text
stringlengths 65
113k
|
|---|
Over 100 million Americans suffer from chronic pain (CP), which causes more disability than any other medical condition in the United States at a cost of $560–$635 billion per year ( ). Opioid analgesics are frequently used to treat CP. However, long term use of opioids can cause brain changes such as opioid-induced hyperalgesia that, over time, increase pain sensation. Also, opioids fail to treat complex psychological factors that worsen pain-related disability, including beliefs about and emotional responses to pain. Cognitive behavioral therapy (CBT) can be efficacious for CP. However, CBT generally does not focus on important factors needed for long-term functional improvement, including attainment of personal goals and the psychological flexibility to choose responses to pain. Acceptance and Commitment Therapy (ACT) has been recognized as an effective, non-pharmacologic treatment for a variety of CP conditions ( ). However, little is known about the neurologic mechanisms underlying ACT. We conducted an ACT intervention in women ( n = 9) with chronic musculoskeletal pain. Functional magnetic resonance imaging (fMRI) data were collected pre- and post-ACT, and changes in functional connectivity (FC) were measured using Network-Based Statistics (NBS). Behavioral outcomes were measured using validated assessments such as the Acceptance and Action Questionnaire (AAQ-II), the Chronic Pain Acceptance Questionnaire (CPAQ), the Center for Epidemiologic Studies Depression Scale (CES-D), and the NIH Toolbox Neuro - QoL (Quality of Life in Neurological Disorders) scales. Results suggest that, following the 4-week ACT intervention, participants exhibited reductions in brain activation within and between key networks including self-reflection (default mode, DMN), emotion (salience, SN), and cognitive control (frontal parietal, FPN). These changes in connectivity strength were correlated with changes in behavioral outcomes including decreased depression and pain interference, and increased participation in social roles. This study is one of the first to demonstrate that improved function across the DMN, SN, and FPN may drive the positive outcomes associated with ACT. This study contributes to the emerging evidence supporting the use of neurophysiological indices to characterize treatment effects of alternative and complementary mind-body therapies.
## Introduction
Over 100 million Americans suffer from chronic pain (CP), which causes more disability than any other medical condition in the United States at a cost of $560–$635 billion per year ( ). Opioid analgesics are frequently used to treat CP. However, long term use of opioids can cause brain changes such as opioid-induced hyperalgesia that, over time, increase pain sensation. Also, opioids fail to treat complex psychological factors that worsen pain-related disability, including beliefs and emotional responses to pain. Cognitive behavioral therapy (CBT) can be efficacious for CP ( ). However, CBT does not focus on important factors needed for long-term functional improvement, including attainment of personal goals and the psychological flexibility to choose responses to pain ( ).
Acceptance and Commitment Therapy (ACT) is a mindfulness-based therapy that focuses on enabling individuals to accept what is out of their control, and to commit to valued actions that enrich their lives ( ). ACT was developed in 1986 by Stephen C. Hayes who began to examine how language and thought influence internal experiences ( ). By emphasizing acceptance instead of avoidance, ACT differs from many other forms of CBT. Although not originally designed for CP, ACT has been shown to be efficacious in terms of clinical outcomes, adherence to treatment, and retention, earning the status of a “well-established” treatment for CP from the American Psychological Association. ACT aims to increase psychological flexibility , and has been associated with improved health outcomes in many randomized controlled clinical trials ( ), including three systematic reviews specific to CP ( ; ; ). Psychological flexibility is defined as an individual’s ability to “recognize and adapt to various situational demands; shift mindsets or behavioral repertoires when these strategies compromise personal or social functioning; maintain balance among important life domains; and be aware, open, and committed to behaviors that are congruent with deeply held values” ( , p. 865). ACT is a “third wave” behavioral treatment that has been shown to be efficacious for treating CP, as well as co-morbid conditions and factors (e.g., goal selection) related to long-term functional improvement ( ; ). Additionally, patients who participate in ACT report greater long-term satisfaction compared to CBT ( ). ACT is transdiagnostic and associated with improvements in physical functioning and pain-related disability, as well as decreases in emotional distress regardless of perceived pain intensity ( ).
Resting-state functional magnetic resonance imaging (rsfMRI) allows for data to be collected while individuals with CP rest in the MRI scanner for a short period of time (<10 min). Thus, data provides information about the natural state of brain function in CP without having to apply any external sensory or cognitive stimulation. Analysis methods of rsfMRI have focused on multiple regions in the brain, targeting inherent and altered measures of connectivity between brain regions and within brain networks ( ). Further, alterations in brain structure and function have been demonstrated in multiple CP syndromes ( ; , ; ). Prior imaging research has suggested that CP results in abnormal hyper-connectivity of brain networks associated with self-reflection (default mode, DMN), emotion (salience, SN), and cognitive control (frontal parietal, FPN) networks ( ; ; ). While ACT has been successful in helping those with CP create a more functional and personally meaningful life ( ), a critical gap in our understanding of the neural mechanisms underlying ACT remains.
Only two prior investigations have used fMRI to assess neural mechanisms of ACT-based interventions for CP. investigated task fMRI activation using pressure evoked pain. Participants with fibromyalgia showed increased activation in the ventrolateral prefrontal cortex (vlPFC) and orbitofrontal cortex (OFC) post-ACT after 12 weeks of ACT. Additionally, results showed pain-evoked changes in connectivity between the vlPFC and thalamus after ACT. conducted an 8-week ACT intervention vs. health education control (HEC) for participants with comorbid CP and opioid addiction. Focusing on DMN and pain regions in the brain, participants receiving ACT exhibited decreased activation during evoked pain in the middle frontal gyrus (MFG), inferior parietal lobule (IPL), insula, anterior cingulate cortex (aCC), posterior cingulate cortex (pCC), and superior temporal gyrus (STG) compared with HEC participants.
In the present study, ACT was delivered to nine women with CP using a quasi-experimental (pre–post) design. fMRI was used to identify changes in brain networks underlying ACT-related behavioral outcomes in CP. Based on our prior work examining ACT in CP ( , ), we hypothesize that: (1) ACT will reduce connectivity strength within and between the DMN, SN, and FPN, and that (2) changes in connectivity strength will correlate with changes in behavioral outcomes from pre-to post-ACT.
## Materials and Methods
### Participants
Nine female participants (47.59 ± 16.54 years, 8 right:1 left-handed) with musculoskeletal pain who did not self-report misusing opioids were enrolled in a 4-week group ACT intervention program ( ). Participants were referred from the outpatient practice of a physician certified in Physical Medicine and Rehabilitation with subspecialty certification in Pain Medicine. The practice involves rehabilitation and management of both acute pain and CMP, with a higher prevalence of females versus male patients reporting CMP ( ).
Patient characteristics.
Participants were required to be at least 18 years of age, speak English, have been living with musculoskeletal CP for 3 or more months, have a Brief Pain Inventory (BPI) Score of ≥4, and have no history of cancer or malignancy, head or severe body trauma in the past 6 months. Participants with neurologic (e.g., history of stroke, brain lesions, or intracranial surgery) or psychiatric disorders not commonly comorbid with CP were excluded. Patients who were not addicted to opioids but were taking opiates on a PRN (“as needed”) basis were eligible to participate, in order to reflect real-world clinic conditions as closely as possible. Only one participant self-reported using PRN opioid medication. Most participants reported having more than one type of chronic pain. Specifically, four patients reported suffering from fibromyalgia, one reported Ehlers–Danlos syndrome, one reported Conradi–Hünermann syndrome, two reported chronic neck pain, three reported chronic lower back pain, and one reported trigeminal neuralgia (for further details regarding the participants’ medical histories, please see ).
### Acceptance Commitment Therapy Protocol
Patients completed two 90-min manualized ACT sessions per week for 4 weeks ( ; ). ACT sessions were administered by two licensed, trained Certified Therapeutic Recreation Specialists (CTRS/L). Behavioral outcomes were measured using validated assessments including the Acceptance and Action Questionnaire (AAQ-II) ( ), the Center for Epidemiologic Studies Scale (CES-D) ( ; ), the Chronic Pain Acceptance Questionnaire (CPAQ) ( ; ; ; ), the NIH Toolbox Neuro - QoL (Quality of Life in Neurological Disorders) scales ( ), and the NIH Patient-Reported Outcome Measurement Information System (PROMIS) measures of pain interference ( , ), administered using an iPad (see ). The study protocol (#6991) was approved by the University of New Hampshire Institutional Review Board on July 19, 2018. All participants provided informed consent.
The behavioral assessment data were entered into Excel spreadsheets using Qualtrics software ( ) for data management. Statistical analyses were conducted using SAS v.9.4. ( ). Paired Student’s t -tests and Wilcoxon signed rank tests were used to assess differences in behavioral measures from pre-to-post ACT (subtracting post minus pre scores). Positive or negative change scores indicated satisfactory results, depending on the specific test in question (e.g., reduced CES-D scores indicated improvements in depression while higher AAQ scores indicated improvements in pain acceptance).
### Resting State fMRI Data Collection
MRI data were collected before and immediately after 4 weeks of ACT using a Siemens Three Tesla Magnetom Prisma scanner at Boston University, Boston, MA, United States. Structural MPRAGE was collected (TR/TE = 2.53 s/1.32 ms, flip angle = 7°, field of view (FOV) = 256 × 320 mm, 0.8 mm resolution) to allow for anatomical registration. Subsequently, two runs of 8 min resting-state fMRI data were obtained using a T2 weighted Echo Planar Imaging sequence (2.5 mm resolution, 60 slices, TR/TE = 1.2 s/30 ms, 300 volumes, FOV = 205 mm, multi-slice interleaved ascending) for all participants. During the resting state scans, participants were instructed to lie still in the scanner with their eyes open, fixating on a crosshair placed in their field of view. Only the first of the two resting state scans were used for analysis. Two rsfMRI scans were collected in the case that one set was unusable (e.g., movement artifact). The first scan set was of high enough quality to use.
### Resting State fMRI Data Analysis
Standard preprocessing steps were carried out using Statistical Parametric Mapping, version 12 (SPM12, ). First, all scan data were imported in the form of DICOM images and converted to Nifti files using the DICOM Import function in SPM12. Functional data were realigned and co-registered to the standard Montreal Neurological Institute (MNI) template in SPM12. Motion correction, band-pass filtering (0.0078–0.08 Hz), slice-timing correction, normalization to remove individual variability for between subject comparisons, and smoothing to increase signal to noise ratio were carried out using SPM12 ( , step 1). Next, each participant’s brain was parcellated into discrete regions of interest representing nodes from the Power atlas ( ) using Mango (Multi-image Analysis GUI; ). The mean time course within seed regions were extracted from the residual images using REX ( ) ( , step 2). FC estimates were then calculated using the pairwise Pearson correlation of seed regions located time course across all 264 nodes resulting with a 264 by 264 connectivity matrix ( , step 3). Finally, connectivity matrices were reduced to a subset of 101 nodes that were associated with the FPN, DMN, and SN.
fMRI data analysis pipeline. Step 1: rsfMRI data preprocessed using SPM12; Step 2: Mean time series extraction from Power ROIs; Step 3: Estimate FC between all nodes using Pearson correlation; Step 4: Use NBS to identify changes in connectivity between pre- and post-ACT.
### Graph Analysis
Graph analysis applied to fMRI is a powerful way of characterizing brain networks. In this context, a network represents a collection of nodes, and the functional connections between pairs of nodes. Nodes in large-scale brain networks represent brain regions, with connections being anatomical, functional, or effective, depending on the type of imaging data analyzed. Application of graph theory-based approaches have identified biologically plausible brain networks found to topologically organize in a non-trivial manner (e.g., network integration and modular structure) and support efficient information processing of the brain ( ; ; ; ). These network analyses allow us to visualize the connectivity pattern across the entire brain and also quantitatively characterize its global organization ( ; ). In the current study, we leverage the ability of graph analytics to identify network connections linking rsfMRI connectivity to ACT treatment.
### Network Based Statistic
We used the Network Based Statistic (NBS; ) to examine FC in the DMN, SN, and FPN. This graph theory-based method provides a statistical approach to identify changes in FC associated with diagnostic status or changing psychological contexts ( ). The NBS is based on the principles underpinning traditional cluster-based thresholding of statistical parametric maps. We use the NBS to identify significant network connectivity differences between pre-ACT and post-ACT ( , step 4). We tested for within network connectivity changes for each of the three networks of interest independently, and then we tested for network connectivity changes across all 101 nodes that make up the FPN, DMN, and SN. Results presented represent functional network differences for t > 2.5 (10,000 permutations). We further examine whether specific pairwise connections in brain networks affected by ACT are related to behavior, and whether connectivity changes associated with ACT correlate with changes in behavior/outcome measures (using Pearson’s r ).
### Causal Mediation Analysis
Causal mediation analyses were conducted, with confounder adjustment, to evaluate associations between FC changes and behavioral changes (pre- to post-ACT). In other words, we tested whether connectivity changes reflected by the NBS ( , step 4) were mediators of changes in depression, social role, and CP acceptance scores. A counterfactual mediation approach was implemented in this analysis using SAS PROC CAUSALMED ( ; , ). Although structural equation modeling (SEM) is frequently used to examine mediation ( ), its limitations include the following: (1) It does not have a clear theoretical foundation for defining causal mediation effects; (2) It does not deal with confounding and interaction effects effectively; (3) It does not treat binary outcomes and binary mediators in a unified manner. The counterfactual approach overcomes these limitations ( ; ).
Causal mediation analysis quantifies and estimates the total, direct, and indirect (or mediated) effects between an independent variable and an outcome. It enables causal interpretations of these effects under the assumptions of the counterfactual framework ( ; ). The causal mediation model decomposes the total effect into a direct effect [e.g., the effect of an independent variable [ A ] on outcome [Y; A = 0 vs. A = 1] and the natural indirect effect (NIE)]. The controlled direct effect (CDE) simulates a randomized controlled trial (RCT) by hypothetically assigning the same value of the mediator to all individuals in the sample, with bootstrapped standard errors ( ). The NIE captures the effect of the mediation pathway (e.g., the average change in Y if the exposure is fixed to the level of the intervention and the mediator changes accordingly [e.g., A = 0 to A = 1]; ). The mediation path is represented by arrows “B” and “C” in .
Rationale for the causal mediation analysis. Change in behavioral measures (pre- to post-ACT), mediated by FC changes. Path “A” shows the direct effect; mediation is estimated by combining paths B and C to produce the NIE.
For this analysis, we used the theoretical constructs underlying ACT to guide our approach (e.g., that psychological flexibility , in the context of CP, includes factors pertaining to acceptance and cognitive defusion (learning to experience uncomfortable thoughts, feelings, and sensations in a way that reduces their interference on valued activities and roles in one’s daily life); ). Thus, we focused on indicators of CP acceptance, pain interference, depression, and social roles as key behavioral outcomes.
## Results
Behavioral change scores from pre-to post- ACT showed statistically significant improvements in clinically relevant outcomes, including depression (measured via the CES-D and the NIH Toolbox Neuro-QoL ), satisfaction with social role (measured via the NIH Toolbox Neuro-QoL ), and pain acceptance (measured via the AAQ-II and CPAQ) ( ). Two specific sub-scores of the CPAQ indicated that there were significant decreases in pain interference and significant increases in willingness to engage in activities despite pain.
Behavioral change measures.
### Network Based Statistic
Significant changes in FC from pre- to post-ACT in the DMN, SN, and FPN were observed. Using the NBS, within network effects of ACT were only observed in the SN which consisted of a sub-network of four nodes and three functional connections ( , t > 2.5, p = 0.039). No effects of ACT were observed within the DMN or FPN. NBS tests comparing pre-ACT vs. post-ACT of the triple network (DMN, FPN, and SN nodes combined) identified a network of 10 nodes and 10 connections displaying decreases in FC associated with completing ACT ( , t > 2.5, p = 0.05). Interestingly, the within network ACT effects observed in the SN were also present in the triple network. Between network changes were also observed in the triple network where all DMN and FPN nodes connected to SN nodes ( ).
Within and between network effects of ACT. (A) Within network decreases in FC following ACT: Pre-ACT < Post-ACT ( t > 2.5, p = 0.039, 10,000 iterations). (B) Between network decreases in FC following ACT: Pre-ACT < Post-ACT ( t > 2.5, p = 0.05, 10,000 iterations). Mean functional connections exhibiting ACT effects are shown for Pre-ACT (blue) and Post-ACT (orange). SN, DMN, and FPN nodes are shown in red, blue, and green, respectively. mCC, midcingulate cortex; aCC, anterior cingulate cortex; MTG, medial temporal gyrus; PFC, prefrontal cortex; rIPL, right inferior parietal cortex; rSMG, right superior medial gyrus. Node numbering below anatomical labels correspond to the node ordering in the Power Atlas (see ).
### Correlation Analysis
Next, we assessed the relationships between brain connectivity changes and behavioral outcomes using Pearson correlation statistics. Six of the ten functional connections that showed significant differences in strength within the Triple Network from pre- to post-ACT (shown in ) were significantly correlated with behavior change scores. Pearson correlations and corresponding p -values for the following significant relationships are shown in . Results indicated that ACT effects in connectivity (functional connections from NBS analyses shown in ) between the MTG and aCC (52, −59, 36; −11, 26, 25) and between the PFC and aCC (38, 43, 15; 0, 30, 27) were correlated with social role ( Neuro - QoL ) and acceptance (AAQ-II) scores. FC changes between the rIPL and aCC (two functional connections connecting 44, −53, 47 with −11, 26, 25 and 0, 30, 27) were correlated with Neuro - QoL depression scores; and changes between rSMG and aCC (55, −45, 37; 10, 22, 27) were correlated with both depression and social role scores. Changes between rSMG and aCC (55, −45, 37; −11, 26, 25) and rIPL and aCC (44, −53, 47; −11, 26, 25) were correlated with reduced pain severity scores.
Correlations between functional connections in triple network and behavioral assessment scores (post minus pre).
### Causal Mediation Model Results
In the causal mediation framework for this study, a baseline behavioral variable (e.g., Pre-ACT Depression Score) was hypothesized to relate to an outcome variable (Post-ACT Depression score) via the causal mechanism that is represented in . As depicted in the diagram, the total causal treatment effect pertaining to the outcome (Post-treatment behavioral score) consists of the following two parts: (1) a direct effect; (2) a mediated (or indirect) effect via the “functional connectivity” variable representing change in FC between two brain regions.
Results from our exploratory causal mediation models ( ) demonstrated that improvements in specific behavioral outcome scores were significantly related to both the direct pathway “A” (e.g., baseline behavior scores predicted post-treatment scores) and through the indirect mediation pathway “B and C” via changes in FC (the NIE). Statistically significant NIE provide evidence in support of our mediation hypothesis for rSMG-aCC for depression, MTG-aCC for social role, both rSMG-aCC and MTG-aCC for CP acceptance, and rlPL-aCC for pain interference. These relationships persisted after covariate adjustment for age, BMI, pain severity, and other potential confounders. By contrast, statistically significant mediation effects were not observed for rlPL-aCC for depression, PFC-aCC and social role, or MTG-aCC and AAQ-II. No significant interactions were detected.
Causal mediation model results.
## Discussion
We examined rsfMRI data of nine women before and after completing ACT in efforts to better understand changes in brain FC and associations with specific behavioral outcomes. Importantly, we used NBS to assess network function with graph analyses and took an innovative approach to study of the relationship between imaging and behavioral measures known as causal mediation analysis. Our results showed significant improvements from pre-to post-ACT in clinically relevant behavioral outcomes, including depression, satisfaction with social role, and pain acceptance. These results confirm findings from other studies ( , ; ; , ) and align with the theoretical principles underlying ACT, specifically that constructs of psychological flexibility including acceptance and cognitive defusion (reduced pain interference) may play important roles in functional improvements for individuals suffering from CP ( ). ACT does not aim to limit exposure to negative experiences, but encourages persons with CP to decrease attention to pain and to move forward in valued life directions despite these experiences. In our study, two specific sub-scores of the CPAQ indicated that there were significant decreases in pain interference and significant increases in willingness to engage in activities despite pain. Other researchers studying mind-body therapies have documented similar results. For example, analyzed health effects of mindfulness- and acceptance-based interventions, including mindfulness-based stress reduction (MBSR), mindfulness-based cognitive therapy (MBCT), and ACT. The authors found small to moderate effects in favor of mindfulness- and acceptance-based interventions compared to controls in pain, depression, anxiety, mindfulness, sleep quality, and health-related quality of life.
We used the NBS ( ) to examine FC in the DMN, SN, and FPN and found within SN effects that extend to brain regions that are components of the DMN and FPN. Prior fMRI studies on mindfulness approaches for treating CP have shown that increased regional activation in the aCC and OFC were associated with reduced ratings of the unpleasantness of pain ( , , ; ; ). Additional multidisciplinary pain treatment programs comprised of daily physical and occupational therapies plus CBT for pain treatment resulted in improved pain measures that correlated with connectivity changes in DMN, SN, and FPN ( ). Specifically, findings showed treatment driven reductions of hyper-connectivity from the left amygdala to the motor cortex, parietal lobe, and CC. also found that connectivity to several regions of the fear circuitry (PFC), bilateral middle temporal lobe, bilateral CC, and hippocampus correlated with higher pain-related fear scores, and that decreases in pain-related fear correlated with decreased connectivity among the amygdala and the motor and somatosensory cortex, CC, and the FPN. Across the few studies utilizing longitudinal randomized controlled designs with active control groups, aCC, PFC, pCC, insula, striatum (caudate, putamen), and amygdala show relatively consistent changes associated with mindfulness meditation ( ; ; ).
These studies, in conjunction with our results, suggest that the neural correlates of ACT for CP affect sensory brain networks and cognitive function. Thus, our results suggest that the neural mechanisms underlying the multi-faceted nature of ACT for CP are not only related to the DMN, but also to the DMN’s relationship to other networks. A consistent finding across several studies is that CP results in hyper-connectivity among the DMN, SN ( ; ), and FPN ( ). Supporting the current analyses, the transition from acute to CP over a 1-year period has been found to result in a gradual ‘shift’ in fMRI activations from nociceptive networks to emotional brain networks ( ). Collectively, these findings provide impetus for further study of associations between rsfMRI and clinical outcomes.
### Behavioral Outcomes and fMRI
Several studies have evaluated the association between fMRI and behavioral outcomes in mindfulness-based interventions (e.g., MBSR), though the majority of these evaluations are not specific to ACT ( ; ; ). A recent study ( ) used a CBT intervention with behavioral activation (an ACT component) and found that the OFC played an important role in improvements in pain intensity post-treatment. Activation of the dorsal pCC at pre-treatment was also associated with improvements of clinical symptoms. ACT has also demonstrated sustained medium-large effect sizes on social functioning ( ; ).
examined ACT-oriented treatment for fatigue in 354 adults with CP. Pearson’s correlations and hierarchical regression were conducted to investigate associations between improvement in fatigue interference and improvements in outcomes. Mixed effects models were used to explore associations between baseline fatigue interference and changes in outcome measures. Results suggested that participants improved in perceptions of fatigue interference, pain, psychological flexibility (PF) processes, and daily functioning. Changes in fatigue interference were associated with changes in pain, PF processes, and daily functioning | r | = 0.20–0.46. Changes in fatigue interference were associated with changes in pain acceptance independent of changes in pain perception. The authors concluded that individuals with fatigue appeared to benefit from the ACT−oriented interdisciplinary treatment for CP, and relatively higher levels of fatigue did not appear to decrease the treatment benefit. Although fatigue was not a focus in our study, the results are similar in terms of demonstrating that the behavioral improvements associated with ACT may persist regardless of pain severity and the presence of other co-morbidities.
examined gray matter volume (GMV) differences between CP patients and healthy controls and found that there were 12 clusters where GMV was decreased in CP patients compared with controls. These clusters included many regions that are considered part of the “pain matrix” involved in pain perception, but also included many other regions that are not commonly regarded as pain-processing areas. The authors also reported that the most common behavioral domains associated with these regions were cognitive, affective, and perceptual domains, suggesting that many of the regions may relate to the constellation of comorbidities that often accompany CP (e.g., fatigue, depression, cognitive, and emotional impairments).
### Integrating Behavioral and Neural Network Changes Using Causal Mediation Analysis
Using causal mediation analysis to assess whether changes in connectivity strength mediated changes in specific behavioral outcomes, we observed statistically significant mediation effects for rSMG-aCC with depression, MTG-aCC with social role, rSMG-aCC and MTG-aCC with CP acceptance, and rlPL-aCC with pain interference. Because the models were adjusted for age, BMI, pain severity, and other behavioral covariates, we were able to determine that the relationships were not confounded by these factors.
We also observed significant mediation effects for rSMG-aCC and rlPL-aCC with pain severity, despite the fact that changes in perceived pain severity are not considered direct targets of ACT. In our unadjusted analyses, perceived pain severity scores did not change significantly from pre- to post-ACT, yet the controlled direct effect in the causal mediation models, with confounder adjustment, demonstrated significant changes in both direct and indirect effects. These exploratory analyses underscore the complexity of measuring pain perception, particularly as other therapeutically targeted behavioral changes and associated neural connectivity changes may be occurring simultaneously.
Notably, the median baseline CES-D score among our participants was 16, indicative of high depressive symptomatology ( ) concurrent with CP. After the ACT intervention, the median CES-D score was reduced to 7, and this change appears to be mediated by decreased rSMG-aCC hyperconnectivity.
By contrast, statistically significant mediation effects were not observed for rlPL-aCC with depression, MTG-aCC with AAQ-II, or PFC-aCC with social role. In these cases, the relationships were confounded by other factors and may operate via more complex multiple mediation pathways that could not be tested in this small exploratory sample. For example, although we observed a statistically significant mediation effect for PFC-aCC and social role in unadjusted models, inclusion of body mass index (BMI), pain severity, and baseline depression nullified this relationship.
A growing body of literature has begun to employ mediation analysis to assess the mechanisms underlying behavioral and clinical outcomes ( ; ; ; ; ; ; ; ). However, few studies have assessed behavioral changes with respect to fMRI data using causal mediation analysis, particularly with respect to CP. described an extension of SEMs applied to data from a fMRI study of thermally induced pain. The results suggested that many classic “pain-responsive regions” such as the anterior insula showed significant mediation of the temperature-induced relationship, and that subjective pain ratings increased near the end of the stimulation period. Other regions, such as the insular cortex appeared to be active during pain judgment. Like our study, this study supports the use of mediation modeling in future research to better understand how connectivity changes among different brain regions affect the subjective experience of pain, and may inform pain management approaches.
### Limitations and Future Directions
In addition to the small sample size, the limitations of our study include the quasi-experimental research design and the lack of long-term follow-up data beyond the immediate post-ACT period. A randomized controlled trial would have provided more methodological rigor to the study, and randomized trials with longer follow-up periods are needed to confirm the findings reported here.
investigated three key aspects of ACT, including its effectiveness, long-term follow-up, social context, and social processes. The authors contend that researchers should include longer follow up periods in clinical studies ( ). This is especially important for treatment-resistant patients (e.g., patients who do not respond to standard, first line treatments), for whom viable treatment options are limited ( ). The variable number of years that participants suffered from chronic pain is also a limitation in our study, although all participants meet the clinical definition of chronic pain lasting more than 6 months. The heterogeneity of the participants’ pain conditions also precludes inference about how ACT affects specific types of chronic pain. Due to the comorbid pain conditions experienced by the participants in our sample, we are unable to evaluate the results of ACT on any single type of musculoskeletal pain.
However, the mechanism of ACT may transcend specific genetic and acquired diagnoses that result in chronic pain, which clinicians may perceive as a strength of the intervention. Central sensitization (the enhanced activity of neurons and circuits in nociceptive pathways) underlies many of the changes in chronic pain ( ), illustrating the contribution of the central nervous system to the generation of pain hypersensitivity across many types of pain diagnoses. Because of central sensitization, chronic pain is not necessarily coupled to the original stimuli source. Moreover, the abnormal pain sensation and sensitivity resulting from central sensitization is enhanced by stress. Aligning with the findings from our study, prior researchers ( ) have highlighted neuroplastic changes in the insula (within the salience network and the DMN) with respect to processes affecting mood disturbances, working memory, fatigue, and body awareness issues observed across different chronic pain conditions. More recently, found that disruptions of multiple networks, including the DMN, salience network, and limbic system, may contribute to the neurophysiological mechanisms underlying postherpetic neuralgia. A growing body of research also suggests that central sensitization may be driven by neuroinflammation in the peripheral and central nervous system, causing widespread chronic pain. For example, reported that sustained increase of cytokines and chemokines in the central nervous system may promote chronic pain that affects multiple body sites. These processes warrant further study in larger randomized controlled trials.
The fact that participants were all female also limits the generalizability of our findings. Although both males and females were recruited and referred for study participation, the study protocol required daytime commitment to therapy and out-of-state travel. Males cited inflexible work schedules as the main reason why they were unable to participate in the study. However, it is also important to note that prior researchers have documented that the prevalence of chronic pain is higher among females than males ( ). Researchers have reported that in addition to psychosocial characteristics that may vary between males and females, differences in genotype and endogenous opioid functioning play a causal role in these disparities, and considerable literature implicates sex hormones as factors influencing pain sensitivity ( ). However, the specific modulatory effect of sex hormones on pain among men and women requires further exploration. Thus, clinicians must consider a myriad of factors when developing a treatment approach to chronic pain, including gender.
Lastly, omitted variable bias may also pose a limitation in our study. For example, factors outside therapy itself, including social processes, may account for up to 33% of improvement in patients undergoing psychotherapy and group-based interventions ( ). It remains poorly understood how the influence of social surroundings longitudinally affects patients’ well-being, social function, and pain perception ( ; ). Prior research suggests that both close and extended social ties may be relevant for positive outcomes ( ). Additional research is needed to better understand the variation in treatment outcomes in relation to an individual’s social and environmental context, using an exposome lens ( ). Future research should also consider different ways of measuring pain perception and should evaluate both mediators and moderators of ACT in pain as well as in other chronic diseases.
## Conclusion
The mechanistic knowledge generated from this study helps to build the evidence base underlying mind-body therapies such as ACT. ACT has been shown to be particularly efficacious for patients who are older ( ) or have co-occurring mood disorders ( ) who may be unresponsive to other psychosocial treatments. Findings from the present study facilitate identification of neural factors predicting patient responses to mind-body therapies. The outcomes of this study will also support the refinement of non-pharmacologic treatment protocols for CP. This is particularly important with the movement toward ‘stepped care’ models of pain management ( ), which aim to treat pain within a primary care setting while enabling the use of a variety of integrated multidisciplinary treatment approaches.
## Data Availability Statement
The datasets presented in this article are not readily available because due to the small sample size, we would need to ask our IRB about any requests to share data. Requests to access the datasets should be directed to SA, [email protected].
## Ethics Statement
The study protocol (#6991) was approved by the University of New Hampshire Institutional Review Board on July 19, 2018. All participants provided informed consent.
## Author Contributions
All authors contributed to the design and implementation of the research, and to the writing of the manuscript. SA, KR, SM, JC, NW, and DR contributed to the analysis. All authors discussed the results and commented on the manuscript. DR supervised the research.
## Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
|
The lifetime prevalence of major depressive disorder (MDD) in adolescents is reported to be as high as 20%; thus, MDD constitutes a significant social and public health burden. MDD is often associated with nonsuicidal self-injury (NSSI) behavior, but the contributing factors including cognitive function have not been investigated in detail. To this end, the present study evaluated cognitive impairment and psychosocial factors in associated with MDD with NSSI behavior. Eighteen and 21 drug-naïve patients with first-episode MDD with or without NSSI (NSSI+/– group) and 24 healthy control subjects (HC) were enrolled in the study. The Hamilton Anxiety Scale (HAMA), Hamilton Depression Scale (HAMD), Adolescent Self-injury Questionnaire, Beck Scale for Suicide Ideation–Chinese Version (BSI-CV), Shame Scale for Middle School Students, Sensation Seeking Scale (SSS) and Childhood Trauma Questionnaire (CTQ) were used to assess depression-related behaviors, and event-related potentials (ERPs) were recorded as a measure of cognitive function. The latency of the N1, N2, P3a, P3b, and P50 components of ERPs at the Cz electrode point; P50 amplitude and P50 inhibition (S1/S2) showed significant differences between the 3 groups. CTQ scores also differed across three groups, and the NSSI– and NSSI+ groups showed significant differences in scores on the Shame Scale for Middle School Students. Thus, cognitive function was impaired in adolescents with MDD with NSSI behavior, which was mainly manifested as memory decline, attention and executive function deficits, and low anti-interference ability. We also found that childhood abuse, lack of social support, and a sense of shame contributed to NSSI behavior. These findings provide insight into the risk factors for MDD with NSSI behavior, which can help mental health workers more effectively diagnose and treat these patients.
## Introduction
Major depressive disorder (MDD) is a common chronic mental disease characterized by persistent sadness, apathy, and anhedonia. MDD is associated with high rates of morbidity, recurrence, and suicide and has a low cure rate, and is often accompanied by cognitive impairment (Bayes and Parker, ). The prevalence of MDD is 4.4% worldwide and 4.2% in China (World Health Organization, ). Among adolescents in China, the rate of MDD is 15–20% and the lifetime prevalence may be as high as 20%, with a male-to-female ratio of 1:2 (Zheng et al., ).
Nonsuicidal self-injury (NSSI) behavior involves direct, intentional injury to one's body without suicidal intent, and is socially and culturally unacceptable (Ross and Heath, ). Common forms of NSSI include skin or wrist cutting, hair pulling, head hitting, biting, beating, scalding, acupuncture, pinching, etc. (Leong et al., ). NSSI behavior is listed as an independent clinical disorder in the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-V) (Andover, ; Zetterqvist, ). The incidence of NSSI behavior among adolescents is 10–20% (Zetterqvist et al., ; Célia et al., ). The co-occurrence of NSSI behavior with MDD in adolescents is mainly related to difficulties in interpersonal relationships, low self-esteem, childhood abuse, and lack of social support (Jiang et al., ; VanDerhei et al., ; Barreto Carvalho et al., ; Wang et al., ). One study found that shame and guilt were significant positive and negative predictors, respectively, of NSSI behavior (Xie et al., ).
Cognitive distortions and negative cognition contribute to adolescent suicide (Xie et al., ). Event-related potentials (ERPs) reflect brain activity and are a reliable indicator of cognitive function, and are thus used to diagnose diseases (Zhang et al., ). P1 latency was shown to be significantly delayed in patients with depression, suggesting a poor ability to attend to and discriminate between stimuli (Zhang et al., ; Liu W. et al., ; Liu Y. H. et al., ). Most studies have used ERPs to explore cognitive function in patients with depression but few have focused on adolescent NSSI behavior, although one study demonstrated that ΔFN [To objectively assess initial response to reward, they utilized the feedback negativity (FN) event-related potential, a well-established psychophysiological marker of reward responsiveness. ΔFN (i.e., FN to losses minus FN to gains)] is a psychophysiologic indicator of NSSI risk (Tsypes et al., ).
We speculated that cognitive deficits underlie NSSI behavior, and that adolescents with MDD with NSSI behavior show specific alterations in cognitive function. To test this hypothesis, we measured ERPs in adolescent patients with MDD and compared these findings to behavioral test scores from a battery of neuropsychological tests, with the aim of clarifying the features of and factors that contribute to MDD with NSSI behavior.
## Materials and Methods
### Participants
The Research Ethics Committee of Shanxi Medical University First Hospital approved the study protocol. The study included 63 subjects aged 10–22 years: 39 drug-naïve patients with first-episode MDD and 24 HC individuals. The drug-naive, first-episode MDD participants were recruited from the Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan, China. The HC subjects were recruited from Taiyuan, China, using advertisement in the community. All participants were evaluated by two trained psychiatrists independently to determine the presence or absence of Axis I psychiatric diagnoses using the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, Five Edition (DSM-V) Axis I Disorders (SCID).
#### Inclusion and Exclusion Criteria for Patients With MDD
The inclusion criteria for MDD patients were as follows: (1) age between 10 and 22 years with no restrictions on gender; (2) met the DSM-V diagnostic criteria for MDD; (3) 24-item Hamilton Depression Scale (HAMD-24) score ≥20; (4) first-episode MDD with no previous use of antidepressant or other psychotropic medications; and (5) volunteered to participate in the study and signed the informed consent form. The exclusion criteria were as follows: (1) previous manic or hypomanic episodes; (2) any co-occurring mental disorder; (3) alcohol dependence or abuse; (4) hereditary and organic diseases; (5) intellectual disability; (6) personal or family history of epileptic seizures; (7) history of electroconvulsive therapy; (8) visual or hearing impairment; and (9) other severe physical disabilities or disorders.
#### Inclusion and Exclusion Criteria for HC Subjects
Inclusion criteria for HC subjects were as follows: (1) age 10–22 years; (2) no mental disorder found in the initial screening; (3) matched to the MDD patients in terms of sex and education level; and (4) participated voluntarily and signed the informed consent form. The exclusion criteria were as follows: (1) organic disease; (2) alcohol abuse within 30 days or alcohol or drug dependence within 6 months prior to the screening; (3) participation in other clinical trials in the previous 3 months; and (4) other conditions that disqualified the subject from the study, as determined by the investigators.
### Measures
Eligible participants were asked to provide sociodemographic information including name, gender, age, education years, occupation, ethnicity, residence, religious affiliations, etc. For correlations between clinically related variables and neural measures, we used the HAMD-24, Hamilton Anxiety Scale (HAMA) to assess the severity of depressive and anxiety symptom. Beck Scale for Suicide Ideation–Chinese Version (BSI-CV) was used to evaluate suicide ideation and attempts.
Behavior and severity of NSSI was assessed using the Youth Self-injury Questionnaire, a 18-items self-report scale that assesses NSSI behavior and severity; According to the assessment of the number of NSSI in the “past year,” it was divided into four grades: 0, 1, 2–4, 5 and above, and the score was 0–3. The assessment of the degree of physical injury was divided into five grades: no, mild, moderate, severe and extremely severe, and the score was 0–4. Eligible patients were categorized in the NSSI group (NSSI+) if they have self-injurious behavior. Patients were included in No self-inflicted injury group (NSSI–) if they don't have self-injurious behavior. The final subgroups included 18 patients categorized as NSSI+ and 21 as NSSI–.
Sensation Seeking Scale (SSS) and Shame Scale for Middle School Students were used to assess social support and shame; Childhood abuse was assessed using the Childhood Trauma Questionnaire (CTQ). The questionnaire consists of five subscales, namely emotional abuse, physical abuse, sexual abuse, emotional neglect, and physical neglect. Each subscale contains five items, and each item is rated on a five scale.
Cognitive function was evaluated by measuring ERPs (P300, N400, N170, and P50 were used to assess, respectively, executive function and memory, language function, face recognition ability and ability to selectively process stimuli).
### ERP Parameters
ERP data were collected using the 64-electrode NEMUS 2 system (EB Neuro, Florence, Italy). Recording electrodes were placed at the Fz, Cz, and Pz positions; the electrode at the Cz position was the standard and those at the Fz and Pz positions were references for waveform identification. Reference electrodes were placed on the mastoid processes (M1 and M2), and the ground electrode was placed in the middle of the parietal lobe.
#### P50 Detection
We measured the auditory ERP P50 component in response to 500-Hz, 60-dB short-range pure tones presented 32–64 times in pairs with superposition. The interval between the first and second stimuli (S1 and S2, respectively) was 0.5 s, with a paired stimulus interval of 10 s. The task had a total duration of 6 min. Electrode resistance was <5 kΩ; a bandpass filter of 0.1–300 Hz was applied; and the data were segmented into the time window from −200 to 800 ms.
#### P300 Detection
The task employed the classic oddball experimental paradigm. The stimulus sequence was composed of a target stimulus (T) and nontarget stimulus (NT) at a probability ratio of 0.2/0.8; T was randomly interspersed among NT, and the task consisted of 60 T and 240 NT. Subjects were required to press a key as soon as T appeared. The stimulus frequency was 0.5–1 time/s; stimulus interval was 1–3 s; and total task duration was 14 min. Electrode resistance was <5 kΩ; bandpass filtering was applied at 0.5–200 Hz; and the time window for data segmentation was −200 to 1,200 ms.
#### N400 Detection
The subjects were required to sit in a chair with their muscles relaxed and remain awake with eyes fixed on the screen. Three words were sequentially displayed on the screen, and subjects judged whether they could form a logical sentence (e.g., “Xiaoming,” “in the playground,” and “playing football”). Each word was presented for 100 ms and the time interval between presented words was 1,000 ms, giving the subjects 1,100 ms to respond. The total duration of the task was 3 min. Electrode resistance was <5 kΩ; the filter range was 0.53–60 Hz; and the time window for data segmentation was −200 to 1,000 ms.
#### N170 Detection
The procedure was similar to that used for N400 detection, except that subjects were presented with images instead of words and had to judge whether these were emotional or nonemotional. Each image was displayed for 300 ms; the time interval between images was 1,500 ms; and total task duration was 8 min. Electrode resistance was <5 kΩ, with bandpass filtering between 0.1 and 100 Hz and data segmented into the time window of −200 to 800 ms.
### Statistical Analysis
Data were analyzed using SPSS v22.0 (SPSS Inc., Chicago, IL, USA). The threshold of statistical significance was set as α = 0.05 for all analyses.
For general demographic data, categorical variables were evaluated with the χ test and continuous variables were evaluated with the t -test or by analysis of variance (ANOVA), which was used for HAMD-24, HAMA, CTQ, SSS, Youth Self-injury Questionnaire, BSI-CV, and Shame Scale for Middle School Students scores. ANOVA was also used to analyze ERP indicators, post-hoc analysis was then used to compare the ERP indicators between groups; the major components of ERPs were identified and their index values determined according to the internationally recognized maximum waveforms of the time analysis window.
Pearson's correlation analysis was performed to determine the relationship between the scores of Adolescen Self-injury Questionnaire and the scores of CTQ, SSS and shame scale in NSSI+ group (MDD with NSSI behavior). The results were considered significant if P < 0.05, corrected by Bonferroni test.
## Results
### Demographics, Clinical, and Psychosocial Characteristics of all Participants
The NSSI–, NSSI+ and HC groups showed significant differences in education years ( P < 0.001); Covariance analysis showed that the influence of education years on the NSSI severity was not affected by grouping ( P > 0.05) ( ; ).
Demographic, clinical, and psychosocial characteristics of all participants ( N = 63).
All subjects were students of Han ethnicity, not married, with no religious affiliation .
Data represent number, mean ± standard deviation .
BSI-CV, Beck Scale for Suicide Ideation–Chinese Version; HAMA, Hamilton Anxiety Scale; HAMD, Hamilton Depression Scale; CTQ, Childhood Trauma Questionnaire; SSS, Sensation Seeking Scale; HC, healthy control; NSSI, nonsuicidal self-injury; NSSI+, MDD with nonsuicidal self-injury; NSSI-, MDD with no self-inflicted injury .
The three groups showed significant differences in HAMD-24, HAMA, Adolescent Self-injury Questionnaire, BSI-CV, emotional and physical abuse, emotional and physical neglect subscales of the CTQ scale scores ( P < 0.05). Covariance analysis showed that the main effect between grouping and HAMA, HAMD, CTQ, SSS, and BSI-CV was not significant ( P > 0.05).
There were no significant differences between the three groups in terms of age, gender, only-child status, residence and the SSS total score ( P > 0.05). There were statistically significant differences in Shame Scale scores between the NSSI- and NSSI+ groups ( P < 0.001).
### Group Differences in Cognitive Function
The results of the ERP analysis showed that compared to HC subjects, the latency of the N1, N2, P3a, P3b, and P50 components was significantly prolonged in the NSSI– and NSSI+ groups; additionally, the amplitude of P50 was decreased, and inhibition of P50 (S1/S2) was increased ( P < 0.05) ( ). Post-hoc analysis showed that, there were no statistically significant differences between NSSI+ group and NSSI– group for the ERP components ( P > 0.05); Compared to the HC group, P300 latency was longer in the NSSI– group and the NSSI+ group ( P < 0.05). On the other hand, N400 latency were shorter, respectively, in the HC group than in the NSSI- group ( P < 0.05). There were no other statistically significant differences between groups for the other ERP components ( ).
ANOVA analysis of ERP results among the NSSI+, NSSI–, and HC groups.
Data represent mean ± standard deviation .
ERP, event-related potential; HC, healthy control; NSSI, nonsuicidal self-injur; NSSI+, MDD with nonsuicidal self-injury; NSSI-, MDD with no self-inflicted injury .
Post-hoc analysis of ERP latency among the NSSI+, NSSI– and HC groups.
Data represent mean difference (P) .
ERP, event-related potential; HC, healthy control; NSSI, nonsuicidal self-injury; NSSI+, MDD with nonsuicidal self-injury; NSSI–, MDD with no self-inflicted injury .
### Correlation Between Psychosocial Factors and NSSI Severity
Pearson correlation was used to analyze the correlation between the NSSI severity and childhood abuse, social support and shame in NSSI+ group, and the results showed that NSSI severity was positively correlated with childhood abuse ( r = 0.667, P < 0.01) and sense of shame ( r = 0.776, P < 0.01), and negatively correlated with social support ( r = −0.464, P < 0.01).
## Discussion
The present study explored cognitive impairment and psychosocial factors in first-episode untreated MDD patients with NSSI behavior. To the best of our knowledge, this study investigated for the first time the differences in cognitive function and psychosocial factors between patients diagnosed with major depression (with and without NSSI) and healthy controls. We found that the latencies on N1, N2, P3a, P3b, and P50 were significantly prolonged and the amplitudes on P50 were significantly decreased in NSSI+ group. The cognitive function of adolescents with self-injury behavior in MDD is impaired, which is mainly manifested as memory loss, dysfunction of attention and execution, and low ability of anti-interference. In addition, we also found that childhood abuse, lack of social support and sense of shame are all causes of self-injury.
### Cognitive Function to MDD With NSSI Behavior in Adolescents
As the main means and index to detect cognitive function, ERP has important clinical significance in the study of cognitive impairment in patients with depression. Patients with MDD have cognitive impairment to a certain extent, which is mainly related to the dysfunction of frontal lobe (executive function) and temporal lobe (memory) (Hansenne et al., ). Based on our findings, compared with NSSI- group and HC group, the latency of N1, N2, P3a, and P3b in NSSI+ group was significantly prolonged and the amplitude decreased. It is speculated that neuronal excitability and cognitive processing speed decreased in NSSI+ group, and there was some impairment of executive function and memory ability.
In this study, we found that the latency and amplitude of P50 in NSSI+ group were worse than those in NSSI– group, and the inhibition of P50 in P50 group was worse than that in NSSI– group. The P50 component of ERP reflects the selective processing of significant external stimuli in the brain. P50 inhibition-measured by S2/S1 ratio-is an indicator of screening or gating intensity (Wang et al., ). The weaker the gating function of MDD patients is, the lower the selective processing ability to important external stimuli is. Combined with previous studies (Wang et al., ), we found that there are some defects in the screening ability of unrelated stimuli in adolescent depression patients with NSSI behavior.
The N400 is used to test language processing ability; the N400 latency reflects the speed of semantic processing in the brain, while the amplitude reflects the speed at which words are processed in context. A smaller N400 amplitude indicates a higher speed (Kutas and Federmeier, ; Zhang et al., ). In this group of MDD patients with self-injury behavior, the latency of N400 was longer and the amplitude was higher, which is consistent with previous studies (Liang and Zhou, ). Compared with NSSI– group, NSSI+ group has more obvious language barrier, which is mainly manifested in slower language processing speed.
N170, ERP faces specific components, is a negative detection of the occipito-temporal region after 130–190 milliseconds of a face, and reflects the structure of the coding phase faced by the brain processing and early detection of face information (i.e., distinguishing face from non-face) (Itier and Taylor, ). The results showed that compared with NSSI- group, the latency of N170 in NSSI+ group was longer, the amplitude was lower, and the speed of face image recognition was slower, so we speculated that the face recognition ability of adolescent depression patients with NSSI behavior decreased.
### Psychosocial Factors Contributing to MDD With NSSI Behavior in Adolescents
#### Childhood Abuse Contributing to MDD With NSSI Behavior in Adolescents
Childhood abuse refers to various forms of physical or mental abuse, sexual abuse, neglect, commercial or other forms of exploitation that cause actual or potential harm to the health, survival, development and dignity of the child, subject to appropriate responsibilities and abilities (Yao et al., ). Severe or moderate abuse; emotional abuse; unwanted sexual contact; repeated contact sexual assault/non-contact sexual assault is one of the main risk factors for self-injury behavior (Yu et al., ). Our study found that the scores of emotional neglect, physical neglect, emotional abuse and physical abuse in the NSSI+ group were higher than those in the NSSI– group. Patients in the NSSI+ group often reported various kinds of abuse and neglect in childhood. Some literature also showed that emotional and physical abuse and neglect experienced in childhood were risk factors for self-injury behavior (Brodsky and Stanley, ; Fergusson et al., ). Therefore, based on our findings, we speculate that the more abuse and neglect experienced in childhood, the greater the probability of self-injury when they grow up.
Childhood abuse, as a negative experience, will affect the normal function of children's brain neurotransmitters and hormones, including the development of brain regions related to coping problems and emotional control. It will have a series of adverse effects on children's physical and mental health and the development of cognitive function, thus increasing the risk of adolescents' risky behaviors (Gilbert et al., ). Factors that affect NSSI behavior include all aspects, although we can't directly determine the childhood abuse and the inevitable cause-and-effect relationship between NSSI behavior (Su et al., ), but it exists as a kind of risk factors, should remind us to strengthen the neglect and abuse of children, to give children a certain support and unconditional love, prevent the happening of the risk behavior.
#### Social Support Contributing to MDD With NSSI Behavior in Adolescents
It is also worth noting that the study showed that the social support score of MDD patients (with or without NSSI) was lower than that of HC subjects, and the social support score of the NSSI+ group was also lower than that of the NSSI– group. Patients in the NSSI+ group often report less family support, their ideas are not understood by others, and there are few companions, so when they encounter stress or setbacks, they cannot or even will not seek help. At this time, self-injury has become an effective way for them to ease their emotions and relieve stress. NSSI behavior is not uncommon in adolescent students, and is particularly common in middle school students between the ages of 13 and 17. Adequate social support can significantly reduce the risk of mental health problems such as NSSI and suicide (Duggan et al., ). Conversely, a lack of social support and childhood abuse is linked to NSSI behavior in later life (Liu W. et al., ; Liu Y. H. et al., ).
Adolescent students are more sensitive, they have extremely unstable emotions, unpredictable and difficult behavior: storms and stress, they are still in an important period of physical and mental development, in this period, we cannot continue to use “storm and stress” to misinterpret adolescent problems. The research on the social development of teenagers mostly focuses on the changes of family and peer roles. In the interaction with their peers, teenagers gradually determine the social factors of their own identity in the process of development, and then determine what kind of person they become (Gao, ). During this period, parents' company, friends' communication and teachers' care all become powerful and effective sources of social support for them.
#### Shame Contributing to MDD With NSSI Behavior in Adolescents
In addition, we also observed the relationship between NSSI behavior and shame. In this study, self-injury was used as a way of self-punishment to study the relationship between NSSI behavior and shame. The results showed that the shame score of NSSI+ group was higher than that of NSSI– group and HC group. Guilt, shame and strong disgust increased before NSSI and decreased after NSSI. According to the results of the study, we found that shame is an important factor affecting self-harm behavior, with the increase of shame, the degree of self-harm will become more and more serious. This result has also been confirmed by many studies (Linehan, ; Tanaka et al., ).
It has been suggested that NSSI is the expression of anger toward oneself, and self-directed anger and self-deprecation are features of individuals who engage in NSSI (Tanaka et al., ; Wang et al., ). There are usually negative emotions before self-injury, leading to depression and self-hatred. Individuals take different behaviors to alleviate these emotions and achieve self-coordination and self-balance, including self-injury. In a study on the influencing factors of NSSI behavior among adolescents, it was found that 70% of teenagers reported “I don't like myself” and 63% chose to say “I'm mad at myself” (Laye et al., ). Therefore, self-punishment is one of the most common causes of self-injury behavior (Linehan, ). In real life, teenagers will have negative emotional experience when they encounter negative life events, and they lack effective ways to deal with emotions, so they choose self-injury to alleviate their negative emotions.
### Limitations
This study had certain limitations. Firstly, the sample size was small, and although we found evidence of cognitive impairment in adolescents with MDD with NSSI behavior, this needs to be validated in a larger cohort. Compared with healthy adolescents, MDD with NSSI behavior in adolescents have obvious cognitive impairment, but compared with adolescent depressive patients without NSSI behavior, there is no significant difference in cognitive function between the two groups. This may be due to the small sample size (18 patients categorized as NSSI+ and 21 as NSSI–), resulting in no significant difference between the two groups. Future studies should include more samples to verify. Secondly, this was a cross-sectional study and there was no long-term follow-up; in the future it would be useful to investigate whether interventions such as psychological counseling, drug treatment, or physical therapy can alter cognitive function in patients with MDD with comorbid NSSI behavior.
## Conclusion
Compared to adolescent patients with MDD with no self-inflicted injury, those with NSSI behaviors had significantly impaired cognitive function, which was mainly manifested as memory loss, inattention, reduced executive function, and poor resource utilization. Additionally, compared to HC subjects, adolescent patients with MDD with NSSI behavior had poor information screening and anti-interference abilities as well as deficits in language processing and face recognition and processing. The main psychosocial factors associated with NSSI in adolescents with MDD were childhood abuse, lack of social support, and a sense of shame. The results of this study highlight the risk factors for MDD comorbid with NSSI behavior, which can help mental health workers more effectively diagnose and treat these patients.
## Data Availability Statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics Statement
The studies involving human participants were reviewed and approved by Ethics Committee of First Hospital of Shanxi Medical University. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin. Written informed consent was obtained from the individual(s), and minor(s)' legal guardian/next of kin, for the publication of any potentially identifiable images or data included in this article.
## Author Contributions
YW, YX, and DQ contributed to study design and were involved in data acquisition, analysis, and interpretation. XZ contributed to data acquisition. SG, NS, CY, MH, and ZL contributed to study design and data interpretation. All authors participated in the drafting or critical review of the article, gave final approval of the version to be published, and agree to be accountable for all aspects of the work.
## Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
|
Objects : To evaluate the feasibility and effectiveness of in-bed wearable elbow robot training for motor recovery in patients with early and late subacute stroke.
Methods : Eleven in-patient stroke survivors (male/female: 7/4, age: 50.7 ± 10.6 years, post-stroke duration: 2.6 ± 1.9 months) received 15 sessions of training over about 4 weeks of hospital stay. During each hourly training, participants received passive stretching and active movement training with motivating games using a wearable elbow rehabilitation robot. Isometric maximum muscle strength (MVC) of elbow flexors and extensors was evaluated using the robot at the beginning and end of each training session. Clinical measures including Fugl-Meyer Assessment of upper extremity (FMA-UE), Motricity Index (MI) for upper extremities, Modified Ashworth Scale (MAS) were measured at baseline, after the 4-week training program, and at a 1-month follow-up. The muscle strength recovery curve over the training period was characterized as a logarithmic learning curve with three parameters (i.e., initial muscle strength, rate of improvement, and number of the training session).
Results : At the baseline, participants had moderate to severe upper limb motor impairment {FMA-UE [median (interquartile range)]: 28 (18–45)} and mild spasticity in elbow flexors {MAS [median (interquartile range)]: 0 (0–1)}. After about 4 weeks of training, significant improvements were observed in FMA-UE ( p = 0.003) and MI ( p = 0.005), and the improvements were sustained at the follow-up. The elbow flexors MVC significantly increased by 1.93 Nm (95% CI: 0.93 to 2.93 Nm, p = 0.017) and the elbow extensor MVC increased by 0.68 Nm (95% CI: 0.05 to 1.98 Nm, p = 0.036). Muscle strength recovery curve showed that patients with severe upper limb motor impairment had a greater improvement rate in elbow flexor strength than those with moderate motor impairment.
Conclusion : In-bed wearable elbow robotic rehabilitation is feasible and effective in improving biomechanical and clinical outcomes for early and late subacute stroke in-patients. Results from the pilot study suggested that patients with severe upper limb motor impairment may benefit more from the robot training compared to those with moderate impairment.
## Introduction
Stroke is the leading cause of long-term disability among adults in the United States (Virani et al., ) and worldwide (Johnson et al., ). More than 795,000 people suffer a stroke in the United States each year (Virani et al., ), and nearly three-quarters of all strokes occur in people over the age of 65 (Virani et al., ). With an ever-increasing elderly population, the stroke will continue to be a major health issue (Virani et al., ). Up to 70% of stroke survivors have hemiparesis affecting the upper extremity and about two-thirds of the stroke survivors demonstrate a long-term reduction in upper limb motor function (Kwakkel et al., ; Lee et al., ), which restrict their ability to perform everyday activities, reduce productivity, and limit social activities (Buma et al., ; Lee et al., ; Johnson et al., ; Virani et al., ). Improving upper limb function is a core element of stroke rehabilitation needed to maximize patient outcomes and reduce disability.
The first few months post-stroke are critical for motor recovery (O’dwyer et al., ; Kwakkel et al., ; Krakauer, ; Mirbagheri et al., ; Lee et al., ; Winstein et al., ; Kundert et al., ), when neural circuits reorganization, including spontaneous recovery and learning–dependent processes, dominate during the acute and subacute stages (Kwakkel et al., ; Krakauer, ; Lee et al., ). However, multiple studies worldwide have shown that for hospitalized stroke patients, 50–70% of the daytime they were inactive in their ward (Bernhardt et al., ; Lang et al., ; West and Bernhardt, ; Luker et al., ), and the time to receive physical therapy and occupational therapy was estimated to be less than 3 h per day (Bernhardt et al., ; West and Bernhardt, ). The duration of the therapeutic session was about 30 min, while the repetition for passive and active movement in the upper limb was about 33–50 (Lang et al., ). Moreover, observation showed that affected upper extremity use is minimal (3.3 ± 1.8 h) during the inpatient rehabilitation stay (Lang et al., ). Patients with severe motor impairment may have few engagements in the physical activity and intervention for the affected limb (Luker et al., ). However, it has been widely recognized that the effective way to promote neuroplasticity and functional motor recovery poststroke is intensive treatments (Buma et al., ) through specific functional (Van Peppen et al., ) and repetitive motor tasks (French et al., ). Apparently, most of the current inpatient stroke rehabilitation interventions cannot provide the desired training.
Over the past two decades, rehabilitation robots, with the capability to increase the number of movement repetitions in a given time compared to conventional therapy and provide individualized foundational tasks without requiring constant therapist involvement, have gained much attention in stroke rehabilitation (Volpe et al., ; Veerbeek et al., ). Moreover, robotic devices may also provide a timely quantitative and sensitive evaluation of the biomechanical performance of the patients (Ren et al., ), which can aid clinicians to manage the rehabilitation program and optimize the treatment goals for individual patients.
Despite increasing literature were presented, the effectiveness of robotics for rehabilitation in upper limb motor poststroke rehabilitation remains inconclusive (Bertani et al., ; Veerbeek et al., ; Ferreira et al., ; Mehrholz et al., ; Chien et al., ). Robotic therapy adjunct to standard-intensity conventional therapy was more beneficial than standard intensity conventional therapy alone (Bertani et al., ; Veerbeek et al., ; Ferreira et al., ; Mehrholz et al., ). However, the meta-analysis also suggested that under similar training intensity, the improvement of upper limb function was comparable between robotic therapy and conventional therapy for stroke survivors (Veerbeek et al., ; Ferreira et al., ; Chien et al., ). It should be noted that those meta-analysis results derived in the aggregate of the general stroke population may not provide the best evidence of practice for stroke survivors with different levels of impairments (Winstein et al., ). Recent robotic rehabilitation studies reported that chronic stroke survivors with moderate deficits achieved greater improvement in motor function from robot-assisted upper limb training than those with mild motor deficits (Hsieh et al., ; Takahashi et al., ; Takebayashi et al., ). Therefore, stratification of stroke participants based on the impairment level is important in terms of estimating the recovery pattern and prognostication of outcomes (Veerbeek et al., ). Moreover, research in robotic training in early stroke rehabilitation is still scarce, particularly for the elbow joint. Elbow extension/flexion is essential for upper limb function such as reaching and grasping, while the elbow joint is also the most common and long-lasting affected post-stroke (Roby-Brami et al., ). To our knowledge, there is a lack of available exoskeleton robots targeting the elbow joint for in-bed stroke rehabilitation. Most of the existing exoskeleton robots are complex and expensive (Veerbeek et al., ) that limits their application in the in-patient clinical setting. Meanwhile, an end-factor controlled robot may not be suitable for subacute patients with moderate and severe upper limb control. As an alternative, a portable exoskeleton elbow robot would be beneficial for in-patient upper limb rehabilitation. Motivated by the unmet need, we have developed a wearable elbow robot that can provide both passive stretching and active game-based training. The active and passive robotic training modalities have been suggested to be feasible and effective in ankle rehabilitation post-stroke (Ren et al., ).
The purpose of the present study was to conduct in-patient rehabilitation training on subacute stroke survivors with moderate and severe upper limb motor impairment using a wearable elbow rehabilitation robot. We aimed to: (1) evaluate the feasibility and effectiveness of a wearable elbow robotic device in subacute stroke in-bed training; and (2) investigate the active motor recovery patterns of stroke survivors with severe and moderate levels of motor impairments. It was assumed that: (1) a 4-week in-bed robot-guided training would improve elbow biomechanical properties and motor function; (2) patients with different motor impairment levels at the baseline would have different motor recovery patterns.
## Materials and Methods
### Participants
Patients with early subacute (7 days to 3 months post-stroke) and late subacute (3–6 months) stroke were enrolled in this study (Bernhardt et al., ). Inclusion criteria were: (1) age of 18–79 years old; (2) first episode of stroke verified through computed tomography or magnetic resonance imaging; (3) within 6 months post-stroke with impaired elbow motor function (grading of hemiplegic elbow joint Medical Research Council <4), (4) absence of any medical contraindication to exercise; (5) no gross visuospatial or visual field deficits which interfered with feedback training using a computer monitor; (6) the ability to understand and follow oral instructions (follow direction by order obey ≥1 step); and (7) medically stable.
Exclusion criteria were: (1) traumatic brain injury; (2) subarachnoid hemorrhage or lacunar infarct without apparent hemiplegia or hemiparesis; (3) previous upper limb amputation; (4) previous musculoskeletal problems on the impaired side including severe arthropathy, arthritis, or complicated orthopedic surgery on either side; (5) other degenerative neurologic problems such as Parkinsonism, Alzheimer’s dementia, or known other dementia; (6) skin lesion, acute infection on application site of the robotic arm; and (7) multiple stroke with neurological sequelae. The study was carried out in conformity with the Declaration of Helsinki; all patients gave their informed consent to participate in the study, which had been approved by the local scientific and ethics committees.
### The Wearable Elbow Robot Device
A wearable elbow robot (Rehabtek LLC, Linthicum Heights, MD, USA) with audiovisual feedback was used for the in-bed elbow movement training ( ). The exoskeleton robot included the upper arm and forearm braces, a servomotor (EC-4 poles, 120W, Maxon Powermax, Sachseln, Switzerland) with a gearhead (GP32C, ratio 86:1, Maxon Powermax, Sachseln, Switzerland) and a bevel gear with a ratio of 3:1. The driving linkage was connected to the forearm brace through a force sensor (MLP-50, nonlinearity 0.05 lb, Transducer Techniques, Temecula, CA, USA) to determine the elbow joint torque. The output axis of the bevel gear was aligned with the elbow flexion axis and flexed/extended the elbow joint through the force sensor and forearm brace.
(A) The wearable elbow robot used for training. (B) Clinical in-bed setup using the wearable rehabilitation robot. It is worn by a patient on the elbow for controlled passive stretching and active movement training with robotic assistance or resistance or with real-time feedback during training. A force sensor was used to detect the elbow flexion and extension torque.
The wearable robot was designed to provide passive stretching, game-based active movement training with the assist-as-needed scheme, and evaluation of biomechanical properties, including muscle strength and elbow range of motion. The wearable robot was interfaced through a touchscreen computer for display and user interface ( ). The user interface allowed adjustment of the applied torque value, movement velocity, and difficulty levels of the active movement games, such as assistance or resistance level (i.e., assisted-as-needed scheme) according to the patient’s ability.
### Elbow Robot Training Set-Up and Procedures
Patients lay supine in bed and wore the wearable robot on the paretic arm, with the shoulder at about 30-degree flexion and 15-degree abduction. The elbow robot was carefully mounted on the affected elbow with the brace adjusted to align the elbow flexion axis along with the wearable robot output axis ( ). The computer monitor was put in front of the patient with height and angle adjusted for proper viewing ( ). To determine a safe range of robot movement, the operator manually moved the elbow to its end of flexion/extension within the tolerance of patients.
The training procedures are shown in . Each training session typically consisted of passive intelligent stretching of the elbow (15 min), active-assisted and/or resisted movement training through movement gameplay (15 min), and passive intelligent stretching for cool down (15 min). Elbow active range of motion (ROM) and maximum isometric voluntary contraction (MVC) of elbow flexors and extensors were measured before and after each session of training. The training protocol would be adjusted individually to accommodate the condition of patients with severe hemiplegia including more passive stretching and less active movement training while therapy intensity was maintained ~150 repetitions of elbow flexion/extension passively or actively.
Elbow robot training procedure.
During the passive intelligent stretching, the forearm was passively moved by the robot in the sagittal plane at 30–40°/s. As the resistance may increase near the extreme positions of the elbow joint, the robot gradually slowed down to stretch the muscle-tendon complex slowly and safely (Ren et al., ). Once a predefined peak resistance torque (e.g., 5 Nm) was reached, the elbow joint was held at the extreme position for 10 s to allow soft tissue stress relaxation (Ren et al., ). During stretching, the patient was instructed to relax, feel the stretch but not to react to it (Ren et al., ). If the patient reacted to the stretching with high resistance, the robot would stop if a resistance torque limit was reached or reverse the direction of movement if resistance torque was beyond the limit (Ren et al., ).
Two types of active movement training were completed by the participants by voluntarily flexing and extending their elbow to play various movement games under real-time feedback, in which the robot could provide assistance after the patients tried but could not finish the movement task, or the robot provided resistance to challenge the patients if they could move the elbow to the target positions in the gameplay. Robot assistance during patient’s active movement training helped the patients reach the target and kept them engaged, while robot resistance continued challenging the patients to generate muscle strength (Waldman et al., ; Ren et al., ). The choice of assistive or resistive type of active movement training was dependent on the patient’s severity of elbow impairment. For patients at the early stage of recovery with little elbow movement capability, the wearable rehabilitation robot constrained the joint at an isometric condition and the patient’s potential re-emerging force-generation was detected sensitively by the wearable robot, and shown in real-time to the patient through visual feedback as a yellow bar on the computer monitor to guide the patient to generate the desired joint torque output ( ).
Participants received four sessions per week during their about 4-week hospital stay, for a total of about 15 sessions. The training protocol would be adjusted individually to accommodate the condition of patients with severe hemiplegia including more passive stretching, and less active movement training while therapy intensity was maintained ~150 repetitions of elbow flexion/extension passively or actively. In addition to the robotic training, all the patients also received their regular inpatient rehabilitation including physical therapy and occupational therapy.
### Outcome Evaluation
Biomechanical outcome measures were conducted immediately before and after the robotic training, including muscle strength measured as a maximum isometric voluntary contraction (MVC) of elbow flexors and extensors. During the measurement, the wearable robot was locked at the 90° elbow flexion and the participant was encouraged to extent and flex his or her elbow maximally. Each measure was done three times with a rest break of 30–60 s to minimize fatigue. The measured data were saved in the robot computer and the averaged value of the three assessments was taken as the corresponding outcome measure.
Clinical outcome measures, including upper limb motor recovery, muscle strength, and spasticity, were made on all the participants before and after all the robot-aided training (i.e., about 4 weeks from baseline) and 4 weeks after the completion of the training.
Fugl-Meyer Assessment of upper extremity (FMA-UE) was used to evaluate motor recovery (maximum score of 66) with a higher score indicating better motor recovery (Gladstone et al., ). The cut-off score for severe, moderate, and mild upper limb motor impairment is suggested to be 26 and 53 (Woodbury et al., ). Muscle strength was also evaluated using Motricity Index for the upper extremities (MI). As a valid muscle strength evaluation scale of stroke recovery in the first 6 months post-stroke, MI assessed the muscle strength of shoulder abduction, elbow flexion, and pinch grip. The total score ranges from 0 to 99, with a higher score corresponding to better muscle strength (Bohannon, ).
Spasticity of the elbow flexors and extensors was evaluated using the Modified Ashworth Scale (MAS). Considering the 1+ score is not ordinal, the scores of 0, 1, 1+, 2, 3, and 4 were adjusted to 0–5 ordinal scores in further analysis, with a higher value indicating more severe muscle spasticity (Pandyan et al., ; Ansari et al., ).
### Data Analysis
The normality of variables was checked using the Shapiro-Wilk test. Due to the small sample size, non-parametric analysis was used for the study. For all outcome variables, the group mean and standard deviation or median and inter-quartile range (IQR) at pre-and post-training, and follow-up were calculated. For the clinical outcome measures, the Friedman test was used to test whether the change between pre-, post-, and follow-up was statistically significant. Paired comparisons using the Wilcoxon signed-rank test were made between pre-and post-training conditions and between pre-and follow-up with Bonferroni adjustment. All statistical analyses were performed using the SPSS statistical software (Version 26, IBM, Armonk, NY, USA). The statistical significance was set at p < 0.05.
Furthermore, for MVC of elbow flexors and extensors, measured before and after each of the treatment sessions, improvement curves over sessions based on a logarithmic fitting equation as below (Kwakkel et al., ; Langhorne et al., ; Chen et al., ):
where x is the number of the training session, y is the MVC value, a denotes the rate of improvement and b indicates the initial muscle strength of the patient. The coefficient of determination ( R ) was calculated to assess the goodness of fit.
## Results
### Participants
Eleven patients (mean age ± SD: 50.7 ± 10.6 years) with moderate to severe upper limb motor impairment [FMA-UE, median (IQR): 28 (14–45)] at subacute stroke stage (post-stroke duration: 2.6 ± 1.9 months) completed the training during their hospital stay of 28 days on average (range: 24–30 days). summarizes the characteristics of each participant at the baseline. Three and eight patients were with severe and moderate upper limb motor impairment, respectively, at the baseline.
Characteristics of the patients at the baseline .
FMA-UE, Fugl-Meyer Assessment-Upper Extremities; MAS, Modified Ashworth Scale; N/A, not applicable to conduct . Median (inter-quartile range); SD, standard deviation .
### Feasibility
We applied the robot training in accordance with their routine in-patient treatment schedule and there was no dropout in the patients. Mild skin compression due to robot fixation and muscle soreness were reported by six patients after the first session of training, but this symptom was relieved within 24 h after onset of the symptoms. In general, the in-bed elbow robot training was well-tolerated by the participants without other adverse events. Every participant was able to complete the 50-min training and reported satisfaction with passive stretching and occasionally mild fatigue following the active movement when asked by the researchers. Except one patient, who was discharged after 13-sessions of training, the other 10 patients completed 15 sessions of training. Due to the researcher applied assistance to patients during the muscle strength testing, the biomechanical measures for three patients were excluded from the analysis. Thus, in the following in-session and curve-fitting analysis, biomechanical data for eight patients over 13 sessions were included.
### Biomechanical Outcomes
#### Improvements After the 3–4 Week Training Program
After 3–4 week of training during the hospital stay, the MVC of elbow flexors significantly increased by 1.93 Nm (95% CI: 0.93 to 2.93 Nm, p = 0.017) and the MVC of elbow extensors significantly increased by 0.68 Nm (95% CI: 0.05 to 1.98 Nm, p = 0.036; ).
Maximum isometric voluntary contraction at baseline and 4-week after the training program . Error bars represent standard deviation. *Indicates a significant difference between two measured time points from Wilcoxon Signed Ranks Test p < 0.05.
#### In-Session Changes Over the 4-Week Training Program
Each session of robot-guided training-induced changes in the MVC of elbow flexors and extensors, as indicated by the pre-and post-session dot plots over 13 sessions are shown in . These recovery curves showed overall improvement in the patients’ motor control ability over 13 training sessions, as well as the improved performance due to each training session, as shown by the differences between the pre-and post-session improvement curves (the blue and red curves respectively). We plot the overall change for eight patients with biomechanical measures, and further plot the recovery curve for patients with moderate upper limb motor impairment (FMA-UE motor >26, n = 5) and severe motor impairment (FMA-UE motor ≤26, n = 2). Overall, the improvement rate derived from the post-session measures was larger than the pre-session measures for both elbow flexion MVC ( ) and extension MVC ( ), which was related to the improvement induced by each training session. For the elbow flexors ( ), overall, patients with severe motor impairment had a lower initial performance value (pre: b = 2.898; post: b = 2.693) than those with moderate motor impairment (pre: b = 3.873; post: b = 4.551), and the post-session improvement rate was larger in the severe motor impairment group ( a = 0.865) compared to the moderate motor impairment group ( a = 0.323).
Session-by-session pre-and post-session maximum isometric voluntary contraction (MVC). Improvement curves across the 13 sessions are fitted with logarithmic function y = a ×ln( x ) + b , where x is the number of the training session, y is the MVC value, a denotes the rate of improvement and b indicates the initial performance capability level of the patients. The coefficient of determination ( R ) was calculated to assess the goodness of fit. (A) MVC of elbow flexors over eight patients. (B) MVC of elbow extensors over eight patients. (C) MVC of elbow flexors over patients with severe motor impairment ( n = 2) and moderate motor impairment ( n = 6). (D) MVC of elbow extensors over patients with severe motor impairment ( n = 2) and moderate motor impairment ( n = 6).
Patient No. 11 with very severe motor impairment (FMA-UE = 5) at baseline was unable to generate active elbow movement in the first five sessions of training. His recovery curve was discontinuous due to the important zero to non-zero motor output change and was modeled separately ( ).
Session-by-session pre-and post-session maximum isometric voluntary contraction (MVC) for patient No.11. This patient started to develop elbow flexors voluntary contraction after five sessions of training.
### Clinical Outcomes
The clinical measures were conducted before and after 15 sessions of training (except for one patient discharged after 13 sessions of training), and 4 weeks after the termination of training. After 4 weeks of training, significant improvements were observed in FMA-UE ( p = 0.003) and Motricity Index ( p = 0.005; ), and the improvements sustained at the follow-up ( p < 0.05). However, no significant differences were observed between the post-training and 4-week follow-up. In addition, subscale value of FMA are presented in . Generally, participants showed improvement in movement, while no significant change was observed in coordination. There was no significant change of muscle spasticity measured by MAS between the baseline, post-training, and follow-up sessions.
Clinical measures at baseline, post-training and follow-up .
FMA-UE, Fugl-Meyer Assessment-Upper Extremities; IQR, inter-quartile range; MAS, Modified Ashworth Scale. *Statistically significant difference using Wilcoxon signed rank test, p < 0.05 .
## Discussion
Our findings suggest that the in-bed wearable elbow robotic rehabilitation training is feasible in early and late subacute stroke survivors with moderate to severe upper limb motor impairment. Over the 4 weeks of training, participants made consistent improvements on elbow biomechanical and clinical outcomes, while the recovery curve showed that patients with severe motor impairment may have a greater rate of improvement compared to those with moderate motor impairment. The current findings address the valuable application of in-bed rehabilitation robotic training for subacute stroke survivors with its advantages in quantifying and monitoring the motor recovery and delivery of the individualized intervention, though our results must be interpreted with obvious caution given the absence of a control group.
The wearable elbow robot training combined with both passive and active movement has several unique features. First, the intelligent control algorithm provides a forceful while safe stretching to the upper limb muscles, which can prevent muscle contracture and joint stiffness (Ren et al., ; Salazar et al., ). Also, the strong stretching may enhance somatosensory input that helps drive neural reorganization (Behm et al., ). Second, an assisted-as-needed scheme was applied in the active movement training, which would be particularly useful for the subacute stroke survivors with moderate to severe motor impairment that has limited capability to generate active movement. If the patient could generate active arm movement, the robot would provide resistance that further enhances the movement control; if the patient could initiate movement but with difficulty to complete the required movement, the robot would help the patient to finish the rest of the movement; or if the patient was unable to generate active movement, the robot would assist the patient to passively move the arm to the desired position. Indeed, applying passive movement before clinical recovery has been proven that can elicit cortical activation patterns that may be critical for the restoration of motor function (Matteis et al., ). The robotic assistance would progressively reduce through the training that further promotes motor learning (Winstein et al., ). Third, the robot with a highly sensitive force sensor can discern the emergence of the subtle change of tiny movement. For patients, this subtle movement change was further augmented and displayed on the computer screen to provide real-time visual feedback that motivates and guides the patient to improve joint torque generation (Ren et al., ). For clinicians, the detection by the robot could provide essential information to optimize the treatment goal and assist rehabilitation plan. In , the patient was unable to generate active muscle strength in elbow flexors until after five sessions of training, and this slight change was successfully detected by the robot.
Utilizing the robot, immediate biomechanical measures can be made before and after each training session, and further plot a recovery curve of the biomechanical outcome change ( ). Though we can observe fluctuation of performance that may occur between sessions, overall, the patients demonstrated an improving trend over the training sessions. However, fluctuation patterns may imply that for longitudinal intervention trials, the multiple-session assessment may provide a better estimation of participants’ condition than a single-session assessment. Furthermore, consistent with previous clinical trials (Takahashi et al., ; Takebayashi et al., ), our stratified recovery curve informed that patients with severe motor impairment were likely to benefit more from robotic training compared to those with moderate motor impairment. We believe this recovery could provide important information to guide the clinical application of robot training (Veerbeek et al., ). First, the recovery curve can assist clinicians to estimate the recovery pattern of the patients, thus optimizing the treatment plan; second, with a larger sample size to plot the recovery curve, we may be able to estimate the minimal training sessions to achieve the desired outcome, which may assist Medicare policymaking.
A limitation of the present study is the lack of a control group to isolate the effect of spontaneous motor recovery. However, we applied the clinical measures including FMA-UE and Motricity Index (MI) for upper limb muscle strength at baseline, immediately after the intervention program, and 1-month after the termination of the program, which could serve as a self-control comparison ( ). Immediately after the training program, the median score of FMA-UE significantly increased to 14, which is larger than 9, the minimal clinically important difference (MCID) value of FMA-UE for subacute poststroke patients (Narayan Arya et al., ). After a 1-month follow-up, there were significant changes in the FMA-UE and MI. Collectively, the outcome of clinical measures could further support the effectiveness of in-bed wearable training in the improvement of upper limb motor limb function and minimize the confounding of spontaneous recovery.
There are limitations to the study. First, patients in the study also received routine in-patient rehabilitation, which could contribute to their improvement. However, this study demonstrated the feasibility to incorporate the in-bed robot training with routine inpatient rehabilitation training, which may not focus on sensorimotor rehabilitation. Anecdotally, the patients enjoyed the robot training and were highly motivated. However, we did not have a treatment satisfaction rating using the Likert system to evaluate patients’ responses, which should be adopted in future studies. Second, patients in the present study had mild or absent elbow spasticity which may limit the generalizability of the study. The mild or absent elbow spasticity would be due to most of the patients being at the early subacute stage (7 days to 3 months post-stroke; Bernhardt et al., ) that spasticity had not been developed yet (Wissel et al., ). In fact, one patient 1-month post-stroke with zero MAS at the elbow showed wrist spasticity of two at the follow-up and two other early subacute patients showed an increase of MAS from zero at the baseline to one at post-training. Also, the forceful passive elbow stretching by the robot might help control spasticity (Ren et al., ). Nonetheless, appropriate quantification of spasticity is important. MAS may not be a valid and ordinal level measure of muscle spasticity (Pandyan et al., ). Future studies can consider using the modified Modified Ashworth Scale, which has been suggested to be better inter-rater and intra-session reliability than the MAS to measure spasticity (Ansari et al., , ). Also, Brunnstrom recovery stages (BRS) can be used to evaluate the changes of muscle tone, synergistic movements, and active isolated movement (Naghdi et al., ). Third, only a small number of in-patients participated in the study. The goodness-of-fit value for curve fitting was thus relatively low. In the future, a strictly designed randomized control trial with a large sample size with different motor impairment levels is needed.
In conclusion, the above study demonstrated the feasibility of using in-bed wearable elbow robot-aided rehabilitation training in subacute stroke survivors with moderate to severe upper limb motor impairment. Furthermore, robotic therapy may result in significant improvement across biomechanical and clinical measures. The recovery curve generated from the robot biomechanical measures could provide useful information to guide the clinical applications of robot-aided training. Patients with severe motor impairment may benefit more from the robot training compared to those with less severe impairment.
## Data Availability Statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics Statement
The studies involving human participants were reviewed and approved by the ethics committee of Presbyterian Medical Center, Jeonbuk, South Korea. The patients/participants provided their written informed consent to participate in this study.
## Author Contributions
All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.
## Conflict of Interest
L-QZ has an ownership in Rehabtek LLC, which received U.S. federal fundings in developing the rehabilitation robot used in this study. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
|
Osteopontin (OPN) is a multifunctional adhesive glycoprotein that is implicated in a variety of pro-inflammatory as well as neuroprotective and repair-promoting effects in the brain. As a first step towards understanding the role of OPN in retinal degeneration (RD), we examined changes in OPN expression in a mouse model of RD induced by exposure to a blue light-emitting diode (LED). RD was induced in BALB/c mice by exposure to a blue LED (460 nm) for 2 h. Apoptotic cell death was evaluated by terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assay. In order to investigate changes in OPN in RD, western blotting and immunohistochemistry were performed. Anti-OPN labeling was compared to that of anti-glial fibrillary acidic protein (GFAP), which is a commonly used marker for retinal injury or stress including inflammation. OPN expression in RD retinas markedly increased at 24 h after exposure, was sustained through 72 h, and subsided at 120 h. Increased OPN expression was observed co-localized with microglial cells in the outer nuclear layer (ONL), outer plexiform layer (OPL), and subretinal space. Expression was restricted to the central retina in which photoreceptor cell death occurred. Interestingly, OPN expression in the ONL/OPL was closely associated with microglia, whereas most of the OPN plaques observed in the subretinal space were not. Immunogold electron microscopy demonstrated that OPN was distributed throughout the cytoplasm of microglia and in nearby fragments of degenerating photoreceptors. In addition, we found that OPN was induced more acutely and with greater region specificity than GFAP. These results indicate that OPN may be a more useful marker for retinal injury or stress, and furthermore act as a microglial pro-inflammatory mediator and a phagocytosis-inducing opsonin in the subretinal space. Taken together, our data suggest that OPN plays an important role in the pathogenesis of RD. |
Transient global cerebral ischemia (tGCI) causes excessive release of glutamate from neurons. Astrocytic glutamate transporter-1 (GLT-1) and glutamine synthetase (GS) together play a predominant role in maintaining glutamate at normal extracellular concentrations. Though our previous studies reported the alleviation of tGCI-induced neuronal death by hypoxic preconditioning (HPC) in hippocampal Cornu Ammonis 1 (CA1) of adult rats, the underlying mechanism has not yet been fully elaborated. In this study, we aimed to investigate the roles of GLT-1 and GS in the neuroprotection mediated by HPC against tGCI and to ascertain whether these roles can be regulated by connexin 43 (Cx43) and cellular-Src (c-Src) activity. We found that HPC decreased the level of extracellular glutamate in CA1 after tGCI via maintenance of GLT-1 expression and GS activity. Inhibition of GLT-1 expression with dihydrokainate (DHK) or inhibition of GS activity with methionine sulfoximine (MSO) abolished the neuroprotection induced by HPC. Also, HPC markedly upregulated Cx43 and inhibited p-c-Src expression in CA1 after tGCI, whereas inhibition of Cx43 with Gap26 dramatically reversed this effect. Furthermore, inhibition of p-c-Src with 4-amino-5-(4-chlorophenyl)-7-(t-butyl) pyrazolo (3, 4-d) pyrimidine (PP2) decreased c-Src activity, increased protein levels of GLT-1 and Cx43, enhanced GS activity, and thus reduced extracellular glutamate level in CA1 after tGCI. Collectively, our data demonstrated that reduced extracellular glutamate induced by HPC against tGCI through preventing the reduction of GLT-1 expression and maintaining GS activity in hippocampal CA1, which was mediated by upregulating Cx43 expression and inhibiting c-Src activity. |
Chronic pain is a main symptom of osteoarthritis (OA). Moreover, a high percentage of OA patients suffer from mental health problems. The endocannabinoid (EC) system has attracted attention as an emerging drug target for pain treatment together with its activity on the mesolimbic reward system. Understanding the circuits that govern the <i>reward</i> of <i>pain relief</i> is crucial for the search for effective analgesics. Therefore, we investigated the role of the EC system on dopamine (DA) and noradrenaline (NA) in an animal model of OA-related chronic pain. OA rats exhibited significant decreases in DA metabolism in the nucleus accumbens (NAc), striatum (STR) and hippocampus (HC). NA metabolism was also significantly decreased by chronic pain in OA rats; however, this disruption was limited to the frontal cortex (FCx) and HC. URB597 (an inhibitor of EC metabolism) treatment completely reversed the decreased DA metabolism, especially in the brain reward system and the HC. Furthermore, administration of URB597 normalized the impairment of NA activity in the HC but potentiated the decreased NA levels in the FCx. Our results demonstrated that chronic pain in OA rats was reflected by the inhibition of mesolimbic and mesocortical dopaminergic transmission, and may indicate the pro-pain role of NA in the FCx. The data provide understanding about changes in neurotransmission in chronic pain states and may explain the clinical improvement in perceived life quality following cannabinoid treatment. Additional mechanistic studies in preclinical models examining the intersection between chronic pain and reward circuits may offer new approaches for improving pain therapy. |
Many postsynaptic proteins undergo palmitoylation, the reversible attachment of the fatty acid palmitate to cysteine residues, which influences trafficking, localization, and protein interaction dynamics. Both palmitoylation by palmitoyl acyl transferases (PAT) and depalmitoylation by palmitoyl-protein thioesterases (PPT) is regulated in an activity-dependent, localized fashion. Recently, palmitoylation has received attention for its pivotal contribution to various forms of synaptic plasticity, the dynamic modulation of synaptic strength in response to neuronal activity. For instance, palmitoylation and depalmitoylation of the central postsynaptic scaffold protein postsynaptic density-95 (PSD-95) is important for synaptic plasticity. Here, we provide a comprehensive review of studies linking palmitoylation of postsynaptic proteins to synaptic plasticity. |
2,3,5-Triphenyltetrazolium chloride (TTC) staining is a commonly used method to determine the volume of the cerebral infarction in experimental stroke models. The TTC staining protocol is considered to interfere with downstream analyses, and it is unclear whether TTC-stained brain samples can be used for biochemistry analyses. However, there is evidence indicating that, with proper optimization and handling, TTC-stained brains may remain viable for protein analyses. In the present study, we aimed to rigorously assess whether TTC can reliably be used for western blotting of various markers. In this study, brain samples obtained from C57BL/6 male mice were treated with TTC (TTC+) or left untreated (TTC-) at 1 week after photothrombotic occlusion or sham surgery. Brain regions were dissected into infarct, thalamus, and hippocampus, and proteins were extracted by using radioimmunoprecipitation assay buffer. Protein levels of apoptosis, autophagy, neuronal, glial, vascular, and neurodegenerative-related markers were analyzed by western blotting. Our results showed that TTC+ brains display similar relative changes in most of the markers compared with TTC- brains. In addition, we validated that these analyses can be performed in the infarct as well as other brain regions such as the thalamus and hippocampus. Our findings demonstrate that TTC+ brains are reliable for protein analyses using western blotting. Widespread adoption of this approach will be key to lowering the number of animals used while maximizing data. |
Neuroinflammation is initiated with an aberrant innate immune response in the central nervous system (CNS) and is involved in many neurological diseases. Inflammasomes are intracellular multiprotein complexes that can be used as platforms to induce the maturation and secretion of proinflammatory cytokines and pyroptosis, thus playing a pivotal role in neuroinflammation. Among the inflammasomes, the nucleotide-binding oligomerization domain-, leucine-rich repeat- and pyrin domain-containing 3 (NLRP3) inflammasome is well-characterized and contributes to many neurological diseases, such as multiple sclerosis (MS), Alzheimer's disease (AD), and ischemic stroke. MS is a chronic autoimmune disease of the CNS, and its hallmarks include chronic inflammation, demyelination, and neurodegeneration. Studies have demonstrated a relationship between MS and the NLRP3 inflammasome. To date, the pathogenesis of MS is not fully understood, and clinical studies on novel therapies are still underway. Here, we review the activation mechanism of the NLRP3 inflammasome, its role in MS, and therapies targeting related molecules, which may be beneficial in MS. |
Neuronal calcium sensor (NCS) proteins, a sub-branch of the EF-hand superfamily, are expressed in the brain and retina where they transduce calcium signals and are genetically linked to degenerative diseases. The amino acid sequences of NCS proteins are highly conserved but their physiological functions are quite distinct. Retinal recoverin and guanylate cyclase activating proteins (GCAPs) both serve as calcium sensors in retinal rod cells, neuronal frequenin (NCS1) modulates synaptic activity and neuronal secretion, K channel interacting proteins (KChIPs) regulate ion channels to control neuronal excitability, and DREAM (KChIP3) is a transcriptional repressor that regulates neuronal gene expression. Here we review the molecular structures of myristoylated forms of NCS1, recoverin, and GCAP1 that all look very different, suggesting that the sequestered myristoyl group helps to refold these highly homologous proteins into very different structures. The molecular structure of NCS target complexes have been solved for recoverin bound to rhodopsin kinase (RK), NCS-1 bound to phosphatidylinositol 4-kinase, and KChIP1 bound to A-type K channels. We propose that N-terminal myristoylation is critical for shaping each NCS family member into a different structure, which upon Ca -induced extrusion of the myristoyl group exposes a unique set of previously masked residues that interact with a particular physiological target.
## Introduction
Intracellular calcium (Ca ) regulates a variety of neuronal signal transduction processes in the brain and retina (Berridge et al., ; Augustine et al., ). The effects of changes in neuronal Ca are mediated primarily by a subclass of neuronal calcium sensor (NCS) proteins (Ames et al., ; Braunewell and Gundelfinger, ; Burgoyne and Weiss, ; Burgoyne et al., ; Weiss et al., ) that belong to the EF-hand superfamily (Moncrief et al., ; Ikura, ; Ikura and Ames, ). The human genome encodes 14 members of the NCS family (Weiss and Burgoyne, ). The amino acid sequences of NCS proteins are highly conserved from yeast to humans (Figure ). Recoverin, the first NCS protein to be discovered, and the guanylate cyclase activating proteins (GCAPs) are expressed exclusively in the retina where they serve as Ca sensors in vision (Dizhoor et al., , ; Palczewski et al., , ; Stephen et al., ). Other NCS proteins are expressed in the brain and spinal cord such as neurocalcin (Hidaka and Okazaki, ), frequenin (NCS1) (Pongs et al., ; McFerran et al., ), visinin-like proteins (Bernstein et al., ; Braunewell and Klein-Szanto, ), K channel interacting proteins (KChIPs) (An et al., ), DREAM/calsenilin (Buxbaum et al., ; Carrion et al., ), and hippocalcin (Kobayashi et al., , ; Tzingounis et al., ). Frequenin is also expressed outside of the central nervous system (Kapp et al., ) as well as in invertebrates including flies (Pongs et al., ), worms (Gomez et al., ), and yeast (Frq1) (Hendricks et al., ; Huttner et al., ; Hamasaki-Katagiri et al., ). The common features of these proteins are an approximately 200-residue chain containing four EF-hand motifs, the sequence CPXG in the first EF-hand that markedly impairs its capacity to bind Ca , and an amino-terminal myristoylation consensus sequence.
Amino acid sequence alignment of selected NCS proteins (sequence numbering is for S. pombe NCS1). Secondary structure elements (helices and strands), EF-hand motifs (EF1 green, EF2 red, EF3 cyan, and EF4 yellow), and residues that interact with the myristoyl group (highlighted magenta) are indicated. Swiss Protein Database accession numbers are Q09711 ( S. pombe Ncs1), Q06389 ( S. cerevisiae Frq1), P21457 (bovine recoverin), and P43080 (human GCAP1).
The amino acid sequences of the NCS proteins are all quite similar and their sequence identities range from 35% to 60% (Figure ). Residues in the EF-hand regions are the most highly conserved, particularly in the Ca -binding loops and exposed hydrophobic residues in EF1 and EF2 (W30, F35, C39, F49, I52, Y53, F69, F82, L89). The sequence in the fourth EF-hand is somewhat variable and may explain why Ca binds to EF4 in some NCS proteins [frequenin (Cox et al., ; Ames et al., ) and GCAPs (Peshenko and Dizhoor, ; Stephen et al., )] but not in others [recoverin (Ames et al., ), and VILIPs (Cox et al., ; Li et al., )]. Non-conserved residues are also found near the C-terminus and linker between EF3 and EF4 that both interact with target proteins/membranes and may play a role in target specificity.
The structurally similar NCS proteins have remarkably different physiologic functions (Table ). Perhaps the best characterized NCS protein is recoverin that serves as a calcium sensor in retinal rod cells. Recoverin prolongs the lifetime of light-excited rhodopsin (Kawamura, ; Erickson et al., ; Makino et al., ) by inhibiting rhodopsin kinase (RK) only at high Ca levels (Calvert et al., ; Chen et al., ; Klenchin et al., ; Komolov et al., ). Hence, recoverin makes receptor desensitization Ca -dependent, and the resulting shortened lifetime of rhodopsin at low Ca levels may promote visual recovery and contribute to the adaptation to background light. Recoverin may also function in the rod inner segment (Strissel et al., ) and was identified as the antigen in cancer-associated retinopathy, an autoimmune disease of the retina caused by a primary tumor in another tissue (Polans et al., ; Subramanian and Polans, ). Other NCS proteins in retinal rods include the GCAP1 and GCAP2 that activate retinal guanylate cyclase only at low Ca levels and inhibit the cyclase at high Ca (Dizhoor et al., ; Palczewski et al., , ). GCAPs also bind functionally to Mg at low Ca levels in light-adapted photoreceptors (Peshenko and Dizhoor, , ), and Mg binding to the second and third EF-hands stabilizes a conformational form of GCAPs needed to activate the cyclase (Peshenko and Dizhoor, ; Lim et al., ). GCAPs are important for regulating the recovery phase of visual excitation and particular mutants are linked to various forms of retinal degeneration (Semple-Rowland et al., ; Sokal et al., ; Baehr and Palczewski, ; Bondarenko et al., ; Jiang and Baehr, ). Yeast and mammalian frequenins bind and activate a particular PtdIns 4-OH kinase isoform (Pik1 gene in yeast) (Hendricks et al., ; Kapp et al., ; Strahl et al., , ) required for vesicular trafficking in the late secretory pathway (Hama et al., ; Walch-Solimena and Novick, ). Mammalian frequenin (NCS1) also regulates voltage-gated Ca and K channels (Weiss et al., ; Nakamura et al., ). The KChIPs regulate the gating kinetics of voltage-gated, A-type K channels (An et al., ). The DREAM/calsenilin/KChIP3 protein binds to specific DNA sequences in many genes, including prodynorphin and c-fos (Carrion et al., ; Mellstrom et al., ). DREAM forms a tetramer that binds to DNA only in the absence of Ca (Carrion et al., , ) and serves as a calcium sensor and transcriptional repressor for pain modulation (Cheng et al., ; Lilliehook et al., ). Hence, the functions of the NCS proteins all appear to be quite diverse and non-overlapping.
Function of NCS proteins .
Mass spectrometric analysis of retinal recoverin and some of the other NCS proteins revealed that they are myristoylated at the amino terminus (Dizhoor et al., ; Kobayashi et al., ; Ladant, ). Recoverin contains an N-terminal myristoyl (14:0) or related fatty acyl group (12:0, 14:1, 14:2). Retinal recoverin and myristoylated recombinant recoverin, but not unmyristoylated recoverin, bind to membranes in a Ca -dependent manner (Zozulya and Stryer, ; Dizhoor et al., ). Likewise, bovine neurocalcin and hippocalcin contain an N-terminal myristoyl group and both exhibit Ca -induced membrane binding (Ladant, ). These findings led to the proposal that NCS proteins possess a Ca -myristoyl switch (Figure ). The covalently attached fatty acid is highly sequestered in recoverin in the calcium-free state. The binding of calcium to recoverin leads to the extrusion of the fatty acid, making it available to interact with lipid bilayer membranes or other hydrophobic sites. The Ca -myristoyl switch function by recoverin also enables its light-dependent protein translocation in retinal rods (Strissel et al., ).
Schematic diagram of calcium-myristoyl switch in recoverin. The binding of two Ca ions promotes the extrusion of the myristoyl group and exposure of other hydrophobic residues (marked by the shaded oval). This figure was adapted from and originally published by Zozulya and Stryer ( ).
In this review, the atomic-level structures of various NCS proteins and their target complexes will be discussed and compared with that of calmodulin. We begin by examining the large effect of N-terminal myristoylation on the structures of recoverin, GCAP1, and NCS1. Ca -induced extrusion of the myristoyl group exposes unique hydrophobic binding sites in each protein that in turn interact with distinct target proteins. An emerging theme is that N-terminal myristoylation is critical for shaping each NCS family member into a unique structure, which upon Ca -induced extrusion of the myristoyl group exposes a unique set of previously masked residues, thereby exposing a distinctive ensemble of hydrophobic residues to associate specifically with a particular physiological target.
## Structure of recoverin's calcium-myristoyl switch
The x-ray crystal structure of recombinant unmyristoylated recoverin (Flaherty et al., ; Weiergraber et al., ) showed it to contain a compact array of EF-hand motifs, in contrast to the dumbbell shape of calmodulin (Babu et al., ) and troponin C (Herzberg and James, ). The four EF-hands are organized into two domains: the first EF-hand, EF-1 (residues 27–56, colored green in Figures and ), interacts with EF-2 (residues 63–92, red) to form the N-terminal domain, and EF-3 (residues 101–130, cyan), and EF-4 (residues 148–177, yellow) form the C-terminal domain. The linker between the two domains is U-shaped rather than α-helical. Ca is bound to EF-3 and Sm (used to derive phases) is bound to EF-2. The other two EF hands possess novel features that prevent ion binding. EF-1 is disrupted by a Cys-Pro sequence in the binding loop. EF-4 contains an internal salt bridge in the binding loop that competes with Ca binding. Myristoylated recoverin, the physiologically active form has thus far eluded crystallization.
Three-dimensional structures of myristoylated recoverin with 0 Ca bound (A), 1 Ca bound (B), and 2 Ca bound (C). The first step of the mechanism involves the binding of Ca to EF-3 that causes minor structural changes within the EF-hand that sterically promote a 45° swiveling of the two domains, resulting in a partial unclamping of the myristoyl group and a dramatic rearrangement at the domain interface. The resulting altered interaction between EF-2 and EF-3 facilitates the binding of a second Ca to the protein at EF-2 in the second step, which causes structural changes within the N-terminal domain that directly lead to the ejection of the fatty acyl group.
The structures of myristoylated recoverin in solution with 0, 1, and 2 Ca bound have been determined by nuclear magnetic resonance (NMR) spectroscopy (Tanaka et al., ; Ames et al., ) (Figure ). In the Ca -Stateplacefree state, the myristoyl group is sequestered in a deep hydrophobic cavity in the N-terminal domain. The cavity is formed by five α-helices. The two helices of EF-1 (residues 26–36 and 46–56), the exiting helix of EF-2 (residues 83–93), and entering helix of EF-3 (residues 100–109) lie perpendicular to the fatty acyl chain and form a box-like arrangement that surrounds the myristoyl group laterally. A long, amphipathic α-helix near the N-terminus (residues 4–16) packs closely against and runs antiparallel to the fatty acyl group, and serves as a lid on top of the four-helix box. The N-terminal residues Gly 2 and Asn 3 form a tight hairpin turn that connects the myristoyl group to the N-terminal helix. This turn positions the myristoyl group inside the hydrophobic cavity and gives the impression of a cocked trigger. The bond angle strain stored in the tight hairpin turn may help eject the myristoyl group from the pocket once Ca binds to the protein.
The structure of myristoylated recoverin with one Ca bound at EF-3 (half saturated recoverin, Figure ) (Ames et al., ) represents a hybrid structure of the Ca -free and Ca -saturated states. The structure of the N-terminal domain (residues 2–92, green and red in Figure ) of half saturated recoverin (Figure ) resembles that of Ca -free state (Figure ) and is very different from that of the Ca -saturated form (Figure ). Conversely, the structure of the C-terminal domain (residues 102–202, cyan and yellow in Figure ) of half saturated recoverin more closely resembles that of the Ca -saturated state. Most striking in the structure of half saturated recoverin is that the myristoyl group is flanked by a long N-terminal helix (residues 5–17) and is sequestered in a hydrophobic cavity containing many aromatic residues from EF-1 and EF-2 (F23, W31, Y53, F56, F83, and Y86). An important structural change induced by Ca binding at EF-3 is that the carbonyl end of the fatty acyl group in the half saturated species is displaced far away from hydrophobic residues of EF-3 (W104 and L108, Figures ) and becomes somewhat solvent exposed. By contrast, the myristoyl group of Ca -free recoverin is highly sequestered by residues of EF-3 (Tanaka et al., ).
The structure of myristoylated recoverin with two Ca bound shows the amino-terminal myristoyl group to be extruded (Ames et al., ) (Figure ). The N-terminal eight residues are solvent exposed and highly flexible and thus serve as a mobile arm to position the myristoyl group outside the protein when Ca is bound. The flexible arm is followed by a short α-helix (residues 9–17) that precedes the four EF-hand motifs, arranged in a tandem array as was seen in the x-ray structure. Calcium ions are bound to EF-2 and EF-3. EF-3 has the canonical “open conformation” similar to the Ca occupied EF-hands in calmodulin and troponin C. EF-2 is somewhat unusual and the helix-packing angle of Ca -bound EF-2 (120°) in recoverin more closely resembles that of the Ca -free EF-hands (in the “closed conformation”) found in calmodulin and troponin C. The overall topology of Ca -bound myristoylated recoverin is similar to the x-ray structure of unmyristoylated recoverin described above. The root-mean-square (RMS) deviation of the main chain atoms in the EF-hand motifs is 1.5 Å in comparing Ca -bound myristoylated recoverin to unmyristoylated recoverin. Hence, in Ca -saturated recoverin, the N-terminal myristoyl group is solvent exposed and does not influence the interior protein structure.
The Ca -induced exposure of the myristoyl group (Figures and ) enables recoverin to bind to membranes only at high Ca (Zozulya and Stryer, ; Lange and Koch, ). Recent solid-state NMR studies have determined the structure of Ca -bound myristoylated recoverin bound to oriented lipid bilayer membranes (Figure ) (Valentine et al., ). Membrane-bound recoverin appears to retain approximately the same overall structure as it has in solution (Valentine et al., ). The protein is positioned on the membrane surface such that its long molecular axis is oriented 45° with respect to the membrane normal. The N-terminal region of recoverin points toward the membrane surface, with close contacts formed by basic residues K5, K11, K22, K37, R43, and K84. This orientation of membrane-bound recoverin allows an exposed hydrophobic crevice (lined primarily by residues F23, W31, F35, I52, Y53, F56, Y86, and L90), near the membrane surface that may serve as a potential binding site for the target protein, RK (Figure ).
Main chain structure (A) and space-filling representation (B) of myristoylated recoverin bound to oriented lipid bilayers determined by solid-state NMR [Valentine et al., ( )]. Hydrophobic residues are yellow, bound Ca ions are orange, and charged residues are red and blue.
## Structural diversity of NCS proteins
### Myristoylation reshapes structure of NCS proteins
Three-dimensional structures have been determined for myristoylated NCS proteins: recoverin (Ames et al., ), GCAP1 (Stephen et al., ), and NCS1 (Lim et al., ) that each contain a sequestered myristoyl group (Figure ). Surprisingly, the myristoylated forms of GCAP1, NCS1, and recoverin all have very distinct three-dimensional folds (Figure ). The overall RMS deviations are 2.8 and 3.4 Å when comparing the main chain structures of Ca -free NCS1 with recoverin and GCAP1, respectively. These very different structures reveal that the N-terminal myristoyl group is sequestered inside different protein cavities at different locations in each case. In NCS1, the N-terminal myristoyl group is sequestered inside a cavity near the C-terminus formed between the helices of EF3 and EF4 (Figure ). The fatty acyl chain in NCS1 is nearly parallel to the helices of EF3 and EF4 that form walls that surround the myristoyl moiety (Figure ). This arrangement in NCS1 is in stark contrast to recoverin where the myristoyl group is sequestered inside a protein cavity near the N-terminus (Figure ). The myristate in recoverin is wedged perpendicularly between the helices of EF1 and EF2 (Figure ) that contrasts with the parallel arrangement in NCS1 (Figure ). For GCAP1 (Figure ), the myristoyl group is located in between the N-terminal and C-terminal domains. In essence, the myristate bridges both domains of GCAP1 by interacting with helices at each end of the protein. The structural location and environment around the myristoyl group is very different in the various NCS proteins (Figure ). We suggest that each Ca -myristoyl switch protein may adopt a distinct structure because its N-terminal myristoyl group associates with patches of hydrophobic residues that are unique to that protein. We point out, however, that myristoylation is not required for the function of GCAP2 (Olshevskaya et al., ), suggesting that additional factors besides myristoylation must also play a role.
Main chain structures of Ca -free myrisoylated NCS1 (PDB ID: 212e) (A), recoverin (PDB ID: 1iku) (B), and GCAP1 (PDB ID: 2r2i) (C). Close-up views of the myristate binding pocket in NCS1 (D) , and recoverin (E) . EF-hands and myristoyl group (magenta) are colored as defined in Figure . Adapted from and originally published by Lim et al. ( ).
Non-conserved residues of NCS proteins interact closely with the N-terminal myristoyl group and help stabilize the novel protein structure in each case. NCS1, recoverin, and GCAP1 all have non-conserved residues near the N-terminus (called an N-terminal arm highlighted purple in Figure ) that make specific contacts with the myristoyl moiety. GCAP1 also contains an extra helix at the C-terminus that contacts the N-terminal arm and myristoyl group (Figure ). Thus, non-conserved residues at the N-terminus, C-terminus, and loop between EF3 and EF4 all play a role in creating a unique environment around the myristoyl group. In NCS1, the long N-terminal arm and particular hydrophobic residues in the C-terminal helix are crucial for placing the C14 fatty acyl chain in a cavity between EF3 and EF4 (Figure ). By contrast, the much shorter N-terminal arm in both recoverin and GCAP1 prevents the myristoyl group from reaching the C-terminal cavity and instead places the fatty acyl chain between EF1 and EF2 (Figure ). We propose that non-conserved residues at the N-terminus, C-terminus, and/or loop between EF3 and EF4 may play a role in forming unique myristoyl binding environments in other NCS proteins, such as VILIPs, neurocalcins, and hippocalcins that may help explain their capacity to associate with functionally diverse target proteins.
### Structures of Ca -bound NCS proteins
Three-dimensional structures have been determined for unmyristoylated forms of Ca -bound neurocalcin (Vijay-Kumar and Kumar, ), frequenin (Bourne et al., ), KChIP1 (Scannevin et al., ; Zhou et al., ), Frq1 (Ames et al., ), and GCAP2 (Ames et al., ). The first eight residues from the N-terminus are unstructured and solvent exposed in each case, consistent with an extruded myristoyl group that causes Ca -induced membrane localization of NCS proteins (Zozulya and Stryer, ; Bourne et al., ; Spilker et al., ). The overall main chain structures of the Ca -bound NCS proteins are very similar in each case, which is not too surprising given their sequence relatedness. However, if the main chain structures are so similar, then how can one explain their ability to bind unique target proteins? One distinguishing structural property is the number and location of bound Ca . Recoverin has Ca bound at EF-2 and EF-3; KChIP1 has Ca bound at EF-3 and EF-4; and frequenin, neurocalcin, and GCAP2 have Ca bound at EF-2, EF-3, and EF-4. Another important structural property is the distribution of charged and hydrophobic residues on the protein surface. Surface representations of hydrophobicity and charge density of the various NCS structures are shown in Figure . All NCS structures exhibit a similar exposed hydrophobic surface located on the N-terminal half of the protein, formed primarily by residues in EF-1 and EF-2 (F35, W31, F56, F57, Y86, and L90 for recoverin in Figure ). The exposed hydrophobic residues in this region are highly conserved (labeled and colored yellow in Figure ) and correspond to residues of recoverin that interact with the myristoyl group in the Ca -free state (Figure ). A similar hydrophobic patch is also seen in membrane-bound recoverin (Figure ). These exposed residues in the hydrophobic patch have been implicated in target recognition from mutagenesis studies [Ermilov et al. ( ); Krylov et al. ( ); Olshevskaya et al. ( ); Tachibanaki et al. ( )], and these residues very likely form intermolecular contacts with target proteins as has been demonstrated in the recent crystal structure of KChIP1 (see below).
Space-filling representations of the Ca -bound structures of recoverin (A), frequenin (B), neurocalcin (C), and KChIP1 (D). Exposed hydrophobic residues are yellow, neutral residues are white, and charge residues are red and blue.
The distribution of charged (red and blue) and hydrophobic (yellow) residues on the surface of the C-terminal half of the NCS proteins is highly variable (Figure ). Frequenin exhibits exposed hydrophobic residues in the C-terminal domain that fuse together with the exposed hydrophobic crevice in the N-terminal domain, forming one continuous and elongated patch (Figure ). By contrast, recoverin (Figure ) has mostly charged residues on the surface of the C-terminal half, whereas neurocalcin (Figure ) and KChIP1 (Figure ) have mostly neutral residues shown in white. The different patterns of charge distribution on the C-terminal surface of NCS proteins might be important for conferring target specificity.
Ca sensitive dimerization of NCS proteins is another structural characteristic that could influence target recognition. Neurocalcin (Vijay-Kumar and Kumar, ), recoverin (Flaherty et al., ), VILIP-1 (Li et al., ), and KChIP1 (Zhou et al., ) exist as dimers in their x-ray crystal structures. Hydrodynamic studies have confirmed that neurocalcin and DREAM form dimers in solution at high Ca and are monomeric in the Ca -free state (Olshevskaya et al., ; Osawa et al., ). Indeed, the NMR structure of Ca -bound DREAM forms a dimer in solution with intermolecular contacts involving Leu residues near the C-terminus (Lusin et al., ). By contrast, GCAP2 forms a dimer only in the Ca -free state and is monomeric at high Ca (Olshevskaya et al., ). Ca sensitive dimerization of GCAP-2 has been demonstrated to control its ability to activate retinal guanylate cyclase. Ca -sensitive protein oligomerization is also important physiologically for DREAM: the Ca -free DREAM protein serves as a transcriptional repressor by binding to DNA response elements as a protein tetramer (Carrion et al., ). Ca -induced dimerization of DREAM appears to disrupt DNA binding and may activate transcription of prodynorphin and c-fos genes (Carrion et al., ). In a related fashion, Ca -bound KChIP1 forms a dimer in solution and in complex with an N-terminal fragment of the Kv4.2 K channel (Zhou et al., ). By contrast, the full-length Kv4.2 channel tetramer binds to KChIP1 with a 4:4 stoichiometry (Kim et al., ), suggesting that KChIP1 dimers may assemble as a protein tetramer to recognize the channel. Such a protein tetramerization of KChIP1 may be Ca sensitive like it is for DREAM. The dimerization of VILIP-1 has been implicated in the trafficking of the dimeric α-subunit of the α β nicotinic acetylcholine receptor (nAChR) (Lin et al., ; Zhao et al., ; Li et al., ). In short, the oligomerization properties of some NCS proteins appear to be Ca sensitive and may play a role in target recognition.
## Target recognition by NCS proteins
### Recoverin bound to rhodopsin kinase fragment (RK25)
The structure of Ca -bound recoverin bound to the N-terminal region of rhodopsin kinase (residues 1–25, hereafter referred to as RK25) was the first atomic-resolution structure of a Ca -myristoyl switch protein bound to a functional target protein (Ames et al., ) (Figure ). The structure of this complex revealed that RK25 forms a long amphipathic α-helix, whose hydrophobic surface interacts with the N-terminal hydrophobic groove of recoverin described above (Figure ). The structure of recoverin in the complex is quite similar to that of Ca -bound recoverin alone in solution (RMS deviation = 1.8 Å). The structure of RK25 in the complex consists of an amphipathic α-helix (residues 4–16). The hydrophobic surface of the RK25 helix (L6, V9, V10, A11, F15) interacts with the exposed hydrophobic groove on recoverin (W31, F35, F49, I52, Y53, F56, F57, Y86, and L90). Previous mutagenesis studies on recoverin (Tachibanaki et al., ) and RK (Higgins et al., ; Komolov et al., ) have shown that many of the hydrophobic residues at the binding interface are essential for the high affinity interaction. These hydrophobic contacts are supplemented by a π-cation interaction involving F3 (RK25) and K192 from recoverin (Zernii et al., ). Dipolar residues on the opposite face of the RK25 helix (S5, T8, N12, I16) are solvent exposed. The helical structure of RK25 in the complex is stabilized mostly by hydrophobic intermolecular interactions with recoverin, as free RK25 in solution is completely unstructured.
Ribbon diagrams illustrating intermolecular interactions for recoverin bound to RK25 (A), KChIP1 bound to Kv4.32 (B), NCS1 (N-domain) bound to Pik1(111–151) (C), NCS1 (C-domain) bound to Pik1(111–151) (D), and space-filling view of NCS1 bound to Pik1(111–151) (E). In each case, a target helix (magenta) is inserted in groove formed by the helices of the EF-hands. The intermolecular interactions are mostly hydrophobic as described in the text.
The Ca -myristoyl switch mechanism of recoverin (i.e., Ca -induced extrusion of the N-terminal myristoyl group, Figure ) is structurally coupled to Ca -induced inhibition of RK (Calvert et al., ; Chen et al., ; Klenchin et al., ). The exposed hydrophobic residues of recoverin that interact with RK correspond to the same residues that contact the N-terminal myristoyl group in the structure of Ca -free recoverin (Ames et al., ). The size of the myristoyl group is similar to the length and width of the RK25 helix in the complex, which explains why both effectively compete for binding to the exposed hydrophobic groove (Figure ). The Ca -induced exposure of the N-terminal hydrophobic groove, therefore, explains why recoverin binds to RK only at high Ca levels. In the Ca -free state, the covalently attached myristoyl group sequesters the N-terminal hydrophobic groove and covers up the target-binding site. Ca -induced extrusion of the myristoyl group of recoverin causes exposure of residues that bind and inhibit RK (Figure ). This mechanism elegantly explains how recoverin controls both the localization and activity of RK in response to light. In the dark (high Ca ), Ca -bound recoverin binds to RK [thereby inhibiting it (Calvert et al., ; Chen et al., ; Klenchin et al., ; Komolov et al., )] and delivers RK to the membrane via a Ca -myristoyl switch that pre-positions RK near rhodopsin. Upon light activation (low Ca ), recoverin rapidly dissociates from both RK and the membrane, allowing RK to bind efficiently to its nearby substrate, rhodopsin, and cause rapid desensitization.
Schematic diagram of calcium-myristoyl switch coupled to target regulation illustrated for NCS1 (A) and recoverin (B). Adapted from and originally published by Lim et al. ( ).
### NCS1 bound to phosphatidylinositol 4-kinase fragment (Pik1)
The structure of Ca -bound NCS1 [or yeast Frq1 (Strahl et al., )] bound to a functional fragment of Pik1 [residues 111–159, hereafter referred to as Pik1(111–159)] was determined by NMR (Lim et al., ) (Figures ). The structure of NCS1 in the complex is very similar to the crystal structure in the absence of target (Bourne et al., ) with a concave solvent-exposed groove lined by two separate hydrophobic patches (highlighted yellow in Figure ). These two hydrophobic surfaces represent bipartite binding sites on NCS1 that interact with two helical segments in Pik1(111–159) (Figure ). The structure of Pik1(111–159) in the complex adopts a conformation that contains two α-helices (residues 114–127 and 143–156) connected by a disordered loop. The N-terminal helix contains hydrophobic residues (I115, C116, L119, and I123) that contact C-terminal residues of NCS1 (L101, W103, V125, V128, L138, I152, L155, and F169). Interestingly, these same hydrophobic residues in Ca -free NCS1 make close contacts with the myristoyl group. Therefore, Ca -induced extrusion of the myristoyl group causes exposure of hydrophobic residues in NCS1 that forms part of the Pik1 binding site (Figures and ). The C-terminal helix of Pik1(111–159) contains many hydrophobic residues (V145, A148, I150, and I154) that contact the exposed N-terminal hydrophobic groove of NCS1 (W30, F34, F48, I51, Y52, F55, F85, and L89), very similar to the exposed hydrophobic groove seen in all NCS proteins (Figure ). The two helices of Pik1(111–159) do not interact with one another or with the unstructured connecting loop and are highly stabilized by interactions with NCS1.
Non-conserved residues in NCS1 at the C-terminus and immediately following EF3 may be structurally important for explaining target specificity. The non-conserved C-terminal region of NCS1 (residues, 180–190) is structurally disordered in the target complex, in contrast to a well-defined C-terminal helix seen in Ca -free NCS1 in the absence of target (Lim et al., ). The C-terminal helix in Ca -free NCS1 (target free state) makes contact with the myristoyl group and residues in EF3 and EF4 (L101, A104, M121, I152, F169, and S173). These same residues in Ca -bound NCS1 make contact with Pik1 in the complex (Figure ). Therefore, the N-terminal Pik1 helix appears to substitute for and perhaps displace the C-terminal helix of Frq1, likely leading to the observed C-terminal destabilization in the complex (Figure ). The corresponding C-terminal helix of KChIP1 is similarly displaced upon its binding to the Kv4.3 channel (Pioletti et al., ; Wang et al., ) but not upon its binding to Kv4.2 (Zhou et al., ). The C-terminal helix in recoverin forms a stable interaction with EF3 and EF4, enabling the C-terminal helix to perhaps serve as a built-in competitive inhibitor that would presumably block its ability to bind to targets like Pik1 and Kv4.3. This role for the C-terminus may explain why the C-terminal sequences of NCS proteins are not well conserved (Figure ). Another non-conserved region of NCS1 implicated in target specificity is the stretch between EF3 and EF4 (residues 134–146). This region of NCS1 adopts a short α-helix in the complex that contacts the N-terminal helix of Pik1. By contrast, the region between EF3 and EF4 is unstructured in many other NCS proteins (Ames et al., ; Bourne et al., ; Scannevin et al., ; Zhou et al., ; Lusin et al., ).
The structure of the NCS1-Pik1 complex (Figures and ) suggest how a Ca -myristoyl switch might promote activation of PtdIns 4-kinase (Figure ). Under resting basal conditions, NCS1 exists in its Ca -free state with a sequestered myristoyl group buried in the C-domain that covers part of its binding site for PtdIns 4-kinase (highlighted yellow in Figure ) and prevents binding of NCS1 to Pik1. The fatty acyl chain has the same molecular dimensions (length and width) as the N-terminal helix of Pik1(111–159), which explains why the myristoyl group and Pik1 helix can effectively compete for the same binding site in NCS1. A rise in cytosolic Ca will cause Ca -induced conformational changes in NCS1, resulting in extrusion of the N-terminal myristoyl group. Ca -induced extrusion of the myristoyl group exposes a hydrophobic crevice in the C-terminal domain of NCS1 and, concomitantly, Ca -induced structural changes in its N-domain result in formation of a second exposed hydrophobic crevice, also seen in all other Ca -bound NCS proteins examined to date (Braunewell and Gundelfinger, ; Burgoyne and Weiss, ; Haeseleer et al., ; Zheng et al., ). These two separate hydrophobic sites on the surface of Ca -bound NCS1 are different from Ca -bound recoverin that contains only one exposed hydrophobic patch (Figure , inset) that interacts with a single target helix in RK (Ames et al., ). The two exposed hydrophobic sites on NCS1 bind to the hydrophobic faces of the two antiparallel amphipathic α-helices in Pik1(111–159) (colored magenta in 9). The Ca -induced binding of NCS1 to PtdIns 4-kinase may promote a structural changes that cause increased lipid kinase activity. Simultaneously, NCS1 binding to PtdIns 4-kinase will also promote membrane localization of the lipid kinase, because Ca -bound NCS1 contains an extruded myristoyl group that serves as a membrane anchor. Thus, NCS1 controls both delivery of PtdIns 4-kinase to the membrane where its substrates are located and formation of the optimally active state of the enzyme.
### Mechanisms of target recognition
NCS proteins bind to helical target proteins analogous to the target binding seen for CaM (Hoeflich and Ikura, ) (Figure ). Helical segments of target proteins bind to an exposed hydrophobic crevice formed by the two EF-hands in either the N-terminal or C-terminal domain in NCS proteins. In recoverin, the two N-terminal EF-hands form an exposed hydrophobic groove that interacts with a hydrophobic target helix from rhodopsin kinase (RK25) (Ames et al., ) (Figure ). The N-terminal EF-hands of KChIP1 interact with a target–helix derived from the T1 domain of Kv4.2 channels (Figure ) (Zhou et al., ) and Kv4.3 channels (Pioletti et al., ). The orientation of the target helices bound to recoverin and KChIP1 are somewhat similar: the C-terminal end of the target helix is spatially close to the N-terminal helix of EF-1. By contrast, the Pik1 target helix binds to NCS1 in almost the exact opposite orientation (Figure ). The N-terminal end of the Pik1 helix is closest to EF1 (green) in NCS1, whereas the C-terminal end of the RK25 target helix is closest to the corresponding region of recoverin. Non-conserved residues in NCS1 (G33 and D37) make important contacts with the Pik1 target helix and presumably assist in imposing the observed orientation of the helix. Thus, the requirement that the helix (in this case, from Pik1) must bind to NCS1 with a polarity opposite to that observed for the helices in other target-NCS family member complexes could clearly contribute to dictating the substrate specificity of frequenins, as compared to other NCS sub-types. Another important structural feature seen in the NCS1-Pik1 interaction is that two helical segments of the target are captured in the complex, whereas in the target complexes characterized for recoverin and KChIP1, only one helix is bound. Therefore, selective substrate recognition by NCS proteins may be explained by both by the orientation of the bound target helix; and, the number of target helices bound.
## Conclusions
We reviewed the molecular structures of NCS proteins and examined structural determinants important for target recognition. N-terminal myristoylation has a profound effect on the structures of Ca -free recoverin, GCAP1 and NCS1. Surprisingly, the sequestered myristoyl group interacts with quite different protein residues in each case and, therefore, is able to reshape these homologous NCS proteins into very different structures. The structures of the Ca -bound NCS proteins all contain an extruded N-terminus with an exposed hydrophobic crevice implicated in target binding. We propose that N-terminal myristoylation is critical for shaping each NCS family member into a unique structure, which upon Ca -induced extrusion of the myristoyl group exposes a unique set of previously masked residues, thereby exposing a distinctive ensemble of hydrophobic residues to associate specifically with a particular physiological target. Differences in their surface charge density and protein dimerization properties may also help to explain NCS target specificity and functional diversity. In the future, atomic resolution structures of additional NCS proteins both with a sequestered myristoyl group and in their extruded forms bound to their respective target proteins are needed to improve our understanding of how this structurally conserved family of proteins can uniquely recognize their diverse biological targets.
### Conflict of interest statement
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
|
Protein homeostasis serves as an important step in regulating diverse cellular processes underlying the function and development of the nervous system. In particular, the ubiquitination proteasome system (UPS), a universal pathway mediating protein degradation, contributes to the development of numerous synaptic structures, including the Drosophila olfactory-associative learning center mushroom body (MB), thereby affecting associated function. Here, we describe the function of a newly characterized Drosophila F-box protein CG5003, an adaptor for the RING-domain type E3 ligase (SCF complex), in MB development. Lacking CG5003 ubiquitously causes MB γ axon pruning defects and selective CG5003 expression in pan-neurons leads to both γ axon and α/β lobe abnormalities. Interestingly, change in CG5003 expression in MB neurons does not cause any abnormalities in axons, suggesting that CG5003 functions in cells extrinsic to MB to regulate its development. Mass spectrum analysis indicates that silencing CG5003 expression in all neurons affects expression levels of proteins in the cell and structural morphogenesis, transcription regulator activity, and catalytic activity. Our findings reinforce the importance of UPS and identify a new factor in regulating neuronal development as exemplified by the synaptic structure MB.
## Introduction
Diverse behavior outputs rely on compartmentalized brain structures that function in a circuitry fashion. Drosophila mushroom body (MB) is the main olfactory-associative learning center in the adult brain and composed of three types of Kenyon cells (KCs) derived from four neuroblasts, each of them sequentially generates the γ,α′/β′, and α/β neurons (Ito et al., ; Lee et al., ; Noveen et al., ). By late larval stage (L3, 3rd instar), γ axons bifurcate into dorsal and medial lobes, both are completely pruned by 18 h after puparium formation (18 h APF), then re-projected to form the medial γ lobes in adults. α′/β′ neurons begin also in the larval stage to develop with axons projecting along a peduncle tract anteriorly, then bifurcates into dorsal α′ and medial β′ lobes. Likewise, MB α/β neurons then develop into dorsal α and medial β lobes at the beginning of puparium formation. These developmental and remodeling events make MB a great system to analyze intrinsic or extrinsic mechanisms regulating neuronal development.
The ubiquitination proteasome system (UPS) is a widely used mechanism to control protein turnover (Dikic, ; Pohl and Dikic, ). The UPS degradation machinery comprises a major enzymatic cascade that targets and covalently links the ubiquitin (Ub) chains to specific substrates. After the E1 activating enzyme utilizes ATP to form a high-energy thioester bond with Ub, the activated Ub is transferred to the E2 conjugating enzyme. The E3 ligase, either HECT or Cullin-based RING-type, recognizes specific substrates and catalyzes Ub-substrate conjugation from E2. Ultimately, the ubiquitinated substrates are sent for destruction by the 26S proteasome. The S phase kinase-associated protein 1 (SKP1)–cullin 1 (CUL1)–F-box protein (SCF) complex, a better-studied multi-subunit RING-type E3 ligase, provides the substrate specificity via the adaptor F-box protein (Ho et al., , ). Substrates targeted for ubiquitination are often phosphorylated and interact with the substrate-binding domain of F-box protein (like WD repeats or leucine-rich repeats LRR).
Previous studies have shown that UPS regulates MB development (Watts et al., ; Zhu et al., ; Shin and DiAntonio, ; Wong et al., ; Meltzer et al., ). For instance, the E3 ligase Highwire is involved in MB axon guidance (Shin and DiAntonio, ), whereas Cul-1 and Cul-3 have been reported to regulate MB axon pruning and regrowth (Zhu et al., ; Wong et al., ), all in a cell-autonomous fashion. Here we report a newly characterized Drosophila F-box protein CG5003. CG5003 contains an F-box motif and interacts with Cul-1. Lack of CG5003 in the mutant background causes pruning defects of γ axons, indicating that CG5003 contributes to MB neuron remodeling. Also, selective CG5003 expression in pan-neurons, but not MB neurons, glia, nor DA neurons, causes both unpruned γ axons and thinned α/β lobes. Finally, mass spectrum analysis revealed possible CG5003 downstream effectors. These results suggest that CG5003 functions extrinsically to regulate MB development. Our findings identify a new factor in the UPS pathway that contributes to MB development.
## Materials and Methods
### Fly Strains and Genetics
Flies were maintained on standard fly food at 25°C with 70% humidity. All fly crosses were carried out at 25°C with standard laboratory conditions unless noted otherwise. All strains were obtained from Bloomington Stock Center, the Vienna Drosophila RNAi Center (VDRC), or as gifts from colleagues. Fly microinjection was conducted by the Drosophila Core Facility, Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences.
### Immunohistochemistry and Western Blot Analysis
Whole-mount Drosophila adult brains were first dissected and fixed with 4% paraformaldehyde for 45 min. Samples were washed with PBT (PBS + 0.3% TX-100) three times and dissected further to remove additional debris in PBS solution. Clean and fixed brains were blocked in PBT solution with 5% Normal Donkey Serum (NDS) and subsequently probed with primary and secondary antibodies in solution with 5% NDS at 4°C overnight. Primary antibodies used in this study included: mouse anti-FasII-1D4 (1:50, DSHB) and mouse anti-Trio (1:50, DSHB), and anti-CG5003 (1:100). Secondary antibodies used from Jackson Lab included: rabbit anti-HRP-TRITC (1:500), donkey anti-mouse-Cy5 (1:200), donkey anti-rabbit Cy3 (1:200), and donkey anti-rabbit Cy5 (1:200).
For western blot analysis, fly tissue samples from adult heads were collected and lysed in lysis buffer. Protein extracts were then subjected to SDS-PAGE gel using antibodies against CG5003 and β-Actin.
### Confocal Microscopy and Statistical Analysis
Images of brains at different developmental stages were acquired by merging a serial Z-stack of average 35–40 sections, each of 0.35–0.5 μm thickness, using the Nikon A1 confocal microscope with 40× or 60× objective. Depending on the desired regions, the whole brains were positioned so that they can be scanned anteriorly to posteriorly (top to bottom). Approximately 40 sections were scanned and merged for visualizing the anterior MB lobes. The acquired MBs labeled by GFP or antibodies such as α-FasII were analyzed and quantified for the lobe defects. Average 10–15 brains (20–30 α/β lobes) were analyzed. The exact N number for each genotype is indicated in all Figures. Data were shown mean ± SEM. P-values of significance (indicated with asterisks, * p < 0.05, ** p < 0.01, *** p < 0.001) were calculated using one-way ANOVA with Bonferroni multiple comparison test among three groups or above. Prism and SPSS software were used to complete the statistical analysis.
## Results
### CG5003 Mutant Exhibits MB γ Axon Pruning Defects
To investigate CG5003 function in MB development, we first verified if CG5003 is a component of the SCF complex. Co-IPpull-downs demonstrated that CG5003 interacts with Cul-1 ( ), suggesting that CG5003 is an adaptor protein for the SCF complex. Next, a p-element insertion fly line f02616 with the p-element inserted at the second exon of the gene region was examined (Flybase, ). This line exhibits pupal lethality, indicating the possibility that CG5003 expression levels are affected by the insertion. An EMS screen was then conducted to isolate additional alleles of CG5003 for experimental purposes. Among all lines, line 9-1 was found pupal lethal, and trans-heterozygotes of 9-1 over f02616 ( 9-1/f02616 ) also caused pupal lethality. These results indicate that flies of line 9-1 fail to complement f02616 and might carry a point mutation in CG5003 . To further support the notion, western blot analysis was done to examine CG5003 protein levels in these flies. Interestingly, CG5003 protein levels were drastically reduced in 9-1/f02616 mutant flies, indicating that these trans-heterozygotes are suitable for examining the consequence of lacking CG5003 ( ).
9-1/f02616 is a CG5003 mutant fly line. (A) Representative western blot images for CG5003 and Cul-1 interaction. Note that endogenous CG5003 proteins are in the same eluates with Cul-1, as pulled down by the anti-CG5003 antibodies. (B) Schematic diagram on CG5003 gene region. Note that f02616 is a p-element insertion line located at the second exon. (C) Representative western blot images for analyzing CG5003 protein levels in the 9-1/f02616 mutants. Note a decrease in the CG5003 protein levels in the trans-heterozygotes. (D) Representative images of fly pupae with the indicated genotypes at 90 h after puparium formation (APF). Note that white-eye 9-1/f02616 mutant pupae exhibit head eversion normally in a similar time scale with the control.
Taking advantage of 9-1/f02616 mutant flies, we first examined the developmental progress of these mutants. The head eversion occurs normally in 9-1/f02616 mutant pupae, suggesting that flies lacking CG5003 develop in a similar time scale as the wild-type flies ( ). Next, MB morphologies of 9-1/f02616 mutants at 3rd instar larvae, 18 h After Puparium Formation (APF), and 24 h APF were analyzed using the anti-FasII antibodies, a marker that stains α/β lobes strongly and γ lobes weakly (Crittenden et al., ). By 18 h APF, the γ axons were completely pruned in the wild-type MB. Whereas no significant difference of FasII-positive γ lobes across different genotypes was observed at 0 h APF, vertical γ lobes were present and left unpruned in 9-1/f02616 mutants at 18 h APF and 24 h APF (arrows and dashed areas in ). Statistical analysis indicated that a significantly higher portion of unpruned γ lobes was present in the 9-1/f02616 mutants ( ). Given that the head eversion occurs normally, it is likely that the overall animal development is normal until the lethal pupal stage. However, due to the presence of these unpruned γ lobes, we were not able to discern potential α/β lobes. Thus, we cannot rule out the possibility that a delay in MB development as shown by the possible absence of α/β lobes at this stage occurs. To demonstrate that CG5003 expression affects axon pruning, CG5003 is re-introduced into the mutant background by expressing CG5003 under the control of the pan-neuronal elav-Gal4 driver. Interestingly, pan-neuronal CG5003 expression partially rescued the mutant γ pruning defect ( ). Altogether, these results suggest that CG5003 is involved in MB γ axon pruning.
CG5003 mutants exhibit mushroom body (MB) γ axon pruning defects. (A–E) Representative confocal images (A,B) and quantifications (C–E) of MB γ lobe formation of control and CG5003 mutant fly brains at different developmental stages: 0 h APF, 18 h APF, and 24 h APF. Note that FasII-positive γ lobes are left unpruned at 18 h APF and 24 h APF in CG5003 mutant MBs. elav driven CG5003 expression partially rescued the mutant pruning defects. White arrows and dashed lines encircle the area of unpruned γ lobes. White arrowheads indicate the vertical α lobes. The N number of brains dissected and quantified for each genotype is indicated in the figure. Scale bar: 50 μm.
### Pan-Neuronal CG5003 Expression Causes MB γ Axon Pruning Defects
Based on the rescue results, we next examined whether pan-neuronal CG5003 expression alone causes any defects in γ axon pruning. Transgenic flies carrying the RNAi targeting CG5003 ( CG5003-RNAi , VDRC#26679) or CG5003 were expressed using elav-Gal4 . Efficiencies of these transgenes were validated by western blot analysis, revealing a corresponding reduction or increase in neuronal CG5003 protein levels ( elav>CG5003-RNAi or CG5003 , ). These flies develop as the head eversion occurs normally ( ). As we examined the MB morphologies in 18 h APF, γ axons in a small portion of flies with pan-neuronal CG5003-RNAi expression were left unpruned, whereas the ones in flies with pan-neuronal CG5003 expression were largely uneliminated and distorted ( ). Manipulation of CG5003 expression using other Gal4 s, such as C739-Gal4 (expresses in α/β neurons, ), 201Y-Gal4 (expresses in γ neurons, ), or TH-Gal4 (expresses in dopaminergic DA neurons, ), did not cause significant γ axon pruning defects. These results indicate that CG5003 likely functions in cells other than MB or DA neurons to regulate MB γ axon pruning.
Pan-neuronal CG5003 expression causes MB γ axon pruning defects. (A,B) Western blot analysis on the protein extracts collected from fly pupae with the indicated genotypes. Note that CG5003 protein levels increase or decrease when CG5003 or CG5003-RNAi are expressed, respectively. β-actin serves as an internal control. (C) Representative images of fly pupae with the indicated genotypes at 90 h APF. Note that red-eye elav>CG5003 or CG5003-RNAi pupae exhibit head eversion normally in a similar time scale with the control. (D,E) Representative confocal images (D) and quantifications (E) of MB γ lobe formation of control, CG5003 , and CG5003-RNAi fly pupae at 18 h APF. Note that FasII-positive γ lobes are left unpruned in MBs expressing CG5003 or CG5003-RNAi . White arrows and dashed lines encircle the area of unpruned γ lobes. White arrowheads indicate the vertical α lobes. The N number of brains dissected and quantified for each genotype is indicated in the figure. Scale bar: 50 μm.
### Pan-Neuronal CG5003 Expression Causes MB α/β Lobe Defects
In addition to γ axon pruning, we also investigated different stages of MB development such as α/β lobe formation in a later timeline. Interestingly, silencing CG5003 expression in all neurons or glia did not cause significant distortion in FasII-positive MB α/β and γ lobes of 3-day-old adult flies ( ). Neuronal overexpression of CG5003 , however, causes significant thinnings of α/β lobes, indicating that too much neuronal CG5003 disturbs the proper development of MB α/β lobes ( ). On the other hand, MB α/β lobes remain normal upon either upregulating or downregulating CG5003 expression in glia, suggesting that glial CG5003 does not play a significant role in regulating MB α/β lobe integrity ( ). Taken together, these results indicate that pan-neuronal CG5003 expression regulates MB α/β lobe development in addition to γ axon pruning.
Pan-neuronal CG5003 expression causes MB α/β lobe defects. (A–F) Representative confocal images (A,B) and quantifications (C–F) of MB α/β lobe formation of control and experimental fly brains at the adult stage. Note that FasII-positive α/β lobes are severely disrupted when CG5003 is expressed in all neurons. White arrows indicate the thinned α/β lobes. The N number of brains dissected and quantified for each genotype is indicated in the figure. Scale bar: 50 μm. ns, not significant; **** p < 0.000001.
### Selective CG5003 Expression in Subtypes of MB Neurons Does Not Affect MB α/β Lobe Integrity
To further investigate whether CG5003 functions in a cell type-specific manner, transgenic CG5003-RNAi or CG5003 was expressed under the control of different MB Gal4s that target all or subsets of MB neurons. Interestingly, no significant distortion in MB α/β and γ lobes of 3-day-old adult flies was observed when expressing CG5003-RNAi or CG5003 using OK107-Gal4 or mb247-Gal4 , suggesting that the defects caused by elav-Gal4 driven CG5003 expression do not depend on MB neurons ( ; ). Furthermore, expressing CG5003-RNAi or CG5003 using C739-Gal4 , C305α-Gal4 (targeting α’/α’ neurons), 201Y-Gal4 , or TH-Gal4 did not cause significant distortion of MB α/β, α’/β’, and γ lobes, indicating that CG5003 does not act intrinsically in MB or DA neurons ( ; ). Taken together, these results implicate that CG5003 likely functions in cells other than MB or DA neurons to regulate MB development.
α/β lobes remain normal when altering CG5003 expression in all or subsets of MB neurons. (A–O) Representative confocal images (A–M) and quantifications (B,C,E,F, H,I, K,L, N,O) of MB α/β lobe formation of control and experimental fly brains at the adult stage. Note that FasII-positive α/β lobes remain intact when CG5003 or CG5003-RNAi is expressed in all MB ( OK-107-Gal4 ), α/β ( C739-Gal4 ), α’/β’ ( C305 α-Gal4 ), γ ( 201Y-Gal4 ), or DA ( TH-Gal4 ) neurons. The N number of brains dissected and quantified for each genotype is indicated in the figure. Scale bar: 50 μm. ns, not significant.
### Mass Spectrum Analysis Reveals Possible CG5003 Downstream Effector Proteins
To gain insights into the mechanism of CG5003-mediated MB development, mass spectrum analysis was performed using samples from control and elav>CG5003-RNAi animals. A total of approximately 4,000 proteins that exhibit differential expression levels between two samples were identified and categorized following the protocol described previously (Li et al., ). Approximately 20 out of these proteins were identified with the highest values in the difference of expression levels were shown in . Interestingly, several novel genes including uncharacterized CG genes were identified. Moreover, a greater number of proteins involved in catalytic and transcription regulatory activity and fewer number of proteins involved in the structural morphogenesis were found to be affected by downregulating CG5003 expression in all neurons. Vice versa, upregulated CG5003 levels affect mainly the protein expression in the structural morphogenesis levels. Taken together, these results provide insights on possible CG5003 downstream effectors regulating MB development.
Mass spectrum analysis of CG5003 downstream effector proteins. (A,B) Representative proteins identified in mass spectrum analysis comparing samples from control and elav>CG5003-RNAi animals. The significance values are calculated based on protocols described previously (Li et al., ). Upregulated (A) and downregulated (B) proteins in different categories are shown. Protein annotations are indicated in each category.
## Discussion
UPS is commonly recognized as an important pathway regulating protein homeostasis using controlling protein degradation. Due to its prevalent roles, it is conceivable that UPS regulates the function and development of the nervous system. Our findings identify a new factor in the pathway, an adaptor F-box protein that regulates the development of MB. Lacking CG5003 in all tissues causes MB γ axon pruning defects, whereas overexpressing CG5003 in pan-neurons, but not MB nor DA neurons, leads to both γ axon pruning and α/β lobe thinning defects. These results demonstrate that CG5003 likely functions in cells other than MB or DA neurons in regulating MB development, further reinforcing the importance of UPS in neuronal development.
Previous studies have indicated that lacking the expression of the ubiquitin-activating enzyme (E1) or proteasome subunits in MB block γ axon pruning, suggesting that UPS is required for this process (Watts et al., ). It has also been shown that the E3 ligase Highwire regulates axon guidance of α/β neurons in a non-cell-autonomous fashion (Shin and DiAntonio, ). These findings all indicate an important requirement for UPS in the MB development. Interestingly, by analyzing the endogenous CG5003 expression using a CG5003 promoter-driven GFP fly line, it was found predominantly expressed in neuronal nuclei near the MB calyces, with some MB cell bodies expressing CG5003 within the calyces ( ). This expression pattern implicates that: first, CG5003 might function non-cell-autonomously in regulating MB development as its expression is predominantly seen outside of the MB calyces; second, a nuclear expression of CG5003 might help explain its control over various transcription factor activity, potential CG5003 downstream effectors as revealed by the mass spectrum analysis.
Our findings identify a new F-box protein CG5003 that regulates MB development in two aspects: axon pruning and axon integrity. These regulations are likely from other extrinsic neurons to MB neurons. Intriguingly, the lack of CG5003 causes similar pruning defects as pan-neuronal CG5003 expression. It is possible that the induced CG5003 expression results in an artificially higher level of CG5003 that causes a dominant-negative effect on pruning. Furthermore, the unpruned γ lobes were not detected in the adult stage, suggesting that other complementary mechanisms exist later in the MB development timeline to ensure pruning occurs and developmental events progress properly.
Since the change in CG5003 expression affects both axon pruning and axon integrity, it is likely that CG5003 regulates a master step upstream of MB axon development, for instance, MB neuron differentiation. By identifying possible downstream substrates of CG5003 using mass spectrum analysis, the detailed mechanism of how UPS regulates MB development will be unraveled. Some of these identified proteins may be expressed in MB and regulated by CG5003. Future work will be required to investigate this unique aspect of UPS-mediated neuronal development.
## Data Availability Statement
The original contributions presented in the study are included in the article/ , further inquiries can be directed to the corresponding author.
## Author Contributions
MY and MH conceived and designed the study. MY, YG, SW, CC, and YC performed the experiments. MY and MH analyzed the data and wrote the article. All authors read and approved the manuscript.
## Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
|
Parkinson’s disease (PD) is the second most common neurodegenerative disease associated with age. Early diagnosis of PD is key to preventing the loss of dopamine neurons. Peripheral-blood biomarkers have shown their value in recent years because of their easy access and long-term monitoring advantages. However, few peripheral-blood biomarkers have proven useful. This study aims to explore potential peripheral-blood biomarkers for the early diagnosis of PD. Three substantia nigra (SN) transcriptome datasets from the Gene Expression Omnibus (GEO) database were divided into a training cohort and a test cohort. We constructed a protein–protein interaction (PPI) network and a weighted gene co-expression network analysis (WGCNA) network, found their overlapping differentially expressed genes and studied them as the key genes. Analysis of the peripheral-blood transcriptome datasets of PD patients from GEO showed that three key genes were upregulated in PD over healthy participants. Analysis of the relationship between their expression and survival and analysis of their brain expression suggested that these key genes could become biomarkers. Then, animal models were studied to validate the expression of the key genes, and only SSR1 (the signal sequence receptor subunit1) was significantly upregulated in both animal models in peripheral blood. Correlation analysis and logistic regression analysis were used to analyze the correlation between brain dopaminergic neurons and SSR1 expression, and it was found that SSR1 expression was negatively correlated with dopaminergic neuron survival. The upregulation of SSR1 expression in peripheral blood was also found to precede the abnormal behavior of animals. In addition, the application of artificial intelligence technology further showed the value of SSR1 in clinical PD prediction. The three classifiers all showed that SSR1 had high predictability for PD. The classifier with the best prediction accuracy was selected through AUC and MCC to construct a prediction model. In short, this research not only provides potential biomarkers for the early diagnosis of PD but also establishes a possible artificial intelligence model for predicting PD.
## Introduction
Parkinson’s disease (PD) is a neurodegenerative disease principally defined by the motor symptoms of resting tremor, rigidity, and bradykinesia. These symptoms occur mainly because of the progressive loss of dopaminergic neurons in the substantia nigra pars compacta (SN; Damier et al., ; Kalia and Lang, ). However, the mechanism behind this neuronal loss remains largely unclear (Dauer and Przedborski, ). There is no cure for PD. The mainstay of its management is symptomatic treatment with drugs that increase dopamine concentrations or directly stimulate dopamine receptors (Kalia and Lang, ). Clinical diagnosis of PD is based on the presence of Parkinsonian motor features, but a significant proportion of nigral neurons are lost before the onset of motor symptoms (Lang and Lozano, ), meaning that clinical diagnosis is likely to occur too late for the administration of disease-modifying therapies. Therefore, in the management of PD, it is urgent to find reliable diagnostic and prognostic biomarkers of PD to prevent the loss of dopaminergic neurons at an early stage (Parnetti et al., ).
The latest biomarkers mainly detect α-synuclein (Visanji et al., ) and neuroimaging modalities (Brooks and Pavese, ). Cerebrospinal fluid (CSF) is close to the central nervous system, making it an ideal source of diagnostic markers for ongoing pathological processes. CSF α-synuclein appears to be reasonably sensitive and specific for PD (Hong et al., ; Mollenhauer et al., ). Total α-synuclein levels have been significantly decreased in PD patients compared with controls (Mollenhauer et al., , ). However, obtaining CSF is difficult, and repeated lumbar puncture is not conducive to long-term monitoring. The detection of α-synuclein in plasma and serum remains controversial; some researchers found that it was unaffected in PD patients (Smith et al., ), while another study found that it was lower in them than controls (Besong-Agbo et al., ). Dopamine transporter imaging and magnetic resonance imaging of the SN are sensitive and specific tools for PD (Benamer et al., ; Kagi et al., ; Lehericy et al., ). Although these techniques are very sensitive, they are expensive and involve radiation exposure, and it is not known how useful they are for the early detection of atypical PD (Frosini et al., ). As blood is easier, cheaper, and less invasive to obtain than cerebrospinal fluid (Thambisetty and Lovestone, ), people have focused on biomarkers in blood (Chahine et al., ; Lin et al., ; Grossi et al., ), especially for longitudinal evaluation. Uric acid, miR-124, and other molecules can be used as biomarkers for the diagnosis of PD in peripheral blood (Angelopoulou et al., ; Lawton et al., ). However, a single biochemical marker is unlikely to be sufficient for the early diagnosis of PD, while a combination of them may be useful. Therefore, there is a need to find more PD biomarkers in peripheral blood, and the development of reliable and accurate peripheral-blood biomarkers will greatly promote the early detection of PD and the identification of its biological characteristics.
Massively parallel microarray analysis can reliably assess the relationships between gene expression and clinical manifestations on a global scale and reveal the etiology of complex diseases by identifying abnormalities in genes or pathways (Schadt et al., ). Weighted gene co-expression network analysis (WGCNA) and protein–protein interaction networks (PPI) were constructed here to identify hub genes underlying PD. Longitudinal studies over time are a common method for studying degenerative diseases. We established a time axis to explore the dynamic changes in hub gene expression in a PD model and their potential as biomarkers in the early stage of the model. Finally, machine learning is a key method of modern medical research, and it is often used to diagnose diseases or to screen biomarkers of them (Deo, ). In this study, we used random forest (RF), K-nearest neighbor (KNN) and support vector machine (SVM) to establish a PD prediction model (Zhang, ; Kriegeskorte and Golan, ). A previous study combined KNN with a genetic algorithm to achieve high classification accuracy (Zhang et al., ). Here, after comparing the AUC and MCC of three classifiers, an SVM was selected to build an artificial intelligence prediction model of PD in the early stage.
## Materials and Methods
### Gene Expression Data and Subsequent Processing Based on GEO Databases
The Gene Expression Omnibus (GEO ) is a public functional genomics data repository of high-throughput gene expression data, chips, and microarrays. As shown in the flow chart ( ), we searched GEO with the following keywords: “(Parkinson’s disease) and (substantia nigra striatum)”, which yielded many datasets (Edgar et al., ). Four gene expression datasets [GSE28894, GSE20141, GSE20295, and GSE20292] were chosen and downloaded from GEO. The GSE28894 dataset contained 60 PD samples and 86 normal samples. GSE20141 contained 10 PD samples and eight normal samples. GSE20295 contained 40 PD samples and 53 normal samples. First, GSE20141 was chosen to run WGCNA to identify candidate hub genes. Second, GSE28894, GSE20141, and GSE20295 were used to construct a PPI network. GSE20292 was used to do external verification ( ). Then we searched for the keywords “(Parkinson’s disease) and (whole blood) and (early stage)” and obtained three datasets: GSE6613 GSE72267, and GSE99039. We performed whole blood verification of the hub genes in all the three datasets. GSE6613 was used to calculate the area under the receiver operating characteristic curve (AUC) of SSR1 and to build our machine learning model. We finally retrieved the datasets GSE85426, GSE51759, GSE89093, GSE138118, and GSE167914 for Alzheimer’s disease (AD), Huntington’s disease (HD), endometrial carcinoma, bladder cancer, and thyroid carcinoma, respectively, which were used to calculate the specificity of SSR1 to PD. Detailed of all data sets can be seen in .
Flow chart of the analysis process.
The information of Gene Expression Omnibus (GEO) datasets.
### WGCNA
In WGCNA, the correlation between modules and clinical subtypes is calculated according to the feature vector of each network module. Module eigengenes actually formulate the expression patterns of all genes within a given module into a single characteristic expression profile. Module eigengenes can be regarded as the first principal component of the gene module. The correlation between each gene in these modules was quantified by the gene significance (GS) value. Accordingly, the module significance (MS) of a certain module is defined as the averaged GS values of all genes included in it. Modules are ranked according to the MS score, and the top five modules are considered key modules relevant to clinical outcomes for further analysis. Hub genes in the co-expression network are a class of genes that have high connectivity within a network module and are significantly correlated with biological function (Chen et al., ). In this study, we measured the absolute value of the gene significance (GS) score, which represents the correlation between the genes in these modules and each phenotype (Yang et al., ). We screened candidate genes using the cutoff criteria |MM| ≥ 0.8 and |GS| ≥ 0.5 because such genes are biologically meaningful. |MM| ≥ 0.8 indicates that the gene is strongly related to the module, and |GS| ≥ 0.5 requires that the gene expression profile be closely related to each module.
### PPI Network Construction and Module Analysis
The differentially expressed genes (DEGs) between PD and normal samples were screened using GEO2R ). GEO2R is an interactive web tool that allows users to compare two or more datasets in a GEO series to identify DEGs across experimental conditions (Edgar et al., ). The adjusted P -values ( P ) and Benjamini and Hochberg false discovery rates were applied to provide a balance between the discovery of statistically significant genes and the limitation of false positives. An absolute value of the logarithm of the fold change (logFC) >1 and P < 0.01 were considered statistically significant.
The PPI network was predicted using the Search Tool for the Retrieval of Interacting Genes (STRING ) online database. Analyzing the functional interactions between proteins may provide insights into the mechanisms of the generation or development of diseases. The PPI network of DEGs was constructed using the STRING database, and an interaction with a combined score >0.4 was considered statistically significant. Cytoscape is an open source bioinformatics software platform for visualizing molecular interaction networks. The plug-in Molecular Complex Detection (MCODE) of Cytoscape is an app for clustering a given network based on its topology to find densely connected regions. The PPI networks were drawn using Cytoscape, and the most significant module in the PPI networks was identified using MCODE. The criteria for selection were as follows: MCODE scores >5, degree cutoff = 2, node score cutoff = 0.2, max depth = 100 and k -score = 2. The hub genes in the PPI network were those with degree ≥10.
### Functional Analysis of Hub Genes and Enrichment Analysis of DEGs
The overall survival and disease-free survival analyses of hub genes were performed using Kaplan-Meier curves in cBioPortal . The expression levels of six hub genes in the brain were determined from the NCBI database. The Database for Annotation, Visualization, and Integrated Discovery (DAVID ) is an online biological information database that integrates biological data and analysis tools and provides a comprehensive set of functional annotation information on genes and proteins for users to extract biological information. The Kyoto Encyclopedia of Genes and Genomes (KEGG) is a database resource for understanding high-level functions and biological systems from large-scale molecular datasets generated by high-throughput experimental technologies. Gene Ontology (GO) is a major bioinformatics tool to annotate genes and analyze the biological processes of these genes. To analyze the functions of DEGs, biological analyses were performed using the DAVID online database. P < 0.01 was considered statistically significant.
### Classifier Construction and Machine Learning
Three ML algorithms, SVM (De Martino et al., ), kNN (Cover and Hart, ), and RF (Ho, ) were built both to verify if SSR1 can distinguish PD patients well and to determine which best classifies SSR1 in PD datasets. The RF method is a commonly-used classification method containing a number of decision trees. A final classification label was determined based on the class with the most votes from all trees. RF is easily parallelizable and can be enhanced with boosting or bagging. kNN performs classification by assigning a point to the class that is most prevalent out of the k points closest to it. At the same time, kNN is simple to implement and can utilize Multi-task learning. SVM maps each data item into an n-dimensional feature space where n is the number of features. It then identifies the hyperplane that separates the data items into two classes while maximizing the marginal distance for both classes and minimizing the classification errors. It is important to note that each technique has its own advantages and disadvantages. We hope to use different algorithms for verification with complementary advantages to more comprehensively verify the feasibility of SSR1 as a biomarker.
All models were learned from the same training data generated by selecting 80% of the data, and the remaining 20% were used as validation data to measure and compare the performance of the model. Each algorithm was also tested with combinations of parameters; finally, we found that c = 2 for the SVM, k = 4 for the kNN, 60 trees for RF produced the best results. To evaluate the overall performance of each model, a 10-fold cross validation was performed. Of the 10 divided sets from the data, the process by which the learned model predicts the remaining one set was repeated 10 times, and eventually, all data were used for validation. All ML algorithms were implemented in the python package sklearn .
### Performance Evaluation
In order to find out the best classifier for further study, the performance of data validation was calculated according to the area under the curve (AUC) from 0.5 to 1 and the Matthews Correlation Coefficient (MCC) from −1 to 1, a parameter able to reflect classifier effectiveness (Chicco and Jurman, ).
TP is the number of samples correctly predicted as PD in PD samples, FN is the number of samples incorrectly predicted as NORMAL in PD samples, FP is the number of samples incorrectly predicted as PD in normal samples, and TN is the number of samples correctly predicted as NORMAL in normal samples. MCC ranges from −1 to 1, with a completely wrong classification at −1 and perfect classification at 1.
MCC of classification is defined as:
s is the total number of samples, c is the total number of correctly predicted samples, is the number of all samples in class k , and and is the number of correctly predicted samples in class k . MCC of pan-cancer classification for perfect prediction is 1, but the minimum is somewhere between −1 and 0, depending on the number and distribution of the actual labels (Kim et al., ). Eventually, the classifier with the greatest AUC and MCC value was identified as the optimal PD classifier.
### Animal Experiments
All experimental protocols were performed following the guidelines on animal research provided by the institutional ethics committee at Nantong University and were approved by the committee.
6-OHDA Lesion: Adult C57BL/6J male mice (25–30 g) were maintained under a 12-h light/12-h dark cycle in cages and acclimated to the experimental environment for 1 week before modeling. The mice received a unilateral intrastriatal injection of 6-OHDA (Sigma-Aldrich, St. Louis, MO, USA). The animals were pretreated with desipramine (Sigma-Aldrich, St. Louis, MO, USA). A total dose of 12 μg of 6-OHDA dissolved in 3 μl PBS (16 μmol/ml) was infused into the right striatum at the following coordinates: anterior-posterior (AP), +0.09 cm; medial-lateral (ML), +0.22 cm; dorsal-ventral (DV), −0.25 cm relative to the bregma.
MPTP model : In the same mice, MPTP (Sigma-Aldrich, St. Louis, MO, USA) was intraperitoneally injected four times at an individual dose of 12 mg/kg dissolved in 200 μl PBS with a 2-h interval between the injections. Te control animals received saline only.
Behavioral Testing: All the tests were performed 0 d, 1 d, 3 d, 5 d, 7 d, 14 d, and 28 d after 6-OHDA injection in comparison with the normal group. In the pole test , the mice were placed head-upward on top of a rough-surfaced iron pole (50 cm in length and 1.0 cm in diameter) and could climb down to the base of the pole. The time that it took for each mouse to turn completely downward and then reach the floor was measured, with a cutoff of 120 s. The average of three measurements was taken as the result. In apomorphine-induced rotation , the mice were allowed to habituate for 10 min in a white 30 × 30-cm chamber. After an intraperitoneal injection of 0.5 mg/kg apomorphine hydrochloride (Sigma-Aldrich, St. Louis, MO, USA), the full rotations in the chamber were recorded with a video camera for 30 min and counted by a blinded examiner.
Tissue Preparation: Perfusion was performed with a cold saline solution, and fixation was then performed with 4% paraformaldehyde in 0.1 M phosphate buffer. Each brain was dissected, postfixed overnight in buffered 4% paraformaldehyde at 4°C and stored in a 30% sucrose solution at 4°C until it sank. Frozen sectioning was performed on a freezing microtome (Leica, CM3050S) to generate 20-μm-thick coronal sections.
Mouse Plasma Extraction : The researcher grabbed the scruff of the mouse with the left thumb, index finger, and middle finger, and the little finger and ring finger fixed the tail. The skin of the eye that needed to be removed was lightly pressed to make the eyeball become congested and prominent. Surgical scissors were used to cut off the beard of the mouse to prevent blood from leaving the beard and causing hemolysis. The eyeball was grasped with tweezers and quickly removed, and the blood flowed from the eye socket into an Eppendorf tube, which was supplemented with a 1:9 ratio of the anticoagulant. The supernatant obtained after centrifugation at 3,000 rpm for 5–10 min was plasma.
### Immunohistochemistry
The prepared tissue sections were washed with PBS, permeabilized with 0.25% Triton X-100 for 10 min at RT, and treated with 10% goat serum blocking buffer for 2 h at RT. Tissue sections were costained with primary antibody against tyrosine hydroxylase (TH; 1:300, Abcam, UK) as a marker for dopaminergic neurons overnight at 4°C. After washing, indirect fluorescence by incubating sections at room temperature in the dark for 1 h with goat anti-rabbit IgG conjugated with Alexa Fluor 568 (1:1,000, Life Technologies). The coverslips were then washed with PBST and treated with an antifade mounting medium with Hoechst 33342. Images were obtained under a microscope (Zeiss LSM700, Carl Zeiss Microimaging GmbH, Jena, Germany). All photographs were taken using the same exposure time. For immunocytochemistry, six to nine fields (two to three fields × three independent samples) were selected randomly from each group, and for immunohistochemistry, three sections from each animal (three mice) were randomly selected.
### Western Blotting Analysis
The brain tissue was homogenized in RIPA lysis buffer (EpiZyme, China), protease inhibitor cocktail (MCE, USA), and phosphatase inhibitor cocktail I (MCE, USA) and then centrifuged at 1,600× g at 4°C for 20 min. The supernatant was collected, and the protein concentration was determined using a BCA Protein Assay Kit (Beyotime, China). An aliquot of the supernatant was diluted in SDS-PAGE Sample Loading Buffer 28 (Beyotime, China), and the proteins were separated in Omni-PAGE HEPES-Tris Gels (EpiZyme, China) and transferred to a polyvinylidene difluoride membrane (Millipore, USA). The membrane was blocked for 1 h at RT in blocking buffer comprising TBS with 5% Difco skim milk (Becton, Dickinson and 606 Company, USA) and 0.1% Tween 20. It was then incubated with the following primary antibodies overnight at 4°C: rabbit anti-GAPDH (Abcam, UK), and rabbit anti-TH (Abcam, UK). The membrane was washed in TBST and incubated with goat anti-rabbit IgG (H + L) and cross-adsorbed secondary antibody (conjugated to horseradish peroxidase; Thermo Fisher, USA) for 1 h at RT. The membrane was then washed three times in TBST for 5 min. The antigen–antibody peroxidase complex was detected using High-sig ECL Western Blotting Substrate (Tanon , China) according to the manufacturer’s instructions, and images were obtained using the Tanon 5200CE Chemi-Image System. The intensity of each band was determined with ImageJ Fiji 1.53c.
### RNA Extraction and Quantitative Real-Time PCR
Total RNA of the SN was extracted using TRIzol reagent (Tiangen, Beijing, China). The total RNA of plasma was extracted using an EZ-press Serum/Plasma RNA Purification Kit (EZBioscience, Beijing, China). The RNA of 3 mice was filtered through a filter column. Reverse transcription of the RNA into cDNA and quantitative polymerase chain reaction (qPCR) were performed according to the instructions of the PrimeScript RT Reagent Kit with gDNA Eraser (Takara, Dalian, China) and TB Green Premix Ex Taq II (Takara). Relative expression levels were obtained by normalizing glyceraldehyde phosphate dehydrogenase (GAPDH). Each reaction was performed in triplicate. The relative mRNA expression level was calculated by the comparative 2 method.
### Statistical Analysis
All data are presented as the means ± SEM and were analyzed using GraphPad Prism 8.0. The difference between two groups was analyzed by a two-tailed Student’s t -test, and one-way ANOVA followed by Tukey’s post hoc analysis was used for multiple comparisons among two or more groups. Significant difference among groups was assessed as ns p > 0.05, * p < 0.05, ** p < 0.01, and *** p < 0.001.
## Results
### Determination of Hub Modules and Genes in WGCNA
The expression profiles of several modules are included in , and each gene was classified into different modules ( ). We processed the gene expression profiles using variance analysis on the GSE20141 dataset, which included the most genes. The top five gene modules were used to select the hub gene module. To ensure that the network was a scale-free network, we ran an empirical analysis to choose an optimal parameter β. Both the scale-free topology model fit index and mean connectivity reached the steady state when β was equal to 4 ( ). A total of five gene modules were identified via average link age hierarchical clustering, and each module is represented in different colors. We drew a heat map to explore the correlations between module eigengenes and clinical traits ( ). Each column in displays the correlation and corresponding p -value: the darker the color, the stronger the correlation coefficient. We found that five module eigengenes had the highest correlations. Scatter plots of the degree and P -value of Cox regression in the five modules are shown in the . Accordingly, we selected the genes that had cutoff criteria |MM| ≥ 0.8 and |GS| ≥ 0.5, which are SSR1, RNF130, GTF2H5, HMGA2, and CD79B. WGCNA can reflect the continuity of potential co-expression information and avoid information loss by setting artificial threshold parameters (Langfelder and Horvath, ). However, WGCNA only focuses on a single dataset, so it lacks universality. To make up for this, we also performed a PPI network analysis.
Determination of soft-thresholding power in WGCNA analysis. (A) Dendrogram of all differentially expressed genes clustered based on a dissimilarity measure. (B) Analysis of the scale-free fit index for various soft-thresholding powers β. (C) Analysis of the mean connectivity for various soft thresholding powers. (D) Heatmap of the correlation between module eigengenes and clinical traits of Parkinson. (E) Clustering of module eigengenes.
### PPI Network Analysis and Hub Gene Selection
After standardization of the microarray results, DEGs were identified. The overlap between the three datasets contained 226 genes, as shown in the Venn diagram ( ), consisting of 154 downregulated genes and 72 upregulated genes in PD patients vs. healthy controls. We performed KEGG and GO analysis on the 226 genes and listed the top eight pathways in both analyses ( ). GO function annotation results displayed that changes at the biological process (BP) were observably focused in dendrite morphogenesis, dendrite development, negative regulation of catabolic process, neuron projection organization, axonogenesis, and negative regulation of protein catabolic process ( ). Changes of DEGs significantly in cell component (CC) were mostly in transport vesicle, transport vesicle membrane, membrane raft, membrane microdomain, synaptic vesicle, and membrane region. The most enriched molecular function (MF) annotations were hormone receptor binding, dystroglycan binding, vinculin binding, ATPase regulator activity, nuclear hormone receptor binding, and protein transmembrane transporter activity. In addition, the results of the KEGG pathway analysis in the bubble chart revealed that DEGs were remarkably concentrated in the Viral myocarditis, Adherens junction, Arrhythmogenic right ventricular cardiomyopathy (ARVC), Vasopressin-regulated water reabsorption, Vascular smooth muscle contraction, and protein processing in the endoplasmic reticulum ( ). The pathways of hub genes were further investigated to determine the mechanism by which hub genes can act as biomarkers of PD. The PPI network of DEGs was constructed, and the most significant module was obtained using Cytoscape ( ). The results showed that the network contained six hub genes. These genes were identified as hub genes by virtue of having a degree ≥10. The genes shared in common by the WGCNA and PPI analysis were SSR1, GTF2H5, and RNF130. Since these genes were identified by two analytical methods, they will be the most reliable and representative of genes for our purposes. The names, abbreviations and functions of these hub genes are listed in .
Venn diagram and PPI network. (A) The PPI network of DEGs was constructed using Cytoscape. Upregulated genes are marked in light red; downregulated genes are marked in light blue. (B) DEGs were selected with the absolute value of fold change >1 and P -value <0.01 among the mRNA expression profiling sets GSE28894, GSE20141, and GSE20295. The three datasets showed an overlap of 226 genes.
The Go terms and KEGG pathways enrichment analysis of 226 DEGs in PD. (A) The Go terms conclude the biological process, cellular component, and molecular function. (B) KEGG pathway revealed that DEGs were remarkably concentrated in the viral myocarditis, adherens junction, arrhythmogenic right ventricular cardlomyopathy (ARVC), vasopressin-regulated water reabsorption, vascular smooth muscle contraction, and protein processing in endoplasmic reticulum.
Three hub genes and functions.
### Whole-Blood Sample Verification and Hub Gene Analysis
To further explore whether the abnormally expressed hub genes in the brain could be detected in peripheral blood in patients at an early stage (at the onset of motor symptoms), we observed the difference in expression between the normal group and PD group in three whole-blood datasets and found that all three genes showed significantly upregulated in peripheral blood ( ). Their differential expression in peripheral blood was basically consistent with that in the brain. The overall survival analysis of the hub genes was performed using Kaplan-Meier curves. PD patients whose period blood highly expressed these genes showed good overall survival and disease-free survival ( ). SSR1, GTF2H5, and RNF130 were expressed highly in brain tissue ( ), which means they meet the fundamental requirements of biomarkers of PD.
Analysis of the correlation between three hub genes and PD based on bioinfarmatics. (A–C) Verification of hub genes based on peripheral blood datasets: GSE72267, GSE99039, and GSE6613. (D–F) Overall survival and disease-free survival analyses of three hub genes were performed using cBioPortal online platform. P < 0.05 was considered statistically significant. (G) Expression level of three hub genes in brain.
### Expression Levels of Hub Genes In vivo
To analyze the accuracy and reliability of the above bioinformatic analysis, Quantitative Real-Time PCR was used to detect the expression levels of the hub genes in the SN and period blood of PD model mice. We used 6-OHDA and MPTP models for tissue verification. Compared with the value in normal SN tissue (non injected mice) and SHAM group, the expression level of SSR1 was significantly upregulated ( P < 0.05) after 6-OHDA, as well as after MPTP injury ( ). GTF2H5 showed no significant difference in the 6-OHDA model and MPTP model ( ). RNF130 was not different in either model ( ). Considering the results above, we chose the 6-OHDA model to detect blood changes in hub genes. Surprisingly, SSR1 and GTF2H5 were both upregulated to varying degrees ( ). However, because they showed no obvious change in brain tissue, we thought that the changes in peripheral blood of GTF2H5 might not be directly related to PD. The imbalance of SSR1 both in the tissues and in peripheral blood after injury suggested that it may play an important role in the occurrence and progression of PD.
The mRNA relative expression levels of SSR1, GTF2H5, and RNF130 in PD model mice. (A–C) The expression levels of SSR1, GTF2H5, and RNF130 in SN (substantia nigra) in vivo in PD animal model constructed with 6-OHDA. (D–F) The expression levels of SSR1, GTF2H5, and RNF130 in SN (substantia nigra) in vivo in PD animal model constructed with MPTP. (G–I) The expression levels of SSR1, GTF2H5, and RNF130 in whole blood in vivo in PD animal model constructed with 6-OHDA. Norrnal group (non injected mice), SHAM group (PBS injected mice); ns p > 0.05, * p < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001 vs. each group.
### Longitudinal Study of SSR1 Expression In vivo
To further explore the relationship between the hub gene SSR1 and dopaminergic neurons in the SN, we established a time axis of 0, 3, 5, 7, 14, and 28 d ( ). We detected TH and SSR1 in the SN tissue of 6-OHDA-injured mice and with PBS-injected mice. We used Western blot and immunohistochemistry to analyze the change in TH. Immunohistochemical fluorescence showed that on day 7 after injury, dopaminergic neuron number began to decrease significantly. In the following days, the number of dopamine neurons remained low ( ). Western blot showed similar results: TH decreased below 60% of the control level at day 7, and from day 14 to day 28, it was lower than 20% ( ). Using qPCR to detect the expression trend of SSR1 at the same time, we found that the increase in SSR1 was divided into three stages ( ). It increased significantly from 0 d to 3 d, remained stable from 3 d to 7 d, and increased again from 7 d to 28 d, which was consistent with the decreasing trend of dopamine neurons. Then we performed a correlation analysis of SSR1 and TH in SN ( ). Regression of TH neuron number on SSR1 concentration showed a negative correlation, with a goodness of fit (R ) of 0.8834. The results show that in animal models, SSR1 has a strong negative correlation with TH neurons. SSR1 may be related to damage to TH neurons and to a certain extent can reflect the degree of damage to them.
Longitudinal study of SSR1 expression in vivo . (A) The flowchart of the construction of the 6-OHDA subacute model, behavioral tests, and sacrifice. (B) Tyrosine hydroxylase (TH) staining of the substantia nigra (SN) of the above mice. Scale bars: 200 μm. (C) Western blot analyses of TH in SN of the above mice. (D) The mRNA relative expression levels of SSR1 in SN of the above mice. (E) The correlation analysis of SSR1 and TH in SN. (F) Pole tests and apomorphine-induced rotation were conducted by a blinded observer after 6-OHDA treatment. (G) The mRNA relative expression levels of SSR1 in the whole blood of the above mice. ns p > 0.05, * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001 vs. Control group.
To explore whether SSR1 could be a biomarker in the early stage of PD, we measured the correlation between changes in animal behavior and SSR1 expression in blood. Most preclinical experiments have focused on late-stage, chronic, fully DA-depleted states (Stanic et al., ; Grealish et al., ; Boix et al., ; Zhang et al., ). Few studies have focused specifically on the early-phase behavioral responses after 6-OHDA lesions in the SNc (Fornaguera and Schwarting, ; Rosa et al., ). In the 6-OHDA model, few researchers have focused on behavioral disorders in the first week after SN striatum injury. We thought it would be interesting to study the early time course of changes occurring in the emergence of the parkinsonian lesion in the standard 6-OHDA model and whether SSR1 might be predictive of the severity of the lesion. Behavioral changes began to appear at 7 days after 6-OHDA injection, and significant differences appeared from 14 days to 28 days. There were few abnormalities in 3D and 5D (in apomorphine-induced rotation, when the number of rotations is >7 r/min, it is considered a successful model; ). The rotation experiment induced by apomorphine further suggested that the number of dopamine neurons decreased to less than 20% of the control level at 14D-28D. The expression of SSR1 in peripheral blood began to be upregulated as early as day 3, when behavioral disorders were not obvious ( ). As the course of the disease progressed, the expression of SSR1 in peripheral blood stayed high. These results show that in the early stage of a PD model (with few or no behavioral abnormalities), SSR1 is significantly upregulated in both the brain and blood. This abnormal expression may indicate the degree of damage to dopaminergic neurons and make SSR1 a promising biomarker of early PD.
### Machine Learning
RF, KNN, and SVM were used to construct classifiers to distinguish PD patients from healthy controls based on GSE6613, which shows the best performance in both classifiers ( ). To identify the best predictors of each classifier, we added these upregulated genes to each classifier one by one in order of rank. The RF classifier based on SSR1 had good predictive power (AUC: 0.91; ). In addition, we validated the PD specificity of our gene expression classifier by testing it on two different protein aggregation disease datasets: one Alzheimer’s disease dataset (GSE85426) and one Huntington’s disease expression dataset (GSE51799). The AUCs of SSR1 for AD and HD were low ( ), which indicates that our expression classifier has no prediction power for Alzheimer’s disease or Huntington’s disease but is efficient and specific for PD. Given that the expression level of SSR1 in other organs ( ), such as the bladder, thyroid, and endometrium, was similar to that in the brain, we chose three datasets of these diseases and tested the AUC power of SSR1 in cases not specific to PD. As expected, these curves had AUCs lower than 0.6 ( ), which means that SSR1 has extreme specificity to Parkinson’s disease, while it behaves normally in other diseases. The KNN and SVM classifier yielded similar results as the RF classifier ( ). By comparing the AUC value of three classifiers: RF(AUC:0.91), KNN (AUC:0.89), SVM(AUC:0.93), we can find SVM classifier behaviors best. To confirm the results above, MCC was implemented to select the optimal classifier to use in clinical applications. As illustrated in , SVM had the highest MCC all the time, which represents high recognition accuracy and precision. To sum up, the SVM classifier has the best precision of SSR1 in PD.
SSR1 in artificial intelligence prediction model. (A) The ROC curve of the sensitivity for the diagnosis of PD based on SSR1 from RF (left), KNN analysis (middle), and SVM analysis (right). (B–F) The ROC curve of the specificity for the diagnosis of PD based on SSR1 in AD (B) , HD (C) , Bladder cancer (D) , Thyroid carcinoma (E) , and Endometrial carcinoma (F) .
Graphs show the performance of the three ML models according to the number of genes for binary classification. The red curves indicate SVM classifiers. The green curves indicate KNN classifiers. The blue curves indicate RF classifiers. The SVM classifier had the highest Matthews correlation coefficient (MCC) value, which means the highest recognition accuracy and precision.
## Discussion
Studies on PD biomarkers based on the GEO datasets have mostly used the peripheral-blood datasets (Wang et al., , ; Wu et al., ; Yuan et al., ). Biomarkers corresponding to PD molecular neuropathological characteristics based on its pathogenesis can not only predict PD at an early stage but also assess the condition of PD patients and judge their prognosis. Therefore, it would b valuable to find biomarkers that are not only related to the pathogenesis of PD but also abnormally expressed in peripheral blood. This study is the first to combine brain tissue and peripheral-blood datasets to find potential biomarkers of PD. We used WGCNA to select five hub genes and constructed a PPI network through GEO data analysis to find six key genes that are abnormally expressed in the brain tissue of PD patients. We selected the three upregulated genes shared by the two analytical methods for further study. Since the ultimate goal was to find peripheral-blood markers, we verified the expression of the three hub genes in the peripheral-blood datasets. This combined with survival analysis showed that all three hub genes were significantly upregulated and were associated with the overall survival of patients. Through bioinformatics analysis, we further confirmed the applicability of the hub genes in animal models, which suggests they can be useful in the clinic. Through qPCR verification, we successfully reproduced the SSR1 disorders in the mouse SN, which was consistent with the bioinformatic analysis. However, GTF2H5 and RNF130 were verified in only one model, and we were unable to verify their value in both models, so we will not further study them in PD.
From the loss of dopamine neurons and the time curve of SSR1 brain expression, the imbalance of SSR1 expression is closely related to the loss of dopamine neurons. The more dopamine neurons are lost, the higher the expression of SSR1. Although we have not fully proven that SSR1 is involved in the damage to TH neurons, our experimental results do show that SSR1 is highly correlated with the damage to TH neurons and may indicate the severity of TH damage. Our results also show that when TH neuronal damage was below 20%, SSR1 expression was maintained to a certain degree. This suggests that the expression of SSR1 may be the response of glial cells to TH neuron damage. We also compared the behavioral curve with the curve of SSR1 expression in peripheral blood. SSR1 was upregulated in the early PD model or even when there is no obvious abnormality in behavior. SSR1 showed a certain degree of predictive power for PD in animal models.
The signal sequence receptor subunit (SSR) is a glycosylated endoplasmic reticulum (ER) membrane receptor associated with protein translocation across the ER membrane. The SSR consists of two subunits, one of which is SSR1. The main function of the endoplasmic reticulum is the synthesis and folding of secretory proteins. Changes in ER function will increase oxidative stress or protein N-glycosylation dysfunction, leading to the accumulation of misfolded proteins in the ER and triggering ER stress. Through KEGG analysis, we found that SSR1 was involved in ER stress. In a recent model of ER stress, it was found that long-term endoplasmic reticulum stress can induce the upregulation of mRNA encoding TARPa, namely, SSR1 (Nguyen et al., ). However, the significance of SSR1 in the PD model has never been confirmed. The impact of ER stress in PD has been a concern in recent years. It was first discovered in the PD model induced by MPP + rotenone. Long-term ER stress participates in the unfolded protein response (UPR) through high expression of genes involved in the pathological process of PD (Ryu et al., ). UPR-related signaling pathways are an adaptive cellular mechanism designed to restore ER homeostasis. Misfolded proteins can activate it to limit ER stress (Hetz et al., ). The activation of the UPR is controlled by the PERK, IRE1α, and ATF6 receptors on the ER membrane. Under normal circumstances, BiP binds to related receptors to inhibit its phosphorylation and the activation of downstream pathways. Under pathological conditions, α-synuclein directly interacts with BiP to trigger the phosphorylation of BiP, promote the dissociation of BiP from related receptors, and activate the UPR (Cooper et al., ; Jiang et al., ; Bellucci et al., ), thereby inducing downstream activation of the PERK axis, the IRE1α-XBP1 axis, and the EIF2α axis (Prell et al., ). Autopsy analysis of Parkinson’s patients has found that compared with the control group, patients with PD showed more phosphorylated PERK in the SN dopaminergic neurons. eIF2α, phosphorylated PERK, and α-synuclein coexist in the dopaminergic neurons of PD patients (Hoozemans et al., ), which further suggests that α-synuclein and long-term ER stress in PD patients are closely linked. The ER stress induced by tunicamycin can also lead to the accumulation of oligomeric α-synuclein (Jiang et al., ), indicating that the ER stress may also reversely aggravate the aggregation and toxicity of α-syn, forming a vicious cycle and exacerbating PD deterioration. We speculate that SSR1 may be a UPR-related mRNA that reflects the degree of ER stress. In the early stage of injury, abnormally aggregated α-synuclein activates the UPR to promote the upregulation of the SSR1 gene by binding to BiP to relieve acute ER stress. Therefore, the compensatory effect of dopamine neuron damage is not obvious at this time. When the ER stress becomes chronic, it exacerbates the accumulation of oligomeric α-synuclein, and the compensatory effect of the UPR cannot counteract the increasing accumulation of abnormal α-synuclein, which further triggers inflammation. At this time, dopamine neurons are significantly reduced, and animal behavior is also significantly abnormal. The expression of SSR1 continues to be upregulated. α-Synuclein activates the PERK axis in astrocytes, and the regulation of the UPR by α-synuclein is not limited to neurons. Considering that astrocytes participate in a variety of brain functions and support neuronal activity, activation of the UPR in these cells by α-synuclein may lead to harmful consequences. This may explain why SSR1 is still highly expressed when the expression of TH neurons in late PD is extremely low. Therefore, SSR1, which has abnormal expression in the early stage of PD (before obvious movement disorders), can be used not only as an early marker but also as an effective indicator of the severity, progression, and prognosis of PD.
For the first time, we applied the timeline of an animal model to the verification and exploration of hub genes, instead of knocking out target genes in an organelle. Exploration in mice may also lay the foundation for the next step toward clinical application. The most commonly used machine learning includes SVM, KNN, RF, and ANN (Artificial Neural Network). Since ANN is a multivariate input, it has no way to predict only SSR1. So we choose the other three classifiers to analyze SSR1 temporarily. Based on our analysis, we selected SVM to construct a computer model for clinical prediction. The application of artificial intelligence to the medical industry has gradually progressed, especially in the fields of early PD prediction and severity prediction (Zhan et al., ; Gupta et al., ). Recent advances in SVM have enabled the creation of computer models that can accurately perform many tasks involving prognosis of the disease and early diagnosis (Kaya, ). SVM has identified PD patients’ dopaminergic imaging markers (Prashanth et al., ), walking protocols (Rehman et al., ) and idiopathic REM sleep behavior disorder (Christensen et al., ) for early prediction. In this study, we established a SVM classifier model by identifying the peripheral-blood data of different samples that were from healthy or PD patients and continuously consolidated and improved the accuracy of the model through continuous calculation and screening of the data. In the future, as the number of clinical data samples increases, we can further improve the training results.
In future studies, we would like to further investigate whether the abnormal expression of SSR1 in PD patients is dominated by dopaminergic neurons or astrocytes. We also plan to study the possible mechanisms within cells. To improve the accuracy and sensitivity of diagnosis, the combination of neuroimaging and peripheral-blood biomarkers can provide better discrimination between parkinsonisms. The SVM can combine peripheral-blood data and images and differentially weight the two kinds of data to form an accurate judgment classifier model. This method is easily accessible and clinically applicable. It provides opportunities to develop an early diagnostic tool for PD patients, helping to save their dopaminergic neurons as early as possible.
## Data Availability Statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ .
## Ethics Statement
The animal study was reviewed and approved by Animal experiment ethics committee of Nantong University. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.
## Author Contributions
MC and QZ designed the experiments. WZ and YW performed the experiments. WZ, YW, KC, and JS analyzed the data. MC and JS contributed to reagents, materials, and analysis tools. WZ and QZ wrote the article. All authors contributed to the article and approved the submitted version.
## Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s Note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
|
The use of electronic cigarette (e-cigarette) has been increasing dramatically worldwide. More than 8,000 flavors of e-cigarettes are currently marketed and menthol is one of the most popular flavor additives in the electronic nicotine delivery systems (ENDS). There is a controversy over the roles of e-cigarettes in social behavior, and little is known about the potential impacts of flavorings in the ENDS. In our study, we aimed to investigate the effects of menthol flavor in ENDS on the social behavior of long-term vapor-exposed mice with a daily intake limit, and the underlying immunometabolic changes in the central and peripheral systems. We found that the addition of menthol flavor in nicotine vapor enhanced the social activity compared with the nicotine alone. The dramatically reduced activation of cellular energy measured by adenosine 5′ monophosphate-activated protein kinase (AMPK) signaling in the hippocampus were observed after the chronic exposure of menthol-flavored ENDS. Multiple sera cytokines including C5, TIMP-1, and CXCL13 were decreased accordingly as per their peripheral immunometabolic responses to menthol flavor in the nicotine vapor. The serum level of C5 was positively correlated with the alteration activity of the AMPK-ERK signaling in the hippocampus. Our current findings provide evidence for the enhancement of menthol flavor in ENDS on social functioning, which is correlated with the central and peripheral immunometabolic disruptions; this raises the vigilance of the cautious addition of various flavorings in e-cigarettes and the urgency of further investigations on the complex interplay and health effects of flavoring additives with nicotine in e-cigarettes.
## Introduction
The use of electronic nicotine delivery systems (ENDS), also known as electronic cigarettes (e-cigarettes) has been dramatically increasing in recent years, and it has become a serious public health issue. Despite the lack of either health data or the demonstrated efficacy in promoting smoking cessation, the e-cigarette is often advertised as a safer alternative or cessation aid to conventional tobacco cigarette smoke, mainly due to its much lower levels of toxic/carcinogenic chemicals ( ; ; ). Although the popularity of e-cigarette use continues to increase, research evidence based on scientific knowledge is lacking and the main focused aspect of e-cigarettes include their beneficial roles in tobacco smoking cessation or reduction, their health risks, and their environmental consequences ( ; ).
The major composition of e-cigarettes usually consists of propylene glycol and vegetable glycerol (PG/VG) as odorless liquid vehicles to generate vapor, nicotine which is the main addictive substance, and a wide variety of flavorings ( ; ). As the number of users grows exponentially worldwide, liquids of e-cigarettes are available in a dramatically large combination of flavor additives, with more than 8,000 flavorings ( ; ). A recent increase in the prevalence of e-cigarettes among young adults and adolescents may largely be due to their widely available flavors which appeal to the youth ( ; ; , ). Epidemiological survey data have shown that the most common flavor categories include fruit, menthol, and tobacco. Menthol flavor was shown to be one of the most popular flavors among young users ( ) and the extent of satisfaction with vaping varies among unique flavor users ( ; ). However, the incorporated effects of flavorings when added in the nicotine-containing vapor and the underlying mechanisms are largely unknown.
Many previous studies have confirmed the association between cigarette smoking and neurodegeneration ( ; ; ), cognition and memory ( ; ; ), and mental disorders, such as attentional deficits ( ; ) and schizophrenia ( ; ), considering the wide distribution of nicotinic acetylcholine receptors (nAChRs) throughout the brain ( ). Based on the fact that nicotine is one of the main addictive components of an e-cigarette, there is increasing recognition that e-cigarettes impact brain functions, for instance, e-cigarettes impaired the integrity of the blood-brain barrier (BBB) and exacerbated the cognitive dysfunction ( ), mental disorders ( ), vascular inflammation ( ), metabolic imbalance ( ), and neurotoxicity ( ) in the brain of human and animal models, while the effects of ENDS with specific flavor on the behaviors need further disclosure. Furthermore, it is the utmost emergency to understand the molecular architectures sculptured in the brain and the peripheral system that synergistically respond to the ingredients of e-cigarettes.
The aim of the current study is to provide a comprehensive behavioral analysis of ENDS with menthol flavor in male mice ( ). We sought to characterize the impacts of the immunometabolic signals on the key brain regions that may account for the behavioral changes. The proteomic cytokine array of the serum was also investigated to detect the circulating immunological signals that were influenced by menthol flavor in ENDS. The correlation analyses among the behavioral parameters and the central and peripheral immunometabolic indices were conducted to reveal the systemic responses mediated by menthol flavor in ENDS.
## Materials and Methods
### Animals
Male C57BL/6J mice (Hunan SJA Laboratory Animal Co., Ltd., Hunan, China) aged 8 weeks old, were maintained in standard housing conditions on a 12/12 h day/night cycle (lights on at 7 a.m. and off at 7 p.m.) with ad libitum access to food and water. All behavioral tests were conducted at a fixed time period during the light cycle. All mice were handled for 15–20 min per day for 3 days before behavioral assays to reduce the stress introduced by contact with an experimenter. All animal experiments and procedures were carried out in accordance with the protocols approved by the Animal Care and Use Ethics Committee of the Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences.
### E-Cigarette Exposure System
The inExpose e-cigarette device (SCIREQ Scientific Respiratory Equipment Inc.) was used in our experiments for vapor exposures of accurate amounts of nicotine between groups, as shown in . During the experiments, 8 mice were treated in one single run, in a closed whole body exposure chamber with relatively spacious space. The action state of animals in the chamber was recorded in real-time by a camera connected to a computer. A supporting software (IX-2PD-4DIO-ECIG inExpose) was applied for controlling the e-cigarette vapor generation and postprocessing. For details of the running program, the exposure duration was set as 30 min per run, one time per day. The gas flow rate was set as 2 L per min ( ). The purified exhaust gas was expelled after the exposure. The e-cigarette vapor exposures were carried out for 7 days per week, for 43 days, and the following behavioral tests were performed with a continuous daily vapor exposure except that the elevated plus maze was assessed when the ENDS had been withdrawn for 48 h ( ). The mice were randomly assigned to one of the three treatment groups and exposed daily as follows: (1) Veh cont: Vehicle control of 50% propylene glycol (PG) + 50% vegetable glycerin (VG); (2) Nico: 4% Nicotine + 46% PG + 50% VG; (3) Nico + ment: nicotine with menthol flavoring: 10% of menthol flavoring agent + 4% of Nicotine + 35% of PG + 51% of VG. All behavioral tests were conducted approximately around 15–16 h following the e-cigarette exposure to avoid its acute effects, except the Elevated plus maze test performed at the 48 h withdrawal period.
Schematic diagram of a precise substance vapor device. Each part of the device is represented by a stick figure. (A) Flexiware software: the exposure temperature, patterns of smoking and humidity, and the exposure time and duration can be modified practically by the computerized exposure system; (B) a Uint-Wise controller connected with (C) e-cigarette vapor generator; (D) whole body exposure chamber with relatively spacious space, eight mice for every single run, and the action state of animals in the chamber was recorded in real-time by a camera (E) connected with (A) ; a gas purifier before it is discharged.
Experimental design of this study. (A) Diagram of experimental procedures. The duration of vapor exposure is 30 min per day and 7 days a week. Veh cont, Vehicle control of propylene glycol/vegetable glycerin; Nico, Nicotine; Nico + ment, nicotine with menthol flavor. (B) Schematic workflow of this study. Veh cont, Vehicle control of 50% propylene glycol (PG) + 50% of vegetable glycerin (VG); Nico: 4% of Nicotine + 46% of PG + 50% of VG; Nico + ment: 10% of menthol flavoring agent + 4% of Nicotine + 35% of PG + 51% of VG.
### Behavioral Assessment
#### Three-Chamber Sociability and Social Novelty Test
The three-chamber test (TCT) is widely used to observe the sociability and social novelty of rodents. An opaque white box (42 cm length × 60 cm width × 25 cm height) was made of acrylic and each chamber measured 42 cm length × 20 cm width × 25 cm height. Before the test, the mice were placed in the corner of the center chamber to habituate to the 3 chambers and two empty cups for 10 min. In the TCT, a subject mouse is allowed to explore two opposing chambers containing another mouse (social stimulus) or empty cage in the sociability test; and to explore two opposing chambers containing the familiar mouse or the novel mouse in the test of preference for social novelty. In the first session (sociability test), the test mice were placed in the corner of the center chamber, a new male mice of the same age were placed into the cup in the left chamber, while no mice were placed in the right chamber. In the second session (social memory test), the mice in the left chamber remained unchanged, and another set of new male mice of the same age was placed into the cup in the right chamber. Each session was monitored for 10 min. Time around and the number of interactions with each wire cage, which either housed the mice or not, and the time spent in each chamber zone was recorded. The contact zone was considered to be at a 2-cm distance from each cup. The 3-chamber box and the cups were cleaned with 70% of ethanol between the sessions.
For sociability sessions,
For social novelty sessions,
#### Elevated Plus Maze
To measure the anxiety levels in e-cigarette withdrawn mice, the elevated plus maze (EPM) was assessed by using a plastic elevated plus maze constructed from two white open arms (25 cm length × 5 cm width) and two white enclosed arms (25 cm length × 5 cm width × 15 cm height) extending from a central platform (5 cm length × 5 cm width) at 90° which form a plus shape. The maze was placed 65 cm above the floor. A camera was set directly above the EPM apparatus for video recording. The mice were individually placed at the center, with their heads facing the open arms. The number of entries and the amount of time spent in the same type of arms were recorded during the 5-min sessions.
#### Tail Suspension Test
The tail suspension test (TST simulates the behavioral despair states similar to depression, and this behavioral test was performed as described before ( ; ). After 1 h of habituation in the experimental environment, each mouse was suspended on a metal bar 50 cm above the floor of the suspension box with an adhesive tape placed approximately 1 cm from the tip of the tail for 6 min. At the beginning of the test, the animals exhibited escape behaviors, which after a period of struggle, became more subtle. These subtle movements were considered as the immobility time. Immobility was defined as the absence of any limb or body movements, except those caused by respiration. The activities of the mice were recorded by a camera, and the immobility time during the 6-min testing period was calculated. During the test, the mice were recorded separately to prevent animals from observing or interacting with each other. After each animal had completed the test, the suspension box was thoroughly cleaned to eliminate olfactory effects.
#### Visual Looming Test
The visual looming test (VLT) was performed in a closed Plexiglas box (40 cm length × 40 cm width × 30 cm height) with a sheltered nest in the corner. For upper field looming stimulus (LS), an LCD monitor was placed on the ceiling to present multiple LS, which was a black disc expanding from a visual angle of 2° to 20° in 0.3 s, expanding the speed of 60° per second. The expanding disc stimulus was repeated 15 times in quick succession (totally 4.5 s). This together with a 0.066 s pause between each repeat forms the total upper visual field LS that lasts 5.5 s. Behavior was recorded using an HD digital camera (Sony, Shanghai, China). The latency between the placement and the first overt behavioral signs, such as escape behavior and time staying in the nest were recorded. Animals were handled and habituated for 10–15 min in the looming box 1 day before testing. During the looming test session, the mice were first allowed to freely explore the looming box for 5 min. No observable adaptation was observed in all our experiments.
#### Hot-Plate Test
To measure the basal responsiveness to nociceptive stimulation, the mice were placed on a hot-plate set at 55 ± 1°C. The antinociceptive response was the latency from the placement of the mouse on the heated surface until the first overt behavioral sign of nociception, such as licking a hind paw, vocalization, or jumping off the plate. The time between the placement and the first overt behavioral sign was recorded as a pain threshold in this test and the mouse was immediately removed from the hot plate immediately after responding or after a maximum of 30 s (cut-off), to prevent tissue damage.
#### Y Maze Spontaneous Alternation Test
The Y maze test was conducted to detect spatial memory and spontaneous alternation performance. The Y maze used in this study is composed of three arms (42 cm length × 4 cm width × 25 cm height) projecting from a central triangular area. The mice were placed in the central area and were allowed to explore freely for 8 min. The observer recorded an arm entry when the hind paws were completely within the arm. Spontaneous alternation was defined as successive entries into the three different arms (without returning to any arm). The percentage alternation was calculated as the ratio of actual to possible alternations (the total number of arm entries - 2) × 100. The arms were cleaned with 70% ethanol between sessions.
### Tissue Processing and Western Blot
The mice were sacrificed immediately after behavioral experiments. They were anesthetized with isoflurane (0.3 ml per 25 g mouse) and euthanized by exsanguination. The brain regions of the frontal cortex and the hippocampus were dissected out on the ice and stored at -80°C for later use. The samples were homogenized in a Radioimmunoprecipitation (RIPA) lysis buffer with 1 time protease inhibitor cocktail and 1 time phenylmethylsulfonyl fluoride (PMSF). Homogenates were incubated on ice for 30 min and centrifuged at 12,000 × rpm for 10 min at 4°C. The concentration of total protein in each sample was measured using a bicinchoninic acid (BCA) kit. Then, the sample was mixed with 6 times loading buffer and boiled at 100°C for 10 min. The denatured samples containing 20 μg of total protein were separated by sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE), and then transferred to the nitrocellulose membrane. The membrane was blocked with 5% non-fat milk in Tris-buffered saline (TBST; 0.1% Tween 20) at room temperature for 1 h, then incubated in primary antibodies overnight at 4°C. The next day, the membrane was incubated with HRP-conjugated secondary antibodies for 1 h at room temperature. For detection, the ECL super signal chemiluminescence kit was used according to the manufacturer’s protocol. The gray intensity analysis of the bands was performed using Image J software (NIH, United States).
### Blood Collection and Proteome Profiler Mouse Cytokine Array
Orbital sinus blood samples were collected before sacrifice from the chronic ENDS-exposed mice. After collection, the blood samples were kept on ice and then centrifuged (3,000 rotations per minute for 10 min at 4°C), and the serum was separated. The serum samples were used fresh or kept at -80°C until further processing. The Proteome Profiler Mouse Cytokine Array Kit (Panel A; R&D Systems) was used to profile cytokines in 50 μL of serum samples according to the manufacturer’s protocol. The visualization of the array membranes was achieved using an enhanced chemiluminescence detection and exposure to X-ray film (Kodak, United States). Densitometry analysis was carried out using Quantity One.
### Urine Collection
To confirm the exposure of nicotine e-cigarettes in the appropriate treatment group, urinary cotinine levels in all the groups were measured at random days after aerosol exposure by using ultraperformance liquid chromatography coupled with tandem mass spectrometry (UPLC-MS/MS) method. Urine was collected during aerosol exposure from the plastic film located at the bottom of the exposure chamber using a pipette and transferred to a microcentrifuge tube. Separate films were replaced between groups in the exposure trials.
### Ultraperformance Liquid Chromatography Coupled With Tandem Mass Spectrometry Analysis
The UPLC–MS-MS analysis was performed on a Waters Acquity ultra-performance liquid chromatography (UPLC) system interfaced with a Waters Xevo TQ MS. Chromatographic separation of cotinine was achieved with an HSS T3 column (2.1 × 100 mm; 1.8 μm particle size). The temperature of the column was maintained at 40°C. A portion of 2.0 μL of the extracted sample was injected onto the column and the gradient elution was performed with 0.1% (v/v) formic acid in deionized water (mobile phase A) and (methanol mobile phase B) at a flow rate of 0.2 mL/min. The MS detection of cotinine was conducted by electrospray ionization (ESI) in the positive ion mode, using the multiple reaction monitoring (MRM) for analyte identification. The following ESI conditions were applied: capillary voltage of 1.5 kV; source temperature of 150°C; desolvation temperature of 400°C; and desolvation gas flow (nitrogen) of 800 L/h; The analysis time was approximately 5 min per sample. About 100 μL of the test urine sample was first diluted by 900 μL of ultrapure water in the centrifuge tube, and then eddied for 1 min and centrifuged at 10,000 rpm for 10 min. The supernatant was taken for testing.
### Statistical Analyses
Experiment data are expressed as the mean ± SEM of the number of tests stated. Statistical comparisons were made using one-way ANOVA followed by Tukey’s HSD test, as indicated in the figure legends. All the statistical tests were performed using the Prism 8.0 software (GraphPad Software Inc., San Diego, CA, United States). Spearman’s correlation analysis was used to conduct the correlations among the behavioral parameters and the AMPK activation in the hippocampus, as well as the altered cytokine levels in the sera. A p -value of less than 0.05 was considered statistically significant.
## Results
### Compensatory Enhanced Sociability in Mice Exposed to Electronic Nicotine Delivery Systems With Menthol Flavor
The e-cigarette products in the market usually offer a very wide variety of flavoring agents mixed with nicotine, which is one of the biggest health concerns of e-cigarettes ( ; ). Here, we aim to evaluate whether the social functioning is modified by ENDS with a flavoring compound. Menthol is one of the most prevalent and common flavors used in e-cigarettes; so, we compared the behavioral responses in vapor-exposed mice between the nicotine alone group and nicotine group mixed with menthol flavoring. To do this, adult male C57BL/6J mice were randomly assigned to three treatment groups ( n = 8 per group) which were exposed to the following: (1) propylene glycol and vegetable glycerol as vehicle control (50:50, PG/VG, Veh cont); (2) PG/VG with 4% (vol/vol) nicotine (Nico); (3) Vapor of 4% nicotine with 10% of menthol flavorings (Nico + ment). Here, we selected the inhalation model with a short duration (30 min) per day and long-term vapor exposure (>40 days) period. After a daily vapor exposure of 30 min and 7 days a week for 43 days, the three-chamber sociability and social novelty tests were evaluated between the above groups ( ). Since we focused to investigate the merged effects of menthol flavor in the nicotine vapor, here, we have only used the PG/VG as vehicle control in the following sets of behavioral assessments. To verify the exposure constituents, the levels of urine cotinine (i.e., nicotine metabolite) were assessed for the mice in all three vapor exposure groups. Mean cotinine levels averaged from multiple mice per exposure run on the random days were higher in urine from the mice exposed to vapors of nicotine alone (319.3 ± 33.99 ng/mL) or nicotine with menthol flavor (452.8 ± 92.71 ng/mL) compared to the Veh control (50.97 ± 13.36 ng/mL) [ F = 12.65, p = 0.0070, ]. The body weight was measured weekly and no significant differences were observed in weight gain among all the treatment groups under our vapor exposure condition ( ).
Social interaction is a complex and highly conserved neuropsychiatric behavior that safeguards survival ( ). Whether the additive of menthol flavor into e-cigarette would change the social interaction was unknown. We evaluated the social behaviors of mice via three-chamber social tests in this study. Sociability was investigated in the sociability session of the test ( ). Mice exposed to long-term ENDS with or without menthol flavor showed normal sociability as assessed by interaction time and time spent in the target chamber. Mice from the Nico group appeared normal while having slightly less contact and socialization with the stranger mouse 1 than in the empty arena compared with that in the Veh control group (No statistical significance, ). Interestingly, the mice in the Nico + ment group prefer and spent more time (129.3 ± 21.01 s) to socialize with the stranger mouse 1 compared to the Nico group (83.67 ± 5.558 s, ) while the time spent in the chamber of stranger mouse 1 and the preference of chamber was similar to the mice of all the groups [ F = 1.411, p = 0.2672, ]. In the test session of preference for social novelty ( ), mice spent more time making contact and socializing with the newly introduced unfamiliar mouse (stranger 2) than stranger 1, as a normal manifestation of social memory and preference for social novelty [ F = 3.005, p = 0.0723, ]. No significant difference were observed among the groups in their interacting time, the number of interaction, and the preference index with the novel mouse ( ), while mice exposed to nicotine alone spent less time in the chamber of the stranger 2 [ F = 4.542, p = 0.0236, ], and mice of Nico + ment group also showed an increased preference for staying in the chamber of the newly introduced stranger 2 mice than with the familiar mouse, the stranger 1, compared to the mice in the group of nicotine vapor alone ( ). Our data suggested that long exposure to menthol-flavored ENDS may have compensatory enhancing effects on the sociability and preference for social novelty compared to the vapor exposure of nicotine alone.
Social behavioral assessments in mice after long-term vapor exposure. Schematic diagram of two parts of the Three-Chamber Test (TCT); stage one (A) and stage two (B) . (A,B) Left: the schematic diagram of the sociability session and the social novelty session of the TCT. Center: the trajectory graphs represent the movements of the tested mouse during the sociability session and the social novelty session, respectively. Right: the heatmaps represent the trajectory of motions in mice. (C–G) Social behavioral assessments of tested mice in the sociability session. Time around target mouse [ F = 3.383, p = 0.0543] (C) and the number of interactions with stranger 1 [ F = 0.9427, p = 0.4062] or empty cage (D) . Time spent in the chambers of stranger 1 side or the empty side (E) . (F) Preference index of stranger 1 among groups. F = 4.040, p = 0.0336. (G) Preference of stranger 1 chamber against the empty side among the exposure groups [ F = 0.9022, p = 0.4216]. (H–L) Social behavioral assessments of tested mice in the social novelty session. Time around target mouse (H) and the number of interactions [ F = 0.5234, p = 0.6004] (I) with stranger 2 mouse or stranger 1 mouse. (J) Time spent in the chambers of stranger 2 sides or the stranger 1 side. (K) Preference index of stranger 2 among the groups [ F = 0.2197, p = 0.8047]. (L) Preference of stranger 2 chamber against stranger 1 side among the exposure groups [ F = 4.081, p = 0.0326]. The calculations of Preference index and Preference of chamber are presented in Methods. Data are shown as mean ± SEM. * p < 0.05, p < 0.01, p < 0.001, and p < 0.0001 as determined by ordinary one-way ANOVA, within-group analyses using paired t -test, with the factors of test condition (sociability or social novelty), cages, and chamber sides (e.g., stranger 1 side or the opposite side). Veh cont, Vehicle control; Nico, Nicotine; Nico + ment, nicotine with menthol flavor.
### Effects of Long-Term Electronic Nicotine Delivery Systems Exposure With Menthol Flavor on Anxiety, Depression-Like Behaviors
The previous study has demonstrated the interplay between social behaviors and anxiety status ( ; ). Since social stress is one of the major risk factors for the progression of anxiety disorders ( ) ( ), and shared neuronal circuits between them has been confirmed ( ; ), we assessed the anxiety and the depression-like behaviors of the mice after long-term vapor exposure. We compared the 48 h-withdrawal responses in vapor-exposed mice with nicotine or nicotine plus menthol. By performing the behavioral tests of EPM (after 48 h-withdrawal) in mice, we observed that long-term vapor exposure of daily half-hour ENDS with or without menthol flavor did not cause significant withdrawal responses evaluated by anxiety-like behaviors in EPM at the 48-h-ENDS withdrawal period ( ). Specifically, we analyzed the immobility time for all groups during the EPM test [ F = 0.5963, p = 0.5599, ; no significant differences were observed. Further, a tail hanging test was performed for the evaluation of depressive-like behaviors. No significant changes were observed on immobility time in the tail suspension test [ F = 0.7295, p = 0.4940, ]. Our current data suggested that long-term e-cigarette usage with short daily nicotine exposure time did not induce anxiety or depression-like behaviors in mice, even after adding the menthol flavor in the e-liquid. These data suggested that the enhancement of menthol flavor in ENDS on social functioning is independent of the emotional status.
Anxiety, depressive-like behavioral assessments in mice exposed to long-term vapor exposure. (A) The trajectory and heatmap of mice in the EPM. (B) The percentage of time of staying in the open arm to the time of one trial ratio (%) [ F = 2.222, p = 0.1333]. (C) The immobility time of mice during the trial in EPM (s). (D) Immobility time of mice during Tail suspension test (s). The value of * p < 0.05 as determined by ordinary one-way ANOVA and multiple comparisons with every other group. Bars represent marginal means ± SEM. N = 8 per group. Veh cont, Vehicle control; Nico, Nicotine; Nico + ment: nicotine with menthol flavor. EPM, Elevated plus maze. TST, Tail suspension test.
### No Innate Visual or Perceptual Behavioral Alterations in Mice After Long-Term Electronic Nicotine Delivery Systems Exposure With Menthol Flavor
We further evaluated whether the social functioning changes induced by the menthol flavor in ENDS are associated with any alternations on innate visual or perceptual behaviors ( ; ; ). Behavioral paradigms, such as innate fear and heat pain response were used in our experiments. In the VLT, innate fear responses were quantified. Unexpected salient visual cues stimulate the animal’s defensive behaviors, such as shying away and hiding back in the nest ( ). We analyzed the onset latency of mice to such behaviors. There were no changes in the latency of flight to nest, the flight-to-nest latency, as well as the duration in the nest in the Nico group when compared to both Veh control and Nico + ment groups ( ). Chronic ENDS inhalation with daily limited-duration may not affect the innate fear responses in mice, and the same was also observed when menthol flavor was added. In the Hot-Plate Test (HPT), the pain response to a thermal stimulus was assessed by the onset of latency, and we found no differences among all groups [ F = 1.157, p = 0.3337, ]. These data suggested that chronic nicotine vapor with or without menthol under our exposure conditions did not affect the innate visual or perceptual behaviors.
The innate visual or perceptual behavioral assessment in mice after long-term ENDS exposure. The behavioral tests were performed in order during ENDS exposure as described in . (A) The latency to the onset behavior of mice during the Visual looming test (VLT; s) [ F = 0.4033, p = 0.6732]. (B) The latency of flight to the nest behavior of mice during VLT (s) [ F = 0.4649, p = 0.6345]. (C) Duration of mice hiding in the nest (s) [ F = 0.2317, p = 0.7952]. (D) The latency to the onset behavior of mice during the Hot-plate test (s). The value of * p < 0.05 as determined by ordinary one-way ANOVA and multiple comparisons with every other group. Bars represent marginal means ± SEM. N = 8 per group. Veh cont, Vehicle control; Nico, Nicotine; Nico + ment, nicotine with menthol flavor.
### Normal Spatial Learning and Memory in Mice With Long-Term Exposure of Electronic Nicotine Delivery Systems With or Without Menthol Flavor
Previous studies have suggested a potential relationship between social activity and the overall executive functioning, working memory, and visuospatial abilities in healthy older adults ( ; ; ). Here, we used the Y-maze test to measure the cognition and spatial memory of mice after exposure to e-cigarette vapor. The percentage of alternation in Y-maze arms was analyzed ( ). There were no significant changes of alternation [ F = 0.6216, p = 0.5467] in the mice of either Nico group (64.07 ± 1.119 %) or Nico + ment group (64.25 ± 1.973 %) compared to the PG/VG group (66.53 ± 1.978 %). These data indicated that menthol flavor combined with nicotine in e-cigarette had no effect on spatial learning and memory.
Spatial memory assessment in mice exposed to long-term vapor exposure. (A) Schematic diagram of recorded alternation of one trial in Y-maze. Spontaneous alternation was defined as successive entries into the three different arms (without returning to any arm). (B) The percentage alternation was calculated as the ratio of actual to possible alternations (the total number of arm entries - 2) × 100. The value of * p < 0.05 as determined by ordinary one-way ANOVA and multiple comparisons with every other group. Bars represent marginal means ± SEM. N = 8 per group. Veh cont, Vehicle control; Nico, Nicotine; Nico + ment, nicotine with menthol flavor.
### The Reduced Adenosine 5′ Monophosphate-Activated Protein Kinase Activation in the Hippocampus by Chronic Electronic Nicotine Delivery Systems Exposure With Menthol Flavor
We further investigated the underlying mechanisms of the effects of chronic vapor on nicotine with or without menthol flavor. The prefrontal cortex and the hippocampus are the interconnected brain regions and hubs for modulating high brain functions and neuropsychiatric behaviors especially social behaviors ( ; ). We here analyzed the AMPK/ERK signaling pathways which are involved in neuronal metabolism, neuroinflammation, and synaptic plasticity. By using Western blotting, we found that the hippocampal activation of ERK1/2 (as shown by phosphorylated/total ERK1/2) and AMPKα (as presented by phosphorylated/total AMPKα) in the hippocampus was decreased by menthol flavor when added into the nicotine vapor ( ). Further, we also evaluated the expression levels of presynaptic protein, synaptin-1, and the postsynaptic protein, PSD95 in the prefrontal cortex and the hippocampus to assess the alterations in synaptic plasticity. A slight reduction of synapsin-1 was observed in the hippocampal region without statistical significance ( ). The AMPK/ERK signaling in PFC ( ), and the expressions of Synapsin I and PSD95 in PFC and the hippocampus were not significantly changed under our vapor exposure condition ( ). These data suggested that the menthol flavor in ENDS might inactivate the AMPK-ERK signaling in the hippocampus.
The immunometabolic signals and synaptic protein analyses in the PFC/hippocampus of mice after long-term vapor exposure. Western blot analyses of the expressions of p/t-ERK1/2, p/t-AMPKα, synaptic proteins, such as Synapsin-1 and PSD95 in the PFC (A) and the hippocampus (B) . The quantification data of the above molecules are presented in (C–J) , respectively. Data are expressed as means ± SEM. The values of * p < 0.05 as determined by ordinary one-way ANOVA and multiple comparisons with every other group. Veh cont, Vehicle control; Nico, Nicotine; Nico + ment, nicotine with menthol flavor.
### Alterations of Peripheral Cytokine Levels Responded to Menthol Flavor in Electronic Nicotine Delivery Systems
Multiple cytokines and chemokines have been investigated regarding their roles in neuropsychiatric behaviors ( ; ). No significant differences in weight gain among all the treatment groups were observed ( ). Here, we profiled multiple cytokines in the sera to assess the peripheral effects of chronic ENDS vapors which might respond to the social behavioral changes. Forty cytokines were measured in our experiment and we observed that the sera levels of CXCL12 and TIMP-1 were significantly reduced while that of the CXCL5 was dramatically increased after nicotine vapor exposure, and a further decline of TIMP-1 and CXCL13 were detected in the group of menthol-flavored ENDS compared to the Nicotine alone group. The serum expressions of CXCL12 were decreased in vapor groups with or without menthol flavor compared to the Veh control group, suggesting that the menthol flavor had no additional effects in ENDS on the serum level of CXCL12. The sera level of M-CSF was only reduced in Nico + ment group compared to the Vehicle control group. The C5 level in the sera was found dramatically decreased in the vapor group of nicotine with menthol flavor compared to either vehicle or ENDS exposure of nicotine only, suggesting a strong downregulation of C5 in the sera of menthol flavorings in ENDS ( ). These data suggested that menthol flavor may modulate the serum expressions of cytokines that responded to the alteration on the social activity by ENDS.
The cytokine profile in the sera is altered by long-term vapor exposure. (A) Representative blots from serum samples of mice treated with long-term vapor exposures of vehicle control, ENDS, or ENDS with menthol flavor that presented levels of 40 known cytokines using the murine Proteome Profiler Cytokine Array. Spots showing differential expression are boxed. (1): CXCL13; (2): C5; (3): sICAM-1; (4): TIMP-1; (5): CXCL5; (6): M-CSF; (7): CCL2; (8): CXCL12. (B) Heatmap of quantification revealed significant changes greater than 1.2-fold, data were determined by ordinary one-way ANOVA and multiple comparisons with every other group. * p < 0.05, ** p < 0.01, *** p < 0.001 compared to the Veh cont; p < 0.05 compared to Nico group, accordingly. Veh cont, Vehicle control; Nico, Nicotine; Nico + ment, nicotine with menthol flavor.
### Linear Correlations Among the Central/Peripheral Immunometabolic Indices and the Response of Social Behaviors to Menthol Flavor in Electronic Nicotine Delivery Systems
To elucidate the correlation among the social activity and the immunometabolic indices in the hippocampus and in the sera which were affected by menthol flavor in ENDS, we conducted the Spearman’s correlation analysis among social behavioral indices that activated the AMPK in the hippocampus and the cytokine levels in the sera ( p < 0.05, ). The serum level of C5 was found to be negatively correlated with the preference for social novelty as measured by the target exploration time in Stage 2 ( r = -0.7). The serum level of C5 was also positively correlated with the activation of ERK (p-ERK/ERK, r = 0.79) and AMPK (p-AMPK/AMPK, r = 0.87) in the hippocampus. The levels of M-CSF and TIMP-1 in the sera were also found to be correlated with the AMPK-ERK signaling in the hippocampus ( ). This set of correlation data suggested that the menthol flavor in ENDS may induce comprehensive immunometabolic responses in the brain nuclei and in the sera corresponding to the social activity change.
Correlations among immunometabolic signals presented as the activation of AMPK-ERK in the PFC and the hippocampus, cytokines in the sera, and the social behavioral parameters (p < 0.05). Through pairwise comparison of the social behavioral index and biochemical indicators, we selected and displayed those with p -value less than 0.05, and used the area and color depth of the dot to represent the r -value. (1): Time around the target in the sociability session of Three-Chamber Test (TCT); (2): Preference index in the sociability session; (3): Time around the target in the test session of preference for social novelty; (4): Preference index in the test session of preference for social novelty; (5): the level of p-ERK/ERK in the hippocampus; (6): the level of p-AMPKα/AMPKα in the hippocampus; (7–11): the serum level of CXCL13 (7), C5 (8), CXCL12 (9), M-CSF (10), and TIMP-1 (11).
## Discussion
The e-cigarette is among the focus of controversy since it may be harm-reducing for traditional smokers seeking to quit, while harm-initiating for former or never smokers, particularly among the youth ( ; ; ). The US FDA has been seeking to reduce nicotine concentrations in conventional tobacco cigarettes to non-addictive levels while emphasizing other nicotine delivery products, such as the role of e-cigarettes in attenuating the harmful effects of combustible tobacco ( ). However, further scientific evidence is needed to convince the safety of e-cigarette and their aid on smoking cessation, given the widespread use of chemicals/artificial flavors to mimic natural flavors commonly used in the e-cigarettes.
A wide variety of flavor options of e-cigarettes on the market has grown in popularity and entices young generations to smoke ( ). The menthol flavor is among the most commonly used flavorings in e-cigarettes, and an exception in which the flavored e-cigarettes have been banned by Federal regulations recently ( ). It has been well-documented that e-cigarettes cause systemic toxicity, including lung and liver injuries; the cytotoxicity induced by e-cigarette flavoring chemicals has also been determined in cell lines and humans. Repeated exposure to menthol was found to significantly decrease cell viability ( ); therefore additional research is urged to understand the mechanisms of the toxicity of flavorings and the chemical combinations in ENDS.
Smoking may increase the risk of mental disorders and non-affective psychoses. A systematic review of literature from 1946 to 2017 followed by a meta-analysis suggested that chronic tobacco smoking was strongly associated with neuropsychological deficits and cognitive impulsivity ( ). The e-cigarette products in the market are generally composed of nicotine with flavor. Menthol flavor was the top choice among teen vapers according to research in the US ( ). In this case, it is necessary to understand the neuropsychiatric roles and their effects on the brain as well as the peripheral system of menthol-flavored e-cigarettes. However, there is limited evidence on the neuropsychiatric roles of menthol flavor in ENDS that was specifically evaluated in vivo in animal models or humans.
Social behaviors are fundamental for the survival of any vertebrate species. Epidemiological data indicated that smokers endorse socializing as a reason to smoke ( ) and social functioning was found enhanced in smokers which was supposed to be related to nicotine ( ). However, very limited work has been implemented to depict the effect of e-cigarettes on social functions in animal models to reveal the underlying molecular mechanisms. In our current work, we have used a standard and precise vapor device to mimic the exposure of ENDS, which avoided the restraint stress that might have been caused by the nose-only aerosol exposure, and evaluated the social behaviors after long-term exposure with daily 30-min inhalation. As shown in the previous study, the effects of nicotine on social behaviors are complex regarding the dose, the schedule of administration, housing, and individual differences; nicotine may increase the social interaction at low doses but reduce it at high doses ( ); and it was also presented to improve the sociability and reduced repetitive behaviors in a mouse model of autism at certain doses while no effects were observed in the normal mice ( ). Consistent with some of these literature, we found in our experiment that, although the slight decrease in the social activity (no statistical difference) was observed in nicotine-vapored mice, the long exposure to menthol flavored ENDS was found to have compensatory enhancing effects on the sociability and preference for social novelty compared to the vapor exposure of nicotine alone, suggesting the antagonistic effect on the social functioning of menthol flavoring as a combinational ingredient with nicotine in ENDS.
Since social behaviors are instinctive with flexibility ( ), and influenced by other psychiatric behaviors, we further assessed the behaviors related to emotion, cognition, and innate state in the mice after ENDS exposure with or without menthol flavor. Interestingly, under our ENDS exposure condition, which was a short-term treatment per day and it lasted for the long term; the ENDS exposure with menthol flavor did not change the anxiety/depressive-like behaviors measured by the elevated plus maze and tail suspension test; the innate visual or perceptual behavioral responses measured by the VLT, HPT, and the spatial memory evaluated by Y-maze in mice. One limitation of this study was that only male mice were used with a limited sample size. It will be necessary to understand how female individuals cope with e-cigarettes and to further confirm the complex interplay of menthol flavoring with nicotine in ENDS.
Further, we characterized the central and peripheral changes induced by the vapors of nicotine alone or combined with menthol flavor that may be related to the alterations in social activities. The prefrontal cortex and hippocampus are hub regions that are dominant in many complex behaviors including social activities and social cognition ( ; ; ). Increasing attention has been paid to the role of the crosstalk between metabolic, inflammatory, and neuropsychiatric disorders ( ), such as the activation of ERK and the AMPK levels ( ). The adenosine 5′ monophosphate-activated protein kinase (AMPK) is a heterotrimeric serine/threonine kinase that promotes ATP generation and is regarded as a key regulator of cellular energy metabolism and mitochondrial homeostasis ( ; ). Therefore, we measured the immunometabolic molecular signals in the prefrontal cortex, hippocampus, and the cytokines in the sera altered by nicotine vapor with or without menthol flavor and investigated their correlations with social behavioral changes. Our results suggested a negative correlation of preference for social novelty and C5 level in the sera of mice. Further, the serum level of C5 was also found to positively correlate with the activation of AMPK-ERK signaling in the hippocampus, which may hint at a coordinated response in the central and peripheral system to ENDS which contribute to the social behavioral enhancement induced by the menthol flavor. Previous studies have shown that anxiety was associated with low levels of many cytokines in sera, such as CCL11, CCL2, CCL5, and IL-6; and lower peripheral levels of CXCL5 was observed in people with psychiatric disorders, such as schizophrenia and recurrent depressive disorder with suicidal ideation ( ). The cytokine profile observed in our study indicated that the menthol flavor in ENDS may act to reverse the potentially reducing effects of nicotine on social activities.
In conclusion, our present study profiled the social behaviors modulated by menthol flavor in ENDS. We presented the compensatory enhanced social activity induced by menthol flavor in the nicotine-containing e-cigarette. The striking enhancement in social activity induced by menthol flavoring, in combination with nicotine in ENDS, may explain the increased severity of nicotine dependence in menthol-flavored e-cigarette vaporer and the popularity of menthol/mint-flavored e-cigarettes in the market. The ENDS induced the immunometabolic alternations in the hippocampus, as well as in the sera that correspond to social behavioral changes, suggesting the disruption of systemic homeostasis are only induced by nicotine but also by other flavorings in the e-cigarette. Although our current data indicated the mild influences of the neuropsychiatric behaviors in the mice due to long-term ENDS exposure with daily intake limit; the phenomenon of enhanced social functions induced by menthol flavor in ENDS highly alerts us with the information that e-cigarette flavoring additives may have complex interplay with nicotine and lead to increased addiction as well as immunometabolic disruption among the e-cigarette users who definitely need further investigations.
## Data Availability Statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics Statement
All animal experiments and procedures were carried out in accordance with protocols approved by the Animal Care and Use Ethics Committee of the Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences.
## Author Contributions
X-AL designed the experiments. ZX, ZM, A-XL, JT, JL, and JW performed the experiments. ZX, YT, CD, ZC, A-XL, X-YJ, JT, and XL performed the data analyses. X-AL, ZC, YT, A-XL, JT, and XG contributed to the manuscript writing. TL, ZC, ZL, LW, and SL revised the manuscript. All authors have read and approved the manuscript.
## Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s Note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
|
A growing wave of evidence has placed the concept of protein homeostasis at the center of the pathogenesis of amyotrophic lateral sclerosis (ALS). This is due primarily to the presence of pathological transactive response DNA-binding protein (TDP-43), fused in sarcoma (FUS) or superoxide dismutase-1 ( SOD1 ) inclusions within motor neurons of ALS postmortem tissue. However, the earliest pathological alterations associated with ALS occur to the structure and function of the synapse, prior to motor neuron loss. Recent evidence demonstrates the pathological accumulation of ALS-associated proteins (TDP-43, FUS, C9orf72-associated di-peptide repeats and SOD1) within the axo-synaptic compartment of motor neurons. In this review, we discuss this recent evidence and how axo-synaptic proteome dyshomeostasis may contribute to synaptic dysfunction in ALS.
## Introduction
Proteome homeostasis (proteostasis) refers to the controlled maintenance of each protein in the proteome in its precise conformation, concentration and location for each cell to carry out its function. Maintaining proteostasis requires various biological mechanisms to regulate the synthesis, degradation, (re)-folding and trafficking of the proteome. The mechanisms required to maintain proteostasis are finely tuned in all cell types. However, an inability of cells to maintain proteostasis can lead to the accumulation of misfolded proteins, particularly within uncharacteristic cellular compartments, leading to cellular dysfunction and death.
Neurons are particularly susceptible to proteostasis network dysfunction compared to other cell types ( ). An inability for the neuron to maintain its proteome leads to the accumulation of misfolded/aggregating proteins, subsequently causing proteome collapse and the formation of inclusions ( ). An accumulation of misfolded/aggregated proteins and inclusions can be observed within distinct neuronal cell types and anatomical regions in several neurodegenerative disorders including Alzheimer’s Parkinson’s, Huntington’s disease ( ). Similarly, amyotrophic lateral sclerosis (ALS), which causes the progressive loss of upper and lower motor neurons also exhibits the accumulation of misfolded proteins and inclusions within motor neurons ( ; ). ALS cases are generally categorized as either familial (family history of genetic mutation) or sporadic (no family history). Despite the origin of disease, a common characteristic is the presence of proteinaceous inclusions (Bunina bodies, basophilic inclusions, skein-like inclusions and hyaline inclusions) within motor neurons and neighboring cells. The particular inclusion type and anatomical region observed can vary based on the presence or absence of ALS mutation ( ). However, proteostasis dysfunction that allows aggregates to proceed into inclusions across all forms of indicate this is a common mechanism underlying ALS.
Whilst, the presence of inclusions are observed in post-mortem tissue, on the other end of the disease timeline, clear evidence indicates one of the earliest pre-symptomatic and functional changes associated with ALS occurs distally in the motor neuron axons and at the synaptic terminals ( ). In line with this, electrophysiological evidence demonstrates distal changes in excitability are an early hallmark of ALS ( ). While it is clear that electrophysiological changes are intimately linked with ALS pathology, the underlying molecular alterations that result in such physiological outcomes remains unknown.
There has been much focus on perturbations of global/somal motor neuron proteostasis and axo-synaptic function in ALS. Despite the axo-synaptic compartment yielding ∼95% of the motor neuron volume ( ), little investigation of the proteostasis status within this compartment has been carried out. However, recent landmark studies indicate the presence of a disturbed proteome within the axo-synaptic compartment that can lead to synaptic dysfunction and motor neuron degeneration ( ). Here in this review, we focus on how perturbations to proteome homeostasis within axo-synaptic compartments may contribute to ALS, particularly focusing on critical areas in: (1) understanding the susceptibility of axons and synaptic terminals to misfolded proteins, (2) their perturbed responses to proteostasis disturbances, and (3) how these responses contribute to the synaptic changes observed in ALS.
The possible effects of pathological accumulation of ALS-associated proteins within the axo-synaptic compartment of motor neurons. Pathological accumulation of ALS-associated proteins (TDP-43, FUS, C9orf72-associated di-peptide repeats and SOD1) have been observed in the axo-synaptic compartment of motor neurons. The axo-synaptic proteome is supersaturated and conducive to protein misfolding and aggregation that have the capacity to propagate to neighboring cells. The accumulation of these proteins can lead to stress granule formation and translational stalling of key axo-synaptic proteins. Pathological accumulation of ALS-associated proteins can alter Ca dynamics and synaptic vesicle release. Collectively, the impairment of these processes may contribute to the synaptic dysfunction associated with ALS, leading to the progressive neurodegeneration of spinal cord motor neurons.
## Maintaining (sub-) proteome homeostasis in motor neurons
Proteome dyshomeostasis is a common feature of neurodegenerative diseases. The vulnerability of specific cell types within various neurodegenerative disorders can be rationalized to lie inherently within their proteome ( , ; ). Using transcriptomic and proteomic data from healthy spinal cord motor neurons, we demonstrated that ALS-vulnerable spinal cord motor neurons contain many proteins expressed at levels higher than their expected solubility compared to ALS-resistant ocular motor neurons ( ). These proteins are deemed supersaturated and vulnerable to misfolding and aggregation, particularly under conditions of proteostatic stress. A consequence of this supersaturated proteome is that it requires a greater reliance on the proteostasis network of the cell to prevent a collapse. However, previous work indicates motor neurons already have a reduced proteostasis capacity, including reduced mounting of the heat shock response ( ) and ubiquitin expression ( ), highlighting their vulnerability to proteome dyshomeostasis.
Motor neurons are a uniquely long neuronal sub-type, spanning distances of up to 1 meter, containing a vast proteome. A spinal cord motor neuron can innervate up to 1000 muscle fibers with each synaptic terminal populated by up to 1 million individual protein molecules, creating numerous complex sub-proteomes for the motor neuron to maintain. Whilst many proteins are trafficked from the soma to the synaptic terminal, recent advances in understanding proteostasis with increased granularity demonstrate the synaptic terminal has a local transcriptome and translatome that responds to intra- and -extracellular stimuli ( ; ; ). Traditionally centrifugal isolation has been used to distinguish the transcriptomic and proteomic profiles of soma and axo-synaptic compartments. However, there have been recent technological advances including, the use of soma-axonal culturing chambers, RNA-scope and non-canonical amino acid labeling to provide improved spatiotemporal resolution to dissect differences within sub-compartments ( ). Utilization of these techniques have aided in comprehending the difficulties in maintaining proteostasis at the synapse, yet primarily investigate hippocampal pyramidal cells and interneurons. However, these findings may only be extrapolated in spinal cord motor neurons, considering the vast distance between the soma and their unique presynaptic terminals (neuromuscular junction).
The difficulty in maintaining synaptic proteostasis has been postulated as a potential origin of in several neurodegenerative diseases, notably Alzheimer’s and Parkinson’s disease ( ; ). Synaptic dysfunction is a common theme across many neurodegenerative diseases and typically occurs early in disease progression. Recent work suggests that many supersaturated proteins are involved in synaptic processes ( ). Consistent with this, we demonstrated that the sub-proteome of synaptic terminal spinal cord motor neurons is more supersaturated than the entire motor neuron proteome ( ). This indicates that the synaptic terminal sub-proteome is particularly vulnerable to proteome stress and misfolding, in an already vulnerable proteome. Furthermore, we have found that many genes down-regulated in ALS are enriched in the synapse and encode axo-synaptic proteins integral to their function ( ; ). Whilst, proteostasis and synaptic dysfunction are common hallmarks of neurodegenerative diseases, there is a growing focus on investigating proteostasis disturbances within the axo-synaptic compartment, yet many questions remain as to how synaptic proteostasis dysfunction may contribute to disease pathology.
## Evidence of proteome dyshomeostasis at the axon and synapse
### Fused in sarcoma
Fused in sarcoma (FUS) is a nucleic acid-binding protein, playing a role in RNA metabolism. An accumulation of mislocalized cytoplasmic FUS, can be observed in ALS postmortem tissue, where it can form insoluble aggregates ( ). Of all the primary ALS-associated aggregation-prone proteins, FUS has the most well-defined role within axons and synaptic terminals. Localization of FUS within RNA granules has been repeatedly reported in the axon, dendrites and pre-and post-synaptic terminals ( ; ; ), including the neuromuscular junction of healthy tissues ( ; ). Cortical and hippocampal cultures indicate FUS is localized in both excitatory and inhibitory synapses, where it is preferentially located between the endoplasmic reticulum and synaptic vesicle pools ( ).
Synaptic FUS primarily localizes to exons and 3′UTRs of RNAs, indicating a role in RNA transport, local translation and stabilization ( ). In particular, synaptic FUS binds to RNAs encoding many receptors and transporters involved in glutamatergic and GABAergic signaling. Synaptic FUS has been demonstrated to aid in dendritic spine formation and regulate mRNA translation ( ), highlighting its importance in synaptic integrity and function. ALS patient-derived motor neurons show an increase in FUS-positive clusters within the axon and synapses. Concurrently, increased Bassoon (pre-synaptic marker) and Homer1 (post-synaptic marker) clusters were also observed within synapses ( ). Given the known roles of FUS at the synapse, these results suggest FUS pathologically accumulates at the synapse in ALS, potentially altering local mRNA control and induce aggregation, collectively perturbing local proteostasis.
Axo-synaptic FUS accumulation has been observed in Fus mice ( , ). Fus mice show an ALS/FTD phenotype with cognitive and motor impairments at 4 and 10 months, respectively, compared to FUS control mice. Axo-synaptic FUS accumulation was associated with altered cortical synaptogenesis, predominantly inhibitory interneurons. Furthermore, six-month old mice showed differential synaptic RNA levels, with a large proportion of genes up-regulated. An enrichment of mRNAs up-regulated were direct binders of FUS; however, a large majority of mRNAs down-regulated were not known synaptic FUS targets, indicating an in-direct mechanism of regulation. Axo-synaptic FUS was associated with increased stability of many genes (enriched in mRNAs containing exonic regions) corresponding to the synaptic-specific function. Additionally, decreased stability (enriched in mRNAs containing 3′UTR) was observed in mRNAs encoding for ribosomal localization, gene expression and translation, processes responsible for maintaining proteostasis. In line with the aforementioned transcriptional changes, protein level alterations were also observed in numerous GABAergic and glutamatergic signaling proteins, indicating that axo-synaptic FUS accumulation subsequently changes the synaptic sub-proteome that likely contributes to synaptogenesis impairments. Whilst, loss of spinal cord motor neurons are observed in the model, synaptic changes of spinal cord motor neurons have not been examined. Considering the number of GABAergic and glutamatergic boutons that extend along spinal cord motor neurons, there is scope to investigate how axo-synaptic FUS accumulation may affect receptor expression and signaling at the post-synaptic terminals that extend along spinal cord motor neurons.
Mutations in the FUS gene are causative of ALS ( ; ). Intra-axonal mutant FUS has demonstrated to accumulate in cultured primary neurons derived from m Fus /hg FUS mice ( ). Furthermore, spinal cord RNA expression profiles of m Fus /hg FUS and m Fus /hg FUS mice showed a down-regulation of genes encoding for glutamate signaling, in addition to ribosomal proteins and protein translation ( ). Similarly, up-regulated genes encode for the eIF2a signaling pathway. eIF2a is a crucial factor for translation initiation and is phosphorylated as part of the integrated stress response (ISR), to stall protein synthesis to alleviate the proteome load ( ). Consistent with this, increased intra-axonal FUS accumulation, pEIF2a and reduced protein synthesis within the sciatic nerve were observed in m Fus /hg FUS and m Fus /hg FUS mice compared to wild-type and FUS controls ( ). Collectively, this work suggests that the accumulation of mutant FUS within axo-synaptic compartments activates the integrated stress response, leading to reduced protein synthesis. Whilst, this study did not examine if a specific set of genes were translationally stalled, it will be important to know how stalling of these genes encode for synaptic functions that may contribute to motor neuron degeneration.
### Transactive response DNA-binding protein-43
Transactive response DNA-binding protein (TDP-43) is a nuclear ribonucleoprotein that binds to UG-rich repeats of target RNAs to regulate gene transcription, mRNA splicing and transport. TDP-43 is predominantly localized to the nucleus, but contains a nuclear export sequence to aid in the nuclear-cytoplasmic shuffling of target RNAs. In healthy neurons, TDP-43 has been shown to localize with axons, synapses and neuromuscular junctions, aiding in RNA transport and stability ( ; ; ). Furthermore, examination of axonal TDP-43 suggests subpopulations of TDP-43 RNP pools with different biophysical properties dependent on their distance from to the soma, suggesting diverse physiological roles and aggregation propensity ( ).
Postmortem examination of a large majority of ALS cases shows aberrant mislocalization of TDP-43 in the cytoplasm and depletion within the nucleus of motor neurons ( ). recently observed elevated levels of TDP-43 and its pathological phosphorylated form, pTDP-43, in intra-muscular nerves of a small number of sporadic ALS patient biopsies. Furthermore, a more extensive retrospective study of 114 patient biopsies with no history of ALS found biopsies containing axonal pTDP-43 within intra-muscular nerve bundles were later diagnosed with ALS, suggesting diagnostic potential of pTDP-43 in peripheral axons ( ). Furthermore, axonal pTDP-43 has also been observed in post-mortem tissue ( ; ) and C9orf72 patient iPSC-derived motor neurons ( ). Similar findings were reported in intra-muscle axons and even the neuromuscular junction of TDP mice ( ). Axonal accumulation of pTDP-43 was shown to colocalize with the ribonucleoprotein component, G3BP1 and RNAs, indicating the formation of ribonucleoprotein (RNP) condensates within axons. The formation of RNP condensates within the soma represses RNA translation and is believed to be a compensatory mechanism to alleviate the misfolded proteome load in ALS. In line with this, TDP-43 mislocalization was associated with reduced protein synthesis within the axons and presynaptic terminals of C9orf72 iPSC-derived neurons, primary neuromuscular co-cultures and TDP mice ( ). Proteomic analysis of axoplasmic lysates from TDP mice found a reduction in nuclear-encoded mitochondrial proteins, including ATP5A1, Cox4i1 and Ndufa4, despite showing modest increases in mRNA abundance, suggesting axonal TDP-43 containing RNPs sequester and impair the local translation of these mitochondrial transcripts. Furthermore, it was demonstrated that this impairment of local synaptic and mitochondrial protein synthesis led to reduced neuromuscular junction function and neurodegeneration ( ). This work not only highlights that aggregation-prone proteins such as TDP-43 pathologically accumulate in the axo-synaptic compartments, but that their presence within these compartments can have detrimental consequences that can lead to neurodegeneration.
### C9orf72
Abnormal expansion of GGGGCC hexanucleotide repeats within the C9orf72 gene is the most frequent genetic association with ALS. It has been proposed that GGGGCC expansion repeats may play a pathogenic role through several mechanisms, including loss of C9orf72 expression and function. C9orf72 protein has been shown to localize to the pre-synaptic terminals, where it interacts with the RAB3 family of proteins involved with synaptic vesicle release ( ; ). Furthermore, GGGGCC hexanucleotide repeats within the C9orf72 gene can generate the synthesis of di-peptide repeat (DPRs) species produced by repeat-associated non-AUG (RAN) translation ( ). In C9orf72 cases, the production of DPR species has been shown to disrupt nuclear-cytoplasmic transport and mislocalization of TDP-43 ( ). As previously discussed, C9orf72 mutations have shown to produce axo-synaptic accumulation of pTDP-43 and synaptic dysfunction ( ), suggesting an indirect role of DPRs on axo-synaptic proteostasis.
In addition to the in-direct role DPRs have on axo-synaptic proteostasis, they may also have a more local and direct role. There are five DPRs produced from sense and antisense (poly-GA, poly-GR, poly-GP, poly-PR and poly-PA). The contribution of each individual DPR species is still unclear. Poly-GA are the most abundant within cytoplasmic inclusions. Furthermore, poly-GA has shown to be present within dystrophic neurites of the cortex, but not in the spinal cord of ALS cases ( ). Poly-GA has been shown to be present in the neurites GA mouse model ( ). In primary motor neuron cultures, GA aggregates were mobile and inversely correlated with GA repeat length. Furthermore, neurons containing GA aggregates showed a reduction in synaptic vesicle release that was associated with a reduction in the synaptic vesicle protein, SV2, as a consequence of GA aggregates sequestering SV2 mRNA. Furthermore, a similar decrease in SV2 mRNA and protein expression was observed in C9orf72 iPSC-derived neurons. However, up-regulation of SV2 was able to reverse synaptic vesicle release impairments ( ). Whilst it is unclear if other DPRs show similar localization and effects, this study highlights the potential of targeting synaptic function as a potential therapeutic avenue for ALS-associated axo-synaptic proteostasis imbalance.
### Superoxide dismutase-1
There have been >160 mutations identified within the superoxide dismutase-1 ( SOD1 ) gene associated with ALS. Unlike FUS and TDP-43, SOD1 is not an RNA-binding protein and is an antioxidant enzyme that functions to catalyze superoxide free radicals to molecular oxygen or hydrogen peroxide. However, similarly SOD1 inclusions form in the cytoplasm of motor neurons in familial ALS cases ( ). Although misfolded SOD1 also been suggested to be associated with non-SOD1 familial- and sporadic-ALS cases ( ; ; ; ). SOD1 is primarily localized to the cytoplasm and mitochondria. However, it can also be found in the nucleus ( ). To our knowledge, it has yet to be confirmed if SOD1 is present in the axons or synaptic terminals. Although, transcriptomic and proteomic analyses indicate that SOD1 mRNA and protein are localized and translated within synaptic terminals, including the neuromuscular junction ( ; ).
There have been several SOD1 mouse models developed over the last few decades. However, none is better characterized than the SOD1 model. SOD1 mice show spatiotemporal synaptic impairments, which have been previously summarized in detail ( ). SOD1 aggregates have been extensively reported in spinal cord motor neurons of SOD1 mice ( ; ). However, evidence for their axo-synaptic localization is limited. Immunoblots of sciatic nerves have shown mutant SOD1 aggregates ( ), indicating their presence in axo-synaptic compartments. In line with this, mutant SOD1 accumulation has been observed in ventral roots as early as four weeks of age, before their presence within the ventral horn and onset of motor impairment ( ). This indicates mutant SOD1 may accumulate in a distal-proximal fashion. However, it has yet to be investigated if SOD1 aggregates are located more peripherally, such as in the neuromuscular junction of motor neurons.
In support of the distal-proximal accumulation of SOD1, sciatic nerve inoculation of spinal homogenates from paralyzed SOD1 mutant mice in SOD1 mice produces motor deficits and motor neuron pathology ( , ). The formation of SOD1 inclusions pathology are initially observed in the ipsilateral DRG and follow a retrograde trajectory along neuroanatomical tracts toward the brainstem ( ). The route of SOD1 inclusions in this model along neuroanatomical tracts is suggestive of trans-synaptic prion-like propagation spread and is supported by the observation of SOD1 inclusions within the neuropil of spinal cord and brainstem ( ). In line with the spatiotemporal spread of ALS symptom progression, there is evidence to suggest that the primary aggregation prone proteins (SOD1, TDP-43 and FUS) are capable of forming prion-like seeds and propagating, potentially via trans-synaptic pathways ( ). Considering that the motor neuron synapse is supersaturated, it may provide an environment conducive for spreading of misfolded protein conformations.
Whilst there has been limited investigation if aggregated SOD1 is present within axo-synaptic compartments, mutant SOD1 has shown to disrupt synaptic function. Presynaptic infusion of mutant SOD1 or SOD1 protein in the squid giant synapse has been demonstrated to inhibit anterograde transport, whilst an infusion of SOD1 only showed modest impairments ( ; ). Furthermore, infusion of SOD1 has shown to rapidly impair synaptic vesicle availability and release, which slowly returns to normal following the passive diffusion of SOD1 out of the presynaptic terminal ( ). SOD1 -induced vesicle release impairments were shown to be mediated via synaptic and peri-synaptic Ca levels and localization alterations. However, the mechanistic links that caused these changes are still, but may be necessary for identifying intervention targets. Collectively, these studies demonstrate that the presence of synaptic mutant SOD1 can disrupt synaptic function. However, the molecular mechanisms that lead to these changes are still unclear. Therefore, it is unclear what contribution potential axo-synaptic pathological aggregates have on the numerous reports of synaptic dysfunction in this model. Furthermore, evidence indicates axo-synaptic FUS and TDP-43 accumulation may exert their pathological effects via their RNA binding targets. However, SOD1 does not bind RNA and therefore may represent a more “pure” system of how proteostasis collapse leads to synaptic dysfunction.
## Concluding remarks
There is now established evidence that ALS-associated proteins have a pathological role in the axo-synaptic compartment that may contribute to disease pathology. Whilst, the supersaturated environment of the synapse indicates it may be a more conducive environment for proteins to misfold, it is still not clear if this process begins in the synapse or if they are trafficked from the soma. Based on this initial body of evidence that ALS-associated proteins can pathologically accumulate in the axo-synaptic compartment, understanding if the synaptic excitability changes observed in ALS are caused by more local proteostasis alterations warrants investigation. Whilst the currently available treatments for ALS, riluzole and edaravone, have shown to work in part by improving synaptic function, their limited efficacy may be due to their inability to modulate toxic protein species ( ; ; ). Preclinical and clinical trials using anti-sense oligonucleotides are on-going with encouraging results ( ). Whilst, these will fail to directly remove aggregates already localized to the soma and axo-synaptic compartment, they may ease the continual axo-synaptic accumulation of toxic species and proteostasis load. In addition, there is promising work investigating the potential of proteolysis- and autophagy-targeting chimeras to facilitate the removal of toxic protein species ( ), however, to our knowledge it remains unclear if these are able to transported/synthesized within the axo-synaptic compartment. It is likely that the best therapeutic avenue will need to be multi-modal and remove toxic species and restore axo-synaptic proteostasis and function. Proteostasis encapsulates the synthesis, maturation, transport and degradation of the proteome, therefore understanding each aspect of proteostasis within the axo-synaptic compartment and how these are perturbed in ALS is vital. Importantly, dissecting the role of pathological accumulation of proteins within the soma and axo-synaptic compartment and the local and global responses may lead to identifying more targeting points of therapeutic intervention.
## Author contributions
JL and JY conceptualized, directed, and wrote and edited the manuscript. Both authors contributed to the article and approved the submitted version.
|
In the developing nervous system synaptic refinement, typified by the neuromuscular junction where supernumerary connections are eliminated by axon retraction leaving the postsynaptic target innervated by a single dominant input, critically regulates neuronal circuit formation. Whether such competition-based pruning continues in established circuits of mature animals remains unknown. This question is particularly relevant in the context of adult neurogenesis where newborn cells must integrate into preexisting circuits, and thus, potentially compete with functionally mature synapses to gain access to their postsynaptic targets. The hippocampus plays an important role in memory formation/retrieval and the dentate gyrus (DG) subfield exhibits continued neurogenesis into adulthood. Therefore, this region contains both mature granule cells (old GCs) and immature recently born GCs that are generated throughout adult life (young GCs), providing a neurogenic niche model to examine the role of competition in synaptic refinement. Recent work from an independent group in developing animals indicated that embryonically/early postnatal generated GCs placed at a competitive disadvantage by selective expression of tetanus toxin (TeTX) to prevent synaptic release rapidly retracted their axons, and that this retraction was driven by competition from newborn GCs lacking TeTX. In contrast, following 3-6 months of selective TeTX expression in old GCs of adult mice we did not observe any evidence of axon retraction. Indeed ultrastructural analyses indicated that the terminals of silenced GCs even maintained synaptic contact with their postsynaptic targets. Furthermore, we did not detect any significant differences in the electrophysiological properties between old GCs in control and TeTX conditions. Thus, our data demonstrate a remarkable stability in the face of a relatively prolonged period of altered synaptic competition between two populations of neurons within the adult brain. |
In drug users, drug-related cues alone can induce dopamine release in the dorsal striatum. Instructive cues activate inputs to the striatum from both dopaminergic and cholinergic neurons, which are thought to work together to support motor learning and motivated behaviors. Imbalances in these neuromodulatory influences can impair normal action selection and might thus contribute to pathologically repetitive and compulsive behaviors such as drug addiction. Dopamine and acetylcholine can have either antagonistic or synergistic effects on behavior, depending on the state of the animal and the receptor signaling systems at play. Semi-synchronized activation of cholinergic interneurons in the dorsal striatum drives dopamine release via presynaptic nicotinic acetylcholine receptors located on dopamine terminals. Nicotinic receptor blockade is known to diminish abnormal repetitive behaviors (stereotypies) induced by psychomotor stimulants. By contrast, blockade of postsynaptic acetylcholine muscarinic receptors in the dorsomedial striatum exacerbates drug-induced stereotypy, exemplifying how different acetylcholine receptors can also have opposing effects. Although acetylcholine release is known to be altered in animal models of drug addiction, predicting whether these changes will augment or diminish drug-induced behaviors thus remains a challenge. Here, we measured amphetamine-induced stereotypy in BAC transgenic mice that have been shown to overexpress the vesicular acetylcholine transporter (VAChT) with consequent increased acetylcholine release. We found that drug-induced stereotypies, consisting of confined sniffing and licking behaviors, were greatly increased in the transgenic mice relative to sibling controls, as was striatal VAChT protein. These findings suggest that VAChT-mediated increases in acetylcholine could be critical in exacerbating drug-induced stereotypic behaviors and promoting exaggerated behavioral fixity. |
Neural circuit activity increases the release of the purine neuromodulator adenosine into the extracellular space leading to A<sub>1</sub> receptor activation and negative feedback via membrane hyperpolarization and inhibition of transmitter release. Adenosine can be released by a number of different mechanisms that include Ca<sup>2+</sup> dependent processes such as the exocytosis of ATP. During sustained pathological network activity, ischemia and hypoxia the extracellular concentration of calcium ions (Ca<sup>2+</sup>) markedly falls, inhibiting exocytosis and potentially reducing adenosine release. However it has been observed that reducing extracellular Ca<sup>2+</sup> can induce paradoxical neural activity and can also increase adenosine release. Here we have investigated adenosine signaling and release mechanisms that occur when extracellular Ca<sup>2+</sup> is removed. Using electrophysiology and microelectrode biosensor measurements we have found that adenosine is directly released into the extracellular space by the removal of extracellular Ca<sup>2+</sup> and controls the induced neural activity via A<sub>1</sub> receptor-mediated membrane potential hyperpolarization. Following Ca<sup>2+</sup> removal, adenosine is released via equilibrative nucleoside transporters (ENTs), which when blocked leads to hyper-excitation. We propose that sustained action potential firing following Ca<sup>2+</sup> removal leads to hydrolysis of ATP and a build-up of intracellular adenosine which then effluxes into the extracellular space via ENTs. |
Visual information in birds is to great extent processed in the optic tectum (TeO), a prominent laminated midbrain structure. Retinal input enters the TeO in its superficial layers, while output is limited to intermediate and deeper layers. In addition to visual information, the TeO receives multimodal input from the auditory and somatosensory pathway. The TeO gives rise to a major ascending tectofugal projection where neurons of tectal layer 13 project to the thalamic nucleus rotundus, which then projects to the entopallium. A second tectofugal projection system, called the accessory pathway, has however not been studied as thoroughly. Again, cells of tectal layer 13 form an ascending projection that targets a nucleus known as either the caudal part of the nucleus dorsolateralis posterior of the thalamus (DLPc) or nucleus uveaformis (Uva). This nucleus is known for multimodal integration and receives additional input from the lateral pontine nucleus (PL), which in turn receives projections from layer 8-15 of the TeO. Here, we studied a particular cell type afferent to the PL that consists of radially oriented neurons in layer 9. We characterized these neurons with respect to their anatomy, their retinal input, and the modulation of retinal input by local circuits. We found that comparable to other radial neurons in the tectum, cells of layer 9 have columnar dendritic fields and reach up to layer 2. Sholl analysis demonstrated that dendritic arborization concentrates on retinorecipient layers 2 and 4, with additional arborization in layers 9 and 10. All neurons recorded in layer 9 received retinal input <i>via</i> glutamatergic synapses. We analyzed the influence of modulatory circuits of the TeO by application of antagonists to γ-aminobutyric acid (GABA) and acetylcholine (ACh). Our data show that the neurons of layer 9 are integrated in a network under strong GABAergic inhibition, which is controlled by local cholinergic activation. Output to the PL and to the accessory tectofugal pathway thus appears to be under strict control of local tectal networks, the relevance of which for multimodal integration is discussed. |
The olfactory system has a unique capacity for recovery from peripheral damage. After injury to the olfactory epithelium (OE), olfactory sensory neurons (OSNs) regenerate and re-converge on target glomeruli of the olfactory bulb (OB). Thus far, this process has been described anatomically for only a few defined populations of OSNs. Here we characterize this regeneration at a functional level by assessing how odor representations carried by OSN inputs to the OB recover after massive loss and regeneration of the sensory neuron population. We used chronic imaging of mice expressing synaptopHluorin in OSNs to monitor odor representations in the dorsal OB before lesion by the olfactotoxin methyl bromide and after a 12 week recovery period. Methyl bromide eliminated functional inputs to the OB, and these inputs recovered to near-normal levels of response magnitude within 12 weeks. We also found that the functional topography of odor representations recovered after lesion, with odorants evoking OSN input to glomerular foci within the same functional domains as before lesion. At a finer spatial scale, however, we found evidence for mistargeting of regenerated OSN axons onto OB targets, with odorants evoking synaptopHluorin signals in small foci that did not conform to a typical glomerular structure but whose distribution was nonetheless odorant-specific. These results indicate that OSNs have a robust ability to reestablish functional inputs to the OB and that the mechanisms underlying the topography of bulbar reinnervation during development persist in the adult and allow primary sensory representations to be largely restored after massive sensory neuron loss.
## Introduction
The mammalian olfactory system has a remarkable capacity for regeneration of its primary sensory neurons (olfactory sensory neurons; OSNs) after loss due to injury, infection, or exposure to toxins. Even after a virtually complete loss of all OSNs, the population is restored to a level nearly indistinguishable from the original in terms of neuronal number and topography of odorant receptor (OR) protein expression (Schwob et al., ; Iwema et al., ). These newly-generated OSNs must reestablish convergent axonal connections with their appropriate targets in the olfactory bulb (OB). During normal development, the axons of all of the several thousand OSNs expressing the same OR converge onto just a few (2–4) of the ~1600 glomeruli in the OB (Mombaerts et al., ). Because individual glomeruli serve as functional units in the central coding and processing of odor information, reestablishing appropriate connections between OSNs and glomeruli is likely critical for normal olfactory function. For example, errors in the reinnervation of glomeruli may underlie olfactory dysfunction in humans recovering from olfactory loss due to trauma or infection (Doty, ; Meisami et al., ). More generally, reestablishing appropriate neural connectivity is a prerequisite for the full recovery of function of any sensory or motor system.
Previous studies have demonstrated that, in the adult, the targeting of OSN axons to glomeruli after lesion is subject to errors that do not occur during development (Schwob et al., ; Costanzo, ; St. John and Key, ; McMillan Carr et al., ; Blanco-Hernández et al., ). These errors include a lack of exclusive convergence of OSNs onto the same glomerulus and projection of at least some axons of a given OSN population to multiple, dispersed glomeruli (Costanzo, ; St. John and Key, ; Blanco-Hernández et al., ). The impact of this mistargeting on odor representations remains largely unclear, however: targeting has been examined for only three OR-defined group of OSNs out of the approximately 1000 ORs expressed in the rodent olfactory system (Gogos et al., ; St. John and Key, ; Blanco-Hernández et al., ). Thus there is no generalized picture of the effect of OSN loss and regeneration on functional odor representations in the CNS.
To address this question, we used mice expressing synaptopHluorin in OSNs (Bozza et al., ) to functionally assess how odor representations recover after lesioning the olfactory epithelium (OE) with the olfactotoxin methyl bromide (Schwob et al., ). We found that despite apparent errors in the exclusive convergence of OSNs onto glomeruli, odor representations involving multiple glomeruli largely recovered after lesion, with a topographic organization and overall magnitude similar to that seen before lesion. These results indicate that the olfactory system shows a robust capacity to regenerate functional inputs to the CNS in a manner that, in all but the most severe cases, preserves the broad spatial organization of odor representations that was present before injury. Thus, primary representations of odor information can be largely reconstituted in the adult even after large-scale neuronal loss, an ability unique among sensory systems.
## Materials and Methods
### Animal strains and care
We used olfactory marker protein-synaptopHluorin (OMP-spH) mice (Bozza et al., ) that had been backcrossed into the C57/Bl6 strain and bred with the 129/SvImJ strain to generate first generation (F1) hemizygous mice. Only males were used in the study due to their sensitivity to methyl bromide. The F1 animals were housed under standard conditions in ventilated racks until 12 weeks of age before being exposed to MeBr. All mouse colonies were bred and maintained at the Boston University animal care facility. Animals were transported to Tufts University School of Medicine for exposure and were returned to Boston University on the same day. All surgical and experimental procedures were approved by the Boston University and Tufts University Institutional Animal Care and Use Committees.
### Surgery
Seven to ten days before lesioning, custom made head caps were implanted on the skull using aseptic surgical procedures. The head cap consisted of a custom-milled aluminum plate that fit the skull snugly and to which two M2 bolts were attached. Animals were anesthetized with isoflurane, placed in a stereotaxic holder and the head cap was placed with its anterior edge aligned to the coronal suture and attached to the skull using dental acrylic. A piece of 30 gauge metal tubing was embedded in the dental acrylic posterior to the OB to serve as a fiducial marker for chronic imaging experiments. The acrylic, which was darkened to reduce autofluorescence, was extended from the head cap to the frontal-nasal fissure, forming a well surrounding the dorsal OB.
For 3 days after surgery, wound margins were treated topically with the anesthetic bupivacaine (1 mg/ml, Sigma-Aldrich, St. Louis, MO) and cleaned with Betadine. Animals were also injected with the nonsteroidal anti-inflammatory carprofen (5 mg/kg SC, Pfizer, New York, NY) and the antibiotic Baytril (3 mg/kg, IM). Animals were monitored closely for a 2 week span following surgery, the first imaging session, and MeBr exposure. Animals were given carprofen and Baytril as necessary.
### Chronic and acute optical imaging
Animals outfitted with head caps were imaged at a minimum of three time points: a “pre-exposure” session to obtain baseline odor response maps, a “post-exposure” session 4 days after exposure to determine whether MeBr successfully induced lesions, and a final “recovery” session 12–13 weeks after exposure to determine the extent of regeneration and recovery of functional responses.
Immediately prior to pre-exposure imaging (minimum 3 days before MeBr exposure), animals were anesthetized with isoflurane and placed in a custom head holder mounted on goniometers and x - and y -translation stages that allowed for independent positioning and rotation of the animal. The bone over the OBs was thinned to transparency, sparing a thin wall of dental acrylic surrounding the OB to form a well around the imaging window. After the first imaging session, the bone was dried and a layer of ethyl 2-cyanoacrylate glue (Instant Krazy Glue, KG925, Elmer’s Products, Inc., Columbus, OH) was applied to the window. After the glue had set, the well was filled with silicone sealant (Kwik-Sil or Kwik-Cast, World Precision Instruments, Sarasota, FL). In nearly all cases, the combination of ethyl cyanoacrylate and silicone based adhesive preserved window translucency for approximately 1 month. There was more variability in preservation of window quality for the ~14 week period of the study. When windows were no longer translucent at the recovery time-point, the bone was re-thinned prior to imaging.
OB alignment across repeated imaging sessions was performed using one of two systems. For the first system, the platform position was fixed relative to a custom objective mount using an alignment bar. The micrometer translation stages allowed for adjustments relative to the initial fixation point. For the second method, a fiducial marker was embedded in the dental cement of the head cap. During the first imaging session, an image of the fiducial marker was taken for later alignment. The marker was placed so that it was visible in the field of view when the OB was centered under the objective.
For acute imaging sessions, anesthetized animals were placed on the custom head holder and the bone over the OB was thinned to transparency and a coverslip and mouse Ringer’s solution placed over the OBs.
### Olfactometry
Odorants were selected and delivered using a 16 channel computer-controlled olfactometer, as described previously (McGann et al., ). Odorant concentration across imaging sessions was confirmed before each session using a portable photoionization detector (miniRAE 2000, RAE Systems, San Jose, CA). All olfactometer parts (including the odorant chambers and anesthesia mask) were made from Teflon or PTFE tubing. Isoflurane was used as an anesthetic to maximize survival across multiple imaging sessions. Isoflurane and odorant were delivered to the animal through a custom anesthesia/odorant delivery mask that fit tightly around the mouse’s snout. Isoflurane was vaporized (EZ-155, Euthanex Corp, Palmer, PA) and mixed with medical grade oxygen. To maintain constant oxygenation levels throughout the experiment, a solenoid was used to pass odorized air into the gas mask during odor presentation and filtered air between trials. The filtered air was set to match the flow rate of the odor line. Thus, the total flow was maintained at 0.9 L/min during and between odor presentation (0.5 L/min isoflurane and oxygen with either 0.4 L/min air or odorized air). In acute experiments, we used a conventional concentric delivery nozzle, described previously (Lam et al., ). In this case, total air flow was 0.5 L/min.
Odorants used (and their purities) included 2-hexanone (98%), 2-butanone (99.5%), ethyl butyrate (99%), methyl valerate (99%), trans -2-methyl-2-butenal (96%), isovaleraldehyde (97%), 2-aminoacetophenone (98%), hexyl acetate (99%), benzaldehyde (99%), phenylacetaldehyde (90%), and methyl salicylate (99%) from Sigma-Aldrich; butyric acid (99.5%) and butyl acetate (99.5%) from MP Biomedicals Inc.; and eugenol (99%), menthone (97%), acetophenone (98%) and methyl benzoate (98%) from Fluka.
### Methyl bromide lesion
Animals were exposed to MeBr as previously described (Schwob et al., , ; Chen et al., ). F1 OMP-spH heterozygous (C57/Bl6 × 129SvImJ) males were exposed unilaterally to MeBr at 12 weeks of age. One side was protected by insertion of a plug made from polyethylene tubing and suture (Cummings et al., ) and sealed at the external naris with superglue. Animals were placed into a Plexiglas chamber and exposed to MeBr gas (Matheson Gas Products, East Rutherford, NJ). MeBr was diluted into purified air (210–240 ppm), with total flow at 10 L/min and length of exposure set at 6 or 8 h. Nose plugs were removed the following day.
### Data acquisition and analysis
Optical signals from the dorsal OB were acquired with standard wide-field epifluorescence microscopy as described previously (Bozza et al., ). Epifluorescence imaging was performed using an Olympus BX51 illumination turret with a 150-W Xenon arc lamp (Opti-Quip, Highland Mills, NY) at 50% intensity (attenuated with an ND50 filter), with the following fluorescence filter set from Chroma Technology (Rockingham, VT): HQ480/40 (exciter), Q505LP (dichroic), HQ535/50 (emitter), with either a 4X (0.28 N.A.) air or 20X (0.95 N.A.) water immersion objective. Odorant-evoked signals were recorded and digitized at 14-bit resolution using a back-illuminated CCD camera (NeuroCCD, SM-256; RedShirtImaging, Decatur, GA) at 256 × 256 pixel resolution and a frame rate of 7 Hz. Data acquisition was performed with Neuroplex software (RedShirtImaging).
For display in the figures, odorant-evoked response maps were spatially low-pass filtered using a Gaussian kernel (sigma values given in text) and displayed, unless where noted, with fluorescence normalized to 95% of the maximum value of that map. In order to compare maps across imaging sessions in chronically imaged animals, image registration was performed by maximizing the correlation between resting fluorescence images or, when possible, using implanted fiducial markers (see above). In acutely imaged animals, OB positions were roughly aligned using the resting fluorescence image and the midline and posterior sinus as landmarks (Wachowiak and Cohen, ; Bozza et al., ). For calculating response amplitudes and positions of input to glomeruli, regions of interest (ROIs) were defined for all spH foci in the response maps using criteria based on focus size, signal-to-noise ratio and optical signal time-course to identify presumptive activated glomeruli (Wachowiak and Cohen, ; Bozza et al., ).
Consensus response map topographies (e.g., Figure ) were generated as described in Wachowiak et al. ( ). Briefly, individual response maps were aligned relative to the midline and caudal sinus, normalized to their own maxima, thresholded at 50%, summed together, then smoothed with a 6 × 6 pixel mean kernel and the resulting maps renormalized and displayed as in Figure . For statistical comparison of response map topographies (e.g., Figures ), maps were smoothed with a 3 × 3 pixel kernel, thresholded to include the top 70% of responses and centroids for each individual response map calculated from the mean of the positions of thresholded pixels. For comparison of centroid positions across animals, x - and y -positions were mapped to the zero point defined by the intersection of the sagittal midline and the anterior limit of the caudal sinus (Wachowiak and Cohen, ; Bozza et al., ). For determining domain separation, the sum of the x -position squared and the y -position squared (the squared displacement) was used. For the calculation of foci diameter (e.g., Figure ), response maps were first slightly smoothed with a low-pass filter (Gaussian kernel, σ = 10 μm) to remove noise and odorant-evoked foci chosen for analysis based on their signal-to-noise ratio and time-course of the odorant-evoked fluorescence change. Focus sizes were measured by fitting the amplitude profiles of each ROI at perpendicular axes across each focus and taking the full-width at half-maximum (FWHM) of the fit along each axis. FWHM values for each axis were averaged to obtain a size value for each focus. To construct consensus odorant response maps for focus size analysis we projected individual, normalized response maps onto a single image using the maximal projection algorithm for confocal z -stacks (ImageJ).
### Two-photon imaging and analysis
All animals undergoing 2-photon laser scanning microscopy (2PLSM) at the terminal imaging session were anesthetized with pentobarbital before removal of the bone over the OB. The dura was also removed and agarose (1.2% in mouse Ringer’s) was placed onto the OB and coverslipped; petroleum jelly was used to seal the cranial window. Imaging was performed on a custom microscope that allowed for wide-field epifluorescence or multiphoton imaging through the same objective (20X, 0.95 N.A., water immersion; Olympus, Melville, NY). A 150-W Xenon arc lamp provided wide-field illumination at 2.8–6% of full intensity through the same filter set as described above. Two-photon fluorescence was excited by a mode-locked Ti:Sapphire laser (Spectra-Physics, 150 fs, 76 MHz; pumped by a 5W Millenia Vs. laser); emitted light was reflected through a mirror placed at the back aperture of the objective and directed to a bialkali photomultiplier (HC125-02, Hamamatsu Corporation, Bridgewater, NJ) fitted with an emission filter (Omega Optical, 535/45). Image acquisition was controlled by custom software in LabView (developed by Dr. J. Mertz, Boston University). For imaging odorant-evoked responses, acquisition rate was 8 Hz with a pixel resolution of 1.6 μm. Response maps obtained with 2PLSM were averaged from 5 to 10 trials to improve signal-to-noise ratio. Relative fluorescence changes were calculated using the eight frames before odorant onset as the baseline fluorescence and an average of eight frames at the peak of the evoked signal as the response. ROIs were defined using resting multiphoton resting fluorescence images.
### Confocal microscopy and histology
Following the terminal imaging session, animals were overdosed with pentobarbital and perfused intracardially with mouse Ringer’s solution (20 ml), followed by cold 4% paraformaldehyde (20 ml, 0.05 M PBS, pH 7.0). For confocal imaging, the bone surrounding the OBs was removed under alkaline PBS (0.1 M, pH 7.9) and the OBs were scanned in situ with a confocal microscope (LSM 510, Carl Zeiss MicroImaging Inc., Thornwood, NY) to assess OSN innervation of glomeruli using OMP-spH fluorescence. The OE and OB were then preserved in 4% paraformaldehyde until cryoprotection. Coronal sections of the olfactory tissue from the OE to OB were prepared using a cryostat at 50 μm/section. Frozen sections were counterstained with cresyl violet and mounted.
## Results
We used the gaseous olfactotoxin MeBr to unilaterally lesion the OE of mice expressing spH, an optical reporter of transmitter release, in all OSNs (OMP-spH mice; Bozza et al., ). Male, hemizygous OMP-spH mice were used in all experiments and lesioned at 12 weeks of age (see Section Materials and Methods). In all experiments one naris was protected from MeBr exposure with a plug that was removed after the exposure period (Cummings et al., ). Exposure to MeBr gas has been used extensively to lesion the OE of rats, and the severity of lesion can be controlled by varying MeBr concentration and duration of exposure (Schwob et al., , ; Iwema et al., ). The time-course and cellular changes underlying degeneration and subsequent recovery of the OE after MeBr exposure have also been well-characterized (Schwob et al., ). Our goal was to assess the degree to which OSNs regenerate functional connections to glomeruli of the OB, where the central representations of odor information are initially formed. The general approach was to compare glomerular odor representations using spH-mediated optical signals (Bozza et al., ) before lesion and after a recovery period.
### Long-term, chronic imaging of sensory inputs in olfactory marker protein-synaptopHluorin (OMP-spH) mice
It was first necessary to establish the stability of odorant representations over a time-period sufficient to allow for OSN recovery—at least 60 days (Schwob et al., )—and under conditions that allow for repeated optical imaging in the same animal. We have previously shown that OSN inputs can be chronically imaged in OMP-spH mice and remain stable over at least 7 days (Bozza et al., ). Here, we extended this time-period. We installed a chronic imaging window over the dorsal OB (see Section Materials and Methods) and imaged odorant-evoked spH response maps in three OMP-spH mice over periods of 111, 124 and 174 days.
Figure shows examples of spH response maps imaged at different time-points in three animals. In all three animals, response maps remained similar across this period. The most significant variability in maps arose from differences of up to 50% in relative signal magnitude in different glomeruli (highlighted by arrows, Figure ); these differences could affect the absolute number of glomeruli activated above an arbitrary threshold level. Such variability likely reflects differences in overall sensitivity in different imaging sessions, due (for example) to changes in nasal patency (Oka et al., ), experience-dependent plasticity (Jones et al., ; Kass et al., ) or modulatory influences (McGann et al., ; Pírez and Wachowiak, ). Nonetheless the approximate number and relative position of activated glomeruli remained consistent across imaging sessions (Figures ), indicating that the procedures involved in chronic imaging (head cap and imaging window implantation, repeated anesthesia sessions and odorant presentations) did not induce apparent changes in functional connections between OSNs and their target glomeruli.
Long-term stability of OSN representations during chronic imaging from the OB. (A) spH response maps evoked by the same odorant (methyl valerate, 1% s.v.) imaged at four time points in the same animal (expressed as days after initial imaging session). The approximate outline of the dorsal OB is shown; crossbars are for comparisons across maps. Each map is normalized to its own maximum; absolute maximum signal amplitudes (“max ΔF/F”) are given below each map. Arrows indicate signal foci that are apparent in each map but show reduced amplitudes over time. Maps were smoothed with a Gaussian kernel ( σ = 25 μm, kernel width = 70 μm) for display. (B) Comparison of spH signal foci locations for responses at day 0 and each later time-point, taken from maps in (A) . Dots identify signal foci with amplitudes above 30% of maximal amplitude. Green dots indicate foci in initial session, red indicates foci in the current session, yellow indicates colocalized foci. (C) spH response maps for two additional animals imaged at 111 and 124 days time-points; odorant: 2-methyl 2-butenal.
To assess the precision with which response maps could be monitored across time-points, we compared the positions of the few (2–4) most strongly-activated glomeruli in maps evoked by the same odorant in different sessions. OB images were aligned as described in the Section Materials and Methods, then the distance between each glomerulus at the initial time-point and its nearest neighbor at the later time-point was measured. Using this measure, the average change in the position of spH signal foci between baseline and the later time-point (111–176 days) was 71.0 ± 29.1 μm (mean ± s.d . ; n = 21 glomeruli taken from three animals and using nine odorants). Thus, in unlesioned animals, we are able to chronically map functional inputs to glomeruli with a spatial precision of smaller than the average diameter of a glomerulus (Bozza et al., ).
### MeBr exposure eliminates odorant-evoked responses in the olfactory bulb (OB)
MeBr potency has not been as extensively characterized in mice (Chen et al., ) as it has in rats (Schwob et al., , ; Iwema et al., ); in addition, the relationship between the initial loss of OSNs and functional inputs to the OB immediately after MeBr exposure is unclear. Thus we next examined the effect of MeBr exposure on odorant-evoked spH signals and on OSN loss. In these animals spH signals were imaged shortly (4–10 days) after lesion, after which the mouse was sacrificed and damage to the OE assessed histologically. We used different MeBr exposure protocols that resulted in a range of lesion severity.
At a MeBr exposure of 215 ppm for 8 h ( n = 10 mice) or 230 ppm for 6 h ( n = 4), exposure resulted in a complete loss of detectable odorant-evoked spH signals in half (7/14) of all mice (Figure ). Higher dosages resulted in significant rates of mortality (not shown). In animals showing a loss of odorant-evoked signals, resting spH fluorescence on the MeBr-exposed side was also diminished although not eliminated entirely at 4–10 days post-lesion (Figure ). Resting fluorescence and spH response maps remained robust on the protected side of all mice (Figure ). In approximately 20% of MeBr-exposed animals (3 of 14), resting fluorescence and evoked spH signals were still detectable but weaker on the exposed side compared to the pre-lesion imaging session or the protected side (Figure ). Further quantification from similarly-lesioned mice in a different cohort is provided below. In the remaining approximately 30% of animals (4 of 14), resting fluorescence and spH signals were similar in magnitude to the pre-lesion session or to those on the protected side (Figure ). These results indicate that MeBr exposure can eliminate OSN responsiveness unilaterally and that the effectiveness of exposure is more variable than has been previously observed in rats (Schwob et al., , ).
Effect of MeBr lesion on functional OSN inputs to the OB. (A) Resting fluorescence images ( top ) and response maps ( bottom ) imaged in the same animal 10 days before ( left ) and 4 days after ( right ) unilateral MeBr exposure. This animal showed a decrease in resting fluorescence and a complete loss of odorant-evoked spH signals on the exposed side. Responses on the protected side were unaffected. Odorant, ethyl butyrate (1% s.v.). (B) Response maps overlaid on resting fluorescence images ( left column ) and post-hoc H&E-stained nasal cavity sections (“Epithelium”, right column ) for three additional animals showing different degrees of functional loss after MeBr exposure. Effects were classified as complete ( i ), intermediate ( ii ) or ineffective ( iii ) based on the amplitude and sensitivity of the odorant-evoked spH signal on the MeBr-exposed side (See the text). Response maps scaled as in (A) but thresholded at 40% of maximal δF/F. The nasal cavity showed widespread damage to the OE in all three animals. In the complete lesion (i) , the full tangential extent of the dorsomedial epithelium has been lesioned and in many areas the damage extends through the basal lamina ( arrowheads ), leading to an exudation that will become organized as endonasal scar tissue (cf. asterisks in Figure ). In the intermediate lesion (ii) , the full tangential extent of the dorsomedial epithelium is damaged but there are some residual neurons along the roof of the dorsal meatus ( arrowheads ). In the “ineffective” lesion (iii) , the full tangential extent of the dorsomedial epithelium is also damaged but there are residual neurons along the roof of the meatus in the area between the two arrowheads . Resting spH fluorescence in the OB is sharply diminished in the “complete” and “intermediate” lesions, but appears normal in the “ineffective” lesion.
In this cohort, mice were sacrificed immediately after imaging and MeBr-induced damage to the OE was assessed histologically. Acutely after exposure, damaged areas were easily evident in hematoxylin and eosin (H&E)-stained sections by the sloughing of sustentacular cells and neurons, as previously described (Schwob et al., ). Mice showed some variation in the severity of the damage from animal to animal even when carefully controlled for age, weight and strain such that sparing was seen in some areas while in other areas damage was so severe that the basal lamina was breached leading to a serum exudate in the nasal cavity—a circumstance that precludes regeneration of the epithelium (Schwob et al., ). The portion of the OE that projects to the region of the dorsal OB imaged in these experiments corresponds roughly to the territory defined by lack of staining with anti-OCAM/mamFasII antibodies (Schwob and Gottlieb, ; Uchida et al., ); thus further description of the OE after lesion recovery (see below) is limited to that area.
Mice showing a complete loss of odorant-evoked spH signals were characterized by complete or near complete destruction of the neuronal and sustentacular cell populations in the dorsal half of the OE (Figure ). In these cases, the full extent of the dorsal OE was damaged based on comparison with the protected side, and across the vast majority of that epithelium the neuronal population was destroyed completely (see “exposed” side of OE image, Figure ). Mice showing weakened spH signals and classified as having intermediate functional lesion (Figure ) also showed significant damage to the OE, but substantial areas of the dorsal epithelium were spared, particularly at posterior levels of the nasal cavity. Surprisingly, even those mice that retained robust spH signals and so were classified as having ineffective functional lesion showed at least moderate OE damage, particularly in the far anterior and far posterior nasal cavity (Figure ). Overall, these results indicate that functional imaging of odorant-evoked spH signals is a stringent assay for the effectiveness of MeBr lesion: animals showing a complete loss of spH signal likely have only a small fraction of OSNs surviving after lesion, with larger survival rates reflected in robust odorant-evoked signals.
### Functional inputs to olfactory bulb (OB) glomeruli recover after MeBr lesion
To assess the functional recovery of OSN connections to OB glomeruli after MeBr lesion, we imaged spH odorant response maps at three time-points: 4–10 days before lesion, approximately 4 days after lesion to assess lesion effectiveness, and 12–13 weeks after lesion to assess recovery. Ten mice were unilaterally exposed to MeBr using a dosage and exposure protocol (235 ppm for 6.5 h) similar to that used to assess lesion effectiveness (above). Of these animals, four showed persistent odorant-evoked responses at the assessment session and so were excluded from further analysis; the remaining six animals showed complete loss of spH signals on the exposed side at assessment. In none of the mice did we observe any obvious behavioral changes either immediately after unilateral exposure or during the recovery period.
In all six of these mice, odorants evoked clear spH signals on the lesioned side at 12 weeks post-lesion. Figures show odorant response maps and spH signal traces in a representative animal. Evoked spH signals 12 weeks post-lesion appeared roughly similar to those observed before lesion, consisting of spatially heterogeneous responses with numerous discrete signal foci (Figure ). In many cases these signal foci appeared in locations that were nearly identical to those observed before lesion (Figure , arrows). Using the ROIs determined at baseline imaging for both the exposed and protected OBs, we were able to identify and measure odorant-evoked spH signals after the 12 weeks recovery period. The time-course of the odorant-evoked spH signal was also similar before lesion and after recovery (Figure ). Across animals, the peak amplitude of the spH signal was similar before lesion and after recovery for these animals (pre-lesion: 2.6 ± 1.5%; mean ± s.d.; recovery: 3.2 ± 0.7%; p = 0.45, paired t -test, n = 22 odorant pairs across six animals; Figure ).
Functional recovery of OSN inputs to the OB after MeBr lesion . (A) Resting fluorescence and response maps imaged in the same animal before (“Pre-exposure”), 4 days after (“Post-exposure”) and 96 days after (“Recovery”) unilateral exposure to MeBr. Odorant was ethyl butyrate in all cases. Odorant-evoked responses were eliminated post-exposure; responses and resting fluorescence (not shown) reappeared at Recovery. (B) Traces showing time-course of the odorant-evoked spH signal from one location (indicated by arrow in (A) ), which was similar before lesion and after recovery. Fluorescence decrease at the “Post” exposure time-point reflects intrinsic hemodynamic artifacts described previously. (C) Summary data showing spH response amplitudes on the exposed and protected sides before lesion and after recovery. See the text for details. Error bars indicate s.d. (D) Odorant response maps imaged in a single acute session at 84 days post unilateral MeBr exposure. (i) Resting spH fluorescence. (ii) Odorant-evoked response maps evoked by three different odorants appear similar on the exposed and protected OBs, with the most variance appearing as different relative amplitudes of the spH signal. Note presence of putative homologous individual glomeruli on each side, especially in the anterior OB. To facilitate comparison, response maps from each side were normalized separately to their own maximum (same pseudocolor scale as in (A) ).
To address potential confounds of the chronic imaging window on OSN recovery (Xu et al., ), a separate set of four mice were exposed unilaterally to MeBr (215 ppm for 8 h) and spH signals imaged in a single session 12 weeks after exposure. Thus in these mice there was no baseline session or assessment of lesion effectiveness, but odorant-evoked spH response maps were compared between the exposed and protected sides. In these animals, response maps appeared qualitatively similar to those seen on the protected OB, and included individual signal foci that were located in a position that was symmetric with foci on the unlesioned side (Figure ). Peak-amplitude spH signals were similar on the exposed ( n = 28 glomeruli from four animals) and unexposed sides ( n = 32 glomeruli, four animals; p = 0.35, unpaired t -test). Thus, OSNs that are replaced after MeBr lesion reestablish convergent functional connections to glomeruli of the OB.
### Recovery of sensory input map topography after olfactory sensory neuron (OSN) regeneration
Projections of OSNs to OB glomeruli show at least two levels of spatial organization: (1) OSNs expressing a given OR converge onto a single glomerulus whose position within a domain varies by several hundred microns in different animals and remains relatively constant in the same animal over time (Strotmann et al., ; Schaefer et al., ; Costanzo and Kobayashi, ; see also Figure ); and (2) projections show a broad topography in which OSNs of a particular class project within spatial domains spanning large regions of the bulbar surface (Nagao et al., ; Bozza et al., ; Pacifico et al., ). Chronic imaging of odorant response maps before lesion and after recovery showed that spH signals in lesion-recovered animals often differed slightly in the precise location of individual signal foci, but that spH signals remained clustered in locations similar to those seen in pre-lesion response maps (Figures ). These examples suggest that, while precise targeting of OSNs to pre-existing glomerular locations may be disrupted in regenerated OSNs, projections to the OB may recover with sufficient precision to preserve the topography of functional domains related to particular odorants.
Odor response map topography is reestablished after MeBr lesion and OSN recovery . (A) spH resting fluorescence and bilateral response maps imaged before unilateral MeBr exposure (“Pre-exposure”) and after recovery in the same animal, shown for ethyl butyrate. On the protected side, blood vessel pattern and position of brightly fluorescent glomeruli is near-identical, as are locations of strongly-activated glomeruli ( arrowheads ). On the exposed side, brightly fluorescent glomeruli and spH responses are similar in amplitude and similar but not identical in location after recovery. (B) Additional examples showing spH response maps for two odorants that preferentially evoke input to the anterior ( top ) and caudal-lateral OB ( bottom ), respectively. For each odorant, response maps are topographically similar at both time-points. White circles indicate the location of the centroid of each map, calculated after smoothing and thresholding (See the text). Arrowheads indicates a particular spH focus (glomerulus) whose position is consistent in both pre-exposure and lesion-recovered maps. (C) Consensus topographies for anterior (ANT) and caudal-lateral (CL) odorant response maps compiled from chronically-imaged mice ( n = 5 mice, 4–8 odorants per mouse) unilaterally exposed to MeBr and imaged before exposure (“Pre”) and after lesion recovery (“Recovery”). Pseudocolor scale indicates relative density of odorant-evoked spH signal across all response maps in each category. Black contour indicates arbitrary 50% cutoff of relative density plot. See the text for analysis details. (D) Quantitative analysis of response topographies in chronically-imaged mice (same animals and odorants as in (C) ). Crosshairs and shaded areas show centroid locations before lesion (black crosshairs, “Pre-exposure”) and after recovery (red crosshairs, “Recovery”) for anterior (ANT, yellow) and caudal-lateral (CL, green) odorants (See the text for list). The centers of the cross hairs denote average centroid location across all pooled odorants, with the arms and ellipses extending to 1 s.d. in x - and y -directions. Domains remained distinct for each time point and similar across time-points (note that this analysis differed slightly from that used to produce consensus topographies in (C) ; See the text for analysis details). (E) Centroid locations analyzed and plotted as in (D) for acutely-imaged animals, showing similar distribution of centroid locations for exposed and protected sides imaged in a single recovery session.
To analyze the topography of lesion-recovered OSN projections more thoroughly, we examined response maps for odorants that have been previously shown to preferentially activate glomeruli in either the anterior (ANT) dorsal OB (aliphatic aldehydes and acids and some esters) or the caudolateral (CL) dorsal OB (ketones and aromatics) (Uchida et al., ; Wachowiak and Cohen, ; Bozza et al., , ; Takahashi et al., ; Matsumoto et al., ). Because different odorants were tested in different animals, response maps were pooled into either ANT-activating or CL-activating groups depending on odorant identity. ANT odorants were: ethyl butyrate, hexaldehyde, 2-methyl-2-butenal, butyl acetate and butyric acid; CL odorants were: acetophenone, 2-hexanone, menthone, methyl benzoate and eugenol. For a qualitative comparison of response map topography before and after lesion recovery, we generated consensus topographies as described previously (Wachowiak et al., ) and in Section Materials and Methods, compiled from pre-lesion and lesion recovery imaging sessions in the same chronically-imaged animals. ANT and CL odorants evoked the strongest responses in similar OB regions before lesion and after recovery (Figure ).
For quantitative comparison of response map topographies, maps from ANT and CL odorants and between pre-lesion (baseline) and recovery sessions were compared using the centroid of each response map (see Section Materials and Methods; Figure ). Centroid positions were compared statistically using a 4-factor ANOVA with the following factors: ANT-odorants at baseline; ANT-odorants at recovery; CL-odorants at baseline and CL-odorants at recovery. There were a minimum of six response maps (at least six different odorants) for each factor; MeBr exposed and protected sides were analyzed separately. In the pre-exposure (baseline) session, as expected, maps for ANT- or CL-activating odorants were located in largely non-overlapping domains in the ANT- or CL- OB, respectively (Figure ), with distinct centroid positions as determined by the 4-factor ANOVA ( F (3, 49) = 2.885, p < 0.05) and a post-hoc test comparing ANT- and CL-odorant centroids at baseline (Fisher’s exact test: p < 0.05). However, there was no significant change in ANT- or CL-odorant map topography after lesion recovery (Figure ), with post-hoc analyses reporting no difference in centroid locations between baseline and recovery sessions (Fisher’s exact test; ANT: p > 0.34; CL: p > 0.50). In agreement with the results in chronically-imaged animals, in the four animals that were exposed to MeBr and acutely imaged at the recovery stage, ANT- and CL-odorants evoked inputs to regions with statistically distinct centroids (unpaired Student’s t -test, ANT vs. CL positions, n = 23, p < 0.001), similar to those seen in the unexposed OB of the same animals (Figure ). These results indicate that OSNs preferentially regenerate axonal projections to targets within their original functional domains on the OB surface, thus reconstituting the broad topography of glomerular activation that is a hallmark of primary odor representations in the OB.
### Atypical convergence of olfactory sensory neurons (OSNs) to olfactory bulb (OB) targets after lesion recovery
Close inspection of lesion-recovered response maps revealed numerous examples of spH signal foci that appeared smaller than a typical glomerulus. These smaller foci—or the presence of more diffuse spH signals—could reflect OSN axons that failed to converge or that only partially innervated a glomerulus (St. John and Key, ; Blanco-Hernández et al., ). To examine these signals more carefully we imaged responses at higher-magnification and smaller depth of field (20X, 0.95 N.A. objective, 3.5 μm pixel resolution) using the same animals as in the above analysis. Imaging at this resolution confirmed that in lesion-recovered mice, odorant-evoked spH signals often appeared in foci that were subglomerular in size (Figures ). Such foci were also apparent in acutely-imaged MeBr-treated animals (Figure ), indicating that these were not a result of chronic window implantation. spH signals from subglomerular-sized foci displayed response dynamics that were similar to those from unexposed animals or larger foci (Figure ).
Evidence for atypical convergence of OSNs onto OB targets after recovery from MeBr lesion . (A) Composite odorant response maps imaged at higher-magnification (20X objective) in unlesioned (i) and lesion-recovered (ii, iii) OBs. Maps are maximal-value projections of responses to all odorants tested in a given session (see Section Materials and Methods). In both chronically-imaged (ii) and acutely-imaged (iii) mice, odorants evoked spH signal foci that were smaller in size than a typical glomerulus. Boxes in (i and iii) indicate regions rescaled in (B) . (B) Response maps from the regions in (A) evoked by a single odorant (methyl valerate, 1% s.v.), scaled to their own maximum ( left ) and to 50% of their maximum ( right ) to highlight weaker-activated regions for both Pre-exposure (i) and Recovery conditions (ii) . Smaller-sized foci are still apparent after rescaling. (C) Time-course of spH signal in typical and undersized foci in unlesioned (a, b) and lesion-recovered (c, d) animals. Traces taken from locations indicated in (B) . Unlesioned and lesion-recovered traces are offset and scaled separately to compare signal time-course. (D) Normalized intensity profiles through spH foci taken from response maps in unlesioned (a, b) and lesion-recovered animals (1, 2, 3; see A , ii ), indicating smaller focus size in recovered animals. (E) Histogram of spH focus sizes for pre-exposure and lesion-recovered preparations. Lesion-recovered animals show an increase in the number of foci below 60 μm full-width at half-maximum (FWHM). Bin size of the histogram (20 μm) is 1 standard deviation of the FWHM values for pre-lesion OBs.
We quantitatively compared spH signal foci sizes in maps taken from baseline and lesion-recovered imaging sessions by fitting the signal intensity profile of discrete foci to a Gaussian and measuring the FWHM of the fit (see Section Materials and Methods and Meister and Bonhoeffer, ). These measurements were made in acutely-imaged, MeBr-exposed animals imaged at the recovery time-point. On the side exposed to MeBr, there was a larger number of small-diameter foci (Figures ), leading to significant reduction in the mean focus size from 90.7 ± 20.8 μm. ( n = 45 glomeruli from three animals) to 67.6 ± 33.5 μm ( n = 100 glomeruli from four animals; p < 0.0001, unpaired t -test). Thus, OSN inputs to lesion-recovered OBs frequently converge onto structures smaller than the size of typical glomeruli.
To investigate the underlying anatomical structure of lesion-recovered OSN inputs to the OB, we used confocal microscopy to scan the intact dorsal OB of imaged preparations (see Section Materials and Methods). In control animals and on the protected side of unilaterally-lesioned animals, OSN axons formed large bundles that coalesced into well-defined glomeruli defined by discrete, roughly spherical areas of OSN axon terminals (Figure ). In contrast, in lesion-recovered OBs OSNs often converged onto smaller structures and glomerular boundaries appeared less well-defined (Figures ). Qualitatively similar results were seen in chronically- and acutely-imaged lesion-recovered animals. Finally, nearly all lesion-recovered OBs showed at least some regions of the dorsal OB with no clear spH fluorescence, indicating a lack of reinnervation by OSNs (Figures ). The OE of these preparations had undergone substantial, although incomplete, reconstitution of the OSN population (Figures ). For example, the chronically-imaged mouse shown in Figures showed substantial regeneration of the OE but nonetheless had patches where the OE thickness was thinner than the contralateral, unlesioned side (Figure ). Similarly, in the acutely-imaged example shown in Figures , a greater extent of the dorsal OE does not recover fully or at all (Figure ), consistent with the lesser degree of glomerular reinnervation that was observed in this animals (Figure ).
Anatomical evidence for atypical convergence of OSNs onto OB glomeruli . (A) Confocal scan of a representative unlesioned OB (maximal z -stack projection). Inset shows detail of the glomeruli noted by yellow arrows . Glomeruli are clearly delineated and relatively uniform in size. (B, C) Confocal scans from two lesion-recovered animals, one chronically-imaged (B) and one acutely imaged (C) . In both cases, glomerular borders are less distinct and OSNs often terminate in smaller structures ( yellow arrowheads, detail in insets ). In addition, regions of the central dorsal OB appear to lack innervation by OSNs. (D–F) H&E-stained sections of the OE from the same animals shown in (A–C) . In (E) , there is substantial but not complete recovery of the epithelium. Arrowheads indicate areas with a reduced contingent of neurons as compared to the protected side. In (F) , much of the epithelium remains less than fully recovered. Arrowheads indicate areas that are grossly abnormal and completely lacking in neurons.
To directly compare spH signal foci with the underlying anatomical structure of OSN inputs in lesion-recovered animals, we imaged spH signals using in vivo 2PLSM in a subset of preparations ( n = 3 chronically-imaged, 3 acutely-imaged, and 2 unexposed animals). Figure shows resting fluorescence and odorant-evoked response maps imaged with wide-field epifluorescence and with 2PLSM in an unlesioned animal. OSN innervation of distinct glomeruli is clearly resolved in vivo using 2PLSM, and odorant-evoked spH signals are readily detectable. Odorants evoke spH signals throughout the glomerulus but with hot spots in smaller domains within it (Figure ), in agreement with previous reports (Wachowiak et al., ).
Atypical glomerular convergence of OSNs confirmed with in vivo two-photon imaging of spH signals. (A) Resting fluorescence and evoked spH response maps imaged with wide-field epifluorescence ( left ) and 2-photon laser scanning microscopy (2PLSM; right ) in the same unlesioned animal. Epifluorescence image taken with 4x objective. Odorant, butyl acetate. Dashed box indicates area imaged at higher-magnification with 2PLSM. With 2PLSM, spH fluorescence increases are apparent throughout the glomerulus, with “hot-spots” of high signal amplitude in subglomerular regions. (B) 2PLSM resting fluorescence ( top ) and response map ( bottom ) from a lesion-recovered animal. Glomerular boundaries are less well-defined (compare to (A) ); in this example several relatively discrete structures are apparent (indicated by dashed ovals), one of which is only 30–40 μm in diameter ( top ). Response map ( bottom ) shows spH signals with approximate boundaries of the four structures overlaid. In two of these structures, odorant (ethyl butyrate, 5% s.v.) only evokes signals in a few foci, with the rest of the area showing no response. (C) Low-magnification wide-field ( left ) and high-magnification 2PLSM imaging ( right ) from another lesion-recovered animal, showing odorant-specific distribution of spH signals lacking a clear glomerular structure. Top : Resting fluorescence of the imaged regions. 2PLSM image is a projection of a z -stack through the olfactory nerve and glomerular layers. Middle, bottom : response maps evoked by ethyl butyrate and butyl acetate. Epifluorescence maps are unsmoothed (unlike previous figures). spH signals imaged with 2PLSM from the regions containing the strongest responses to both odorants (dashed box) reveal no clear glomerular structure from resting fluorescence. Instead, signals are distributed in small “hot-spots” ( white arrows ) whose distribution differs for the two odorants.
spH signals imaged with 2PLSM in lesion-recovered animals revealed qualitatively different signals with respect to glomerular structure. OSN axons often converged to atypically small structures (Figure ) or failed to delineate glomeruli with clear boundaries (Figure ). In these areas odorants often evoked spH signals appearing as “hot-spots” that appeared in only a portion of the glomerular structure (Figure ). Nonetheless, different odorants evoked different spatial distributions of spH signals (Figure ), consistent with their activating distinct (although smaller) populations of convergent OSNs. Overall, these results suggest that the smaller-sized spH signal foci observed in lesion-recovered OBs reflect OSN axon projections that do not converge to a canonical glomerular structure but which nonetheless provide functional input to OB targets.
### Functional recovery of olfactory sensory neuron (OSN) inputs after severe and lasting damage to the OE
Exposure to higher doses of MeBr can lead to more pronounced damage to the OE that allows for only a limited recovery and regeneration of OSNs (Schwob et al., , ). To test the limits at which OSNs can recover functional connections to the OB, we unilaterally exposed an additional six animals to a higher MeBr dosage (240 ppm, 8 h). This dosage was lethal in all but three animals. In these animals, spH signals were imaged in a single, acute session after the 12 week recovery period.
Confocal scans of the dorsal OB of these animals showed reduced resting spH fluorescence and no clear glomerular structure (Figure ); the OB on the protected side appeared normal. Histological examination of the OE of these animals showed extensive and lasting damage on the exposed side, such that there was little reconstitution of the neuronal population (Figure ). Instead, the majority of the epithelium had undergone respiratory metaplasia, in which damaged OE is replaced by respiratory epithelium after destruction of globose basal cells by severe MeBr exposure (Schwob et al., ; Jang et al., ). In these cases, the architecture of the epithelium and underlying lamina propria is distorted by fibrosis within what was the nasal cavity and the formation of synechiae bridging across the cavity from turbinate to septum (Figure ). In all three mice, this type of scarring was more prevalent in the anterior nasal cavity.
OSNs can partially reestablish functional inputs to the OB after severe and lasting trauma to the olfactory epithelium . (A) Confocal scan (maximal projection) of the dorsal OBs of an animal imaged at recovery stage after high-dosage unilateral MeBr exposure (See the text). The OB on the protected side appears normal; the OB on the exposed side fails to show OSNs terminating in glomerular structures. (B) H&E-stained section of OE from the animal shown in (A) . On the lesioned side there is almost no reconstitution of the neuronal population, although neurons are apparent in some areas of the epithelium ( arrowheads ). Scar tissue fills much of the dorsal meatus in this animal (asterisks). (C) Epifluorescence image and spH response map from the same animal as in (A, B) imaged at the recovery stage. Resting fluorescence is low on the exposed side. Very weak odorant-evoked spH signals were detected on this side and were confined to the lateral margin of the dorsal OB ( bottom ). Inset shows evoked signals from this region after rotating the head for improved optical access and scaling responses in this region to their own maximum. (D) Response maps evoked by ethyl butyrate and two additional odorants imaged from the same animal at higher-magnification (20X objective) with wide-field optics. Multiple spH signal foci, nearly all of which are subglomerular in size, are evoked by each odorant, although the patterns of activation are distinct. Arrows indicate signal foci that appear specific for a given odorant.
In vivo , the OB on the lesioned side of all three animals showed greatly reduced resting spH fluorescence (mean ± s.d., lesion: 4153 ± 516 arbitrary fluorescence units, unexposed: 8884 ± 981, n = 3 animals), indicative of poor regeneration of OSN inputs (Figure ). In addition, odorant-evoked spH signals were severely attenuated. However, in each of the animals at least some odorants evoked weak but detectable spH signals; in all cases these were confined to the lateral margins of the dorsal OB (Figures ). Higher-magnification (20x) imaging of this region revealed numerous small spH signal foci or diffuse, nonfocal signals. Despite the small amplitudes and greatly perturbed spatial organization of spH signals in these animals, different odorants still evoked spatially distinct response patterns (Figure ). These results indicate that at least some OSNs are capable of regenerating and reestablishing odorant-specific functional connections with the OB even in the face of severe and lasting damage to the OE.
## Discussion
We assessed the capacity of the mammalian olfactory system to reestablish functional connections to the CNS and to recapitulate odor representations at the level of the OB after wholesale destruction of the OSN population. By imaging odorant-evoked spH signals from OSNs to OB glomeruli before peripheral lesion and after a 12 week recovery period, we found that this regenerative capacity is robust: odor “maps” involving many glomeruli (and thus many ORs) were reconstituted with little or no change in their topographic organization across the dorsal OB. We also obtained evidence that mistargeted OSNs—which have previously only been observed anatomically—make functional connections to the OB. Finally, we found that OSNs were able to at least partially reestablish functional connections to the OB even after lesions severe enough to permit only minor recovery of the OSN population. These results expand on earlier anatomical studies that have reported regeneration and glomerular convergence of a few OR- and histologically-defined OSN populations (Schwob et al., ; Costanzo, ; St. John and Key, ; McMillan Carr et al., ; Blanco-Hernández et al., ) and are consistent with a recent report that discriminative odor memories are preserved after OSN lesion and recovery (Blanco-Hernández et al., ).
Several lines of evidence suggest that the process of installing a chronic imaging window did not substantially affect OSN targeting. First, in unlesioned (but windowed) controls, we found that odor maps remained stable for at least 13 and for as long as 25 weeks. Second, in unilaterally-lesioned animals, we observed differences in the fine structure of response maps (described below) between the MeBr-exposed and protected sides, despite the presence of an imaging window on each side. Third, we obtained qualitatively and quantitatively similar results in animals imaged only at the 12 week recovery time-point and exposed bilaterally to MeBr. Thus, it is unlikely that the chronic imaging procedure or unilateral lesion affected the process of OSN regeneration and targeting to glomeruli.
### Recovery of odor map topography after lesion
In the mammalian OB, many odorants preferentially evoke activity in glomeruli clustered in spatial domains covering several hundred microns (Imamura et al., ; Uchida et al., ; Johnson et al., ). These domains are associated with odorants of a particular chemical class and are innervated by molecularly and functionally distinct OSN types (Bozza et al., , ; Takahashi et al., ; Matsumoto et al., ; Pacifico et al., ). We found that this domain organization, as assessed functionally across the dorsal OB, is largely preserved after OE regeneration. This result does not simply reflect the reconstitution of normal zones of odorant receptor (OR) expression in the OE and the maintenance of rhinotopic projections from the OE to OB (Schoenfeld et al., ; Cummings et al., ; Iwema et al., ), as OSNs projecting to OB domains are interspersed in the OE (Bozza et al., ). The reconstitution of functional topography after OSN regeneration is consistent with earlier anatomical studies examining the targeting of P2- or M72-expressing OSNs or of immunohistochemically-defined OSN subsets (Cummings et al., ; St. John and Key, ; McMillan Carr et al., ; Blanco-Hernández et al., ); these studies found that OSNs project to glomeruli in topographically similar locations as in control animals, although with clear errors in targeting. The fact that, in this study, spH response maps—even those involving many glomeruli—retain a spatial organization that matches that before lesion suggests that regenerated OSN axons do not randomly converge onto OB glomeruli but instead preferentially target their appropriate domain on the OB surface. In addition we note that many lesion-recovered response maps included individual signal foci (i.e., glomeruli) that appeared in a similar location to that observed before lesion (e.g., Figure ) or to that of a focus on the unexposed side (e.g., Figure ), suggesting that the precision of targeting in regenerated OSNs may be higher than of a functional domain.
The mechanisms mediating this targeting remain unclear but may include OR identity (Feinstein et al., ), OSN cell type (Bozza et al., ) and axon guidance cues (Schwob, ; Schwarting and Henion, ). In many systems such guidance mechanisms function only during a restricted developmental window; our results suggest that they are at least partially effective in guiding OSN axons in adults. The precise targeting of OSNs to glomeruli may also be affected by activity-dependent mechanisms driven by exposure to ambient odorants (Nakatani et al., ; Zou et al., ; Kerr and Belluscio, ). Examining the recovery of OSNs expressing markers for ORs for which ligands are known in combination with functional imaging of inputs to all glomeruli will be useful for testing this possibility.
It remains possible that a minority of OSNs expressing the same OR fail to converge onto glomeruli in their appropriate domains, or that a minority of OSN populations converge onto glomeruli in inappropriate locations. Quantifying the degree to which such mistargeting occurs is difficult since functional domains can only be loosely defined by odorant responsiveness (Wachowiak and Cohen, ; Bozza et al., ; Wachowiak et al., ) (i.e., nearly all odorants activate at least some OSN input to glomeruli outside of their preferred domain) and because molecular (e.g., OR-based) tags to define domain boundaries are themselves derived from OSN convergence patterns (Bozza et al., ; Pacifico et al., ). It is also possible that spH signal foci that occur in similar locations before and after lesion may reflect activation of OSNs that express different ORs but have a similar odorant-specificity. An ideal approach to more precisely define the precision of OSN retargeting would be to tag the postsynaptic neurons associated with a given OSN population—for example, using transysnaptic transgene expression driven by OSNs expressing a particular OR.
### olfactory sensory neuron (OSN) convergence onto individual glomeruli
Another fundamental feature of OSN projections to the OB is the exclusive convergence of OSNs expressing the same OR onto a glomerulus (Mombaerts et al., ; Treloar et al., ). We found evidence for errors in this convergence after OE regeneration: lesion-recovered animals showed an increase in the number of small-sized (<60 μm) spH foci compared to controls, indicating either reduced numbers of OSN axons forming a glomerulus or partial innervation of a glomerulus by OSNs expressing a given OR. We found evidence for both possibilities using high-resolution two-photon imaging of spH signals in register with the underlying structure of OSN axons. The time-course and odorant-specificity of spH signals in these subglomerular foci was similar to spH signals in larger foci or in unlesioned controls, suggesting that transmitter release from these OSN terminals was functional. These results suggest that regenerated OSNs can provide functional input to mistargeted glomeruli and that this mistargeting is a general phenomenon seen across many OR types.
OSN mistargeting may impact odor perception after OE lesion and recovery (Yee and Costanzo, ; Vedin et al., ). In humans, dysosmias are often reported after trauma to the OE (Doty, ; Meisami et al., ). Innervation of a single glomerulus by multiple OSN types might underlie these effects. Given our evidence that OSN mistargeting is restricted to within a functional OB domain, one prediction is that discrimination of structurally similar odorants will be impaired after OE lesion and recovery, while a second is that discrimination between odorants activating different domains will be unaffected. The former prediction has not, to our knowledge, been tested. The latter prediction is supported by a recent report that behavioral discriminations of two odorants that activate distinct OB domains persist after OSN lesion and recovery (Blanco-Hernández et al., ). However, a second study found that odor discriminations are impaired even after partial lesion that spares many OSNs (Bracey et al., ), in apparent contradiction to the recovery of odor memories after lesion and regeneration. Thus, rigorously testing perceptual effects of OSN mistargeting may be difficult and will likely require combining multiple perceptual assays with imaging of odor maps in the same animals (Bracey et al., ).
### Factors affecting olfactory sensory neuron (OSN) regeneration and axon targeting
While we evaluated the recovery of sensory inputs to the OB after MeBr-induced OSN lesion, previous studies have used a variety of lesion models with qualitatively distinct results. Severing the olfactory nerve at the cribriform plate, which leads to extensive OSN death and subsequent regeneration, results in more extensive mistargeting after regeneration—including a loss of rhinotopic projections—than does chemically lesioning OSNs (Costanzo, ; Christensen et al., ). There also appears to be a correlation between the numbers of OSNs surviving the lesion and the degree of mistargeting (Schwob et al., ). For example, retargeting of P2-expressing OSNs to their appropriate glomerulus is normal if these neurons alone are selectively lesioned using a genetic method while all other OSNs are spared (Gogos et al., ), and chemical lesions that spare the lamina propria appear to permit more precise targeting of recovered OSNs (Blanco-Hernández et al., ).
After severe OE lesion with higher MeBr doses in which there was substantial lasting damage to the OE, we found that odor response maps were severely disrupted, with little or no regeneration of OSN inputs to the dorsal OB and a lack of convergence onto clear glomeruli in the lateral OB. Despite this severe disruption, however, odorants did evoke spH signals in reinnervated areas and did so with odorant-specific (though poorly-defined) spatial patterns, indicating that OSNs are capable of establishing functional inputs to the OB even in the absence of glomerular convergence. Thus the capacity of the OE to reestablish connections to the CNS appears to persist even in the face of extreme peripheral damage.
## Author Contributions
Man C. Cheung, Matt Wachowiak and James E. Schwob designed the experiments, Man C. Cheung performed the imaging experiments and data analysis, James E. Schwob administered the MeBr lesions, James E. Schwob and Woochan Jang performed the histological analysis and Matt Wachowiak and Man C. Cheung wrote the paper.
## Conflict of interest statement
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
|
In the next century, flying civilians to space or humans to Mars will no longer be a subject of science fiction. The altered gravitational environment experienced during space flight, as well as that experienced following landing, results in impaired perceptual and motor performance—particularly in the first days of the new environmental challenge. Notably, the absence of gravity unloads the vestibular otolith organs such that they are no longer stimulated as they would be on earth. Understanding how the brain responds initially and then adapts to altered sensory input has important implications for understanding the inherent abilities as well as limitations of human performance. Space-based experiments have shown that altered gravity causes structural and functional changes at multiple stages of vestibular processing, spanning from the hair cells of its sensory organs to the Purkinje cells of the vestibular cerebellum. Furthermore, ground-based experiments have established the adaptive capacity of vestibular pathways and neural mechanism that likely underlie this adaptation. We review these studies and suggest that the brain likely uses two key strategies to adapt to changes in gravity: (i) the updating of a cerebellum-based internal model of the sensory consequences of gravity; and (ii) the re-weighting of extra-vestibular information as the vestibular system becomes less (i.e., entering microgravity) and then again more reliable (i.e., return to earth).
## Introduction
On earth, gravity is a force to which we are constantly exposed starting from the day we are born (Lacquaniti et al., ). During our everyday activities, it is vital that the brain accounts for the physical force of gravity (Honeine et al., , ; Jansen et al., ). This is because, during our natural behaviors, gravity produces disequilibrium torques that must be counteracted by our motor systems to maintain balance and prevent falls. Accordingly, our feedforward compensatory pathways, postural strategies, and locomotor patterns all require taking gravity into account to ensure the maintenance of equilibrium during everyday activities—including quiet standing, arm reaching and locomotion (Cordo and Nashner, ; Papaxanthis et al., ; Sylos-Labini et al., ; Honeine et al., ; Lacquaniti et al., ; Macaluso et al., ). Moreover, the physical force of gravity provides a vital world-based reference to which we can anchor our perception of spatial orientation as well as control of balance (Lackner and DiZio, ; Lacquaniti et al., ; Panic et al., ).
Importantly, during space exploration missions, the force of gravity becomes minimal. As a result, nearly 70% of all astronauts experience impaired balance, locomotion, gaze control, dynamic visual acuity, eye–head–hand coordination, and/or motion sickness within the first 3–4 days of both space flight and then again after returning to earth (Lackner and Dizio, ; Souvestre et al., ). These symptoms arise because changes in gravity alter the sensory input from the vestibular system, which in turn generates a persistent conflict (i.e., mismatch) between expected and actual sensory vestibular inputs during active movements (Oman and Cullen, ). Then, in the days following such changes in gravity, astronauts show sensorimotor adaptation that results in improved motor performance. As discussed below, recent experiments using ground-based models have furthered our understanding of the neural mechanisms that underlie sensorimotor adaptation and thus have important implications regarding the interpretation of the results from flight-based studies. Notably, these studies have established the neural mechanisms that underlie the brain’s computation of an estimate of gravity and self-motion during active behaviors and have also provided evidence for the re-weighting of vestibular inputs in conditions where it becomes less reliable. In this review, we consider these findings in the context of experiments that have studied the neurovestibular adaptation during and after space flight and the implications for improving human performance during and following space exploration.
## Gravity Is Important on Earth: Posture, Perception, and Behavior
The findings of theoretical as well as behavioral studies have led to the longstanding hypothesis that the brain builds an internal model of the expected sensory consequence of our own actions (Wolpert et al., ; Wolpert and Ghahramani, ). During self-motion, this internal model is required for the maintenance of posture, accurate spatial orientation, and the generation of precise voluntary movement (Cullen, ). Specifically, by comparing the incoming sensory information from different modalities (i.e., vestibular signals with information from the proprioceptive, somatosensory, and visual systems) with that predicted by its internal model, the brain anticipates and validates the consequences of the force of gravity (McIntyre et al., ; Zupan et al., ). On earth, the expectation of the constant force of gravity is an inherent component of this internal model. However, during space exploration, the force of gravity becomes negligible resulting in a mismatch between the brain’s expectation of the sensory consequences of movement and that which is actually experienced due to the resultant unloading of the otoliths. This mismatch has important implications for astronauts, during and after space flight, since it results in impaired behavioral performance in the first days of exposure to an altered gravity environment. However, after 1–5 days of space flight and ~1 week after landing these symptoms largely disappear, implying that the brain has adapted to the new gravitational environment.
### Posture and Locomotion in Space
The maintenance of upright posture during quiet standing requires overcoming the force of gravity. The biomechanics of human posture can be well modeled by an inverse pendulum (Winter et al., , ). Accordingly, we constantly oscillate around an equilibrium point and small corrective movements are required to prevent falling. The neural mechanisms that stabilize upright posture in 1 g generally persist when initially exposed to microgravity, even though they are no longer necessary (Clément et al., ; Mouchnino et al., ; Massion et al., ; Vernazza-Martin et al., ; Baroni et al., ). Indeed, the human body naturally assumes a more neutral posture in microgravity characterized by a semi-crouched torso, flexed arms and legs, and forward bent neck and head (Andreoni et al., ; Han Kim et al., ). While the brain’s internal model of postural control appears structurally stable in the short-term, it remains unknown whether the neural mechanisms that stabilize the upright posture in 1 g continue to operate during long missions in space. However, it appears likely that this is not the case. Indeed, postural stability is often related to the Hoffman reflex, an otolith-spinal reflex (Chen and Zhou, for review). Muscle activity associated with the Hoffman reflex has been shown to reach low values with longer delays after 7 days in space (Reschke et al., ; Watt et al., ). It appears that in microgravity, the information coming from the otolith organs to the motoneurons is gradually reinterpreted. Similarly, following re-entry into 1 g , the neural mechanisms that stabilize the upright posture appear to be largely disrupted following both short (1–2 weeks) and long (4–6 month) duration spaceflight (Jain et al., ; Wood et al., ). Interestingly, more severe and persistent deficits occur in the latter case. Rapid recovery is reported on the first day after return, with more gradual improvement in the following weeks ultimately returning performance to pre-flight levels (Paloski et al., ; Reschke et al., ).
In addition to postural instabilities, astronauts often experience oscillopsia during locomotion following space flight, suggesting that head-trunk coordination is impaired (Bloomberg et al., ). Specifically, the coherence between pitch head and vertical trunk movements is reduced following space flight (Bloomberg et al., ; Mulavara et al., ) similar to what has been observed in patients with altered vestibular input due to peripheral vestibular loss (Mulavara et al., ) or the application of galvanic vestibular stimulation (Moore et al., ). An interesting fact is that head-trunk coordination is better during locomotion after re-entry in more experienced astronauts (e.g., number of flights; Bloomberg et al., ; Moore et al., ) suggest that experience influences the ability to rapidly update a vestibular based internal model for the control of posture and locomotion.
### Perception in Space
During space flight, astronauts also report spatial disorientation and destabilizing sensations. On earth, many aspects of our environment, including ourselves, are “gravitationally polarized.” The brain continually computes our head and body orientation relative to gravity, using vestibular and other sensory information (reviewed in Goldberg et al., ). Spaceflight violates many of the regularities that characterize our orientation on the ground (Lackner and DiZio, ). For example, due to the lack of otolith input that normally signals head orientation relative to gravity, astronauts can lose all sense of spatial anchoring to their surroundings when their eyes are closed (Lackner and Graybiel, ). When their eyes are open, astronauts may intellectually know their position in relation to their surroundings, but they do not experience a normal sense of orientation with respect to the environment (Lackner and DiZio, ). As a result, sensations of inversion, tilt, and virtually every combination of body orientation and vehicle orientation have been reported. With increased flight duration such illusions, which can be experienced immediately on transition into microgravity, tend to abate as astronauts adapt to their new environment (Lackner and DiZio, ).
Perceptual adaptation to altered gravity has also been studied using centrifugation on the ground as well as in space (e.g., Clément et al., ). For example, shortly following transition into microgravity, subjects experience a roll tilt illusion during centrifugation that is similar to that observed during ground-based experiments (~45°). However, during prolonged exposure (i.e., 16 days in microgravity), the illusion of tilt increased, such that subjects reported that they felt as if they were lying on their side (~90°). These results suggest that the brain initially continues to use its ground-based model of the sensory consequences of gravity during early space flight, which it then adapts to account for the new microgravity environment. Likewise, perceptual adaptation was evidenced by larger tilt illusion values upon re-entry compared to the pre-flight values. It is important to note that impaired perception during gravitational transition compromises an astronaut’s ability to control the spacecraft itself (Clément, ). For instance, it has been reported that astronauts that failed to land safely had episodes of spatial disorientation during the procedure (Clark and Bacal, ). These pilots showed vestibular dysfunction that was correlated with their performance in controlling the spacecraft during the landing procedure.
### Voluntary Movement in Space
Finally, there is accumulating evidence that the accurate control of voluntary movements, such as reaching, is correspondingly altered during space flight (Carriot et al., ; Scotto Di Cesare et al., ; Gaveau et al., ; White et al., ). When instructed to reach up or down, human subjects demonstrate asymmetric arm kinematic suggesting that the brain also uses an internal model of gravity to predict and take advantage of its mechanical properties to optimize effort (Gaveau et al., ). Interestingly, this asymmetry disappears in microgravity (Carriot et al., ; Crevecoeur et al., ; Gaveau et al., ). Additionally, pointing accuracy drastically decreases in absence of gravity (Carriot et al., ). While it was initially proposed that this occurs due to the reduction of the arm weight in microgravity (Bringoux et al., ), a subsequent EEG study reported increased activity within the vestibular network during a comparable visuo-motor task (Cebolla et al., ). Moreover, comparable effects have been reported in an ground-based model when vestibular input was ablated via labyrinthectomy (Angelaki, ). Thus, taken together, the altered vestibular inputs experienced during space flight likely contribute not only to the observed impairments in postural and perceptual performance but also to changes in the kinematics and accuracy of voluntary movements.
It is noteworthy that to date, most studies of voluntary movements in microgravity have been performed during parabolic flights and thus it was not possible to investigate long-term adaptation. However, the findings of ground-based centrifugation experiments have shown that reaching patterns can rapidly adapt to new force field environments when tested (i.e., 10–15 movements; Lackner and Dizio, ). Moreover, evidence from studies of astronauts following re-entry is consistent with rapid adaptation. Specifically, sensorimotor learning (Mulavara et al., ), as well as eye–head and head-trunk coordination (Glasauer et al., ; Bloomberg et al., ; Reschke et al., ; Bloomberg and Mulavara, ; Courtine and Pozzo, ; Clément and Wood, ) recover rapidly in the first day after return from short- and long-term missions, with an improvement that is more gradual in the following weeks. Thus, it seems likely that the brain likewise adapts its control of voluntary movements over the long-term in microgravity.
### Conclusions
Overall, the absence of gravity severely impairs motor and perceptual performance. Although the computation of gravity relies on the integration of sensory information from our different senses, the role of the vestibular signal appeared to be omnipresent in most if not all human behaviors in space. At this stage, it is thus fundamental to understand how the gravity signal is computed from vestibular inputs.
## Our Brains Are Wired to Keep Track of Gravity: What Happens to The Vestibular System in Space
### Vestibular Sensory Organs and Peripheral Transmission
To date, many investigators have studied how the vestibular system responds and adapts to the transition from gravity to microgravity ( ). As reviewed above, the absence of gravity leads to unloading of the otoliths such that they are no longer stimulated as they would be on earth by changes in the head’s spatial orientation. Early experiments in rats and frogs suggested that this unloading causes an increase in the mass of the otoconia (i.e., the small crystals of calcium carbonate which couple mechanic forces to the activation of sensory hair cells in the utricle and saccule) following short-term (i.e., 7 days) exposure to microgravity (Vinnikov Ia et al., ; Ross et al., , ; Lychakov et al., ). Correspondingly, experiments in model systems have shown that the opposite phenomenon appears to occur in hypergravity (cichlid fish: Anken et al., ; marine mollusk larvae: Pedrozo and Wiederhold, ; rats: Krasnov, ; reviewed in Cohen et al., ). Most recently, Boyle and Varelas ( ) investigated the structural remodeling that occurs in the otoconia of mice using electron microscopy. Interestingly, these investigators found evidence for a mass addition to the otoconia outer shell, following exposure to long but not short duration spaceflight (or hindlimb unloading), as well as the thinning of the inner shell and cavitation of the otoconia following centrifugation. Likewise, structural changes following hindlimb unloading have been reported following long but not short duration hindlimb unloading (i.e., 90 days, Boyle and Varelas, ) vs. 160 days (Zhang et al., ). Accordingly, taken together these findings suggest that the otoconial mass adapts to fluctuations in the gravitational stimulus to maintain a consistent force on the maculae in astronauts during space flight. Future work will be required to fully understand the detailed time course of these changes.
Neurophysiological short- (<7 days) and long (>7 days) term- adaptation to microgravity and ground-based model. Upper left panel : the mass of the otoconia increases following short-term exposure to microgravity (Vinnikov Ia et al., ; Ross et al., ; Lychakov et al., ). This increase in mass is assumed to be maintained as long as there is no change in the gravitational environment. The opposite phenomenon occurs in hypergravity (decrease in mass; Krasnov, ; Pedrozo and Wiederhold, ; Anken et al., ; reviewed in Cohen et al., ). To date, however, the time course is unknown since testing was only done after long-term centrifugation (>24 days). Middle left panel : After 7–9 days in microgravity, type II vestibular hair cells increase in size and number (Ross, , , ). Then after 2 weeks in microgravity, the number of type I cells increases as well (Ross and Tomko, ). In hypergravity, only type II hair cells show a significant decrease in number (e.g., following 14–30 days of centrifugation; Lychakov et al., ; Ross, ). Bottom left panel : Immediately after entering microgravity, studies across species have reported increases in vestibular afferent baseline activity and sensitivity (Gualtierotti and Alltucker, ; Gualtierotti, ; Boyle et al., ). However, when considered alone, studies in NHPs have been inconclusive as some report increases and other report decreases in sensitivity (Correia et al., ; Cohen et al., ). After 5 days in microgravity, vestibular afferent responses return to ground levels (Bracchi et al., ; Boyle et al., ). On earth, 1 month after complete unilateral vestibular lesion, afferent responses in the intact nerve remain comparable to control levels (Sadeghi et al., , , , ). Bottom right panel : Across animal models, the sensitivities of vestibular nuclei neurons initially increase in microgravity and then return to baseline levels after a week (Pompeiano et al., ; Cohen et al., ). On earth, following labyrinthectomy, vestibular nuclei neurons that normally only respond to vestibular input before lesion, show the emergence of responses to extravestibular inputs (efference copy, proprioception) after lesion (Sadeghi et al., , , , ). During sensory-motor adaptation, vestibular neurons update their response to altered sensory feedback (Brooks and Cullen, ; Brooks et al., ; Mackrous et al., ). Top right panel : The cerebellum displays synaptic reorganization as early as 24 h following the transition to microgravity, which remains for at least 18 days (Holstein et al., ). On earth, following labyrinthectomy, cerebellar neurons lose their ability to discriminate between tilt and translation (Yakusheva et al., ).
The vestibular receptor cells in all mammalian end organs, including the otoliths, are called hair cells and are divided into two subtypes ( , left panel). These subtypes, termed type I and type II hair cells, occur in nearly equal ratios. Type I hair cells are defined by the presence of calyceal afferent innervation, while in contrast Type II hair cells synapse upon discrete bouton afferent terminals ( , left panel; reviewed in Cullen, ). Intriguingly, prolonged exposure to microgravity (>7 days) also appears to primarily impact the structure of type II hair cells (Ross, , , ). For example, ultrastructural analysis has demonstrated statistically significant increases in the number of type II utricular hair cell synapses in mice after a 9-day space flight (Ross, ). After 2 weeks in microgravity, an increase in the mean number of presynaptic processes ending on the calyces of type I cells has also been reported (40%; Ross and Tomko, ). A more recent study interestingly reported a reduction, rather than increase, in the synapse densities of the hair cells in the mouse utricle following 15 days of exposure to microgravity (Sultemeier et al., ). While differences in the approach used by Ross et ( ; electron microscopy) vs. Sultemeier et ( ; immunohistochemistry) complicate direct comparison, taken together the results of these studies suggest that vestibular hair cells, at least in rodents, can demonstrate adaptative changes in response to altered gravity. These peripheral adaptative changes, combined with those occurring at subsequent stages of vestibular processing (detailed below), likely contribute to the changes in utricular function that have been reported in astronauts immediately after returning from space flight (Hallgren et al., ; Reschke et al., ).
Finally, following exposure to microgravity, changes have also been reported at the next stage of peripheral vestibular processing, namely in the vestibular nerve afferents. Initially after entering microgravity both the baseline activities and sensitivities of otolith afferents substantially increase (reviewed in Clément et al., ). This finding is consistent across all non-mammalian animal models that have been tested (toadfish: Boyle et al., ; bullfrog: Gualtierotti and Alltucker, ; Gualtierotti and Bailey, ; Bracchi et al., ; Gualtierotti, ). Otolith afferent baseline activities and sensitivities then appear to return to control levels after ~5 days (Bracchi et al., ) and/or 24 h after returning to the ground (Boyle et al., ). It has been proposed that this initial hypersensitivity of otolith afferents induced by microgravity is due to presynaptic adjustment of synaptic strength in the hair cells reviewed above (Ross, ).
To date it remains unknown whether vestibular afferent sensitives likewise change during the first days of space flight in mammals. Afferent recordings were made in monkeys after 12 and 14 days of flights during two COSMOS missions (COSMOS 2044 and COSMOS 2229, respectively). However, these two missions reported contradictory findings (increased vs. decreased gains relative to pre-flight levels; Correia et al., ; Cohen et al., ). Indeed, given the inherent variability of monkey afferent response gains (Sadeghi et al., ; Massot et al., ; Jamali et al., ), the low numbers of afferents recorded in each study were likely not sufficient to make a pre-post flight comparison. In this context, it is important to note that the vestibular efferent system does not appear to play a significant role in the short-term adaptation of afferent coding in mammals as it does in lower vertebrate species (reviewed in Cullen and Wei, ). Thus, how microgravity influences the responses of vestibular afferents in mammals remains an open question.
### Central Vestibular Processing
Vestibular afferents target neurons in the vestibular nuclei comprise the first stage of central vestibular processing. Most vestibular nuclei neurons integrate inputs from both otolith and canal afferents (reviewed in Goldberg et al., ; Cullen, ). Single unit recordings have been made from the vestibular nuclei of rhesus monkeys on several Russian “Cosmos/Bion” Missions between the Bion 6 (Cosmos 1514) through Bion 11 projects. The findings of these studies are detailed in “Final Reports” submitted by investigators to Russia’s Institute of Biomedical Problem, as well as in some published reports (reviewed in Cohen et al., : Sirota et al., , , , , , , , , , , , , ; Shipov et al., ; Sirota, ; Kozlovskaya et al., , , ; Yakushin et al., , , ; Badakva et al., ). Overall, investigators reported increases in the sensitivities of vestibular nuclei neurons to both linear and rotational head motion during the first days of space flight, with a subsequent return to normal (reviewed in Cohen et al., ). This finding was surprising given that microgravity affects the linear forces sensed by the otolith but not the rotations sensed by the canals (Cohen et al., ). Notably, neural sensitivities to linear head motion reached a maximum by the end of the first week in space while neural sensitivities to rotational head motion increased only within the first days of flight and then returned to normal levels within this same time frame.
The effect of microgravity on vestibular nuclei activity has also been studied by quantifying the expression of the early gene c-fos, which is a neural activity marker. For example, in ground-based models, galvanic stimulation and centripetal acceleration lead to increases in Fos immunoreactivity in the vestibular nuclei (Kaufman et al., , ; Kaufman and Perachio, ). Experiments done in space have likewise reported increased Fos expression in the vestibular nuclei of rats (particularly the medial and descending vestibular nuclei) 24 h postlaunch. Increased Fos expression has also been observed following return from a 17-day mission (Pompeiano et al., ). In contrast, Fos expression levels were comparable to control levels 13 days postlaunch and at 13 days postlanding, consistent with adaption occurring over time in response to altered gravity. Interestingly, while Fos expression was unchanged in autonomic regions that have been linked to motion sickness postlaunch (i.e., area postrema and nucleus tractus solitarius), significant increases were observed in these areas 24 h after landing (Pompeiano et al., ).
The vestibular afferents and vestibular nuclei both send direct projections to the caudal vermis of the cerebellum. In particular, the cerebellar nodulus receives significant input from vestibular otolith afferents. Ultrastructural changes in Purkinje cell dendritic morphology and/or the synaptic organization of their mossy fiber inputs have been reported when measured during 5–18 days of space flight (Krasnov and D’iachkova, ; Krasnov and Dyachkova, ). Similar changes are observed in nodular mossy fiber terminals. Additionally, major changes occur in the Purkinje cell cytoplasm within 24 h, including enlargement of the cisterns of the smooth endoplasmic reticulum, formation of long, stacked lamellar bodies, and the presence of degeneration (Holstein et al., ). Based on the last of these structural alterations, it has been proposed that excitotoxicity may play a role in the short-term changes in neural responses that are observed during space flight (Cohen et al., ).
Taken together, the findings of space-based experiments in central pathways demonstrate that the loss of otolith loading in microgravity (or reestablishment of loading following retry) leads to an increase in the sensitivity of vestibular pathways followed by adaptation over time. Below we consider the implications of ground-based research for providing insight into the neural mechanisms that underlie the sensorimotor adaptation required to ensure postural and perceptual stability, as well as the ability to generate accurate movements after exposure to altered gravity.
## What Are The Implications of Ground-Based Models for Understanding How The Vestibular System Adapts to Microgravity
### The Vestibular Cerebellum and Computation of Head Orientation Relative to Gravity
As reviewed in “Gravity Is Important on Earth: Posture, Perception, and Behavior” section above, there is consensus that the brain builds an internal model of the expected sensory consequence of our own actions (Wolpert et al., ; Wolpert and Ghahramani, ). During self-motion, this internal model is required for the maintenance of posture, accurate spatial orientation, and the generation of precise voluntary movement. The otoliths, like any inertial sensor (i.e., accelerometer), cannot distinguish forces produced by changes in the head’s orientation relative to gravity from those produced during translational self-motion. Thus, to compute a real time estimate of the head’s orientation relative to gravity on earth, the brain integrates rotational head motion information from the semicircular canals with otolith signals (reviewed in Goldberg et al., ). Ground-based single-unit recording experiments in head-restrained monkeys have shown that some Purkinje cells in the nodulus/uvula of the caudal vermis integrate otolith and semicircular canal inputs during passively applied self-motion to provide an estimate of current head orientation relative to gravity (reviewed in Angelaki and Cullen, ). Further, with the loss of canal input (i.e., via canal plugging) these same neurons lose their ability to discriminate between changes in the head’s orientation relative to the gravity and linear head acceleration (Yakusheva et al., ). This finding has led to the proposal that, on earth, the vestibular cerebellum computes internal models of the physical laws of motion to provide an estimate of the head’s orientation relative to gravity (reviewed in Goldberg et al., ).
### Cerebellar Prediction of the Dynamic Sensory Consequences of Gravity During Active Motion
More recently, single-unit recording experiments have further demonstrated that cerebellum-based mechanisms cancel the sensory consequences of gravity during active head movements (Mackrous et al., ). The activity of individual cerebellar output neurons was recorded while monkeys actively reoriented their heads relative to gravity. Strikingly, the robust vestibular responses displayed by neurons to the passive head motion were canceled during comparable active head movements. Indeed, such cancellation is required to maintain accurate postural control and perceptual stability. For example, on earth, vestibulo-spinal reflexes are vital to ensuring postural stability in response to unexpected changes in the head’s orientation; they send compensatory motor commands to the neck and axial/appendicular muscles that stabilize posture relative to space. However, when the same head motion is actively generated, these compensatory reflex responses are counterproductive because they would oppose the intended voluntary behavior through space. Indeed, during active movements, this cerebellum-based mechanism suppresses vestibulo-spinal reflex pathways during active movement relative to gravity (Mackrous et al., ).
It then follows that in space flight, following the transition to microgravity, both active and passive head movements will produce different (i.e., reduced) otolith afferent input compared to what they would produce on the ground. Notably, head tilts will continue to activate semi-circular canal but not otolith afferents. Thus, the brain experiences a mismatch between its expectation (internal model) of the resulting sensory feedback and actual sensory feedback that is experienced during head movements. During active movements, the initial mismatch between expected and actual otolith input will likely result in higher modulation in neurons in the vestibular nuclei as compared to earth. Over time, however, we speculate that cerebellum-based mechanisms underlie the ability to adapt to such mismatches and update the brain’s internal model to account for the new relationship between expected and actual sensory vestibular input that exists in microgravity.
Indeed, to date, such cerebellum-based adaptation has been demonstrated in ground-based experiments where a resistive load was applied to a monkey’s head while it generated voluntary head movements (e.g., Brooks et al., ). Because the application of the load initially altered the relationship between the motor command to move the head and its actual movement, the resultant vestibular sensory feedback was less than expected. Thus, initially, there was a mismatch and vestibular responses were not canceled during active head movements. However, cerebellar output neurons then show trial-by-trial adaptation to the new sensorimotor constraints after many active head movements—until there was again a match between the expected and actual sensory feedback. Once the internal model was updated and there was a match between actual and expected sensory feedback, vestibular responses were again canceled during active head movements. This finding has direct implications for behavior, since these cerebellar output neurons send descending projections to vestibular nuclei neurons that mediate vestibulo-spinal reflexes. Given that these reflexes are essential for maintaining posture and balance, the brain’s ability to adapt its descending commands to account for changes in the environment is essential. We propose that a similar cerebellum-based mechanism accounts for adapting to learning a new match between expected and actual sensory consequences of gravity when an astronaut is initially exposed to microgravity or returns to the ground following sustained exposure to microgravity.
Additionally, over longer time periods in microgravity, vestibular pathways are also likely to reweight this extra-vestibular information to compute more reliable estimates of head orientation. Prior ground-based studies in monkeys following peripheral vestibular loss have insights into the neural mechanisms that underlie the re-weighting of sensory information when vestibular information becomes less reliable (Sadeghi et al., , , ; Jamali et al., ). Notably, in normal rhesus monkeys (and presumably humans), central pathways do not integrate vestibular and proprioceptive signals at the level of the vestibular nuclei; vestibular nuclei neurons are insensitive to passively applied stimulation of proprioceptors. Instead, the integration of vestibular and proprioceptive only occurs at the next levels of vestibular processing, for example in the rostral fastigial nucleus of the cerebellum (Brooks and Cullen, ; Brooks et al., ) and vestibular thalamus (Marlinski and McCrea, ; Dale and Cullen, ). Surprisingly, however, following a peripheral vestibular loss, vestibular nuclei neurons demonstrate strong responses to passively applied stimulation of proprioceptors, suggesting that a form of homeostatic plasticity compensates for the reduced reliability of the vestibular input (Sadeghi et al., , , ).
Thus, the dynamic re-weighting of inputs from different modalities (i.e., extravestibular vs. vestibular) is observed even at the first stage of central processing in the vestibular nuclei following a peripheral vestibular loss. At least two types of extravestibular inputs substitute for the lost vestibular input: (1) proprioception; and (2) motor efference copy signals. Initially, robust responses to passive stimulation of neck proprioceptors are rapidly unmasked (within 24 h) and are linked to the compensation process as evidenced by faster and more substantial recovery of the resting discharge in proprioceptive-sensitive neurons (Sadeghi et al., ). Over the long term, efference copy signals also contribute to neuronal responses such that the efficacy of vestibular pathways is enhanced for active vs. passive self-motion (Sadeghi et al., , , ). Such re-weighting of extra-vestibular information in early vestibular pathways is also likely to occur in microgravity, where otolith organs are unloaded and thus are no longer stimulated as they would be on earth. We speculate that these results have implications for better understanding compensation and adaptation to vestibular functional disruption. Consistent with this proposal, recent MRI studies in astronauts pre- vs. post-flight have provided evidence for vestibular/proprioceptive sensory re-weighting and adaptive neuroplasticity at higher levels of processing in the cortex (Hupfeld et al., ).
Indeed, there is evidence from both space- and ground-based studies that such extra-vestibular sensorimotor feedback can rapidly influence the online processing of vestibular information for the control of balance (Marsden et al., ) and locomotion (Mulavara et al., ; Forbes et al., ). Additionally, we note that vision provides important “extra-vestibular” information about self-motion and spatial orientation, as was elegantly demonstrated by the “visual reorientation illusion” experiments of Howard and colleagues (e.g., Howard and Hu, ; Jenkin et al., ). Future experiments focused on the neural mechanisms responsible for the reweighing of extra-vestibular and vestibular information following exposure to altered gravity are required to fully understand the mechanisms responsible for the changes observed in astronaut performance/strategies during space flight.
In summary, we propose that the results of recent ground-based studies of the neural mechanisms underlying sensorimotor adaptation provide important insights into the central changes that occur in the brains of astronauts before and after space exploration missions.
## Connecting The Dots Between Ground- and Space-Based Neurophysiological Studies of Vestibular Pathways and Their Compensation
Above, we reviewed the results of space-based research that have revealed significant modifications in the cellular and subcellular structure of the vestibular pathways at multiple levels ( )—from the vestibular periphery (increase in the mass of the otoconia, initial hypersensitivity of otolith afferents) to the cerebellum (changes in Purkinje cell dendritic/synaptic morphology). On earth, two key ground-based models: (i) vestibular peripheral lesion (e.g., labyrinthectomy); and (ii) sensorimotor adaptation have proven essential to our fundamental understanding of the adaptive capacity of vestibular pathways and neural mechanisms that underlie this adaptation.
First, the results of ground-based single unit studies using vestibular peripheral lesions have been essential to furthering our knowledge of how central mechanisms compensate for the sustained reduction in vestibular input. Following unilateral labyrinthectomy, peripheral afferent responses in the intact nerve (i.e., the contralesional nerve) are comparable to control levels when recorded >1 month after lesion (Sadeghi et al., ). As reviewed in “What Are the Implications of Ground-Based Models for Understanding How the Vestibular System Adapts to Microgravity” section above, long-term compensation following vestibular peripheral lesion is mediated by “central strategies” based on the re-weighting of extra-vestibular inputs and updating of internal models. Likewise, as reviewed in “Our Brains Are Wired to Keep Track of Gravity: What Happens to the Vestibular System in Space” section above, in nonmammalian species peripheral afferent responses are comparable to control levels after 5 days in space (Bracchi et al., ) and 24 h after returning to the ground (Boyle et al., ). Thus, this latter result in nonmammalian species is similar to what has been observed in the intact nerve following unilateral labyrinthectomy in ground-based studies of primates. Nevertheless, future studies will be required to establish whether and how the sensitivities of mammalian vestibular afferent sensitives initially change during the first days of space flight.
Second, the findings of ground-based single unit studies of sensorimotor adaptation have provided additional insights into the adaptive capacity of central vestibular pathways that have important implications for space flight. As reviewed above, central vestibular neurons and cerebellar output neurons demonstrated trial-by-trial updating of altered sensory feedback during active head rotations (Brooks et al., ; Cullen and Brooks, ). These experiments establish the neural correlate for a cerebellum-based forward model that computes an estimate of the sensory consequences of voluntary motion (Brooks et al., ). Additionally, recent experiments have further made the important discovery that this forward model also continually accounts for the sensory consequences of gravity during active motion (Mackrous et al., ). Thus we predict that, during space flight and then again following landing, these same cerebellar output neurons show comparable updating to account for changes in the force of gravity.
Together, these findings suggest that the design of more effective countermeasures to maintain crew health and performance could be obtained by optimizing exercises that accelerate these early stages of compensation (i.e., sensory re-weighting and the updating of internal models). Further, reports of improved posture and locomotion after re-entry for more experienced astronauts (e.g., number flights; Bloomberg et al., ; Moore et al., ) suggest that experience influences the ability to rapidly update a vestibular based internal model and has interesting implications for the design of pre-flight training regimes. Future work coupling neuronal recordings with vestibular peripheral lesion and sensorimotor adaptation as well as other established ground-based models, such as centrifugation, will likely provide additional insight into the neural mechanisms that underlie and potentially facilitate adaptation during space flight.
## Conclusions
Most astronauts experience motor and perceptual impairments as well as motion sickness during the first 3–4 days of both space flight and then again after returning to earth (Lackner and Dizio, ). As we reviewed above, there are many reasons to believe that such symptoms occur due to a mismatch between the brain’s internal model of the expected sensory consequences of active behaviors and the actual sensory reafference that is experienced. Most notably, changes in the force of gravity alter the sensory input from the vestibular system, because the absence of gravity results in an unloading of the otoliths during space exploration, and then the reloading of the otoliths again upon re-entry. Such marked changes in otolith input initially produce a mismatch between the brain’s expectation of sensory consequence of head motion and that which is experienced. Accordingly, many flight-based experiments have investigated how the vestibular sensory organs as well as central vestibular neural pathways respond and adapt to the transition from gravity to microgravity and vice versa . For example, increased otoconial mass and hair cell numbers are observed following a week in microgravity, as are central changes in the brainstem and cerebellum. Correspondingly, opposite trends are observed upon re-entry.
Importantly, such space-based investigation has been furthered by single unit studies using ground-based models—including labyrinthectomy and sensorimotor learning—in which the relationship between expected and actual vestibular input is systematically altered. The results of these ground-based experiments suggest that the brain uses two key strategies to adapt to altered gravity: (i) the updating of a cerebellum-based internal model of the sensory consequences of gravity; and (ii) the re-weighting of extra-vestibular information as the vestibular system becomes less (i.e., entering microgravity) and then again more reliable (i.e., return to earth). Both strategies have rapid time courses, with the updating of a cerebellum-based model of the sensory consequences of gravity occurring over a few movements (Brooks et al., ; Mackrous et al., ), and significant sensory re-weighting occurring within 24 h and stabilizing after ~5 days (Sadeghi et al., , , ). Strikingly, it is during this time window that astronauts display motion sickness (reviewed in Carriot et al., ). Accordingly, we propose that further advancing our knowledge of the neural mechanisms that mediate adaptation will have important implications for understanding how to optimize training programs that account for the environmental challenges of astronauts before and after space exploration missions. Finally, we note that multiple factors (e.g., changes in plasma volume, heart rate, maximal muscle power, etc.) in addition to altered vestibular input ultimately contribute to impact crew performance during space flight. Understanding the interactions between changes in vestibular input and these additional stressors and their impact on astronaut performance will be an important direction for future research.
## Author Contributions
JC, KC, and IM: conceptualization and writing of the manuscript. All authors contributed to the article and approved the submitted version.
## Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s Note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
|
Social interaction accounts for a large amount of our daily life and represents the cornerstone of human society (Chen and Hong, ; Matthews and Tye, ). Conversely, social impairments are commonly observed in various neurodevelopmental, neuropsychiatric and neurodegenerative disorders, such as depression, autism, and social anxiety disorder (Hari et al., ). How our brain controls social interaction behaviors and what pathological substrates underlie social deficits in distinct neuropsychiatric disorders are current Research Topics of intense investigation. Here, we present a Research Topic that contains a collection of both original research articles and review articles, highlighting recent progress in the developmental, neuroanatomical, and molecular mechanisms of social behaviors.
For group living animals, social hierarchy is a general feature and of critical importance for survival, health, and reproduction (Zhou et al., ). Social hierarchy is plastic in rodents and is under bidirectional regulation of social experience. For example, previous history of winning experiences increases the probability of victory in subsequent competitions, and this depends on long-lasting plastic changes in the thalamo-prefrontal circuit (Zhou et al., ). Interestingly, in this Research Topic, show that a single losing experience destabilizes hierarchy in adolescent but not in adult mice, suggesting a critical window for experience-dependent plasticity of social dominance hierarchy. Moreover, the development of social hierarchy relies on activity of the prefrontal cortex and is regulated by critical period plasticity in primary sensory cortical areas. This study provides a deeper understanding of the mechanisms underlying formation and maintenance of social hierarchy.
To display social behavior, animals need to instantaneously integrate multiple internal states (e.g., motivation, reward, emotion, and memory) with ever-changing sensory information of different modalities (e.g., visual, auditory, olfactory, and somatosensory). Among these, pheromones serve as an important social cue in rodents (Chen and Hong, ). systematically investigate neuronal inputs to the extraorbital lacrimal glands (ELGs), which are well-known exocrine glands to secrete pheromones (Hirayama et al., ). They identify sex-specific differences in the anatomy and physiology of the ELGs. In addition, viral tracing experiments reveal sex-specific differences in their innervations by the axons from the hypothalamus, olfactory areas, and striatum. These valuable observations provide a structural basis for differences in social behaviors between males and females that may involve the ELGs.
Social intelligence represents important abilities to successfully interact with other people in social activities. An fMRI study conducted by provides new evidence for brain organization underlying social intelligence. They find that the caudate nuclei and the theory-of-mind-related regions play an important role in the maintenance of social intelligence. The level of social intelligence is positively linked to the gray matter volume of the caudate. Moreover, they discuss the potential relevance of these findings to the pathogenesis of mental conditions, such as autism spectrum disorder.
Two articles provide an in-depth review of social interaction in substance use disorder (SUD) and depression, contributed by and , respectively. These articles summarize the current knowledge in the field but focus on different aspects. The relationship between social interaction and the SUD is poorly understood due to its complexity. On one hand, social withdrawal and isolation are hallmark of SUD. On the other hand, positive social interaction is protective against mental illness of patients with SUD. State-of-the-art methodology is essential to investigate the extraordinarily complicated brain network. The comprehensive review by provides a thorough review on the application of miniature fluorescence microscope (miniscope) in dissection of neural circuits underlying social interaction and SUD. Miniscopes offer several advantages in studying behavior-related circuits, including the ability to identify specific cell types and ensembles, image at high temporal resolution, target deep brain regions, and monitor neuronal activity in freely moving animals.
Social deficit represents a core behavioral symptom of depression, which is one of the most prevalent mood disorders worldwide. The pathological causes for depression involve complicated environmental and genetic factors. Epigenetics, defined as changes in gene expression without altering the genome sequence, bridges the environmental and genetic mechanism of depression. summarize major epigenetics modification mechanisms in current literatures and compare epigenetic studies on various rodent models and humans with major depressive disorder. Intriguingly, different epigenetic mechanisms have been linked to several rodent models of depression, such as chronic social defeat stress, chronic variable stress, and early life stress, suggesting diverse epigenetic modulations by a broad range of distinct stressful triggers. Also, depression is a sexually dimorphic disorder, and clinically therapeutic effects vary greatly between men and women. However, neural mechanisms underlying these sex-based differences are not well understood. In this review, aim to elucidate the sexually dimorphic epigenetic factors controlling social deficits in depression. They discuss sex-specific epigenetic regulations in both depressive animal models and patients. These comprehensive discussions advance our understanding of sexually dimorphic and specific mechanisms for social deficits of depression.
Finally, provide a theoretical perspective on social recognition memory, the capacity to allow animals recognize and remember their social partners. Since hippocampus is a brain region well-known for learning and memory, they first focus on hippocampal circuits involved in regulation of social recognition memory. The authors then document the circuit mechanisms underlying how the memory was affected by social isolation. They also extensively discuss cell types and molecules that participate in hippocampal regulation of social recognition memory.
In summary, this Research Topic presents a collection of 6 articles that provide new insights and perspectives into the mechanisms of social interactions and their dysregulations in mental disorders. Moreover, the neuronal and molecular components discussed in these articles may serve as therapeutic targets for social deficits in mental disorders.
## Author Contributions
All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.
## Funding
This work was supported by grants from the National Natural Science Foundation of China (32125018, 32071005, 31900729, and 32171079), the Fundamental Research Funds for the Central Universities and the MOE Frontiers Science Center for Brain Science & Brain-Machine Integration, Zhejiang University.
## Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's Note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
|
There is a strong trend toward increased brain size in mammalian evolution, with larger brains composed of more and larger neurons than smaller brains across species within each mammalian order. Does the evolution of increased numbers of brain neurons, and thus larger brain size, occur simply through the selection of individuals with more and larger neurons, and thus larger brains, within a population? That is, do individuals with larger brains also have more, and larger, neurons than individuals with smaller brains, such that allometric relationships across species are simply an extension of intraspecific scaling? Here we show that this is not the case across adult male mice of a similar age. Rather, increased numbers of neurons across individuals are accompanied by increased numbers of other cells and smaller average cell size of both types, in a trade-off that explains how increased brain mass does not necessarily ensue. Fundamental regulatory mechanisms thus must exist that tie numbers of neurons to numbers of other cells and to average cell size within individual brains. Finally, our results indicate that changes in brain size in evolution are not an extension of individual variation in numbers of neurons, but rather occur through step changes that must simultaneously increase numbers of neurons and cause cell size to increase, rather than decrease. |
We report development of a large-scale spiking network model of the cerebellum composed of more than 1 million neurons. The model is implemented on graphics processing units (GPUs), which are dedicated hardware for parallel computing. Using 4 GPUs simultaneously, we achieve realtime simulation, in which computer simulation of cerebellar activity for 1 s completes within 1 s in the real-world time, with temporal resolution of 1 ms. This allows us to carry out a very long-term computer simulation of cerebellar activity in a practical time with millisecond temporal resolution. Using the model, we carry out computer simulation of long-term gain adaptation of optokinetic response (OKR) eye movements for 5 days aimed to study the neural mechanisms of posttraining memory consolidation. The simulation results are consistent with animal experiments and our theory of posttraining memory consolidation. These results suggest that realtime computing provides a useful means to study a very slow neural process such as memory consolidation in the brain.
## 1. Introduction
Memory formation has two stages: memory acquisition and memory consolidation (Dudai, ). A single session of training forms a type of memory which is fragile and persists only a short period up to minutes to hours. This phase is called memory acquisition. After the training, the learned memory, a short-term memory, decays spontaneously and quickly within a day. Meanwhile, repeated training with a sufficient rest between training sessions gradually form another type of memory, a long-term memory, which is robust and persists for days and weeks. This phase is called memory consolidation. Memory consolidation occurs after training but not during training. That is, when we take a rest after training, the brain still continues working to consolidate the learned memory. This posttraining memory consolidation is thought to be the basis of spacing effect (Ebbinghaus, ), in which a massed training is inferior to repeated training to form a robust long-term memory, even if the total training time is equal. Therefore, it is important to study how the brain works after training as well as during training to elucidate the memory mechanisms and our behaviors.
In cerebellar motor learning, both memory formation and consolidation occur within the cerebellum. In gain adaptation of vestibulo-ocular reflex (VOR) and optokinetic response (OKR), parallel fiber-Purkinje cell (PF-PC) synapses in the cerebellar cortex store short-term memory, whereas mossy fiber-vestibular nuclear cell (MF-VN) synapses in the brain stem store long-term memory (Kassardjian et al., ; Shutoh et al., ). OKR is an oculomotor reflex in which the eye moves to the same direction of the visual world's movement to reduce the slip of the retinal image. In OKR adaptation, the amplitude of eye movement, called gain, changes by training. By a single 1-h training, the gain increases quickly, which corresponds to memory acquisition. After the training, the gain decreases naturally to the original level within a day. By repeating the 1-h training everyday, the gain increases gradually throughout 1 week (Shutoh et al., ), which represents memory consolidation. Moreover, injection of muscimol, a γ-Aminobutyric acid (GABA) receptor agonist, to the cerebellar cortex immediately after the training disrupts memory consolidation (Okamoto et al., ), indicating that training alone is not sufficient for memory consolidation. Accumulating evidence suggests that posttraining memory consolidation of OKR gain takes the following steps (Shutoh et al., ). By a single 1-h training, PF-PC synapses undergo LTD induced by conjunctive activation of PFs and the CF innervated to the same PCs (Ito, ), and thereby the OKR gain increases. After the training, PFs gradually recover from the LTD, which erase the memory of learned OKR gain in the cortex. On the other hand, because inhibition exerted by PCs to VN is weakened due to the LTD, the VN is deporalized tonically. This deporalization, combined with presynaptic MF activation, induces LTP at MF-VN synapses (McElvain et al., ; Person and Raman, ), and thereby forming the memory of OKR gain in the nucleus. In this way, while the cortical memory is erased gradually after the training, the nuclear memory forms simultaneously as a long-term memory, as if the learned cortical memory is transferred to the nucleus and consolidated there.
We have proposed a theory of the cerebellar posttraining memory consolidation in OKR adaptation (Yamazaki et al., ). The theory captures an essence of the macroscopic dynamics of synaptic mechanisms underlying the posttraining memory consolidation. On the other hand, the theory does not provide insights on mesoscopic cellular/synaptic dynamics on the posttraining memory consolidation. For example, the theory does not tell us about spatiotemporal spike patterns of individual neurons. To study the detailed cellular/synaptic dynamics, an elaborated, realistic cerebellar model is necessary. A problem of such elaborated models, however, is that they would spend too much computational time. Typically, computer simulation of large-scale spiking network models is 10–100 times slower than the real-world time (Nageswaran et al., ). This means, if we wanted to carry out a computer simulation of memory consolidation for 1 week, and the computer simulation was 100 times slower than real time, the simulation would spend about 2 years in total to complete. This is practically impossible.
In this study, we adopted high-performance computing (HPC) technology to solve these problems. We used graphics processing units (GPUs) to calculate equations of neurons in parallel, which could speed up the numerical simulation drastically. Specifically, we built a very large-scale spiking network model of the cerebellum composed of 1 million neurons, which is a model of 1 mm of cats' cerebellum. Moreover, owing to the parallel computing on GPUs, we were able to conduct the computer simulation fast enough to complete a very long computer simulation in a practical time, Eventually, we achieved realtime simulation, which means that computer simulation of the cerebellar activity for 1 s completes within 1 s in the real-world time (Igarashi et al., ; Yamazaki and Igarashi, ). This is essential for computer simulation of the cerebellar posttraining memory consolidation, because the memory consolidation takes days or even weeks. Using the present cerebellar model, we performed computer simulation of long-term OKR adaptation of training for 5 days, and obtained qualitatively the same results with experiments (Shutoh et al., ) and our previous theoretical model (Yamazaki et al., ). We also examined the detailed spike patterns of neurons, which was abstracted and therefore ignored in our theory.
## 2. Materials and methods
### 2.1. Model
Our cerebellar model is built based on a 1 mm of the cerebellar corticonuclear microcomplex (Figure ) of cats, which is thought to be a functional module of the cerebellum (Ito, , ). The original model had 100,000 granule cells, which is 10 times smaller than cats' cerebellum (Ito, ), and was already reported elsewhere (Yamazaki and Tanaka, ; Yamazaki and Nagao, ; Yamazaki and Igarashi, ). In this study, we extended the previous model as follows. First, the present model includes 1 million granule cells, thereby the model includes the same number of neurons with 1mm of the cats' cerebellum. Second, the present model has synaptic plasticity at mossy fiber-vestibular nuclear cell (MF-VN) synapses, as well as parallel fiber-Purkinje cell (PF-PC) synapses. Except the number of granule cells and MF-VN synaptic plasticity, the previous and present models are the same. Therefore, we summarize the model specification only briefly below. The details are found in our previous papers (Yamazaki and Tanaka, ; Yamazaki and Nagao, ; Yamazaki and Igarashi, ).
Illustration of the cerebellar circuit implemented in this study . (A) Detailed 3D diagram with locations of synaptic plasticity. We implemented six major types of neurons: granule cells, Golgi cells, PCs, basket cells, inferior olivary cell, and VN. They were connected according to anatomical data, and cell parameters were taken from electrophysiological data. Contextual information and error information were conveyed by MFs and a CF, respectively, and the VN provided the final output of the circuit. PF-PC synapses (blue star) and MF-VN synapses (red star) underwent plastic change. The figure was reproduced from Yamazaki and Igarashi ( ). (B) 2D diagram of the connectivity and the number of neurons. PF-PC synapses (blue star) and MF-VN synapses (red star) underwent plastic change as in (A) . Arroheads represent types of synaptic connections (triangle, excitatory; circle, inhibitory). GR, granule cell; GO, Golgi cell; PC, Purkinje cell; BS, basket cell; VN, vestibular nucleus; IO, inferior olive; MF, mossy fiber; CF, climbing fiber; PF, parallel fiber.
The present model is composed of 1,048,576 (=1024 × 1024) granule cells, 1024 Golgi cells, 16 PCs, 16 basket cells, 1 inferior olivary cell, and 1 VN, connected according to cats anatomical data (Yamazaki and Tanaka, ). Neurons are modeled as conductance-based integrate-and-fire units (Gerstner et al., ):
where u ( t ) is the membrane potential at time t , C is the capacitance, g and E are the conductance and reversal potential of the leak current, respectively, g ( t ), g ( t ), g ( t ) are synaptic conductances of excitatory α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) and N -methyl- D -aspartic acid (NMDA), and inhibitory GABA synapses, E and E are reversal potentials, g ( t ) and E are the conductance and reversal potential of after-hyper polarization, respectively, and I ( t ) is an external current. When u ( t ) exceeds a threshold θ at time t , the neuron elicits a spike at time t . Cell parameters are taken from turtles and rodents electrophysiological data (Yamazaki and Tanaka, ). The values used in this study are summarized in Table . Synaptic conductance g ( t ) for type x is calculated as a convolution of presynaptic spike events with an exponential kernel as
where ḡ is the peak conductance, w is the synaptic weight which is constant, S is the set of spikes elicited by presynaptic cell j , t is the spike time for the f th spike, exp ( t ) is the exponential kernel, and Θ( t ) is the Heaviside step function. The exponential kernels used in the present study are summarized in Table , whereas the synaptic weights are shown in Table .
Summary of cell parameters .
GR, granule cell; GO, Golgi cell; PC, Purkinje cell; BS, basket cell; VN, vestibular nuclear neuron; IO, inferior olivary neuron; -, nonexistent .
Summary of synaptic functions .
Abbreviations as in Table .
Summary of synaptic weights .
Abbreviations as in Table .
The model has two distinct synaptic plasticity sites. One is PF-PC synapses, which undergo long-term depression (LTD) by conjunctive activation of granule-cell axons called parallel fibers (PFs) and a climbing fiber (CF) innervating to the same PC (Ito, ), and long-term potentiation (LTP) as well by sole activation of PFs (Lev-Ram et al., ). We modeled these bidirectional plasticity as follows:
where w ( t ) is the synaptic weight between PC i and PF j , τ and τ are time constants where τ ≪ τ , x ( t ) is an internal variable, c and c are constants, PF ( t ) is 1 if PF j on PC i elicits a spike at time t and 0 otherwise, and CF( t ) is 1 if the climbing fiber elicits a spike at time t and 0 otherwise. The term means that PFs that elicit spikes 0–50 ms earlier than the time when the climbing fiber elicits a spike undergo LTD. If n spikes travel along a PF during 50 ms, the weight change becomes n times c . Transmission delay of PF spikes might be essential for plasticity (Knoblauch et al., ). The conduction velocity of PFs has been experimentally estimated as 0.24 m/s (Vranesic et al., ). This results in the transmission delay of 1 mm PF is maximally about 4.2 ms, which could be negligible as long as we assume 50 ms time window for LTD. Therefore, we did not model transmission delays of PF spikes. On the other hand, we do not exactly describe the biological counterpart of x . A potential interpretation of x would be intracellular concentration of some kinases involving PKC-MAPK positive feedback loop, which plays an essential role in maintenance of induced LTD (Kuroda et al., ). The initial values of w and x were set at 1.0 and 0.0, respectively.
The other plasticity is MF-VN synapses, which undergo bidirectional plasticity by a modified Hebbian mechanism. The original equation was proposed by our previous theoretical model (Yamazaki et al., ) based on Zhang and Linden ( ); Person and Raman ( ); McElvain et al. ( ) as follows:
where τ is time constant, v ( t ) is the synaptic weight at MF-VN synapses at time t , MF( t ) is the activity of MFs, VN( t ) is the activity of VN, 〈·〉 is the temporal average over a certain time window (we assumed 6 s), and θ( t ) is a running average of VN( t ), namely θ( t ) = 〈VN( t )〉. The left-hand side represents the temporal increment of v ( t ). The 1st term in the right-hand side represents LTD by sole activation of MFs, and 2nd term represents the Hebbian mechanism, where the weight change correlates with the correlated activity of pre- and postsynaptic neurons. Here, the term θ( t ) acts as a threshold; only when the postsynaptic neuron is activated strongly to exceed θ( t ), the synapses undergo LTP, otherwise LTD or no change. In this way, θ( t ) determines the direction of synaptic change. Moreover, the value of θ( t ) itself changes temporally depending on the temporal history of VN( t ). Higher θ( t ) value makes the synapse harder to undergo LTP. The initial value of v was set at 1.0. The parameters for w and v are summarized in Table .
Summary of learning parameters .
As far as we have tested, the general network dynamics does not change so largely over a wide range of parameter settings. We have found three points that are necessary to achieve robust learning. First, granule-Golgi cell recurrent network should be tuned so as to generate the population code of granule cells robustly. Second, basket cell → PC synaptic connections should not be so strong; otherwise, PCs would be silent completely. Third, PC → VN synaptic connections should not be so strong; otherwise, VN would be silent completely. If we satisfy these three points, the network, as far as we have tested, works robustly.
### 2.2. Simulation paradigm
We conducted computer simulation of long-term OKR gain adaptation as in Shutoh et al. ( ). Specifically, we repeated a 1-h simulated OKR training followed by 23-h rest 5 times corresponding to 5-days training. In each OKR training, simulated optokinetic stimulus is fed to MFs, and retinal slip is fed to a CF. Both optokinetic stimulus and retinal slip are modeled as Poisson spikes with the following firing rate:
where f ( t ) and f ( t ) are the firing rate of MFs and a CF, respectively, and are the mean activity of MFs and a CF, which are set at 15 spikes/s and 1.5 spikes/s, respectively. T is a period of a cycle of optokinetic stimulus, which is assumed to be rotated sinusoidally in front of animal subjects. We set T = 6 s consistently with the experiments (Shutoh et al., ). Because one cycle is 6 s, daily 1-h training consists of 600 cycles of simulated optokinetic stimulus. On the other hand, after training, we assumed that both MFs and a CF elicited spikes spontaneously with the following firing rate:
where and are set at 5 spikes/s and 1 spikes/s, respectively.
Once we define the firing rate of MF and CF as above, and assume that the activity of a simulated neuron (e.g., firing rate) reflects the strength of input signals to the neuron almost linearly as in the case of integrate-and-fire neurons used in this study (Gerstner et al., ), we could estimate the activity of VN as a linear sum of excitatory MF activity and inhibitory PC activity. The PC activity could be estimated as a linear sum of PF activity and basket cell activity, and further by solely MF activity. By substituting the MF and VN activities for MF( t ) and VN( t ) in Equation (4), we could obtain the following simplified equation for v . The detailed derivation is found in our previous paper (Yamazaki et al., ).
where w ( t ) is the average synaptic weight of all PF-PC synapses, and w is a constant that defines the initial weight of PF-PC synapses, namely, 1.0. We used Equation (7) rather than Equation (4) for simplicity to update v ( t ).
### 2.3. Data analysis
We conducted computer simulation of the 5-days OKR training, and obtained spike data of all individual neurons and synaptic weight data of PF-PC synapses and MF-VN synapses. The total simulation time was 5 × 24 × 60 × 60 × 1000 = 4.32 × 10 ms, with temporal resolution of 1 ms.
We analyzed how the OKR gain changed before and after training for each day. To do so, before training for each day, we fed 10 cycles of simulated optokinetic stimulus to the network and obtained the spike data of VN. We made a spike histogram with bin size of 100 ms, fitted the data with a cosine function with the period of 6 s, and calculated the modulation amplitude. We defined the modulation amplitude as the OKR gain before training. We made the same procedure to obtain the OKR gain after training for each day.
We also examined how the granule cells transmit mossy fiber signals robustly against noise in Poisson spike trains. Granule cells must produce almost identical spike pattern in response to the same optokinetic stimulus with different noise across cycles; otherwise, learning at Purkinje cells would fail. To quantify the reproducibility of the granule cell spike pattern in response to the same simulated optokinetic stimulus, we calculated the reproducibility index at time t defined as the normalized cross correlation as follows:
where is the activity of granule cell j at cycle i of simulated optokinetic stimulus at time t , which was calculated by convolution of the spikes with a causal exponential:
where is the set of spikes elicited by granule cell j at cycle i , t is the spike time for the f th spike, τ is the time constant of 8.3 ms, and Θ( t ) is the Heaviside step function. Intuitively, is a temporal trace of EPSPs of PF j on a PC at cycle i , and τ = 8.3 ms is the time constant of AMPA receptor-mediated PF-EPSPs at a PC (Llano et al., ). We calculated the average and standard deviation of the reproducibility index among 10 pairs of two successive cycles.
### 2.4. Numerical method
All equations that govern the network dynamics are solved numerically. Specifically, differential equations describing membrane potentials are solved by 2nd-order Runge-Kutta method with temporal resolution (Δ t ) of 1 ms. The simulation program is written in C with CUDA (Common Unified Device Architecture) (NVIDIA, ) and most of the calculation is made on GPUs.
In our previous study (Yamazaki and Igarashi, ), we used only 1 GPU (NVIDIA GeForce GTX580) to simulate 100,000 granule cells in realtime. On the other hand, the present model has 10 times more granule cells, which makes computer simulation far slower than realtime. The most time-consuming part is to calculate synaptic conductances of Golgi cells, basket cells and PCs, where these cells receive excitatory inputs from granule cells via PFs. Due to the large number of granule cells, the calculation spends too much time. To address this issue, we decomposed the granular layer network composed of granule cells and Golgi cells into 4 identical subnetworks and calculated the dynamics in parallel on 4 GPUs (2 boards of NVIDIA GeForce GTX TITAN Z, each contains 2 GPUs). In the following, we explain how to decompose the network and calculate the conductance on 4 GPUs.
Figure illustrates a part of the granular layer of our model. The granular layer is composed of 1024 × 1024 granule cells and 32 × 32 Golgi cells arranged regularly on a two-dimensional grid. Granule cells are further divided as 32 × 32 clusters, where each cluster consists of 32 × 32 granule cells. Due to short dendrites of granule cells, we assumed that the granule cells in the same cluster shared inhibitory inputs from the same Golgi cells. On the other hand, granule cells receive 4 excitatory MF inputs. We assumed that granule cells receive 4 MF inputs independently of the other granule cells. This structure allows us to decompose the granular layer network into 4 identical subnetworks composed of 512 × 512 granule cells and 32 × 32 Golgi cells, where granule cells are further divided into 32 × 32 clusters in which each cluster contained 16 × 16 = 256 granule cells as shown in Figure . We conducted simulation of each subnetwork on a GPU, thereby we employed 4 GPUs for simulation of 4 subnetworks. In each subnetwork, we calculated quarter of synaptic conductance for each Golgi cell from granule cells in the same subnetwork. We then exchanged the partial conductances across subnetworks over GPUs and obtained the full conductance by summing up the partial values (Figure ). This is made by direct memory exchange between 2 GPUs called peer access, which is much faster than conventional memory exchange via CPUs. Because calculation of synaptic conductance is linear, our split-reduction method over 4 GPUs provides the same result with the conventional method. The same method is used to calculate synaptic conductances of basket cells and PCs as well.
Decomposition of granule cell population into four subpopulations for parallel simulation on 4 GPUs . (A) Schematic of the model granular layer. Granule cells (black dot), Golgi cells (circle) and fictious glomeluri (hexagon) were arranged regularly on a two-dimensional grid. Each Golgi cell was surrounded by 4 glomeluri, and within the rectangle, 32 × 32 = 1024 granule cells were located and constituted a granule-cell cluster. These granule cells were assumed to share the same inhibitory inputs from Golgi cells and receive excitatory inputs for 4 independent mossy fibers, so the granule cells were functionally identical. (B) Decomposition of granule-cell clusters. We decomposed each granule-cell cluster composed of 32 × 32 = 1024 granule cells into 4 subclusters composed of 16 × 16 = 256 granule cells shown by 4 colors. (C) Calculation of synaptic conductances for Golgi cells, PCs and basket cells from granule cells (left small dots). Each postsynaptic neuron must sum up the postsynaptic potentials of all granule cells with certain synaptic weights. At step 1, for each postsynaptic neuron, a quarter of the synaptic conductance was calculated from granule cells on each GPU, which was illustrated by a pie-shape color where a large circle represents a postsynaptic neuron. At step 2, the calculated partial conductances were reduced between 2 GPUs in parallel to obtain a half of the conductance. At step 3, the partial conductances were further reduced and the full conductance was calculated. (A) was reproduced from Yamazaki and Tanaka ( ).
## 3. Results
### 3.1. Simulation time
First, we measured how the simulation time was accelerated by using multi GPUs. Using only 1 GPU, we found that computer simulation of the cerebellar activity for 6 s, corresponding to 1 cycle of simulated optokinetic stimulus, spends 17.7 s. Using 2 GPUs, 9.10 s are spent. Finally, using 4 GPUs, we achieved 5.33 s for 6 s simulation, indicating realtime simulation. Therefore, we used 4 GPUs for further simulation.
### 3.2. Long-term OKR gain change
We conducted computer simulation of long-term OKR adaptation for 5 days. For each day, we performed a simulated 1-h OKR training. During the training, MFs convey simulated optokinetic stimuli, whereas a CF conveys simulated retinal slip error signals. After the training, both MFs and the CF elicit Poisson spikes spontaneously with a constant firing rate, respectively.
Figure plots the OKR gain obtained in our long-term OKR training simulation for 5 days. By daily 1-h training, the OKR gain increases during training, and after the training, the learned OKR gain almost disappears. This indicates memory acquisition of OKR gain. On the other hand, throughout the 5 days, OKR gain gradually increases, indicating memory consolidation. The present numerical result is qualitatively consistent with previous experimental and theoretical results (Shutoh et al., ; Yamazaki et al., ).
Simulated OKR gain . (A) Modulation amplitude of VN, which corresponded to OKR gain. Horizontal axis represents time (day) and vertical axis the firing rate (spikes/s). For each day, the modulation amplitude increased by 1-h training, and decreased after training until the next training in the next day. Throughout the 5 days, the amplitude gradually increased, suggesting consolidation of memory of learned OKR gain. (B) Increase of OKR gain before and after daily 1-h training. Conventions as in (A) . Although the same 1-h training was performed, the increase became larger day by day.
Figure plots the daily increment of learned OKR gain by 1-h training. The increment becomes larger day by day, suggesting that repeated daily training accelerates the memory acquisition. This result is consistent with previous experiments (Shutoh et al., ).
### 3.3. Change of synaptic weights
Figure plots the change of weights at PF-PC synapses ( w ) and MF-VN synapses ( v ) throughout the 5 days training. For w , we calculated the average of all PF-PC synaptic weights with respect to PFs and PCs. Similarly for v , we calculated the average of all MF-VN synaptic weights with respect to MFs. PF-PC synapses undergo LTD during training, and slowly return to the original weight value after training spontaneously. PF-PC synapses repeat the same temporal change 5 times for 5 days, suggesting that PF-PC synapses store only short-term memory of OKR gain for hours. On the other hand, MF-VN synapses change little during training, and slowly increase after training. The synaptic weight accumulates every day after training, suggesting that MF-VN synapses store long-term memory of OKR gain.
Synaptic weight change . PF-PC synaptic weights (blue) repeated quick decrease during training and slow recovery after training for each day, whereas MF-VN synaptic weight started to increase mainly after training and was accumulated throughout 5 days. Horizontal axis represents training period (day) and the vertical axis represents the weight value. For PF-PC synaptic weights, the average value on all presynaptic granule cells and postsynaptic PCs was plotted.
The overall dynamics is as follows. First, memory of OKR gain is formed in the cerebellar cortex by PF-PC LTD during training. Second, after training, learned cortical memory is decayed slowly and disappears completely by the next day, and finally, during the slow decay of the cortical memory, memory is formed in the vestibular nucleus by MF-VN LTP, as if the cortical memory is transferred to the nucleus and consolidated. The present numerical result is consistent with the previous theoretical results (Yamazaki et al., ).
### 3.4. Change of eye movement trajectory
So far, both the current numerical and previous theoretical studies show qualitatively the same results. A benefit of our numerical study is that we could obtain detailed data of individual neurons such as membrane potential and spike trains with a fine temporal resolution of 1 ms, which were abstracted in our theoretical model (Yamazaki et al., ).
Figure plots the firing rate of VN in response to simulated sinusoidal optokinetic stimulus before and after training at the 1st day (A) and the 5th day (B). The firing rate modulates sinusoidally as the input signals. The modulation amplitude increases by daily 1-h training, and the amplitude also increases gradually throughout 5 days. On the other hand, the baseline firing rate does not change largely from 30–50 spikes/s. Here, the modulation amplitude of VN represents the OKR gain (Shutoh et al., ), suggesting that the OKR gain becomes larger by repeated daily training. These results also suggest that realtime simulation allows us to study both macroscopic behaviors of a neural network such as OKR gain, and mesoscopic dynamics of individual neurons in the neural network such as a membrane potential and spike trains.
Firing rate change of VN . (A) Firing rate in response to simulated optokinetic stimulus modulating sinusoidally in time with the period of 6 s before and after training (blue and red, respectively) in the 1st day. Horizontal axis represents time in ms, and vertical axis the firing rate (spikes/s). The data points are fitted with a cosine function and the fitted curves are also plotted to show the modulation clearly. (B) The same firing rate in the 5th day. Conventions as in (A) .
### 3.5. Robust signal transmission by the enormous number of granule cells
Granule cells must transmit information conveyed by mossy fibers to Purkinje cells and interneurons faithfully against input noise, otherwise, learning at Purkinje cells would fail. In OKR, mossy fibers convey information on visual world movement, and granule cells produce a spatiotemporal spike pattern that represents the stimulus reliably. For this purpose, the almost identical spike pattern of granule cells must be produced across cycles of the optokinetic stimulus.
Here, we examined how the enormous number of granule cells help them to transmit mossy fiber information faithfully and robustly against input noise. Specifically, we calculated the reproducibility index (Equation 8) that quantifies the reproducibility of the spike pattern of granule cells across cycles of the simulated optokinetic stimulus on different cycles, while changing the number of granule cells in the network.
Figure plots an example of the spike pattern of granule cells, whereas Figure plots the reproducibility. As can be seen, the reproducibility is better when 1 million granule cells were employed than 0.1 million granule cells. This result suggests that a functional role of the enormous number of granule cells is robust transmission of mossy fiber signals to PCs against input noise.
Granule cell activity . (A) Spike pattern of 1024 out of 1,048,576 granule cells chosen randomly in response to simulated optokinetic stimulus, which modulated sinusoidally with the period of 6 s. Horizontal axis represents time (ms), whereas the vertical axis the neuron number. (B) Reproducibility of the granule-cell spike pattern across different cycles of the simulated stimulus. The reproducibility with 1 million granule cells (blue) was higher than that with 0.1 million cells (red). Horizontal axis represents time (ms), whereas the vertical axis the reproducibility index. The average values on 10 pairs of cycles were plotted in color, while gray regions represented the standard deviation.
## 4. Discussion
### 4.1. Understanding memory consolidation mechanisms
Memory consolidation is a slow process that takes days and weeks. To study the neural mechanisms of memory consolidation, two ways are possible: either conducting experiments or making theoretical models. A theoretical model is a mathematical description of a specific phenomenon. To make such model, we ignore most of experimental details and capture the essence of the phenomenon. For example, in our theoretical model of posttraining memory consolidation in the cerebellum (Yamazaki et al., ), we abstracted all detailed physiology of individual neurons, detailed anatomical structure, and detailed input stimuli. This provides a clear view of how the memory consolidates after training, but we still do not know the detailed neuronal process during the memory consolidation. Large-scale, realistic spiking network models are appropriate for this purpose, but the computational time would be problematic instead.
HPC technology solves this problem. The advantage is two-folds. First, the technology allows us to build a larger-scale model composed of more neurons and synapses with more detailed morphology and biophysical properties than conventional models. Very large-scale functional brain models have been built (Izhikevich and Edelman, ; Eliasmith et al., ). Notably, The Blue Brain Project and Human Brain Project attempt to build a realistic whole brain model, and they recently published a very detailed cortical microcolumn model (Markram et al., ). Second, the technology allows us to carry out computer simulations much faster than that on a single-threaded CPU. The latter makes the above-mentioned long-term computer simulation possible in a reasonable time. For instance, if the computer simulation runs in real time, a simulation of memory consolidation for 1 week completes in 1 week. In our study, we adopted GPUs. Using our large-scale, detailed spiking network model of the cerebellum implemented on multi GPUs, we were able to simulate the detailed temporal dynamics of individual neurons, while observing the slow memory consolidation process simultaneously. The present study is, as far as the author knows, a first demonstration of a very long time computer simulation of an elaborated spiking network model for days. We were able to replicate our previous theoretical results (Yamazaki et al., ), and further examined detailed neuronal and synaptic dynamics during memory consolidation. In cerebellar motor learning, location of motor memory and the role of LTD at PF-PC synapses have been a matter of debate for more than 30 years (Mellvill-Jones, ). The present study could provide an answer from the modeling view point.
### 4.2. Realtime simulation and the programming
The present cerebellar model consists of more than 1 million spiking neurons. In general, computer simulation of such large-scale model takes very long time. The simulation could be 10–100 times slower than the real-world time (Nageswaran et al., ). However, owing to HPC technology, we were able to conduct computer simulation in realtime, where simulation of cerebellar activity for 1 s completes within 1 s in the real-world time. This allowed us to conduct a complete computer simulation of long-term OKR adaptation training for 5 days in a practical time.
We used 4 GPUs simultaneously to perform realtime simulation of 1 million neurons. To do so, we had to write the simulation program in C with CUDA, a platform for GPU computing, and employed some parallel algorithms to use GPUs efficiently. Specifically, we used some algorithms to compute synaptic conductances of Golgi cells, basket cells and PCs that receive excitatory inputs from many granule cells. This is quite technical and difficult, and so there should be a more simple way to adopt the power of parallel computing in neuroscience. One potential way would be to develop a neural simulator primarily designed for GPUs and some other accelerators. Naveros et al. ( ) has reported development of such a spiking neuron network simulator on a GPU. Some groups have used the software for realtime robot control (Garrido et al., ; Casellato et al., ).
Realtime simulation is only a milestone, and we expect even faster computer simulation. Scalability, however, would be a problem. Generally speaking, using more GPUs would employ more latency for communications and overhead of communication operations, which could easily be a bottle neck.
### 4.3. Advantages of large-scale models over theoretical models
Although the present study reproduced qualitatively the same results with our previous theoretical model (Yamazaki et al., ), some results are slightly different. First, the MF-VN synaptic weight in the present model tends to decay spontaneously, whereas that in the theoretical model did not. This is because in the theoretical model, the decay term was canceled out and removed by a mathematical treatment. In experiments (Shutoh et al., ), the learned long-term OKR gain almost vanishes after 2 weeks from the last training, suggesting that it is natural for the synaptic weight to decay spontaneously. Second, the increase of modulation amplitude of VN before and after the 1-h training gradually becomes larger throughout 5 days in the current study (Figure ), whereas the change is constant in the theoretical model. The same experiments demonstrate that the increase becomes larger gradually day by day. This result suggests that the present large-scale model captures the detailed dynamics of long-term OKR gain adaptation better than the theoretical model.
Moreover, the present model allows us to study the detailed temporal dynamics of individual neurons with a fine temporal resolution. We were able to obtain detailed spike data of PCs and VN, and analyzed the firing patterns as in Figure . This is an advantage of an elaborated spiking network model over theoretical models, which abstract detailed temporal dynamics of individual neurons. We will be able to go into the details of molecular mechanisms of memory acquisition and consolidation (Abel and Lattal, ; Ito, ), if the HPC technology advances further.
We were also able to examine how the number of neurons could affect the stability of the network dynamics. In the present model, we incorporated more than 1 million granule cells, because the cats' cerebellum has 1 million granule cells per 1 mm (Ito, ). The cerebellar granule cells constitute the largest population in the whole brain (Azevedo et al., ). A question arises: why does the cerebellum have such an enormous number of granule cells? A theoretical study has demonstrated that incorporating more granule cells makes the network more reliable for controlling hardware robots (Pinzon-Morales and Hirata, ). In the present study, we demonstrated that the enormous number of granule cells makes signal transmission from MFs to PFs more robust as in Figure .
In summary, combination of large-scale, detailed spiking network models with HPC technology for realtime simulation will provide a strong means to study mesoscopic, detailed neural mechanisms for macroscopic behavioral phenomenon that could take very long time for days and weeks such as memory formation.
### 4.4. Data sharing
We will release the source code of the model used in this study under an opensource license upon publication, to facilitate open collaboration and ensure scientific reproducibility, on Cerebellar Platform ( ).
## Author contributions
TY designed research; MS and TY performed research; MS and TY analyzed data; TY and MS wrote the paper.
## Funding
JSPS Kakenhi Grant Number (26430009) and UEC Tenure Track Program (6F15).
### Conflict of interest statement
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
|
Neurodegenerative diseases (NDs) are becoming a serious public health concern as the world’s population continues to age, demanding the discovery of more effective therapies. Excessive formation of reactive oxygen species (ROS) can result in oxidative stress (OS), which can be regarded as one of the common causes of neurodegenerative diseases (NDs). Thus, in this review, we focus on summarizing the consequences of ROS NDs, while taking the four prevalent NDs as examples, including Alzheimer’s disease (AD), Parkinson’s disease (PD), Amyotrophic lateral sclerosis (ALS), and Huntington’s disease (HD), to illustrate the key signaling pathways and relevant drugs. Together, these findings may shed new light on a field in which ROS-related pathways play a key role; thereby setting the groundwork for the future therapeutic development of neurodegenerative diseases.
## Introduction
Neurodegenerative diseases (NDs) are a diverse set of illnesses characterized by the slow loss of anatomically or physiologically relevant neural systems. They are common causes of morbidity and cognitive impairment in the elderly, including Alzheimer’s disease (AD), Parkinson’s disease (PD), Amyotrophic lateral sclerosis (ALS), Huntington’s disease (HD), etc. (Erkkinen et al., ). Amyloidoses, tauopathies, α-synucleinopathies, and TDP-43 proteinopathies are the most frequent conditions in which proteins exhibit aberrant structural features. Excessive production of reactive oxygen species (ROS) has been reported to play an important role in these protein misfolds (Poprac et al., ). The main sources of cellular ROS are mitochondria and NADPH oxidases (NOXs). In most cells, the mitochondrial electron transport chain (ETC) is one of the most important sources of reactive oxygen species (ROS), with a research reporting that mitochondria generate 45% of ROS while NOXs account for the remaining 40% (Wong et al., ). Under physiological settings, the balance between the production of ROS and the clearance of ROS is extremely tightly controlled. When the delicate equilibrium is disturbed in some pathogenic conditions, including mitochondrial dysfunction, protein misfolding, metal ions dyshomeostasis, and glial cells proliferation and activation (Yeung et al., ), ROS levels rise, resulting in OS which contributes significantly to the degeneration of neuronal cells by interfering with the function of biomolecules (DNA, protein, and lipid; ). As a result, ROS involved in neurodegenerative changes has become a research hotspot.
ROSproduction in neurodegenerative diseases. Protein misfolding, metal ions dyshomeostasis, mitochondrial dysfunction, and glial cell proliferation and activation mainly induce the ROS production in NDs.At the same time, the overproduction of ROS can also affect the four pathological processes (Created with ). ROS, reactive oxygen species; NDs, neurodegenerative diseases.
Thus, ROS regulation has emerged as a promising strategy in the NDs field. So far, none of the FDA-approved small molecule drugs for NDs therapies have a clear mechanism for targeting ROS, and there is an urgent need to figure out the complexity of ROS in NDs and identify potential targets in the ROS-related pathway for therapy requirements. Herein, we focus on summarizing the ROS-regulated molecular mechanisms in NDs and their relevant molecular drugs over the recent 5 years.
## ROS in Neurodegenerative Diseases
Occurring as one of the primary hallmarks of a variety of clinical conditions, oxidative stress (OS) is produced by the unchecked generation of ROS, which promotes severe damage to brain tissue. The functions of ROS in the development of NDs are still unclear. In this review, we describe four categories of common NDs and the potential impacts of ROS in these NDs ( ).
Theroles of reactive oxygen species (ROS) in neurodegenerative diseases.The presence of hallmark protein(s) for each neurodegenerativedisease is a common trait, such as Tau and Aβ in AD,α-synuclein in PD, TDP-43, and SOD1 in ALS, and mHTT in HD. (A) In AD, ROS production serves both as a stimulus and aconsequence of activated Nrf2 via PI3K/AKT/GSK3β,p62, p38 MAPK/NF-κB pathways, which is demonstrated toclosely correlates with AD pathogenesis. Besides, ROS induced ADdevelopment through the inhibition of PP2A/CIP2A and the activation of JNK/P53 pathways. Some corresponding drugs are utilized to reverse this and exhibit some initial effects. (B) In PD, ROS also acts as both stimuli and a consequence of activated Nrf2 via PI3K/AKT/GSK3β, DJ-1, and p38 MAPK/NF-κB pathways. Additionally, ROS can activate c/EBPβ/AEP pathway, which leads to dopaminergic neuronal loss and motor disorders. Some drugs are found to reverse the pathology through the above-mentioned pathways. (C) In ALS, the inhibition of GSK3β is reported to activate Nrf2 via PI3K/AKT pathway. SOD1 is an important gene that is relevant to ROS in ALS that inhibits ROS production. ROS activates IκK/p-IκB/NF-κB pathway to inactivate Nrf2. The activation of SOD1 also regulates IGF1R/mTOR pathway to inhibit autophagy which can eliminate misfolded proteins. Rilmenidine is found to reduce autophagy to alleviate ALS development. (D) In HD, mHTT blocks autophagy, and mHTT and the over production of ROS leads to DNA damage to produce ROS through mitochondrial dysfunction, which results in ALS. Metabolic reprogramming can also induce ROS production which leads to ALS. Finally, pridopidine can be found to inhibit mitochondrial dysfunction to reverse this pathology (Created with ). AD, Alzheimer’s disease; PD, Parkinson’s disease; HD, Huntington’s disease; ALS, amyotrophic lateral sclerosis; SOD1, Superoxide Dismutase 1; TDP-43, TAR DNA-binding protein 43; Nrf2, Nuclear factor erythroid 2-related factor 2.
## Alzheimer’s Disease
AD is one of the most common NDs, impacting 45 million individuals worldwide. Deposition of protein aggregates, including extracellular amyloid plaques (Aβ), intracellular tau (forms nerve fiber tangles), and loss of synaptic connections in specific areas of the brain characterize AD (Knopman et al., ). It has been reported that in the early stage of AD, oxidative damage occurs in the brain before significant plaque pathology develops (Butterfield and Halliwell, ).
Several pathways connecting ROS in AD have recently been uncovered. Nuclear factor erythroid 2-related factor 2 (Nrf2) is a crucial redox-regulated gene in controlling ROS levels, with intranuclear Nrf2 decreased in NDs such as AD (Cores et al., ). Kelch-like ECH-associated protein 1 (Keap1) and antioxidant response element (ARE) are important to Nrf2 pathway. Keap1-Nrf2-ARE can be divided into two parts: the cytoplasm and the nucleus. Under normal circumstances, Keap1 binds with Nrf2 in the cytoplasm and stays in an inactive state, where Nrf2 will be ubiquitinated and then degraded. When stimulated by ROS, the binding of Keap1-Nrf2 is unstable. Nrf2 is released and transferred to the nucleus then binds to ARE and promotes the transcription of downstream genes, leading to the translation of a series of related proteins to exert physiological effects (Osama et al., ). These proteins include heme oxygenase-1 (HO-1), glutathione cysteine ligase modulatory subunits (GCLM), etc. which are antioxidant proteins that can reduce ROS production. Nrf2 can also promote autophagy, which helps remove Aβ aggregates and phosphorylated Tau proteins. When Nrf2 binds ARE, the transcription of autophagy-related genes like Atg5, p62, and Map1lc3b are also upregulated. The inhibition of Nrf2 pathway and the dysfunction of autophagy will in turn cause the accumulation of ROS, senescent organelles, and misfolded proteins (Zhang W. et al., ).
A growing number of studies have proved that the activation of nuclear Nrf2 is affected by phosphatidylinositol 3-kinase (PI3K), Akt, and GSK3β. PI3K is a dimer composed of the regulating subunit p85 and the catalytic subunit p110. When it binds to growth factor receptors, it can alter and activate the Akt protein structure and activate or inhibit a series of substrates downstream by phosphorylation (Vidal et al., ), including the inhibition of GSK3β via phosphorylation at Ser9. GSK3β can phosphorylate Nrf2, causing the Nrf2 nuclear export and degradation (Fão et al., ). As studies have shown elevated GSK3β levels in AD and increased GSK3β activity are directly involved in the degradation of Nrf2, the inhibition of GSK3β may be a possible therapeutic strategy for the treatment of AD. In AD mice, a GSK3β inhibitor called tideglusib can reduce tau phosphorylation, decrease Aβ deposition, and increase astrocyte proliferation (Lauretti et al., ). GLP-1 has been shown to improve AD cognition by alleviating Aβ-induced glycolysis declines in astrocytes to reduce ROS production via PI3K/Akt pathway (Zheng et al., ). Korean black bean anthocyanins, a natural antioxidant neuroprotective compound, reduced synaptic and memory loss and neurodegeneration in an AD model by inhibiting Aβ-induced ROS-mediated OS via the PI3K/Akt/GSK3β/Nrf2 pathway in vitro and in vivo (Ali et al., ). Oxyphylla A, a compound extracted from Alpinia oxyphylla, has been also found to reduce Aβ proteins in SAMP8 mice via the activation of the Akt/GSK3β pathway to activate Nrf2 and reduce ROS (Bian et al., ). Rosmarinic acid (RosA) shows the same effect as Oxyphylla A, and it has been reported to attenuate Aβ-induced cellular ROS generation in PC12 cells (Rong et al., ).
Meanwhile, p62 is an intracellular signaling protein involved in a variety of cellular environments. Several reports have proved that p62 can promote Nrf2 activity by triggering Keap1 (Sánchez-Martín et al., ). The phosphorylation of p62 at ser349 strongly enhances its interaction with Keap1, which results in Nrf2 dissociation and activation (Ichimura and Komatsu, ). Pterostilbene has been reported to activate the Nrf2 pathway by promoting the binding of p62 and Keap1 in SH-SY5Y cells, which results in the downregulation of ROS (Xu et al., ). It has been reported that the activation of the Nrf2-mediated p62 signaling pathway can induce autophagy to reduce the Aβ caused cell death in PC12 cells. Autophagy inhibited ROS generation by facilitating mitochondrial turnover as well (Gu et al., ). The p62 also plays an effective and specific role in the clearance of microtubule-associated protein tau (MAPT) by blocking nerve fiber tangles accumulation and pathological diffusion (Xu et al., ).
Besides, the p38 mitogen-activated protein kinase (MAPK), which was found upregulated in AD, has been proved to reduce the nuclear transfer of Nrf2. Anthocyanin, a subfamily of flavonoids with antioxidation, has been reported to reduce ROS expression through increased Nrf2 and HO-1 protein levels in SH-SY5Y cells via inhibiting p38 MAPK (Amin et al., ). Astaxanthin has also been found to reduce ROS and neuronal death through the p38 MAPK signaling pathway (Zhang X. S. et al., ).
Furthermore, the transcription factor nuclear factor-κB (NF-κB) is regulated in a complex manner. It is a master switch of inflammation that is associated with H O production and is also related to Nrf2 regulation (Sies and Jones, ). Gintonin, a glycolipoprotein fraction isolated from ginseng, has been reported to inhibit p38 MAPK and NF-κB pathways to stabilize Nrf2 to reduce ROS (Choi et al., ). Sulforaphane, another positive modulator of Nrf2, reduces Aβ and ROS via the inhibition of NF-κB. It also decreases pro-inflammatory cytokine expression and p65 activation, resulting in increased protein expression levels of HO-1 (Zhao et al., ).
Apart from the Nrf2 pathway, other mechanisms have been found in the regulation of ROS in AD. For instance, protein phosphatase 2A (PP2A), a ubiquitously expressed serine/threonine phosphatase can be inhibited by ROS. PP2A has been proved to inhibit CIP2A in order to phosphorylate Tau and amyloid precursor protein (APP) in mouse brains. Synthetic tricyclic sulfonamide PP2A activators have been proven to decrease Tau and APP phosphorylation via this pathway (Wei et al., ). Moreover, microRNAs (miRNAs) are small, endogenous, non-coding RNAs that act as regulators in a variety of biological processes. The expression changes in miRNAs may cause diseases. The miR-34c has been found upregulated in AD, with intracellular Aβ aggregation and tau hyperphosphorylation in different regions of the brain, together contributing to cognitive deficits (Bazrgar et al., ). A recent study shows that the upregulated miR-34c participates in the pathogenesis of AD via ROS/JNK/P53 pathway and the inhibition of miR-34c can improve memory decline in AD models (Shi et al., ).
## Parkinson’s Disease
PD is the second most common ND, with a prevalence of more than 6 million worldwide. Neuronal loss in the substantia nigra (SN) is a neuropathological characteristic of PD, which leads to striatal dopaminergic insufficiency and the buildup of α-synuclein in neuronal inclusions. The α-synuclein binds to ubiquitin and forms proteinaceous cytoplasmic inclusions of proteins called Lewy bodies (Zhang K. et al., ). Disturbance of physical process and pathway dysfunction, including OS, defective mitochondria, and cellular calcium imbalances, plays a part in ROS imbalance, which is consequently involved in PD etiology all play a part in the etiology of PD (Aarsland et al., ). Furthermore, due to lower glutathione (GSH) levels, the inherent antioxidant defenses in dopaminergic neurons in SN pars compacta are more vulnerable to ROS than in other parts of the brain (Bjørklund et al., ).
Nrf2 is also the main protein involved in the development of ROS-caused PD, while the Akt/GSK3β/Nrf2 axis is extensively targeted by drugs. Protocatechuic aldehyde (PCA) has been found to perform an effective neuroprotective role in MPTP or MPP generated PD mice (Guo et al., ) by correcting mitochondrial dysfunction and relieving ROS damage via the GSK3β/Nrf2 pathway. In addition, schisandra chinensis (Sch) was reported to reduce GSK3β activity and upregulate Nrf2 in the striatum and hippocampus, block NF-κB nuclear translocation, and ameliorate excessive ROS levels in a 6-OHDA-induced PD model (Yan et al., ). Polydatin has also been reported to prevent dopaminergic neurodegeneration by inhibiting microglia activation through AKT/GSK3β/Nrf2 signaling pathway in lipopolysaccharide (LPS)-induced PD models (Huang et al., ). Moreover, dapagliflozin reduces ROS production in the rotenone-induced PD model via the activation of the PI3K/AKT/GSK3β pathway, which results in the attenuation of neuronal injury (Arab et al., ). The p38 MAPK and NF-κB have also been researched in PD. Overproduction of ROS results in the activation of MAPK and NF-κB pathways, providing links between OS and neuroinflammation. A novel synthetic styryl sulfone and a novel chalcone compound have been published to prove this statement. These compounds activate Nrf2 to produce HO-1, which inhibits the production of ROS and the p38 MAPK and NF-κB mediated neuroinflammation and in PD models, rescues the dopamine neurotoxicity (Lee et al., ; Guo et al., ).
Besides those similar pathways in AD pathology, some extra pathways to affect ROS are found in PD. For example, excessive ROS production can diminish mitochondrial membrane potential (MMP), causing the accumulation of PTEN induced putative kinase 1 (PINK1) and E3 ubiquitin ligase Park 2 (Parkin), which activates mitophagy to reduce ROS by combining sequestosome 1 (p62) and microtubule-associated protein 1 light-chain 3 (LC3; Cui et al., ). The mutation of PINK1 and Parkin may block mitophagy, leading to the accumulation of defective mitochondria, ROS increases, and ultimately to neurodegeneration (Wen et al., ). In the rotenone-induced PD model, the rotenone treatment has been found to activate p38 MAPK which disrupts mitophagy and results in ROS increase. And the ROS inhibitor NAC provided protection by restoring cell death and mitochondrial function in this model (Chen et al., ). In addition to functional interactions with PARKIN, PINK1 can also fight ROS by interacting with DJ-1 as a neuroprotective protein. DJ-1 is a small 20 kDa protein that is highly conserved in different species. It can be oxidized at its cysteine residue under OS, thus acting as a ROS scavenger. DJ-1 also stabilizes Nrf2 to enhance antioxidant response (Zhao et al., ). Interestingly, another study has reported that MEHP upregulated ROS production to activate mitophagy, which increases cytotoxicity as a mechanism of cell death (Xu et al., ). Therefore, ROS should be further studied in the mitophagy-related pathogenesis PD pathogenesis.
Recently, scientists identified TTFA (a complex II inhibitor) and Atovaquone (a complex III inhibitor), which are effective in blocking oxidative phosphorylation, strongly elevating ROS, and activating dopaminergic neuronal cell death through the C/EBPβ/AEP pathway, leading to PD (Ahn et al., ).
## Amyotrophic Lateral Sclerosis
ALS is a progressive, fatal neuromuscular disorder characterized by the degeneration of upper and lower motor neurons leading to somatic muscle dysfunction in the body (Grad et al., ). According to the ALS Association, ALS affects roughly 1 in 50,000 people worldwide each year. ALS is implicated in a range of pathogenesis, such as excitatory toxicity, mitochondrial dysfunction/dysregulation, endoplasmic reticulum stress, neuroinflammation, and OS (D’Ambrosi et al., ). The loss of nuclear TAR DNA-binding protein 43 (TDP-43) function may contribute to the progression of ALS (Tziortzouda et al., ). Importantly, increased ROS has been implicated in the etiology of ALS in a number of studies. ROS markers rise in the postmortem brains of people with ALS, as well as in transgenic animal models (D’Ambrosi et al., ).
In the majority of ALS cases, which are defined as sporadic (SALS), the etiology of the disease is unknown, while 5%–10% of cases are hereditary and categorized as familial (FALS). Chromosome 9 Open Reading Frame 72 (c9orf72), Superoxide Dismutase 1 (SOD1), TDP-43, Fused in Sarcoma (FUS), Optineurin (OPTN), and TANK-binding kinase 1 (TBK1) are among the FALS-related genes. SOD1, is the first recognized gene linked to ALS, whose mutations account for roughly 20% of familial forms of ALS (McCampbell et al., ). Functional SOD1 encodes a Cu /Zn -binding SOD, and converse O to H O and O to protect cells from toxic ROS, while in SOD mice, a classic ALS model, ROS levels increase due to the SOD1 mutation (Xiao et al., ).
Similarly, the Keap1/Nrf2 complex is important in controlling ROS levels in ALS, much as it is in AD and PD. Kirby and colleagues found the first indication of a link between SOD1 and Nrf2 when mut-SOD1 (G93A) reduced Nrf2 mRNA expression in the mouse motor neuron-like hybrid cell line NSC34. The mutant SOD1 models also demonstrate ARE dysfunction which leads to ROS overproduction (Kirby et al., ). As written above, GSK3β and NF-κB are also key factors in ALS. Urate has been proved to decrease ROS via Akt/GSK3β/Nrf2/GCLC pathway to protect motor neurons in the ALS model (Zhang et al., ). Neuronal-specific inhibition of IκB lowers motor neuron loss and reactive glial cells in SOD1 mice and TDP-43 mice by reducing misfolded SOD1 levels and TDP-43 translocation into the nucleus. These findings improved the cognitive impairment of ALS transgenic mice, allowing longer lifespans (Dutta et al., ). In addition, inhibiting NF-κB in microglia and astrocytes can reduce brain and peripheral inflammation, as well as extend mouse survival (Ibarburu et al., ). Under ROS, Nrf2/ARE signaling is a critical protective strategy for cell survival. ROS stimulates IκB kinase (IκK) activation and then mediates IB (NF-κB inhibitor) phosphorylation, increasing proteasome degradation and NF-κB release. However, at the transcriptional level, the two opposing pathways can interfere with each other. Nrf2 reduces ROS-mediated NF-κB activation by boosting ROS-neutralizing antioxidant defenses, which decreases NF-κB pathway activation (Sivandzade et al., ).
Additionally, autophagy is the principal intracellular catabolic route for removing misfolded proteins, aggregates, and damaged organelles that cause aging and neurodegeneration like ALS, in which autophagy is frequently disrupted, leading to cytoplasmic separation of the readily aggregated and toxic proteins in neurons, especially dysfunctional SOD1 to produce ROS. Since autophagy is regulated through mTOR-dependent and -independent mechanisms, mTOR is considered as a key target to rescue the impaired autophagy. Accordingly, increasing levels of ROS were found to cause the reduction of mTOR in the larval brain (Chaplot et al., ). And the stimulation of the mTOR system in mutant SOD1 astrocytes is caused by post-transcriptional overexpression of IGF1R (insulin-like growth factor 1 receptor), an upstream positive modulator of the mTOR pathway, according to a recent study. Astrocytes with mutant SOD1 are less toxic to motor neurons when the IGF1R-mTOR pathway is inhibited (Granatiero et al., ). Rilmenidine has been reported to induce autophagy in mutant SOD1 mice, which results in the downregulation of SOD1 and ROS reduction (Perera et al., ).
## Huntington’s Disease
A genetic ailment characterized by movement abnormalities and cognitive deterioration, Huntington’s disease (HD) is inherited in an autosomal dominant manner. Symptoms of HD include a general shrinking of the brain and degeneration of the striatum (caudate nucleus and putamen), as well as the loss of efferent medium spiny neurons in the striatum (caudate nucleus and putamen; MSNs). These symptoms may be related to the widespread expression of mutant huntingtin (the toxic protein that causes HD) in HD patients’ bodies (Jimenez-Sanchez et al., ).
ROS and mitochondrial dysfunction have a role in the neuronal degeneration of HD. A genome-wide association study (GWAS) containing 6,000–9,000 patients identifies DNA repair related genes as major modulators of age at onset and disease severity, with some pathways connected to redox signaling and mitochondrial function. In the presence of ROS, huntingtin works as a scaffold that can localize to DNA damage and modifies its associated complex (Maiuri et al., ). Other research demonstrates that huntingtin is engaged in a variety of mitophagy processes. The existence of the polyglutamine tract in mutant huntingtin alters the formation of these protein complexes and determines mutant huntingtin’s deleterious effects on mitophagy, which result in a buildup of damaged mitochondria and an increase in ROS. In HD, these alterations lead to overall mitochondrial dysfunction and neurodegeneration (Franco-Iborra et al., ). Pridopidine has been reported to increase mitochondrial respiration and reduce ROS in HD models (Naia et al., ). ROS has also been linked to Huntington’s disease-associated region-specific cell death. Studies have demonstrated that mitochondria can undergo metabolic reprogramming by utilizing fatty acids as a source of energy, causing ROS-induced damage in the vulnerable striatum (Polyzos et al., ).
## Conclusions
In this review, we provided an illustration of the responsibilities of ROS in NDs and summarized some related drugs for potential therapeutic purposes. ROS is primarily produced by mitochondria and NOXs, which can cause OS. Some of the intricate mechanisms in which ROS can contribute to the development of NDs have been elucidated, with Nrf2 as the central regulator of ROS in NDs, while other cellular processes such as mitophagy, and neuroinflammation play their crucial roles in controlling ROS in NDs. For instance, in AD, Nrf2 and its related pathways are widely reported. In PD, the influence on mitophagy PINK1/PARKIN pathway has also been further discussed. In ALS, the relationship between SOD1 and ROS has been illustrated, and in HD, ROS has been suggested to affect metabolic reprogramming. However, most of these studies, especially those related to HD, lack in-depth investigations on the specific role of ROS in NDs, when only covering the measurement of the alterations of ROS, which can be a consequence or concomitant phenotype in response to NDs. Thus, more detailed relationships between ROS and the occurrences of NDs should be further exploited.
Moreover, in the concept of alleviating OS caused by ROS, some nutritional factors (e.g., resveratrol and curcumin) that act as antioxidants have been guided to treat NDs. But the use of these antioxidants to control and prevent NDs remains unsatisfactory. The reason for this is that most antioxidants have osmotic limitations due to their inability to pass the blood-brain barrier. Nanoparticles may prove to be an effective vehicle for delivering these medications to the central nervous system with the advancement of nanotechnology. We propose that, rather than using scavengers, direct regulation of ROS production from specific sources with targeted drugs should be used to avoid or limit oxidative damage in neurodegeneration. It has been proven that several ND-related genes/proteins like Nrf2, GSK3β, p38 MAPK, etc., are involved in the regulation of the ROS pathway. By focusing on specific ROS-mediated signaling pathways, we can anticipate the development of more refined redox drugs. Direct inhibition of an enzyme, increased endogenous antioxidants, or increased energy production, will be a promising direction for future therapeutic purposes in NDs.
## Author Contributions
YZho and YZhe wrote the manuscript. GW and BL designed and supervised this article. All authors contributed to the article and approved the submitted version.
## Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s Note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
|
One of the most promising avenues for compiling connectivity data originates from the notion that individual brain regions maintain individual connectivity profiles; the functional repertoire of a cortical area ("the functional fingerprint") is closely related to its anatomical connections ("the connectional fingerprint") and, hence, a segregated cortical area may be characterized by a highly coherent connectivity pattern. Diffusion tractography can be used to identify borders between such cortical areas. Each cortical area is defined based upon a unique probabilistic tractogram and such a tractogram is representative of a group of tractograms, thereby forming the cortical area. The underlying methodology is called connectivity-based cortex parcellation and requires clustering or grouping of similar diffusion tractograms. Despite the relative success of this technique in producing anatomically sensible results, existing clustering techniques in the context of connectivity-based parcellation typically depend on several non-trivial assumptions. In this paper, we embody an unsupervised hierarchical information-based framework to clustering probabilistic tractograms that avoids many drawbacks offered by previous methods. Cortex parcellation of the inferior frontal gyrus together with the precentral gyrus demonstrates a proof of concept of the proposed method: The automatic parcellation reveals cortical subunits consistent with cytoarchitectonic maps and previous studies including connectivity-based parcellation. Further insight into the hierarchically modular architecture of cortical subunits is given by revealing coarser cortical structures that differentiate between primary as well as premotoric areas and those associated with pre-frontal areas. |
Morphometric analysis of neurons and brain tissue is relevant to the study of neuron circuitry development during the first phases of brain growth or for probing the link between microstructural morphology and degenerative diseases. As neural imaging techniques become ever more sophisticated, so does the amount and complexity of data generated. The NEuronMOrphological analysis tool NEMO was purposely developed to handle and process large numbers of optical microscopy image files of neurons in culture or slices in order to automatically run batch routines, store data and apply multivariate classification and feature extraction using 3-way principal component analysis (PCA). Here we describe the software's main features, underlining the differences between NEMO and other commercial and non-commercial image processing tools, and show an example of how NEMO can be used to classify neurons from wild-type mice and from animal models of autism. |
The aim in high-resolution connectomics is to reconstruct complete neuronal connectivity in a tissue. Currently, the only technology capable of resolving the smallest neuronal processes is electron microscopy (EM). Thus, a common approach to network reconstruction is to perform (error-prone) automatic segmentation of EM images, followed by manual proofreading by experts to fix errors. We have developed an algorithm and software library to not only improve the accuracy of the initial automatic segmentation, but also point out the image coordinates where it is likely to have made errors. Our software, called gala (graph-based active learning of agglomeration), improves the state of the art in agglomerative image segmentation. It is implemented in Python and makes extensive use of the scientific Python stack (numpy, scipy, networkx, scikit-learn, scikit-image, and others). We present here the software architecture of the gala library, and discuss several designs that we consider would be generally useful for other segmentation packages. We also discuss the current limitations of the gala library and how we intend to address them. |
Despite the great strides neuroscience has made in recent decades, the underlying principles of brain function remain largely unknown. Advancing the field strongly depends on the ability to study large-scale neural networks and perform complex simulations. In this context, simulations in hyper-real-time are of high interest, as they would enable both comprehensive parameter scans and the study of slow processes, such as learning and long-term memory. Not even the fastest supercomputer available today is able to meet the challenge of accurate and reproducible simulation with hyper-real acceleration. The development of novel neuromorphic computer architectures holds out promise, but the high costs and long development cycles for application-specific hardware solutions makes it difficult to keep pace with the rapid developments in neuroscience. However, advances in System-on-Chip (SoC) device technology and tools are now providing interesting new design possibilities for application-specific implementations. Here, we present a novel hybrid software-hardware architecture approach for a neuromorphic compute node intended to work in a multi-node cluster configuration. The node design builds on the Xilinx Zynq-7000 SoC device architecture that combines a powerful programmable logic gate array (FPGA) and a dual-core ARM Cortex-A9 processor extension on a single chip. Our proposed architecture makes use of both and takes advantage of their tight coupling. We show that available SoC device technology can be used to build smaller neuromorphic computing clusters that enable hyper-real-time simulation of networks consisting of tens of thousands of neurons, and are thus capable of meeting the high demands for modeling and simulation in neuroscience.
## 1. Introduction
In the process of gaining insight into the underlying principles of neural computation, the tools and methods developed and provided by computational neuroscience play a key role. In particular, we rely on the mathematical modeling of neuron, synapse, and neural network models and their numerical simulation to study their complex interaction and network dynamics. Community software for modeling, such as NeuroML (Gleeson et al., ), NMODL (Hines and Carnevale, ), and NESTML (Plotnikov et al., ), and for simulation, such as NEURON (Hines and Carnevale, ), Arbor (Akar et al., ), NEST (Gewaltig and Diesmann, ), and Brian (Goodman and Brette, ) provide such tools. They are complemented by numerical tools for statistical analysis, such as the Electrophysiology Analysis Toolkit Elephant as well as tool support for model validation methodologies, for example, the validation framework NetworkUnit (Gutzen et al., ). The requirements in regard to efficiency, correctness, and replicability and reproducibility of the outcomes place high demands on the whole software ecosystem.
When investigating large scale networks, in general one would like to simulate them as fast as possible. Whereas, real-time simulation is interesting because of the possibility of interacting with real-world applications, hyper-real-time would enable the study of slow processes, such as structural plasticity and long-term memory, and permit researchers to perform more comprehensive parameter scans of faster processes. This is still a major technical challenge (Friedmann et al., ), and not even the fastest supercomputer available today is up to the task.
Consequently, neuromorphic computing and application-specific novel hardware architectures are very attractive as they promise significant acceleration. However, the technical hurdles to making neuromorphic computing a useful tool for neuroscientists are not insignificant either. Crucially, flexibility and efficiency, which are both required for such a system, are opposing goals in the choice of technology (e.g., GPP , FPGA or ASIC ; Noll et al., ). Optimal flexibility is achieved with traditional general purpose processors. The SpiNNaker system (Furber et al., ) is an example for a neuromorphic massively parallel computing platform that is based on digital multi-core chips using ARM processing cores. It is fully programmable, thus flexible in the choice and implementation of the numerical models, and allows large-scale simulations to be performed in real-time. The Heidelberg BrainScaleS system (Schemmel et al., ) and its successor BrainScales-2 (Pehle et al., ), in contrast, are capable of running simulations orders of magnitude faster than real-time. To achieve this, the architecture builds on the physical, i.e., analog, emulation of neuron and synapse models (Schemmel et al., ) in dedicated mixed-signal circuits combined with digital plasticity processors (BrainsScaleS-2) using a “hybrid plasticity” scheme (Friedmann et al., ). Physical, analog emulation thereby restricts the system to its built-in, “silicon-frozen” analog models, and use-cases where technology-related effects, such as fabrication tolerances and thermal noise, are acceptable.
During recent years, programmable device technology and tools have greatly increased in functionality, benefiting from the continued advances in semiconductor technology. Modern field programmable gate arrays (FPGAs) provide a large number of chip resources (e.g., logic cells and memories) allowing to implement complex hardware designs at affordable costs. High-level synthesis (HLS) tools allow the developer to generate hardware implementations from algorithmic descriptions, thus reducing development time and making the technology accessible to non hardware experts. Although the design effort remains high, programmable device technology offers a good compromise between flexibility and efficiency and has therefore been widely recognized as potentially well-suited to neural network simulation. This has been exploited by a number of digital neuromorphic architectures developed in recent years.
In an earlier study, Maguire et al. ( ) made an inventory and revealed the challenges associated with implementing large-scale spiking neural networks on FPGAs, emphasizing the importance of design decisions on system level and its impact on the final performance. Since then, a number of architectural approaches and implementations for different use cases have been published. A scalable modular architecture for closed-loop experiments with in vitro cultures is presented in Pani et al. ( ). The platform is able to simulate small-to-medium size networks in real-time, implementing 1,440 Izhikevich neurons. Bluehive (Moore et al., )—a scalable custom 64-FPGA machine—is dedicated to the simulation of large-scale networks with demanding communication requirements. On a single FPGA, Bluehive can simulate 64,000 Izhikevich neurons in real-time. NeuroFlow (Cheung et al., ) is a platform that builds on top of Maxeler's Dataflow Engine (DFE) technology. A 6-FPGA system can simulate a network of 600,000 neurons. Real-time performance is achieved when simulating a network consisting of 400,000 neurons. The simulation of a plastic 1,000 neuron two-population Izhikevich model for 24 h biological time can be completed in 1,435 s, thus achieving a ~60-fold acceleration. The platform supports several neuron and synapse model types and a spike time dependent plasticity (STDP) rule. NeuroFlow also provides a PyNN interface (Davison et al., )—a common Python interface for neural network simulators. In Wang et al. ( ) and Wang et al. ( ), an architecture is proposed that uses a procedural “on-the-fly” generation scheme for parameters and connections and is able to simulate 20 million to 2.6 billion leaky integrate and fire (IAF) neurons in real-time on a single Stratix V FPGA.
Such large scales come at a price and can only be achieved by accepting limitations regarding functionality, model complexity and simulation accuracy. These limitations may well represent acceptable trade-offs for the intended specific use cases, but can be severe with respect to the requirements of a platform for general neuroscience simulations. For example, in order to save hardware resources and reduce both computational costs and the amount of data to be processed, hardware implementations often use a large update interval of h = 1 ms to progress neuron model dynamics (e.g., Moore et al., ; Cheung et al., ; Wang et al., ). This is 10 times larger than the de facto standard used in digital simulations, and comes at the cost of numerical accuracy, especially for neuron models with stiff equations (Hansel et al., ; Morrison et al., ; Blundell et al., ; Pauli et al., ). A further commonly-used trade-off with similar advantages and disadvantages is to represent neuron state variables in a low-precision fixed-point data format (e.g., Moore et al., ; Wang et al., ). It has been shown, for example, that the accuracy of the numerical integration of the Izhikevich neuron model dynamics is insufficient when a s16.15 representation, i.e., a 32-bit signed fixed-point data format is used (Gutzen et al., ; Trensch et al., ). Model complexity is reduced in the architecture proposed in Wang et al. ( ) and Wang et al. ( ) where individual synaptic connection delays are replaced by an axonal delay, thus avoiding the large memory structures and computational costs required to delay and accumulate incoming spike events.
These examples clearly demonstrate that it is challenging to reach design decisions that are simultaneously performant and flexible. The plethora of neuron and synapse models makes it difficult to come to design decisions that satisfy all requirements equally. There are also many questions relevant for the design which still lack an unambiguous answer and thus keep design decisions in a state of uncertainty. One example is the required numerical precision, which determines the specification of data types and the implementation of arithmetic operations—a design decision that effects implementation complexity, chip area and power efficiency. So far, only a few studies have examined the effects of numerical accuracy on simulation outcomes (e.g., Pfeil et al., ; Trensch et al., ; Dasbach et al., ).
Promising new design possibilities are also enabled by the integration on a single chip of FPGAs together with processor cores and other components to System-on-Chip (SoC) devices. This paves the way toward novel hybrid software-hardware approaches for application-specific implementations and new neuromorphic computing systems, such as the IBM Neural Computer INC-3000; a highly scalable parallel processing system. A single-cage system clusters 432 Xilinx Zynq SoC devices in a high bandwidth 3D mesh communication network (Narayanan et al., ). The system is highly flexible and applications can off-load algorithms and accelerate them using the programmable logic of the Zynq SoC devices. An example of such an application is the implementation of the cortical microcircuit model (Potjans and Diesmann, ) on the INC-3000 presented in Heittmann et al. ( )—a reproduction of an equivalent NEST implementation and on the SpiNNaker neuromorphic system (cf. van Albada et al., ). The model consists of 0.8 · 10 neurons and 0.3 · 10 synaptic connections, was implemented in HLS, and utilizes 305 FPGAs. The simulation achieves an approx. four times speed-up compared with the biological time domain.
In this article, we introduce a novel SoC-based hybrid software and hardware mixed architecture approach for a neuromorphic compute node (henceforth HNC node ) which is intended to work in a multi-node cluster configuration and capable of meeting the high demands for modeling and simulation in neuroscience. The development builds on the Xilinx Zynq-7000 SoC device architecture (Xilinx, ) and takes advantage of the tight coupling of a powerful FPGA device and a dual-core ARM Cortex-A9 processor core. The primary goal of the development is to provide a flexible platform for the accelerated simulation of neural network models which may consist of up to a few tens of thousands of neurons, a scale which covers the vast majority of current spiking neural network modeling studies. With the neuroscience requirement-driven design of the HNC node architecture, our development is to be seen as a complementary yet distinct approach to the neuromorphic developments aiming at brain-inspired and highly efficient novel computer architectures for solving real-world tasks.
We show that such a system can indeed be built, and that acceleration factors with respect to real-time in the order of 10–50 are realistically achievable for moderate workloads, with even higher factors possible for low workloads. We further demonstrate that the use of workload and performance models allow us to predict the performance characteristics of such a system under varying assumptions regarding workload and hardware design choices, some of which showing great potential as a substrate for neural simulations.
This article is organized as follows. Section 2 first gives an overview of the HNC node high-level architecture and the main design ideas. Section 3 presents the results of our performance measurements and an evaluation of the performance characteristics. A detailed presentation of the HNC node hardware and software architecture can be found in Section 4, with a focus on microarchitecture details critical to performance. In Section 5, we develop a workload and performance model to understand the performance characteristics of the HNC node and predict them for alternative assumptions in design space.
## 2. Overview of the Hybrid Neuromorphic Compute (HNC) Node
The HNC node architecture concept combines software-based and hardware-based implementations for the building blocks of a neural network simulation engine, and tightly couples both implementation types on a single chip; specifically, on a device of the Xilinx Zynq-7000 SoC family (Xilinx, ).
The underlying algorithms and the functional principle of the HNC node concept do not differ from those that are typically used in pure software implementations for time-discrete neural network simulations of point neuron models. It follows a hybrid strategy where neuron states are updated synchronously, time-driven, and at fixed intervals (e.g., Δ t = 0.1 ms) and synapses are updated asynchronously and event-driven, triggered when a synapse's presynaptic neuron emits a spike (Morrison et al., ).
While it is sufficient to implement performance non-critical tasks in software and let them be executed by general purpose processors, the performance-critical algorithms profit from mapping them to hardware. Non-critical tasks are, for example, the processes of node configuration, operation and simulation control, data type conversion, network instantiation, and user interaction. Critical to performance and simulation efficiency are the spike events processing and presynaptic data distribution, and the neuron and synapse model computations. The algorithms implemented in hardware bring the data and the operations performed on them close together and can thus alleviate problems which are inevitable with conventional systems, such as the von Neumann bottleneck.
shows the HNC node high-level architecture concept, which consists of three main components: (i) an off-chip external memory (top); (ii) an application processing unit (APU; middle); and (iii) a programmable logic part (PL; dashed box). A more detailed description of the high-level system architecture and the microarchitecture is given in Section 4.1.
Hybrid neuromorphic compute (HNC) node high-level architecture. The highest architectural level of the HNC node comprises three main components: an off-chip external memory (top), an application processing unit (APU; middle), and a programmable logic part (PL; lower dashed box). In order to distribute the workload and parallelize operations, the PL implements 16 identical processing units (P1, P2,.., P16). The red and blue arrows indicate two distinct processes that are critical to performance and primarily determine the performance characteristics and achievable acceleration factors. Red arrows: the process of the neuron and synapse model state update performed by the ordinary differential equations solver pipelines (ODE pipelines) which operate on fast on-chip block RAM memories that constitute the state variables buffer (SVBs). Blue arrows: the process of the presynaptic data distribution and processing which hold the data it operates on in the slow external off-chip memory.
Both the APU and the PL are connected to the off-chip external memory. It contains the node control software (Section 4.2) which is executed by the APU orchestrating the overall node operation, and also holds the node-local connectivity data of the neural network being simulated and buffers the recorded spike data. Storing the connectivity data in a slow, external memory is one of the decisive performance limiting factors of the system. This aspect is discussed in detail Section 4.3.1. However, there are two important factors leading to this design decision. The first is a functional requirement: even though the current development does not yet include plasticity, in order to be able to cope with synaptic and structural plasticity algorithms in future, the synaptic connections must be stored, accessible, and changeable. In contrast, for static networks, performance-efficient solutions have been developed which makes use of a procedural connectivity generation approach (Knight and Nowotny, ; Heittmann et al., ) where the synaptic connections are determined algorithmically during the simulation, thus avoiding having to retrieve them from memory. The second is a resource constraint due to technical limitations of the technology: fast, low-latency, on-chip block RAM (BRAM) would be ideal to hold this data, but BRAM is a limited FPGA resource and the memory requirement for storing a network's connectivity data is demanding. For example, given a 64-bit data item to represent a single connection, a natural dense network, such as the cortical microcircuit model (Potjans and Diesmann, ) comprising 0.8 · 10 neurons and 0.3 · 10 connections requires 2.4 Gbyte of memory in total. That is 24 Mbyte per compute node if a single node processes 10 neurons. The Xilinx Zynq-7000 SoC device used in this work provides only 19.2 Mbit of BRAM, i.e., 10 times less than required.
The PL, i.e., the FPGA part of the SoC, implements 16 identical hardware processing units (P1, P2,.., P16). Each is capable of carrying out the computations for N = 64 neurons. This allows a total of N = 1,024 neurons to be processed on a single chip or HNC node, respectively. The PL and APU are closely coupled through high performance streaming and memory mapped interfaces which allow an efficient data exchange between the two parts. The PL is also directly connected to the off-chip external memory, thus enabling APU-independent memory read and write operations.
Each processing unit processes its 64 neurons in a pipeline fashion, updating the neuron states at fixed intervals of Δ t = 0.1 ms. The neuron states y are thereby held in state vector buffers (SVB) which are implemented as fast block RAM (BRAM) memories on the PL. The associated data paths of this time-driven process are indicated in by the red arrows.
The blue arrows in mark the data paths involved in the event-driven presynaptic data processing. The post-synaptic spike events (up to 16 spike events can occur in parallel at a time; one per processing unit) are serialized and packed for communication and recording. This is handled by the spike events processing module. If a spike event occurs, it initiates read operations from external memory to obtain the network's connectivity data, i.e., the node-local synaptic connections of the firing neuron, from which the synaptic inputs are derived. The presynaptic data distribution module parallelizes this data and delivers the synaptic inputs to the processing units (P1, P2,.., P16); this is indicated by the dashed blue lines in , thereby distributing the workload generated by the incoming presynaptic spike events. The ring buffers (RB) implement the synaptic transmission delays and store the accumulated synaptic inputs, i.e., the lumped excitatory i and inhibitory i values. Since the number of synapses by far predominates, the whole process of presynaptic data distribution and processing is critical to performance.
## 3. Results
### 3.1. Single Node Performance
In the following, we consider an isolated HNC node that is not embedded in a multi-node system for which otherwise inter-node communication and synchronization latencies cannot be ignored. For an isolated node, the previously explained two distinct processes will exclusively determine performance where the neuron state update process (red arrows in ), and the process of presynaptic data distribution and processing (blue arrows in ), contribute to different performance relevant aspects. In Section 5.2, a performance model is presented that is based on the HNC node microarchitecture implementation details explained in Section 4.3. By additionally taking communication latencies, inevitably occurring in a multi-node system, into account, the model will also allow conclusions to be drawn about the acceleration factors achievable for larger network sizes and workloads.
The current HNC node design implements N = 1024 neurons and allows C = 128 target connections per source neuron and node. This is in agreement with a connection probability value of approx. ϵ = 0.1 observed in Braitenberg and Schüz ( ). Note that the possible number of a source neuron's target connections is not restricted to the value of C . It scales linearly with the number of HNC nodes M in a cluster, i.e., it yields MC . A typical cortical neuron connects to between 1,000 and 10,000 other neurons. Consequently, a network of N = 10 where each neuron has 10 connections represents an upper limit with regard to memory requirements and workload; beyond this, the total number of synapses in a network scales linearly rather than quadratically.
In order to evaluate the HNC node's capability to perform in different workload situations, we investigate a two-population network model consisting of 1,000 neurons (see Section 5.3). We measure the time to simulate the network and calculate the acceleration factor as the quotient of the measured simulation duration in wall clock time and the simulated biological time of 300 s. We systematically vary the external input current from i = −3.0 pA to 100.0 pA. The increasing external offset current causes the network to run through a wide range of activity, from quiescence up to an average firing rate of and thus an increase in the workload. According to the workload model described in Sec. 5.1, this results in an average number of spike events per simulation time step ( h = 0.1 ms) ranging from to .
The result of the HNC node performance measurement is shown in . For comparison, shows the results for the same model implemented in NEST 2.20.1 (Fardet et al., ) and executed on an Intel(R) Core(TM) i7-7700K CPU 4.20 GHz (Kaby Lake architecture). If the workload is in the range of a few spike events per simulation time step, the HNC node outperforms the NEST simulation on the Intel Kaby Lake CPU, and this even at ~4.5 W power consumption (see the power report given in the )—with the Intel Kaby Lake CPU, a power consumption of several tens of Watts is to be expected. If the external current is set to zero, the network fires with an average rate of , which corresponds to a number of spikes per time step of . For this workload, the acceleration factor achieved for the NEST simulation is 8.4 compared to a factor of 127.0 measured for the HNC node. The NEST simulator used for the comparison is a runtime-optimized and flexible tool for a wide range of neural network simulations and as such, is a good reference in this regard. Clearly, a CPU-optimized implementation of the specific network model can achieve even better results . However, the difference in performance and efficiency is such that the HNC node performance is beyond the reach of any CPU implementation. At low workloads, the hardware implementation can fully utilize its capabilities. Pipelining and the parallelization of operations increases throughput and reduce latencies. This is mainly to be ascribed to the process of the neuron state update, indicated by the red arrows in . Its implementation benefits from data-locality that is achieved by storing variables in fast, low-latency on-chip BRAM memories.
Performance as a function of workload for the HNC node and NEST. The acceleration factor (wall clock time divided by the biological time) as a function of the average number of spike events per simulation time step of the HNC node using a PL clock frequency of f = 200 MHz (A) and the neural simulation tool NEST on an Intel(R) Core(TM) i7-7700K CPU 4.20 GHz (Kaby Lake architecture) (B) . The measurements were carried out with h = 0.1 ms simulation resolution. In consecutive simulation runs of 5 min simulated biological time, the 1,000 neuron two-population Izhikevich neural network model described in Section 5.3 was stimulated with an increasing external offset current i = {−3.0 pA, .., +100 pA}. Inset in (A) gives a log-lin representation.
As the workload increases, the NEST implementation undergoes a moderate degradation in performance. In contrast, the performance deteriorates rapidly on the HNC node. This is a trivial consequence of the data access latency and limited bandwidth of the external memory decelerating the process of the pre-synaptic data distribution and processing (marked by the blue arrows in ), which now dominates operation. This is examined in greater detail below for different hardware design choices. Moreover, the measurements of the single HNC node and CPU core performances only give an upper baseline. For the simulation of larger networks on multi-node or many-core systems in the following we examine the effect on performance of the additional latencies arising from synchronization and communication.
### 3.2. Performance Characteristics
Based on the HNC node microarchitecture (Section 4.3) and their operating latencies (Section 4.3.3) a performance model is developed in Section 5.2. This model is used in the following to evaluate the performance characteristics of the HNC node as a stand-alone compute node and when operating in a cluster configuration. In order to verify the correctness of the performance model, we repeated the measurements carried out in the previous section using three different PL clock frequencies f = 100/150/200 MHz. The results are shown in where the blue markers indicate the measured acceleration factors and the gray curves are calculated from Equation (7) of the performance model. The predicted acceleration factors are in almost perfect agreement with the measured values.
Performance as a function of PL clock frequency and workload for the HNC node and NEST. (A) Measured acceleration factors of the HNC node (blue markers) as a function of workload for three different clock frequencies in log-lin (A1) and linear (A2) representation. Gray curves show the predictions of the performance model (Section 5.2). (B) As in (A) , but comparing the performance of the HNC node running at a PL clock frequency of f = 200 MHz to that of a NEST implementation using one or four threads on an Intel(R) Core(TM) i7-7700K CPU 4.20 GHz.
The results also reveal that as the workload increases, the achievable acceleration factor is increasingly determined—and thus limited—by external memory access times (i.e., by the term in Equation 7). This can not be compensated by a higher PL clock frequency. However, an acceleration factor of ~100 is achieved for moderate workloads, i.e, , h = 0.1 ms. Such a workload is created, for example, by a network consisting of N = 5,000 neurons with an average firing rate of .
compares the HNC node measurements at a PL clock frequency of f = 200 MHz to the equivalent simulation in NEST on a four-core Intel CPU. At low workloads, the HNC node is an order of magnitude faster than the NEST/CPU implementation. Even at high workloads, the HNC node still simulates substantially faster than a single state-of-the-art processor core. Such high workloads are not only of theoretical interest in benchmarking tasks. As increases linearly with the network size N (Section 5.1), from a single-node workload perspective and assuming a fixed number of neurons per node, a small network at high average firing rates is equivalent to a large network utilizing multiple nodes and exhibiting a low average firing rate.
For example, for the cortical microcircuit model (Potjans and Diesmann, ) which consists of N ≈ 0.8 · 10 neurons, a value of can be expected . At this workload the HNC node achieves an acceleration factor of ~7 while for a single-threaded NEST simulation a factor of ~2 was measured. If the NEST workload is distributed, in the sense of strong-scaling utilizing all four cores of the Intel CPU, the NEST simulation is nearly as fast as the HNC node. Note that is a theoretical value in this case, as the current single node implementation cannot accommodate a network as large as the cortical microcircuit model.
Even though power efficiency was not considered in this work, it is worth mentioning that the SoC device's power consumption is in the order of just a few Watts, and thus achieves a much higher simulation efficiency than the Intel core.
If the HNC node is to be operated in a cluster, the adverse effect that additional inter-node communication has on performance could influence design decisions such as the number of neurons per node and processing unit. For illustration, we consider four sets of design parameters. These are as follows: prototype —the parameter set corresponding to the prototypical implementation generating the measurements presented above, implementing P = 16 processing units, DS = 2 data streams (marked S1 and S2 in Figures 8 , 9 ), and N = 64 neurons per ODE pipeline; high data stream parallelism —as for prototype but assuming that each processing unit connects to its own data stream ( P = 16, DS = 16, N = 64) introducing a factor eight times reduction of external memory access latency; high processing units parallelism —as for high data stream parallelism but implementing twice the number of processing units in order to halve the ODE pipeline iteration latency and increase the maximum achievable single-node acceleration factor ( P = 32, DS = 16, N = 32); and low processing units parallelism —the opposite of high processing units parallelism , reducing the number of processing units ( P = 8, DS = 16, N = 128). The parameter sets are shown in . Note that the parameter sets, with the exception of the prototype configuration, have not been applied to the HNC node. The SoC device selected for this study is limited to the prototype configuration in terms of number of data streams.
Parameter sets.
The acceleration factors are calculated using the performance model (Section 5.2) for four different parameter sets prototype ; high data stream parallelism ; high processing units parallelism ; low processing units parallelism , and for three different workload situations, low, medium , and high as well as with and without inter-node communication. The number of neurons per node N = PN = 1024, the PL clock frequency f = 200 MHz, the transmission latency time T = 500 ns, and the per spike event transmission latency factor α = 0.05 (see main text and Section 5.2 for description) are the same for all parameter sets .
The number of neurons per node ( N = 1024) and the PL clock frequency ( f = 200 MHz) are kept constant across the parameter sets. To describe the effect of inter-node communication on performance, the performance model developed in Section 5.2 introduces two parameters: the transmission latency time T , and the per spike event transmission latency factor α (for a description of the parameters see Section 5.2). Their values were set to T = 500 ns and α = 0.05. They are the same for all parameter sets. The choice for the transmission latency time is motivated by the temporal resolution of h = 0.1 ms and an envisioned acceleration factor of 100, which would be a major breakthrough for reproducible large-scale neuroscience simulations. This assigns T half of the wall clock time that would be available to complete a single simulation step. The value of the per spike event transmission latency factor was arbitrarily chosen and corresponds to 5 additional clock cycles per spike event at a given PL clock frequency of f = 200 MHz.
The upper panels in show the acceleration factors as a function of the workload calculated according to the performance model (Section 5.2, Equations 7, 8) both with and without inter-node communication. In addition, the lower panels in provide an alternative representation of the curves, namely as the respective proportion of performance loss (with respect to the maximum achievable single-node acceleration factor for the corresponding parameter configuration) caused by inter-node communication and by the process of the presynaptic data distribution—mainly the effect of external memory access latency (Section 5.2, Equations 11, 12). shows the calculated acceleration factors for low, medium, and high workload.
Performance characteristics estimation. Performance characteristics of the HNC node are calculated using the performance model (Section 5.2) for the parameter sets prototype ; high data stream parallelism ; high processing units parallelism ; low processing units parallelism . See main text and for details. The upper panels show the achievable acceleration factors as a function of workload with inter-node communication (dashed curves) and without inter-node communication (solid curves); the lower panels show the stacked plots of the respective contributions to the loss of performance with respect to the maximum achievable single-node acceleration factor of the inter-node communication (green) and presynaptic data distribution (blue) (see Section 5.2).
As one would expect, the additional communication latency reduces the maximum achievable acceleration factors. For the prototype configuration ( , prototype, upper panel), for example, the factor decreases from 298.5 to 119.8 ( ). As the workload increases, the effect becomes progressively smaller. For the prototype configuration, for low workload, the factor decreases by 35.5%, for medium workload by 11.2%, and for high workload by 7.9%. For low workload, the achievable acceleration is now determined by inter-node communication latency, but toward higher workload external memory access time is still the main contributor to performance degradation ( , prototype, lower panel).
In the high data streaming parallelism configuration, we therefore assign each processing unit its own data stream, and by this means, introduce eight times higher parallelism in the presynaptic data distribution—the two data streams S1 and S2 ( Figure 8 ) are each split into eight streams, thus reducing external memory access times by a factor of eight. (high data stream parallelism, upper and lower panel) illustrate the effect. For medium workload and with inter-node communication, the acceleration factor increases from 15.0 (for the prototype configuration) to 51.7, i.e., by a factor of 3.4.
One may try to further improve performance by an increase in the parallelism of the neuron and synapse model processing, i.e., by introducing a higher number of processing units. The high processing units parallelism configuration doubles the number of processing units. This configuration achieves a very high maximum acceleration factor of 571.4 for the single node without inter-node communication. In a cluster such high acceleration cannot be realized, even for low workload. Bound by inter-node communication latency, the performance loss in relation to the maximum acceleration is 74%, and for low workload 81.2%. However, for high workload, external memory access time is still the main limiting factor ( , high processing units parallelism, upper and lower panel).
With regard to the hardware footprint and the required FPGA resources—which is an important aspect of hardware designs—the effect of a reduction of the number of processing units is also of interest. The low processing units parallelism configuration, therefore, implements half of the processing units of the prototype configuration ( , low processing units parallelism, upper and lower panel). For low workload and in comparison to the high processing units parallelism configuration, the acceleration factor decreases from 107.5 to 70.9, i.e., by 34%. For high workload, the acceleration factor decreases from 36.9 to 31.3. This is a loss of only 15.2% and might be an acceptable degradation when making design decisions oriented toward a high workload scenario, given that thereby 75% of ODE pipeline hardware resource, namely digital signal processing (DSP) units, can be saved with this configuration. Saving hardware resources reduces power consumption and thus increases simulation efficiency. Considering the above, for medium workload the high data stream parallelism configuration can be a compromise with regard to the achievable acceleration factors for different workload situations and the required chip resources. For the HNC node prototype implementation the utilization of the SoC chip resource are given in the .
The current implementation of the HNC node configured with the prototype parameter set and operated in a cluster would achieve an acceleration factor in the order of 10–50 for medium and small workloads. Such a workload is created, for example, by a network consisting of N = 10,000 neurons with an average firing rate of . To simulate such a network, 10 HNC nodes would need to be clustered.
### 3.3. Correctness
In order to meet the requirement of an accurate and reproducible simulation we evaluated the equivalence of the simulation results produced by the HNC node and a ground truth. In this validation process we aimed for the reproduction of the dynamics of a selected network state obtained from a reference implementation of the two-population Izhikevich network described in Section 5.3. This reference implementation was written in the C language and developed as part of an earlier study (Trensch et al., ). The source code is available online . To create the network state, the ground truth, the network was trained for 1 h biological time using a spike time dependent plasticity (STDP) rule (see the description of the network given in the ). After 1 h of simulated network time, the current state of the network was captured by exporting the network's connectivity data. The connectivity data was then imported back into the C simulation, and with the STDP rule turned off, from 30 min simulated time the spikes were recorded while the network was stimulated with a random input. This recorded network activity data defined the ground truth, that is, the captured network state that defines a reliable reference. For reproduction, we loaded the connectivity data into the HNC node and repeated the simulation. To provide further evidence and to substantiate the correctness of the simulation result generated by the HNC node, the connectivity data was also imported into the NEST simulator and we repeated the simulation again. When simulating a network, it is sufficient to communicate spike events at intervals less or equal to the minimum synaptic delay in the network. The NEST implementation makes use of this and propagates spike events on a 1 ms grid - the minimal synaptic delay in the two-population Izhikevich network. In contrast, the HNC node communicates spike events at 0.1 ms intervals. For progressing neuron model dynamics, an integration step size of h = 1 ms would not be sufficient to achieve the necessary numerical accuracy (Pauli et al., ). Therefore, both NEST and the HNC node use an integration step size of h = 0.1 ms. The NEST Izhikevich neuron model implementation was adapted accordingly. The simulation script and the source code is available online .
From the three obtained data sets of network activity, the probability distribution of the firing rates (FR), the coefficient of variation (CV), and the Pearson's correlation coefficient (CC) were calculated and compared. The statistical measures are described in the . The result of the comparison is shown in . All measures are in close agreement and show statistical equivalence.
Quantitative comparison of statistical measures. Upper two rows from left to right: probability distribution of average firing rate (FR), coefficient of variation (CV), and Pearson's correlation coefficient (CC) for the excitatory (EXC) and the inhibitory (INH) population. The measures were calculated from 30 min simulated time. For the calculation of CC, spike trains were binned at 2 ms. In order to derive the probability distributions from the calculated measures, the Freedman-Diaconis rule was applied to select the width of the bins of the distribution histograms, and a Gaussian kernel was used for density smoothing. The bottom row shows the Kolmogorov-Smirnov statistics calculated from the raw samples of the calculated statistical measures. During the simulations performed on the HNC node, using the NEST simulator, and carried out using the reference C implementation, the network was stimulated with a different random input—a limitation of the HNC node prototype and hardware implementation of the PRNG. All three simulations used the same explicit Forward Euler integration method with an integration step size of h = 0.1 ms. All measures are in close agreement and show statistical equivalence.
Simulation results must not only be reproducible and in agreement with a reliable reference, but also replicable, i.e., spike-identical in repeated simulations. Replicability was tested by repeatedly simulating the two-population Izhikevich network for 20 minutes simulated time. Due to limited numerical precision and rounding errors, operations are not commutative. Therefore, and to strengthen the tests, the network was also logically shifted across processing units in order to assign a logical neuron-id to different hardware resources, and thus force a different spike ordering and scheduling of operations. The simulation results were successfully validated for spike-identicality (data not shown).
## 4. Architecture
### 4.1. System-Level Architecture
We chose the XCZ7045 SoC from the Xilinx Zynq-7000 SoC device family (Xilinx, ) for the technical implementation, and all work presented in this article was carried out on a Xilinx Zynq-7000 SoC ZC706 development board (Xilinx, ). The XCZ7045 integrates a dual-core ARM Cortex-A9 processor (up to 1 GHz) and a freely programmable and re-configurable logic device, i.e., an FPGA with the size of 350,000 configurable logic blocks (CLBs). It provides ~218,000 look-up tables (LUTs), ~437,000 flip-flops (FFs), 19.2 Mbit of fast static block RAM (BRAM) that can be customized for different configurations, and 900 digital signal processing (DSP) blocks for the implementation of arithmetic operations.
shows the system-level view of the implemented HNC node architecture. It details the major components and modules, their interaction and functional assignments. The operation of the HNC node is software-controlled. The program executable is located in the external memory (top right) and executed by the processing system's (PS) application processing unit (APU) (upper dashed box). For user interaction, debugging and data exchange, the HNC node is connected to a Linux host system (upper left) via an Ethernet (ENET) connection for the read-out of recorded spike events, a JTAG connection for programming and debugging, and a serial UART user console interface.
System-level view of the HNC node hardware architecture. The on-chip components are framed by the dashed lines. The lower frame encloses all modules that have been implemented in programmable logic (PL), while in the upper frame the components of the processing system (PS) are shown. Attached to it is an external 1 GiB DDR RAM module (upper right). It stores the node software system executable and the data structures required for operation, for example, the state variables and connectivity information. The external memory also functions as buffer for the recorded spike data. The PS is further connected to a Linux host system (upper left) which provides a serial console to operate the HNC node, the Xilinx Vivado environment for development, and a TCP/IP server to collect the recorded binary spike data.
The simulation engine's core components are realized in programmable logic (PL). They are shown in the lower dashed box in . Function-wise, the hardware components can be assigned to four distinct steps in the process of carrying out a simulation cycle: (i) presynaptic data distribution; (ii) presynaptic data processing; (iii) neuron and synapse model update; and (iv) spike events processing.
Presynaptic data distribution : triggered by postsynaptic spike events, the PS/PL Data Transfer Module initiates read operations from the external memory to obtain the node-local connectivity information (see Section 4.3.1) of the firing neurons. In order to do so and make optimal use of the read bandwidth of the external memory, the PS/PL Data Transfer Module is connected to the PS via a pair of high performance ports (HP1, HP3) capable of working independently of one another. At its outputs, the module connects to a series of first-in-first-out (FIFO) buffers (in referred to as RB FIFOs) which compensate for latencies and to which the presynaptic date is distributed. The RB FIFOs connect the PS/PL Data Transfer Module to 16 identical processing units (P1, P2,.., P16). The processing units parallelize and pipeline the computations for the presynaptic data processing and the neuron and synapse model dynamics.
Presynaptic data processing : In order to derive the synaptic inputs i and i from the presynaptic data, the presynaptic data is fetched from the RB FIFOs and passed through the RB pipelines. The RB pipelines operate on the ring buffers (RBs) and accumulate the synaptic inputs, the values of which are stored and delayed for further processing by placing them into the RBs.
Neuron and synapse model update : The ordinary differential equation solver pipelines (ODE pipelines) retrieve the accumulated synaptic input values from the RBs and progresses the neuron and synapse model dynamics; updating the models' state vectors in the state variables buffers (SVBs). In addition, an XNOR-shift PRNG can provide a random external network stimulus which is directly applied to the neurons in the ODE pipelines.
Spike events processing : In principle, there can be as many spike events occurring in each unit, and in a single simulation time step k , as the number of neurons processed in a pipeline. In other words, in extreme, 16· N = N = 1,024 spike events need be buffered, serialized and packed for local (intra-node) and external (inter-node) spike communication, as well as for recording. The associated components that are related to this process are shown at the lower right in .
In order to enable the APU to perform software-controlled read and write operations on the SVBs to access the state variables, all processing units are chained to one another and connected to a direct memory access (DMA) controller.
The aforementioned modules mainly represent the data paths or operate on them. To orchestrate the control flow, additional components are required for configuration, simulation control, and synchronization. For configuration and simulation control, a bank of 32-bit registers store node control and status information (shown at the mid left in ). All registers are mapped into the APU's address space and thus accessible by the node software. Their settings steer the operation of a finite state machine (FSM) responsible for generating all control signal sequences for the different operating modes (e.g., load state variables, progress simulation by k steps, unload state variables). To preserve the temporal causality and ensure the correct sequence of operations, all spike events of a simulation step k must have been delivered and the RB buffer updates must have been completed before the next simulation step k + 1 can be initiated. This is ensured by an intra-node synchronization logic which monitors the operating status of all modules. The module is shown at the lower left in . Technically, it implements a barrier mechanism that synchronizes the overall processing at the end of every simulation step. In a multi-node configuration this extends to an inter-node barrier message—software simulators, such as NEST (Gewaltig and Diesmann, ) use MPI barrier calls for this purpose.
The entire hardware design—with the exception of the DMA controller and the FIFO blocks for which Xilinx soft IP cores were used—was implemented on the register transfer level (RTL) in VHDL. The decision to take this more arduous and time consuming approach—rather than a high level synthesis (HLS) implementation (Xilinx, )—is motivated by the endeavor to maximize control over the microarchitecture details in order to optimize the timing behavior. The current HNC node implementation works stable up to a PL clock frequency of f = 200 MHz. The software implementation was carried out in the C language. For the development process the Xilinx Vivado Design Suite (Xilinx, ) and the Xilinx Vivado SDK and embedded system tools (Xilinx, ) were used which provide the development tools for hardware-software co-design, synthesis and analysis.
### 4.2. Software System Architecture
outlines the basic architecture of the HNC node software system, which is executed by the SoC's integrated APU. At its lowest level, an abstraction layer provides fundamental routines to drive the hardware functions, for example, to reset and initialize components, to handle interrupts, to establish a basic serial console and TCP/IP communication, and to initiate direct memory access (DMA) transfer operations. Helper- and low-level simulator functions, such as routines to load and unload the state variable buffers, build on top of this layer providing the foundation for the actual simulator functions—the kernel of the software system. The main components here are the Neuron Manager , responsible for the instantiation of neurons, and the Connection Manager , responsible for creating the synaptic connections. At the highest level, a C-API provides Create(..) , Connect(..) , and Simulate(..) function calls, which represent a minimal set of functions required to instantiate and simulate a network. Besides the simulator core-functionality, we implemented functions for system configuration, testing and debugging as well as for user-interactive node control. Access to those is given through a serial user console interface. To minimize the resources footprint and achieve best possible performance, the software system was implemented as bare-metal application, running natively without the use of any underlying operating system. When executed, it makes use of one of the two ARM Cortex-A9 cores that the APU provides. During the execution of a Simulate(..) function call, no operations on the external memory are performed by the APU. This allows the PL to make optimal use of the bandwidth of the external memory while a simulation is running.
HNC node software system architecture. The tiered architecture provides abstraction at different functional levels. (A) The low-level system routines hide technical details about the operation of the implemented hardware components. Based on this, low-level simulator and helper functions (B) form the foundation for the core component of the HNC node software (C) , that is, the simulator functions. (D) At the highest level, a minimal set of functions is provided to instantiate and simulate a network. (E) In addition, the software system implements components that are responsible for control, testing, and debugging and also enable user interaction.
#### 4.2.1. Node-Local Network Instantiation
The current HNC node prototype requires that the neural network model is formulated as a sequence of Create and Connect function calls, which needs to be compiled to an executable. In this object-format it is loaded into the external memory and executed when a Simulate function call is issued. Each Create instantiates a single neuron. The function takes as its arguments a model name, the initial values of the neuron's state variables, and a logical neuron-id, which identifies the neuron on the node. The Create function calls are processed by the Neuron Manager . It maps the logical neuron-id to a dedicated hardware resource identified by a resource-id, i.e., a processing unit and a position in the ODE pipeline. This process mainly consists of setting up the data structures for state variables in memory while administering byte-orders and data type conversions according to the model-specific hardware implementation. The DMA controller operates directly on these data structures when the processing units are “ loaded” and the state variables are moved to the SVBs—and also vice versa when “ unloaded” and the data is read back to external memory. In the current implementation, an interrupt-controlled DMA operation takes ≈30μ s to fill the SVBs while 16KiB of data is transferred in order to load or unload the states of N = 1024 neurons.
Analogous to the Create function call for the instantiation of a neuron, a Connect function call creates a single connection. It expects in its argument list a logical source neuron-id (for a multi-node system extended by a node-id), a logical target neuron-id, as well as the synapse parameters, i.e., a weight and a delay. From the sequence of Connect function calls, the Connection Manager builds the data structures in the external memory that represent the network connectivity. This structure associates each source neuron with a list of synapse target connections.
#### 4.2.2. Recording
The HNC node implements two different solutions for recording the network activity data, one for recording spikes and one for recording state variables. Recording spike events is a fully asynchronous process which is decoupled from the simulation scheduling. During a simulation, the spike events are grouped together as they occur and packed to 64-bit values which are buffered in the Recording FIFO (shown at the bottom in ) before being written to external memory. The high performance port HP3 is used for the write operations. Its read channel is already assigned to the retrieving of the presynaptic data. Sharing the port—and the external memory—does not create any visible read-write contention. Performance measurements carried out with and without spike recording did not show any degradation in performance with active spike recording and led to comparable results for the measured acceleration factors, even at high spike rates. The current design implements a recording buffer with a size of 60MiB capable of caching ~15M spike events. This buffer is written by the recording hardware in a round robin manner and emptied by the simulation kernel's Recording Client ( ), which transfers the data via a TCP connection to a TCP server running on the Linux host system. For the client implementation on the HNC node the open-source lightweight IP (lwIP) TCP/IP stack was used, which comes with the Xilinx board support package, and is included in the Vivado SDK. In order to read out state variables, a running simulation must be halted to allow the DMA controller to access the SVBs. Consequently, capturing state variables significantly reduces performance. On the other hand, the DMA provides the APU with an efficient way to access all state variables at once and at any desired interval.
### 4.3. Microarchitecture
The module microarchitectures presented in this section try to bring the data and the operations performed on them as close together as possible. The implementations aim at optimal low-latency solutions utilizing SoC device features, such as low-latency BRAM and high-performance streaming interfaces for external memory access.
#### 4.3.1. Connectivity Representation and Presynaptic Data Distribution
The structure in which the network connectivity data is stored in memory is determined by the microarchitecture of the PS/PL Data Transfer Module , which is shown in . Upon the arrival of a spike event, it retrieves the list of synapse target connections C associated with a source neuron n , and distributes the data items to the RB FIFO buffers for further processing by the RB pipelines (see also ). Such a retrieved list constitutes the presynaptic data . It is represented by a list of quadruples C = {( s , n , w , d ), .., ()}, where n specifies the target neuron, w and d denote the synaptic weight and delay values, and s is a data path control value assigning a data item to its associated RB FIFO buffer by controlling the demultiplexer circuits (DMUX, ). The data format of the synaptic target list items is detailed in . The demultiplexers connect the data paths alternately with the RB FIFO buffers and thus the processing units. This architecture detail comes in handy when removing, adding, or combining processing units, as it helps to maintain a balanced load on the high-performance ports. The design and implementation of the module aim at lowest possible data access latency and an optimal utilization of the available read bandwidth of the external memory. Therefore, the PS/PL Data Transfer Module , residing in the PL, is interfaced with the PS, and thus with the external memory, through the two high-performance ports HP1 and HP3. This splits the target list into the two lists and assigned to HP1 and HP3, respectively. Their assignment (and associated data paths) are indicated in red and blue in . The two high-performance ports are capable of working in parallel and independently of one another, while for example, the ports HP0 and HP1 would share the same PS resources, hindering full parallelism.
Presynaptic data distribution. Upon the arrival of a spike event, the presynaptic data is read from the external memory in two independent parallel data streams S1 and S2, indicated by the red and blue arrows, and distributed to the RB FIFOs by the demultiplexers (DMUX). While the Processing System (PS) performs the external storage operations bypassing the APU (not shown in the figure), the PS/PL Data Transfer Module controls the two AXI data streams and the high-performance ports HP1 and HP3 through which it connects to the PS. It calculates the memory addresses of two lists, and which constitute the two data streams, that is, the presynaptic data associated with the neuron that has emitted the spike. This data is stored in two different memory regions, marked by the red and blue boxes.
In terms of implementation, the port interfaces follow the Advanced eXtensible Interface (AXI) standard (Arm Limited, ). More precisely, they provide 64-bit AXI3 Slave interfaces. On the PL, the PS/PL Data Transfer Module architecture bundles two AXI Master stream interface implementations that constitute their counterparts. The AXI protocol is based on data bursts. The presynaptic data to be retrieved upon the occurrence of a single spike event is transmitted in two parallel sequences of four bursts, i.e., four bursts on each port, where a burst consists of 16 64-bit data items. The principle is shown in . The red and blue colors correspond to the datapath coloring in . The read address channels describe the address and control information of the data bursts transferred on the read data channels. The addresses are calculated from the neuron-id and node-id ( n , m ) of the source neuron that emitted the spike, the burst length (len ), and the memory base addresses of the two target lists .
AXI stream protocol implementation. To create data streams that are as continuous as possible, data transfers are already scheduled without waiting for the preceding transfer to complete. Per spike event, the transfer of a sequence of four data bursts is initiated on each of the two read data channels associated with the two streams S1 and S2 (marked red and blue). For this purpose, the memory read base addresses of the four burst data packets are transmitted as a block on the read address channels.
In order to generate data streams that are as continuous as possible, read operations that are triggered by subsequent spike events are already scheduled even though the read data channels are still occupied. By this means, the two data streams S1 and S2 are created. Every spike event triggers a transfer of a 1KiB data packet from external memory. For a single data packet, an average transmission time of ~550 ns ( f = 200 MHz) was measured. This corresponds to a data transfer rate of 1818 MiB/s which is a much higher throughput than achievable with a Xilinx AXI DMA soft IP core (Xilinx, )—the common solution for high-bandwidth direct memory access. The DMA soft IP core throughput is specified with 399.04 MB/s at 100 MHz clock frequency (Xilinx, ).
The transfer parameters, the number of bursts and the size of a burst, are configurable in control registers. They were set as discussed above allowing a source neuron to make 128 synapses on a node. In the current prototypical implementation, the transferred data packets are of same size for all spike events. Unused list entries are read from memory but they are not distributed.
The RB FIFO buffers which connect the PS/PL Data Transfer Module with the processing units serve two purposes. First, they buffer the synaptic input derived from incoming spike events for the time that the ODE pipelines are operating on the ring buffers (RBs) and blocking them for parallel read operations, and second, they allow a clock domain crossing. We have not yet investigated the latter, but it would allow the PS/PL Data Transfer Module to operate at a higher clock frequency than the processing units, which could have a positive impact on the latency of external memory data access.
#### 4.3.2. Ring Buffer Processing and Ordinary Differential Equation Solver Pipeline
The HNC node's processing units draw their ability to accelerate computations primarily from the pipelined processing when accumulating the synaptic inputs in the ring buffers, and when progressing the neuron and synapse model dynamics in the ODE pipelines. This capacity builds on the usage of fast, low-latency on-chip BRAM for storing local variables. shows the involved components and their interaction for a single processing unit. In every simulation time step, the ODE pipeline updates the state vectors of 64 neurons ( y → y ) while operating on the state variables buffer (SVB). The SVB is implemented as true dual-port BRAM enabling high pipeline-throughput and minimal iteration latency. The state vectors are implemented as 128-bit data words, where 120 bits are available for use to store the state variables and 8 bits are required for pipeline control. The representation of the 120-bit data word, in terms of the number of state variables, their length and type, is determined by the model's hardware implementation and its counterpart in the software system, namely the function for neuron instantiation as part of the neuron manager. This generic approach allows a certain flexibility with regard to the choice of data types and operations according to the numerical precision required by the model to be implemented. This architecture is open to extensions, as the ODE pipeline module can be exchanged to support a wide variety of neuron and synapse models. An example implementation is given in . It shows the microarchitecture of the Izhikevich model implementation used for the performance evaluation and validation task conducted in this work.
Ring buffer (RB) architecture and interaction of components. Local variables are stored in the ring buffer (RB) and the state variables buffer (SVB). For their implementation fast, low-latency on-chip BRAM memories were used. Shown is the interaction of the RB pipeline and the ODE pipeline which both operate on the RB. To avoid additional write operations by the ODE pipeline to invalidate the RB entries when processed, each entry is provided with a time stamp k that indicates the RB cycle for which it is valid. The value of k is calculated by the RB pipeline—see also the RB update algorithm ( )—and compared with the current simulation time step to verify an entry's validity when read by the ODE pipeline for processing. The principle is illustrated in (B) where the data path is marked in red. The corresponding RB layout is shown in (A) .
An ODE pipeline retrieves the accumulated synaptic inputs i and i from the RB and may also receive input from an external source, such as a PRNG. Like the SVB, the RB is also implemented as true dual-port BRAM. The buffer layout, shown in , consists of K segments subdivided into N = 64 entries - the number of neurons in the pipeline. The RB is read in a round-robin fashion by the ODE pipeline, such that a segment is re-addressed after k + K simulation time steps. The delay resolution—the minimum of which is given by the simulation resolution, i.e., d = h = 0.1 ms—and the number of segments K determine the maximum possible synaptic delay.
RB entries that have already been processed, and are thus outdated, remain in the buffer and may be erroneously reprocessed by the ODE pipeline in subsequent RB cycles. In order to avoid having to add an additional write operation to the ODE pipeline to mark an entry as processed, and thus invalid, we implemented a solution which turns this approach around. When updated, an entry is marked with a time stamp k that indicates the RB cycle for which the entry is valid. The principle is shown in . This valid time stamp is derived from the calculated target simulation step excluding the lower log ( K ) digits. Upon entering the ODE pipeline, the higher order bits of k and the value of k are checked for equality. If this is the case, i and i are valid synaptic inputs. This method further avoids the restoring of RB entries in the situation of an ODE pipeline restart (see below). The disadvantage of this solution is a higher consumption of the scarce BRAM resources.
In contrast to the ODE pipelines that are controlled by a finite state machine, an RB pipeline works in a purely event-driven fashion. When not stalled by ODE pipeline operations, the presynaptic data buffered in the RB FIFO is being fetched. It is then passed through the RB pipeline which executes the ring buffer algorithm detailed in the flow diagram in .
Ring buffer update algorithm. The algorithm is executed by the RB pipelines. The blue arrows indicate in- and out-going data items at different pipeline stages. The dashed box is for simplified illustration and shows the algorithm for the exceptional case of static synapses where a neuron's excitatory and inhibitory synaptic input can be lumped together. In all other cases, the algorithm expands according to the table on the upper right.
The proposed design raises two issues of potential read-before-write conflicts which need to be taken into consideration. Even though RB update operations never address an RB segment that is processed in the current time step k , it may nevertheless happen that an RB write operation is not considered in further processing. This can be the case if a presynaptic data item represents a synapse with d = d , i.e., a 0.1 ms delay. The initiated update on the k + 1 RB segment may have no effect as it is already being fetched into the ODE pipeline for the next simulation step. In such a case, the ODE pipelines must be reset and restarted. This adds an additional latency L to the processing, where L denotes the pipeline depth. In the proposed design the ODE pipeline restart is software controlled. Whether a restart condition is indicated or not depends on the synaptic delay value and is encoded in the presynaptic data (see table in ). This information is passed to the finite state machine that is controlling the ODE pipeline operation and considered when the next simulation step is initiated. Another read-before-write conflict arises in the RB pipeline itself, caused by BRAM read, write, and operation latencies. These must be taken into account if consecutive presynaptic data items initiate updates on the same RB entry. The reading of an entry for which a previous write operation has not yet completed will lead to a wrong synaptic input value. This problem may only arise with multapses. It can also be solved in software by rearranging the lists of synaptic targets in memory.
It is also worth mentioning that a ring buffer shares its read ports between the RB and ODE pipelines. We have investigated the impact of read contention on performance due to concurrent read operations. When not considering an asynchronous external spike input and long ODE pipeline iteration latencies, only an early arriving spike event may find the RB pipeline stalled when placing data in the RB FIFOs. The additional latency is minimal (in the order of a few clock cycles per simulation time step) and thus can be neglected.
#### 4.3.3. Operating Latencies
In order to further examine the design, we extracted the operating latencies from the microarchitecture VHDL implementation, or where that was not possible, measured them with an external logic analyzer. The timing diagrams and the table in details the performance relevant operating latencies of the HNC node in clock cycles and show the timing of the operation scheduling.
Operating latencies. The scheduling of operations and the latencies associated with it, distinguishes two basic cases. (A) If no spike events occur, operation mainly reduces to ODE pipeline processing. (B) Normally, spike events have to be processed which changes and adds latencies. Postsynaptic spike events must be serialized, and for incoming presynaptic spike events the presynaptic data must be retrieved from external memory. (C) Table listing relevant latencies. The value of L cannot be derived from the microarchitecture; its average value was determined using an external logic analyzer.
At simulation start (and restart) the ODE pipelines are empty. An initial memory read operation that fetches the first data items, and the process of filling the ODE pipelines results in the latencies L and L . This is illustrated in for the case of two simulation steps in which no spike events occur. The latency L corresponds to the depth of the ODE pipelines and may differ depending on the implemented model. The same holds for the iteration latency IL , which is the number of clock cycles required to process all N = 64 neurons assigned to a pipeline. At the end of a simulation step a few clock cycles L are required for synchronization.
Spike events can occur in every clock cycle of the ODE pipeline operation, as depicted in . They are serialized and packed, resulting in a latency of L (see also the table in ). Before the presynaptic data can be read from external memory, its memory addresses have to be calculated. The latencies created by this process are summarized in L . The high-performance ports and the memory controller on the PS, as well as the external memory itself, determine the overall read access latency, and hence the value of L as the data is streamed into the RB FIFOs by the PS/PL Data Transfer Module . The components involved are connected to different clock domains and contribute with latencies that are determined by the SoC technology rather than by the implemented user logic. We therefore measured the value as the number of PL clock cycles required for the transfer of a 1KiB data packet—the amount of data which is read from external memory upon the occurrence of a single spike event—for three PL clock frequencies f = 100/150/200 MHz.
At the end of a simulation step in which spike events had to be processed, the RB pipelines might still be filled, and pending RB updates must be finalized. This adds the latencies summarized in L . Finally, the HNC node goes into synchronization to prepare for the next simulation step. This requires a few clock cycles at the end of a simulation step compared to the situation where no spike event occurred. This adds the latency to the processing.
In a multi-node system, the total latency would be extended by inter-node synchronization times. This is not explicitly included in the timing diagrams in but indicated by the red barriers.
## 5. Methods and Materials
### 5.1. Workload Model
The synchronous, time-driven neuron states update process (red arrows in ) generates a computational cost determined almost exclusively by the number of neurons processed by a single processing unit, and thus adds a constant operating latency. In contrast, the computational cost of the asynchronous, event-driven process of presynaptic data distribution and processing (blue arrows in ) depends mainly on the amount of presynaptic data to be processed and retrieved from external memory. The amount of data is determined by the average number of synapses on a node that a source neuron connects to C , as well as the total number of spike events processed by the node. For a given number of neurons per node N —which is a hardware design parameter and a constant—a certain number of nodes M is required to simulate a network of size N . The connection probability ϵ of the network determines the average in/out-degree K = ϵ N , i.e., the number of in- and out-going synaptic connections of a neuron, which grows with the network size. Since the connections are distributed across the nodes, the average number of synapses on a node that a source neuron connects to remains constant for a given ϵ, even if the network size is growing. This is expressed by Equation (1).
Because C is a constant, the average amount of presynaptic data retrieved from external memory is consequently of same size for every spike event. It is therefore practical to consider as an indicator of computational workload the average number of spike events processed per simulation time step k :
where is the average firing rate calculated over all neurons in the network, n is a neuron's total spike count in the interval T , and h defines the temporal resolution, the step size, of the grid-based simulation, i.e., the time interval h = Δ t = t − t . Note that this metric is initially independent of the number of neurons simulated.
### 5.2. Performance Model
We exploit knowledge of the HNC node microarchitecture latencies to derive a performance model that allows conclusions to be drawn about the performance characteristics in different scenarios regarding the workload and design and technology parameters. We make the following assumptions that represent a scenario that maximally challenges the hardware:
All neurons have at least one target connection with a synaptic delay value d = d .
Every spike event will initiate an ODE pipeline restart. This adds the latencies L and L ( ) to every simulation step.
Spike events are distributed uniformly across the neurons in an ODE pipeline and over pipeline iterations .
We assume that the expected value for the timing of a spike event is the middle of an ODE pipeline iteration, i.e., at IL /2. This is justified by the two-population Izhikevich network used for the benchmarking (Sec. 5.3), and the placement of the neurons on the processing units.
All lists of synaptic target connections are the same length .
This is justified by the current design (Section 4.3.1). Upon every spike event, a 1KiB data packet is transferred from external memory to the RB FIFOs.
As explained in the previous section, we take the average number of spike events processed in a single simulation step k as a measure of the workload. The time span to perform a single simulation step becomes minimal if no spike events occur, and is predominantly determined by the number of serially processed neurons assigned to an ODE pipeline. This is reflected in the ODE pipeline iteration latency IL . Together with the synchronization latency L , it sets the upper bound for the single-node acceleration factor at a given clock frequency f . From the timing diagram in we derive:
where k denotes the number of simulation steps, and h specifies the temporal resolution of the simulation, i.e., h = Δ t = 0.1 ms. For k ≫ 1 this simplifies to
where L = IL + L . Analogously to L , which summarizes the processing latencies for the non-spiking case, latencies arising from processing spiking events can be summarized according to the timing diagrams and process scheduling shown in . This consists of the sum of the latencies for the spike events serialization and buffering process ( L = L + L + L ), the latencies incurred by the initiation of the data streams S1 and S2 ( L = L + L ), see , and the latencies resulting from the processing of outstanding presynaptic data items at the end of a simulation step ( L = L + L ). The number of clock cycles for each latency, as well as its description, can be found in . Altogether, this results in
The term IL /2 in Equation (6) reflects the assumption of a uniform distribution of the spike events.
For an isolated node with no inter-node communication, the acceleration factor as a function of the average number of spike events per simulation step can now be formulated as follows:
For , the denominator in Equation (7) consists of two terms corresponding to the spiking and the non-spiking case, where L denotes the per spike event data stream latency. The two branches are equal for . In the absence of spike events, applies (see Equation 5).
Please note that Equation (7) does not consider C - the average number of synapses on a node that a source neuron connects to. The value of C determines the value of L that was measured since it cannot be derived from the microarchitecture. This becomes relevant, for example, when the number of neurons per node N , and thus also C changes (Equation 1). Furthermore, it neglects the possibility that a presynaptic data transfer could complete before all neurons are processed, i.e., IL /2 − L > 0. To account for this, the value of would have to be corrected by adding IL /2 − L . However, one would only see an effect at very low spike rates because applies. Equation (7), therefore, represents a good estimate of the acceleration factors that can be achieved with the proposed HNC node design under different workloads.
Currently, only a single-node prototype exists. In order to estimate the performance characteristics of a multi-node system, we expand the performance model to include inter-node communication latencies. Strongly simplifying the complex effects of communication network topologies, protocols, and low-latency interconnects, we propose three basic assumptions:
Spike events are broadcasted, i.e, communicated to all nodes.
Inter-node connections all have the same and fixed transmission latency time T , which adds to every simulation step. In addition to the times needed to communicate the spike events between nodes, T also includes inter-node synchronization latencies, i.e., barrier messaging times.
To take into account that inter-node communication increase with workload, every spike event adds a transmission latency to the communication, i.e., a variable, workload dependent portion defined as a small fraction of the transmission latency time. It is specified by a factor α and results for a given workload in .
Adding inter-node communication latencies to Equation (7) results in
where L denotes the transmission latency in PL clock cycles derived from the transmission latency time, i.e., L = f T . Note that even in the absence of spike events, L does not vanish as it includes inter-node synchronization times. According to Equation (4), the upper bound for the acceleration factor with inter-node communication then becomes:
From the performance characteristics derived above, the total relative performance loss P with respect to the maximum achievable acceleration can be estimated for different workloads as follows:
The total performance loss can be further subdivided into the losses caused by the HNC node-local spike processing (which mainly consists of retrieving and distributing the presynaptic data)
and the loss caused by the inter-node communication
### 5.3. Verification, Validation, and Benchmarking Model: Two-Population Izhikevich Network
We use a simple two-population model as the basis for both the performance measurements and the verification and validation of the correctness of the HNC node hardware and software implementation. The network consists of 1,000 Izhikevich-type neurons (Izhikevich, ), which follow the dynamics
The network consists of 800 excitatory regular spiking neurons [( a, b, c, d ) = (0.02, 0.2, −65.0, 8.0)] and 200 inhibitory fast spiking neurons [( a, b, c, d ) = (0.1, 0.2, −65.0, 2.0)]. The excitatory population makes random connections to the inhibitory population and to itself. The inhibitory population only projects to the excitatory population. All neurons in the network draw their connections with a fixed in-degree of K = 100 and receive additional input from an external source. A detailed description of the network is given in the .
The choice of this model was motivated by our previous work, where we subjected a two-population Izhikevich network implementation on the SpiNNaker system to a rigorous verification and validation task (Gutzen et al., ; Trensch et al., ).
## 6. Discussion
We presented an SoC-based hybrid software-hardware architecture of a neuromorphic computing node. This is to be seen as a complementary yet distinct approach to the neuromorphic developments aiming at brain-inspired and highly efficient novel computer architectures for solving real-world tasks. The requirements for achieving reproducible hyper-real-time neuroscience simulations are different, so also the technical challenges. We examined the extent to which the proposed architecture and Xilinx Zynq SoC device technology is capable of meeting the high demands of modeling and simulation in neuroscience in terms of flexibility, accuracy, and simulation performance.
### 6.1. Flexibility
The HNC node design exploits the trade-off between flexibility and efficiency offered by the Xilinx Zynq SoC device technology. The tight coupling of programmable logic with a general purpose processor gives the developer the flexibility to cope with rapid developments in neuroscience and changing requirements. For example, the plethora of neuron and synapse models require that the operations and their scheduling performed by the ODE pipelines can be adapted in terms of the implemented numerical algorithms and data types. The application of code generation techniques (Blundell et al., ) can abstract hardware implementation details away from a neuron and synapse modeling task. Therefore, the ODE pipeline architecture was implemented as a replaceable VHDL-module having a defined port interface. This makes the neuron and synapse model hardware implementations accessible to tools, such as NESTML (Plotnikov et al., ). By this means a wide variety of neuron and synapse models can be supported.
The availability of powerful, node-local processor cores also allows us to decentralize; moving tasks onto the neuromorphic compute nodes that are typically carried out on a host system. For example, the generation of the network connectivity could be carried out on a conventional system using established tools, such as PyNN (Davison et al., ) or PyNEST (Eppler et al., ), while the network instantiation process is parallelized by being delegated to the processor cores of the neuromorphic compute nodes. This would reduce network building times, especially when repeated simulations are performed (e.g., parameter scans). Moreover, the integration with the existing workflows for neural network modeling and simulation becomes easier to reach.
The HNC node architecture is open for extension, for example, the implementation of synaptic plasticity rules. Although plasticity models were deliberately left out for the current HNC node prototype, it was considered in the design decisions. In future developments, we intend to exploit the hybrid software-hardware architecture concept of the HNC node in such a way that plasticity algorithms programmed in software run on a dedicated plasticity processor—executed on the APU using the second, so far unused, ARM processor core—supported by accelerators implemented in programmable logic. To enable the implementation of spike-based plasticity rules (Morrison et al., ), the network connectivity data as well as the recorded spike events are stored in the external memory, thus keeping synaptic weights adjustable and spike history accessible to the processor cores. There are a number of different forms of plasticity (Magee and Grienberger, ) and a rapid development in the field which entails some technical challenges. The HNC node provides here a flexible platform as a means to explore novel architecture concepts to implement plasticity algorithms.
### 6.2. Numerical Precision
Particular care must be taken with respect to mathematical operations. Both the choice of data types and algorithms as well as their technical implementation require special attention. The design decisions made regarding the example Izhikevich neuron model ODE pipeline implementation (see Section 4 in the ), e.g., the data types and the numeric integration scheme, are based on the results of our earlier studies (Gutzen et al., ; Trensch et al., ). By conducting a calculation verification task , we concluded that a 32-bit signed fixed-point data type (s16.15) does not provide the necessary numerical precision to capture the dynamics of the Izhikevich neuron model (Izhikevich, ) with sufficient accuracy. For the processing unit's ODE pipelines, we therefore implemented a 40-bit signed fixed-point data type (s16.23)—a decision also made to avoid expensive floating point operations. In combination with an explicit Forward Euler ODE solver method and an integration step size of h = 0.1 ms, we achieve sufficient accuracy—even though it is the simplest numerical method available. Analogously to the calculation verification task carried out in the studies mentioned, we verified the ODE pipeline operation by comparing the subthreshold dynamics and the spike timing to the results of an explicit Runge-Kutta-Fehlberg(4, 5) method with an absolute integration error of 10 .
### 6.3. Verification of Implementations
During implementation, hardware and software components cannot be considered independently of each other and must therefore be developed in parallel in a co-development process. The HNC node software system is written in C and almost all hardware components were developed in VHDL. In contrast to a high-level synthesis approach, where a hardware design is formulated at an algorithmic level in the C language, for example, and the synthesis tool chain generates a reliable hardware description from it, the implementation in VHDL at the RTL level is rather error-prone. A well thought-out test strategy is therefore essential. It must consider the verification of the correctness of the technical implementation of the hardware and software components as well as the validation of the outcome of the simulations performed on the HNC node.
Our approach was that of an embedded hardware-software co-verification, in the sense of a directed software-controlled functional testing. For this purpose, the hardware components under test were connected to the APU of the SoC device through memory-mapped AXI-interfaces and subjected to a series of hierarchical functional tests written in C. These tests range from simple to complex and are executed on the APU. They include basic hardware and software functional tests, integration tests, as well as complex functional tests that also became part of the HNC node software system. Examples of such complex tests are memory read-write pattern tests. They ensure the correct implementation and operation of the DMA data transfer to and from the SVBs and verify data type and endianness conversion. Another example of a complex test scenario is the functional verification of the RB pipeline and RB buffer operation, where a software-controlled spike injection and a subsequent RB read out is used to verify the correctness of the presynaptic data processing.
### 6.4. Performance
Software developers of spiking neural network simulation tools invest much effort in the optimization of their codes to achieve best possible performance and simulation efficiency. They are well aware of the performance-critical nature of retrieving the presynaptic data from memory and its distribution, its accumulation in the ring buffers, and the update process of the neuron and synapse model dynamics performed at every simulation time step. The challenges in finding optimal solutions and implementations are manifold. For example, in large-scale networks, synaptic processing substantially dominates the computational load, and the irregular, random access pattern in retrieving the presynaptic data reduce a processor's cache hit rate and increases data access latencies (see e.g., Kunkel et al., ). The tools of trade here are algorithms that implement high parallelism in computations, “cache-friendly” data structures, and the application of techniques for latency hiding, such as data prefetching (Pronold et al., ). The proposed HNC node design aims to address these problems—which on conventional computer architectures are a consequence of the von Neumann bottleneck—by implementing performance-critical tasks in hardware. Specifically, the process of neuron and synapse model update benefits from the data-locality of state variables stored in fast on-chip BRAM memories. Storing the network connectivity data in an external memory, however, undermines this concept, and toward higher workloads, performance will be bound by external memory access latency. For larger systems and higher workloads, it is therefore crucial to aim for an architecture design that also allows data-locality for the presynaptic data processing. The design of the HNC node is constrained in this respect by the limited BRAM resources.
The ability to model the performance behavior for different design parameters is of great value as it can guide future developments and design decisions. We developed such a model for the HNC node architecture. The implementation strategy, based on the hardware description on register-transfer level (RTL) in the VHDL language has allowed us to derive an accurate performance model from the implemented microarchitecture. To this end we made several simplifying assumptions, in particular, with respect to the inter-node communication latencies. Network technologies are typically optimized for throughput, but not for latency. The value of the transmission latency time ( T = 500 ns) assumed for the performance evaluation is already ambitious. However, low-latency inter-node communication is as important for performance as data-locality is for the computations. Despite these simplifications, the model achieves a good approximation of the performance characteristics. Extrapolating from the single node performance, we predict that small clusters capable of simulating in hyper-real-time networks comprising a few tens of thousands of neurons would achieve acceleration factors in the order of 10 to 50.
### 6.5. Cluster Operation
Although cluster operation is not the focus of this article, some related considerations that influenced design decisions are worth mentioning. Three communication bottlenecks can be identified in the simulation flow that are relevant to the overall performance of a cluster system: the spike exchange between nodes, inter-node synchronization, and external communication for system configuration and operation including the unload of recorded simulation data. The requirements of these tasks differ in regard to latency and bandwidth. Inter-node spike communication and node synchronization require an ultra-low latency interconnect but not high bandwidth. The demand for external communication is completely different. For loading and unloading larger amounts of data, high bandwidth is desirable to achieve low system setup times and eventually real-time recording capability. We are therefore aiming at three different solutions tailored to the respective task, although our cluster concept is not yet fully developed. The HNC node encodes spike events using an Address Event Representation (AER; Mahowald, ). AER-based communication is well established in neuromorphic computing and the basis for low-latency spike-communication. In order to achieve the predicted cluster performance (cf. Section 3.2), it is crucial that the transmission latency time of T = 500 ns for inter-node spike communication assumed by the performance model can be attained in a cluster consisting of a few tens of HNC nodes. The Xilinx Zynq SoC device used for the implementation of the HNC node prototype provides various hardware interfaces that would allow us to establish an efficient chip-to-chip communication, for example, a number of serial gigabit transceivers (GTX/GTH), PCI Express, and low-voltage differential signaling (LVDS) user I/Os (Xilinx, ). A solution for a low-latency spike communication in a 64-node FPGA cluster is, for example, presented in Moore et al. ( ). It exploits high-speed serial links and achieves a hop-latency of 50 ns in a 3D torus topology. For inter-node synchronization, we favor a simple one-wire (e.g., wired-or) solution where a global barrier signal is derived from the intra-node synchronization logic (cf. ). External communication with the HNC node is established using a 10/100/1000 Mb/s tri-speed Ethernet PHY and the TCP protocol—currently used only to stream the recorded simulation data to a host system. For a cluster, we aim at a parallel data move solution, in which each HNC node is connected to its own host system or host node, respectively.
The proposed technology and architecture is an ideal basis for prototyping and design space exploration—the primary domain of programmable logic devices—and for elaborating novel architectures. The reconfigurable logic allows extensive freedom in the implementation of the numerical models while the processor cores opens an elegant way to achieve system integration. They can be an intermediate step toward next-generation neuromorphic systems and neuroscience simulation platforms. In this sense, the proposed HNC node design complements the existing neuromorphic system architecture approaches of SpiNNaker and BrainScales, in regards both to technology and the trade-off between flexibility and efficiency.
## Data Availability Statement
The simulation scripts and source codes used in this work to demonstrate correctness are available online at: ( ).
## Author Contributions
GT developed the System-on-Chip based hybrid architecture and implemented the prototype, developed the workload and performance model, and performed the experiments. GT and AM designed the experiments and wrote the paper. All authors contributed to the article and approved the submitted version.
## Funding
This project has received funding from the Helmholtz Association's Initiative and Networking Fund under project number SO-092 (Advanced Computing Architectures, ACA). Open access publication funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—491111487.
## Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's Note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
|
Cerebral edema contributes to neurological deterioration and death after hemispheric stroke but there remains no effective means of preventing or accurately predicting its occurrence. Big data approaches may provide insights into the biologic variability and genetic contributions to severity and time course of cerebral edema. These methods require quantitative analyses of edema severity across large cohorts of stroke patients. We have proposed that changes in cerebrospinal fluid (CSF) volume over time may represent a sensitive and dynamic marker of edema progression that can be measured from routinely available CT scans. To facilitate and scale up such approaches we have created a machine learning algorithm capable of segmenting and measuring CSF volume from serial CT scans of stroke patients. We now present results of our preliminary processing pipeline that was able to efficiently extract CSF volumetrics from an initial cohort of 155 subjects enrolled in a prospective longitudinal stroke study. We demonstrate a high degree of reproducibility in total cranial volume registration between scans (<i>R</i> = 0.982) as well as a strong correlation of baseline CSF volume and patient age (as a surrogate of brain atrophy, <i>R</i> = 0.725). Reduction in CSF volume from baseline to final CT was correlated with infarct volume (<i>R</i> = 0.715) and degree of midline shift (quadratic model, <i>p</i> < 2.2 × 10<sup>-16</sup>). We utilized generalized estimating equations (GEE) to model CSF volumes over time (using linear and quadratic terms), adjusting for age. This model demonstrated that CSF volume decreases over time (<i>p</i> < 2.2 × 10<sup>-13</sup>) and is lower in those with cerebral edema (<i>p</i> = 0.0004). We are now fully automating this pipeline to allow rapid analysis of even larger cohorts of stroke patients from multiple sites using an XNAT (eXtensible Neuroimaging Archive Toolkit) platform. Data on kinetics of edema across thousands of patients will facilitate precision approaches to prediction of malignant edema as well as modeling of variability and further understanding of genetic variants that influence edema severity. |
<b>Objective:</b> Variants in <i>SYNE1</i> have been widely reported in ataxia patients in Europe, with highly variable clinical phenotype. Until now, no mutation of <i>SYNE1</i> ataxia has been reported among the Chinese population. Our aim was to screen for <i>SYNE1</i> ataxia patients in China and extend the clinicogenetic spectrum. <b>Methods:</b> Variants in <i>SYNE1</i> were detected by high-throughput sequencing on a cohort of 126 unrelated index patients with unexplained autosomal recessive or sporadic ataxia. Pathogenicity assessments of <i>SYNE1</i> variants were interpreted according to the ACMG guidelines. Potential pathogenic variants were confirmed by Sanger sequencing. Clinical assessments were conducted by two experienced neurologists. <b>Results:</b> Two Chinese families with variable ataxia syndrome were identified (accounting for 1.6%; 2/126), separately caused by the novel homozygous <i>SYNE1</i> mutation (NM_033071.3: c.21568C>T, p.Arg7190Ter), and compound heterozygous <i>SYNE1</i> mutation (NM_033071.3: c.18684G>A, p.Trp6228Ter; c.17944C>T, p.Arg5982Ter), characterized by motor neuron impairment, mental retardation and arthrogryposis. <b>Conclusions:</b> <i>SYNE1</i> ataxia exists in the Chinese population, as a rare form of autosomal recessive ataxia, with a complex phenotype. Our findings expanded the ethnic, phenotypic and genetic diversity of <i>SYNE1</i> ataxia. |
This study aims to compare the differences in the kinematic characteristics of crossing obstacles of different heights between stroke survivors and age-matched healthy controls and to identify the changes of balance control strategy and risk of falling. Twelve stroke survivors and twelve aged-matched healthy controls were recruited. A three-dimensional motion analysis system and two force plates were used to measure the kinematic and kinetic data during crossing obstacles with heights of 10, 20, and 30% leg length. The results showed that during leading and trailing limb clearance, (AP) center of mass (COM) velocities of the stroke group were smaller than those of the healthy controls for all heights. The decreased distances between COM and center of pressure (COP) in the AP direction during the both trailing and leading limb support period were also found between stroke survivors and healthy controls for all heights. The COM velocity and COM-COP distance significantly correlated with the lower limb muscle strength. In addition, stroke survivors showed greater lateral pelvic tilt, greater hip abduction, and larger peak velocity in the medio-lateral (ML) direction. There was a positive correlation between the COM-COP distance in the AP direction and the clinical scales. These results might identify that the stroke survivors used a conservative strategy to negotiate the obstacles and control balance due to a lack of muscle strength. However, the abnormal patterns during obstacle crossing might increase the risk of falling. The findings could be used to design specific rehabilitation training programs to enhance body stability, reduce energy cost, and improve motion efficiency. |
Phenylketonuria is a hereditary metabolic disorder due to the deficiency of tetrahydrobiopterin or phenylalanine hydroxylase. Delayed diagnoses of it manifest a progressive irreversible neurological impairment in the early years of the disease. Guthrie test and tandem mass spectrometry aided in early detection and intervention of phenylketonuria, which significantly decreased the disability of patients as well as reducing the need for diagnosis in adults. This is a case report of a 60-year-old Asian man, characterized by severe visual-spatial disorders and bilateral diffuse symmetric white matter lesions on magnetic resonance imaging, who was diagnosed as phenylketonuria with his congenital mental retardation sibling. Heterozygous mutations exist in gene encoding PAH c.1068C>A and c.740G>T. During the diagnosis, we looked up at other late-onset genetic diseases considered to occur rarely but gradually revealed similar clinical manifestations and significant white matter lesions gaining importance in guiding to correct diagnosis and treatment. We made a comprehensive review of phenylketonuria and other inherited diseases with major prevalence in adulthood with prominent white matter involvement. Our study aims to help neurologists to improve recognition of metabolism-related leukoencephalopathies without neglect of the role of congenital genetic factors. |
<b>Purpose:</b> Preliminary evidence indicated that children with a reading disorder (RD) may have deviance in their ability to perform high demanding cognitive tasks, such as reading, depending on somatosensory inputs. Until now, only anecdotical reports suggested that improving somatosensory inputs may influence their ability to maintain a stable perception of the visual world despite continuous movements of our eyes, head, and body. Here, we investigated whether changes in upright perception, the subjective visual vertical (SVV), were modulated by somatosensory inputs in a group of children with RD. <b>Method:</b> The SVV task was used under two distinct conditions, i.e., with or without somatosensory inputs from the foot. We enrolled a group of 20 children with reading disorders and 20 sex-, age-, IQ- matched children with neurotypical development. <b>Results:</b> Responses to the SVV task were found to be significantly less accurate in children with RD than in children with neurotypical development (<i>p</i> < 0.001). In the latter, SVV response did not depend on somatosensory inputs from the foot. In contrast, in children with RD somatosensory inputs, either improved or worsen their SVV depending on the tilt direction (<i>p</i> < 0.01). <b>Conclusion:</b> Our results suggested that SVV responses in children with RD could be related to an immaturity for heteromodal sensory integration, including somatosensory inputs. |
Alzheimer's disease (AD), a common neurodegenerative disease in the elderly and the most prevalent cause of dementia, is characterized by progressive cognitive impairment. The prevalence of AD continues to increase worldwide, becoming a great healthcare challenge of the twenty-first century. In the more than 110 years since AD was discovered, many related pathogenic mechanisms have been proposed, and the most recognized hypotheses are the amyloid and tau hypotheses. However, almost all clinical trials targeting these mechanisms have not identified any effective methods to treat AD. Scientists are gradually moving away from the simple assumption, as proposed in the original amyloid hypothesis, to new theories of pathogenesis, including gamma oscillations, prion transmission, cerebral vasoconstriction, growth hormone secretagogue receptor 1α (GHSR1α)-mediated mechanism, and infection. To place these findings in context, we first reviewed the neuropathology of AD and further discussed new insights in the pathogenesis of AD. |
We propose that multiple sclerosis (MS) is best characterized as a syndrome rather than a single disease because different pathogenetic mechanisms can result in the constellation of symptoms and signs by which MS is clinically characterized. We describe several cellular mechanisms that could generate inflammatory demyelination through disruption of homeostatic interactions between immune and neural cells. We illustrate that genomics is important in identifying phenocopies, in particular for primary progressive MS. We posit that molecular profiling, rather than traditional clinical phenotyping, will facilitate meaningful patient stratification, as illustrated by interactions between HLA and a regulator of homeostatic phagocytosis, MERTK. We envisage a personalized approach to MS management where genetic, molecular, and cellular information guides management. |
<b>Objective:</b> To investigate whether MEG network connectivity was associated with epilepsy duration, to identify functional brain network hubs in patients with refractory focal epilepsy, and assess if their surgical removal was associated with post-operative seizure freedom. <b>Methods:</b> We studied 31 patients with drug refractory focal epilepsy who underwent resting state magnetoencephalography (MEG), and structural magnetic resonance imaging (MRI) as part of pre-surgical evaluation. Using the structural MRI, we generated 114 cortical regions of interest, performed surface reconstruction and MEG source localization. Representative source localized signals for each region were correlated with each other to generate a functional brain network. We repeated this procedure across three randomly chosen one-minute epochs. Network hubs were defined as those with the highest intra-hemispheric mean correlations. Post-operative MRI identified regions that were surgically removed. <b>Results:</b> Greater mean MEG network connectivity was associated with a longer duration of epilepsy. Patients who were seizure free after surgery had more hubs surgically removed than patients who were not seizure free (AUC = 0.76, <i>p</i> = 0.01) consistently across three randomly chosen time segments. <b>Conclusion:</b> Our results support a growing literature implicating network hub involvement in focal epilepsy, the removal of which by surgery is associated with greater chance of post-operative seizure freedom. |
The Stroke Therapy Academic Industry Roundtable (STAIR) has recommended that novel therapeutics be tested in a large animal model with similar anatomy and physiology to humans. The pig is an attractive model due to similarities in brain size, organization, and composition relative to humans. However, multiple pig breeds have been used to study ischemic stroke with potentially differing cerebral anatomy, architecture and, consequently, ischemic stroke pathologies. The objective of this study was to characterize brain anatomy and assess spatiotemporal gait parameters in Yucatan (YC) and Landrace (LR) pigs pre- and post-stroke using magnetic resonance imaging (MRI) and gait analysis, respectively. Ischemic stroke was induced via permanent middle cerebral artery occlusion (MCAO). MRI was performed pre-stroke and 1-day post-stroke. Structural and diffusion-tensor sequences were performed at both timepoints and analyzed for cerebral characteristics, lesion diffusivity, and white matter changes. Spatiotemporal and relative pressure gait measurements were collected pre- and 2-days post-stroke to characterize and compare acute functional deficits. The results from this study demonstrated that YC and LR pigs exhibit differences in gross brain anatomy and gait patterns pre-stroke with MRI and gait analysis showing statistical differences in the majority of parameters. However, stroke pathologies in YC and LR pigs were highly comparable post-stroke for most evaluated MRI parameters, including lesion volume and diffusivity, hemisphere swelling, ventricle compression, caudal transtentorial and foramen magnum herniation, showing no statistical difference between the breeds. In addition, post-stroke changes in velocity, cycle time, swing percent, cadence, and mean hoof pressure showed no statistical difference between the breeds. These results indicate significant differences between pig breeds in brain size, anatomy, and motor function pre-stroke, yet both demonstrate comparable brain pathophysiology and motor outcomes post-stroke. The conclusions of this study suggest pigs of these different breeds generally show a similar ischemic stroke response and findings can be compared across porcine stroke studies that use different breeds. |
Cerebral arteries are usually tortuous, and in the treatment of cerebrovascular diseases with stenting, a stent deployed may be collapsed at one end, leading to reduced blood flow and subsequent stent occlusion. Immediate rescuing measures should be implemented to prevent severe ischemic events. In this case report, we present a case with V4 segment occlusion of the right vertebral artery treated with endovascular stent angioplasty. An Enterprise stent deployed at the occlusion segment was collapsed at the proximal end after withdrawal of the delivery system. Immediate rescuing measures were taken by navigating a micro-guidewire through the lateral stent mesh at the proximal end into the stent lumen followed by advancing a second micro-guidewire right through the reopened proximal stent end into the stent lumen for deployment of a supporting balloon-expandable Apollo stent to prevent stent collapse. Follow-up digital subtraction angiography 6 months later demonstrated patent stents and unobstructed blood flow. |
<b>Objectives:</b> We aimed to identify the factors contributing to comorbid anxiety symptoms over a 12-month follow-up period in Chinese adults with newly diagnosed epilepsy. <b>Methods:</b> Adult patients with newly diagnosed epilepsy (PWNDE) were recruited from First Hospital, Jilin University. Anxiety symptoms were assessed using the Generalized Anxiety Disorder-7 questionnaire (GAD-7; Chinese version) at 12 months. Multivariate stepwise logistic regression analysis was employed to identify the predictors for anxiety symptoms at 12 months. <b>Results:</b> A total of 157 PWNDE completed the study and were included in the final analysis. The percentage of participants with anxiety symptoms significantly decreased from 31.2% at baseline to 23.6% at 12 months (<i>p</i> = 0.027). Multivariate stepwise logistic regression analysis indicated that depressive symptoms at baseline [odds ratio (OR) 3.877 (95% confidence interval (CI) 1.683-8.933); <i>P</i> = 0.001] and the number of antiseizure medications (ASMs) during the follow-up period [OR 2.814 (95% CI 1.365-5.803); <i>P</i> = 0.005] were independent factors contributing to comorbid anxiety symptoms at 12 months. <b>Conclusion:</b> Depressive symptoms at baseline and the number of ASMs during the follow-up period were significant predictors of comorbid anxiety symptoms 12 months after a diagnosis of epilepsy. |
<b>Background:</b> The current study aimed to investigate the predictive value of visual-evoked potential (VEP) latency for post-operative visual deterioration in patients undergoing craniopharyngioma resection via extended endoscopic endonasal approach (EEEA). <b>Methods:</b> Data from 90 patients who underwent craniopharyngioma resection <i>via</i> EEEA with intraoperative VEP monitoring were retrospectively reviewed. P100 latency was compared between patients with and without post-operative visual deterioration, and the threshold value of P100 latency for predicting post-operative visual deterioration was calculated by the receiver operating characteristic curve analysis. In addition, other potential prognostic factors regarding post-operative visual outcomes were also analyzed by multivariate analysis. <b>Results:</b> Patients with post-operative visual deterioration showed a significantly longer VEP latency than those without (<i>p</i> < 0.001). An extension over 8.61% in VEP latency was identified as a predictor of post-operative visual deterioration (<i>p</i> < 0.001). By contrast, longer preoperative visual impairment duration and larger tumor volume were not significant predictors for post-operative visual deterioration. <b>Conclusions:</b> The current study revealed that intraoperative VEP monitoring in EEEA is effective for predicting post-operative visual deterioration, and an extension over 8.61% in VEP latency can be used as a critical cut-off value to predict post-operative visual deterioration. |
<b>Background:</b> Identification of an underlying mitochondrial disorder can be challenging due to the significant phenotypic variability between and within specific disorders. Epilepsy can be a presenting symptom with several mitochondrial disorders. In this study, we evaluated clinical, electrophysiologic, and imaging features in patients with epilepsy and mitochondrial disorders to identify common features, which could aid in earlier identification of a mitochondrial etiology. <b>Methods:</b> This is a retrospective case series from January 2011 to December 2019 at a tertiary referral center of patients with epilepsy and a genetically confirmed diagnosis of a mitochondrial disorder. A total of 164 patients were reviewed with 20 patients fulfilling inclusion criteria. <b>Results:</b> A total of 20 patients (14 females, 6 males) aged 0.5-61 years with epilepsy and genetically confirmed mitochondrial disorders were identified. Status epilepticus occurred in 15 patients, with focal status epilepticus in 13 patients, including 9 patients with visual features. Abnormalities over the posterior cerebral regions were seen in 66% of ictal recordings and 44% of imaging studies. All the patients were on nutraceutical supplementation with no significant change in disease progression seen. At last follow-up, eight patients were deceased and the remainder had moderate-to-severe disability. <b>Discussion:</b> In this series of patients with epilepsy and mitochondrial disorders, we found increased propensity for seizures arising from the posterior cerebral regions. Over time, electroencephalogram (EEG) and imaging abnormalities increasingly occurred over the posterior cerebral regions. Focal seizures and focal status epilepticus with visual symptoms were common. Additional study is needed on nutraceutical supplementation in mitochondrial disorders. |
Biomarker discovery, development, and validation are reliant on large-scale analyses of high-quality samples and data. Currently, significant quantities of data and samples have been generated by European studies on Alzheimer's disease (AD) and other neurodegenerative diseases (NDD), representing a valuable resource for developing biomarkers to support early detection of disease, treatment monitoring, and patient stratification. However, discovery of, access to, and sharing of data and samples from AD and NDD research are hindered both by silos that limit collaboration, and by the array of complex requirements for secure, legal, and ethical sharing. In this Perspective article, we examine key challenges currently hampering large-scale biomarker research, and outline how the European Platform for Neurodegenerative Diseases (EPND) plans to address them. The first such challenge is a fragmented landscape filled with technical barriers that make it difficult to discover and access high-quality samples and data in one location. A second challenge is related to the complex array of legal and ethical requirements that must be navigated by researchers when sharing data and samples, to ensure compliance with data protection regulations and research ethics. Another challenge is the lack of broad-scale collaboration and opportunities to facilitate partnerships between data and sample contributors and researchers, in addition to a lack of regulatory engagement early in the research process to enable validation of potential biomarkers. A further challenge facing projects is the need to remain sustainable beyond initial funding periods, ensuring data and samples are shared and reused, thereby driving further research and innovation. In addressing these challenges, EPND will enable an environment of faster and more disruptive research on diagnostics and disease-modifying therapies for Alzheimer's disease and other neurodegenerative diseases. |
Jet Lag Disorder is a Circadian Rhythm Sleep-Wake Disorder resulting from a misalignment of the endogenous circadian clock and the sleep and wake pattern required by a change in time zone. Jet lag is most severe following eastward travel. This multicenter, randomized, placebo-controlled clinical trial (JET) assessed the physiological mechanism of jet lag induced by a real-life transmeridian flight and evaluated the efficacy of tasimelteon-a circadian regulator acting as a dual melatonin receptor agonist, in the treatment of Jet Lag Disorder (JLD). Tasimelteon-treated participants slept 76 min longer on Night 3 during their second trip (evaluation phase) as compared to their first (observational phase). Over the three travel nights evaluated, transmeridian jet travelers in the tasimelteon group slept 131 min more (TST<sub>2/3</sub>) than those in the placebo group. The JET study demonstrated clinically meaningful improvements in nighttime sleep and daytime alertness in both objective and subjective measures as well as global functioning after a real-world flight. These results suggest that tasimelteon can be an effective therapeutic tool to treat JLD in the context of transmeridian travel. |
To analyze the influence of seizure semiology, electroencephalography (EEG) features and magnetic resonance imaging (MRI) change on epileptogenic zone localization and surgical prognosis in children with epileptic spasm (ES) were assessed. Data from 127 patients with medically intractable epilepsy with ES who underwent surgical treatment were retrospectively analyzed. ES semiology was classified as non-lateralized, bilateral asymmetric, and focal. Interictal epileptiform discharges were divided into diffusive or multifocal, unilateral, and focal. MRI results showed visible local lesions for all patients, while the anatomo-electrical-clinical value of localization of the epileptogenic zone was dependent on the surgical outcome. During preoperative video EEG monitoring, among all 127 cases, 53 cases (41.7%) had ES only, 46 (36.2%) had ES and focal seizures, 17 (13.4%) had ES and generalized seizures, and 11 (8.7%) had ES with focal and generalized seizures. Notably, 35 (27.6%) and 92 cases (72.4%) showed simple and complex ES, respectively. Interictal EEG showed that 22 cases (17.3%) had bilateral multifocal discharges or hypsarrhythmia, 25 (19.7%) had unilateral dominant discharges, and 80 (63.0%) had definite focal or regional discharges. Ictal discharges were generalized/bilateral in 71 cases (55.9%) and definite/lateralized in 56 cases (44.1%). Surgically resected lesions were in the hemisphere (28.3%), frontal lobe (24.4%), temporal lobe (16.5%), temporo-parieto-occipital region (14.2%), and posterior cortex region (8.7%). Seizure-free rates at 1 and 4 years postoperatively were 81.8 and 72.7%, respectively. There was no significant difference between electroclinical characteristics of ES and seizure-free rate. Surgical treatment showed good outcomes in most patients in this cohort. Semiology and ictal EEG change of ES had no effect on localization, while focal or lateralized epileptiform discharges of interictal EEG may affect lateralization and localization. Complete resection of epileptogenic lesions identified <i>via</i> MRI was the only factor associated with a positive surgical outcome. |
Charcot-Marie-Tooth (CMT) patients present mainly lower limbs disability, with slowly progressive distal muscle weakness and atrophy, but hands impairment is a relevant problem affecting the quality of life (QoL). The evaluation of the upper limb is of primary importance. Often these patients present subclinical disorders or report difficulties in manipulating objects, with little evidence in the most used outcome measures. We aim to investigate the impact of hand impairment in the perceived QoL of CMT persons and secondly whether the Disability of Arm, Shoulder and Hand (DASH) scale can be useful in assessing upper limb abilities in CMT. We recruited 23 patients with confirmed genetic diagnosis of CMT. We performed a clinical evaluation with Sollerman Hand Function Test (SHFT), Thumb Opposition Test (TOT) and CMT examination score (CMTES). We completed the clinical assessment with DASH scale and the Short form 36 (SF36) questionnaire for a subjective evaluation of upper limb disability and quality of life. All patients also underwent an instrumental evaluation with a hand-held dynamometer measuring hand grip and tripod pinch and a sensor-engineered glove test (SEGT) to evaluate finger opposition movements in a quantitative spatial-temporal way. As expected, we found significant differences between CMT and control group performances in both clinical and instrumental assessment. Concerning QoL, we found that total score of SF36 and the SF36 Physical Composite Score (PCS) correlate with all clinical and instrumental Outcome Measures (OMs), particularly with Tripod pinch strength and TOT, which are considered major determinants of manual dexterity in CMT. DASH scale correlates with most clinical and instrumental OMs. Not surprisingly, we also found a correlation with DASH work, because CMT affects young patients engaged in work activities. However, we found a low correlation with the TOT and the dynamometer suggesting that DASH may not be the best scale for remote monitoring of upper limb disorders in CMT patients. Nevertheless, the results of our study confirm the usefulness of SF36 in recognizing the impact of upper limb disability in these subjects suggesting its use even in the remote monitoring of physical functioning. |
Labrune syndrome (LS) is caused by <i>SNORD118</i> gene mutations with a particular neuroimaging of white matter disease, intracranial calcification, and cysts. There was no effective treatment until now. An 18-year-old man with infancy-onset LS was first treated with vascular endothelial growth factor (VEGF) inhibitor Bevacizumab for 1 year, resulting in significant clinical and radiological improvements. We adopted a similar regimen in a patient with late-onset LS and demonstrated moderate cognitive improvements but without changes in imaging. As such, Bevacizumab could potentially be clinically effective in adult-onset LS with great safety. |
An 80-year-old woman with rheumatoid arthritis had gait difficulties and frequent falls. MRI of the brain showed an extra-axial enhancing lesion overlying the right frontal–parietal cortex, that progressively extended to the contralateral side. This was accompanied by further decline in her functional status. We discuss the diagnostic and therapeutic approach of a pachy–leptomeningeal process in a rheumatoid patient.
An 80-year-old woman presented to the neurology clinic with a history of unsteady gait and frequent falls and was admitted to our hospital for further evaluation. She had history of arterial hypertension, chronic obstructive pulmonary disease, and long-standing rheumatoid arthritis (RA) treated with prednisone and plaquenil. In her youth, she had pulmonary tuberculosis (TB) treated with streptomycin and para-aminosalicylic acid. Six months prior to her clinic visit, she was admitted to a local hospital for surgical treatment of a small bowel obstruction. Postoperatively, she developed atrial fibrillation. She received intravenous unfractionated heparin and then bridged to warfarin (target INR 2.0–3.0). While at the hospital, she fell and had a mild traumatic subarachnoid hemorrhage (SAH). She made a good recovery and was discharged home on warfarin. Four months following hospital discharge, she fell again and had a left femoral fracture requiring hip replacement.
Neurological examination showed a broad based unsteady gait. A non-contrast head CT scan showed scattered dural and vascular calcifications. T2 weighted brain MRI showed an extra-axial high signal intensity lesion overlying the right parietal–occipital region with mild gyriform contrast enhancement (Figures A,B). There were no signal abnormalities on T1 or gradient echo sequences. A diagnosis of subacute post-traumatic SAH was made. She had successful cardioversion, and was discharged home on amiodarone. Five months later, she presented with worsening unsteadiness and left sided weakness. Neurological examination showed impaired left hand dexterity, mild weakness of the left leg, and a broad based gait. Non-contrast head CT showed gyriform hyperdensity along the lateral aspect of the right frontal cortex associated with sulcal effacement. Gadolinium enhanced brain MRI showed an enhancing hyperintense signal abnormality overlying the right parietal and posterior frontal lobes compatible with leptomeningeal and dural thickening (Figures A,B). Cerebrospinal fluid (CSF) analysis showed 62 red blood cells (RBCs), 2 white blood cells (WBCs), a glucose concentration of 60 mg/dl, and a protein content of 75 mg/dl. Cytology was negative for malignancy. HSV and VZV PCRs in CSF were negative. CSF Lyme titer and cryptococcal antigen were likewise negative. CSF VDRL was non-reactive. Viral, bacterial, acid fast bacilli (AFB), and fungal cultures were negative in CSF and blood. Serum C-reactive protein was 2.9 mg/dl, and erythrocyte sedimentation rate was 35 mm/h. Antinuclear antibodies, anti-Smith, anti-ribonucleoprotein, anti-SSa, anti-SSb, anti-histone, and SCL-70 were negative. Serum rheumatoid factor (RF) was <20. Serum angiotensin converting enzyme (ACE) level was 10 U/L. Contrast CT scan of the chest, abdomen, and pelvis showed low density lesions throughout the spleen, and 5 mm nodules in lungs.
Extra-axial high signal intensity overlying the right parietal–occipital region with mild gyriform contrast enhancement .
Hyperintense signal abnormalities overlying the right parietal and posterior frontal lobes with contrast enhancement, compatible with dural and leptomeningeal thickening .
Symptoms progressed, and a follow-up gadolinium enhanced MRI showed worsening of the right-sided leptomeningeal and dural thickening as well as progression of the leptomeningeal enhancement over the left posterior parietal and occipital lobes (Figures A,B). A second lumbar puncture was performed. CSF was clear with an opening pressure of 8 cm of H O. There were 9 RBCs and 12 WBCs (78% lymphocytes). CSF glucose was 60 mg/dl, and the protein content was 77 mg/dl. Cytology was again negative for malignant cells. Flow phenotyping showed mixed lymphoid population, predominantly T-cells and occasional B-cells, with no evidence of clonality. Bacterial, fungal, and AFB cultures were negative in blood and CSF. Quantiferon gold testing for TB was positive.
Increased right-sided leptomeningeal and dural thickening with worsening of leptomeningeal enhancement over the left posterior parietal and occipital lobes .
A brain/meningeal biopsy was done which showed a chronic inflammatory process with numerous plasma cells with Russell bodies and granulomatous reaction of the leptomeninges and dura. Findings were consistent with rheumatoid pachy and leptomeningitis (Figures A,B). In addition, there was an acute and chronic leptomeningitis with focal giant cell reaction with secondary parenchymal perivascular lymphocytic infiltrates with reactive gliosis (Figures A–D). The leptomeninges contained a focal abscess formation with suppurative inflammation (Figures A,B). Special stains including GMS, AFB, FITE, Gram, and cultures were negative. Sections of the pachymeningitis were sent for mycobacterium TB DNA PCR, and were also negative.
(A,B) Dura matter with chronic pachymeningitis.
(A,B) Acute and chronic leptomenigitis. (C) Acute and chronic leptomeningitis with giant cells. (D) Acute and chronic leptomeningitis with secondary parenchymal perivascular lymphocytic infiltrates.
(A,B) Focal abscess formation.
## Discussion
Direct CNS involvement by inflammatory cells in RA, including rheumatoid meningitis, is extremely rare (Bathon et al., ; Chang and Paget, ; Voller et al., ; Bruggemann et al., ).
Bathon et al. ( ) reviewed 19 patients (10 men and 9 women) with inflammatory rheumatoid CNS disease. Most cases were diagnosed at autopsy. No MRI was performed. Most patients had long-standing RA, with mean duration of illness of 14 years at onset of neurological symptoms. Although rheumatoid CNS involvement may occur during the phase of acute synovitis, less than half of these patients had active synovitis when neurological symptoms developed. The authors concluded that RA duration and activity were unreliable indicators to diagnose rheumatoid CNS involvement. However, rheumatoid meningitis may occur early in the course of the disease and may even precede synovitis (Jones et al., ; Starosta and Brandwein, ).
In Bathon’s review, the clinical presentation of rheumatoid meningitis was characterized by altered mental status in 47% of cases, cranial neuropathies in 26%, hemiparesis/paraparesis in 21%, seizures in 21%, and headaches in 11%. Inflammatory rheumatoid CNS disease may produce pachymeningitis and leptomeningitis, as in the case of our patient (Bathon et al., ; Kato et al., ; Jones et al., ; Shimada et al., ). If the dura matter is predominantly affected, these patients present with pachymeningitis, characterized by headaches and cranial neuropathies caused by inflammation or dural fibrosis. If the leptomeninges are predominantly affected, mental status changes, gait imbalance, memory loss, depression, seizures, or paresis are more common (Kupersmith et al., ).
Along with rheumatoid meningitis, Bathon identified other extra-articular manifestations like subcutaneous nodules in 67% of cases, visceral nodules in 47%, and both in 40%. Our patient had lung and spleen nodules; these may have been a manifestation of RA, or related to her previous TB.
Cerebrospinal fluid findings in rheumatoid meningitis are variable and often non-diagnostic (Kupersmith et al., ). Lymphocytic pleocytosis is not always present. An increased protein level is frequently observed. However, sampling at points distant from the lesion might produce pseudo negative results (Kato et al., ; Kupersmith et al., ; Shimada et al., ). New specific markers are being identified. Determination of RF in CSF is often used as a diagnostic marker because strongly positive results specifically indicate the disease (Kato et al., ). Inflammatory cytokines, including TNF-α, IL-1β, and IL-6, play a central role in RA pathogenesis and in some cases high CSF levels have been identified in patients with rheumatoid meningitis, but their significance is still unknown (Kato et al., ; Shimada et al., ). Gadolinium enhanced MRI is quite useful. Diffuse or patchy enhancing extra-axial high intensity lesions in FLAIR and DWI, are considered characteristics of RA meningitis (Jones et al., ; Starosta and Brandwein, ; Shimada et al., ).
The final diagnosis is ultimately histopathological. In Bathon’s review, rheumatoid meningitis diagnosis was not achieved until autopsy in 17 of 19 patients. In two patients diagnosis was made by open brain biopsy. On gross examination, thickened meninges and nodules or plaques were observed frequently. Microscopic pathological examination demonstrated three abnormal patterns: rheumatoid nodules, non-specific meningeal inflammation, and vasculitis. Nodules were the most common finding (68%) and were histologically identical to subcutaneous rheumatoid nodules. Nodules were located in the cranial meninges (92%) and in the choroid plexus (15%). There were two cases affecting the spinal meninges. Nodules were absent in the brain parenchyma or spinal cord. Non-specific inflammatory infiltrates in the leptomeninges or pachymeninges, with mononuclear cells, particularly plasma cells, and less frequently necrosis and multinucleated giant cells, was found in 63% of cases. Reasons for a meningeal predilection, particularly the dura, rather than brain parenchyma, by invading inflammatory cells are unclear. Autoimmunity to collagen, a major component of the dura but not brain parenchyma, may play a role (Bathon et al., ). Vasculitis was identified in 37% of cases and involved the brain, spinal cord parenchyma, as well as the meninges. Vessel wall infiltrates consisted of lymphocytes and plasma cells. The authors concluded that CNS rheumatoid nodules might be considered specific for CNS rheumatoid disease. In instances of non-specific chronic inflammatory infiltration by vasculitis or meningitis the preponderance of plasma cells may distinguish CNS rheumatoid disease from other connective tissue disorders such as systemic lupus erythematosus (SLE) or Sjögren syndrome. Among patients diagnosed at autopsy, the most common findings are rheumatoid nodules. Among patients diagnosed by biopsy, it is a non-specific inflammatory infiltration, probably secondary to limited tissue sampling (Bathon et al., ; Jones et al., ; Starosta and Brandwein, ; Li and Kuzuhara, ; Shimada et al., ). In our patient, biopsy showed chronic inflammatory infiltration rich in plasma cells in both the pachymeninges and the leptomeninges. There was also a small focal abscess even though cultures were negative. In Bathon’s report, polymorphonuclear infiltrates were found in three patients (25%). Although our patient had a positive quantiferon TB gold test, likely due to her previous TB, AFB stains, cultures, and mycobacterium TB DNA PCR were negative in the meningeal samples.
There are no clear guidelines for the treatment of rheumatoid meningitis. Cyclophosphamide, azathioprine, and methotrexate in combination with corticosteroids have been recommended (Kato et al., ; Kupersmith et al., ; Jones et al., ; Starosta and Brandwein, ; Li and Kuzuhara, ; Shimada et al., ). Improvement has been obtained with corticosteroids alone. This suggests that immunosuppressants may not always be necessary in the induction phase of treatment, although they may be required to achieve tapering or cessation of corticosteroids to avoid potential long-term adverse effects (Shimada et al., ). Recurrence of rheumatoid meningitis in a patient treated with infliximab (Chou et al., ) and pachymeningitis occurring after administration of adalimumab have been reported (Ahmed et al., ).
## Conflict of Interest Statement
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
|
Unilateral major limb amputation causes changes in sensory perception. Changes may occur within not only the residual limb but also the intact limb as well as the brain. We tested the hypothesis that limb amputation may result in the detection of hand sensation during stimulation of a non-limb-related body region. We further investigated the responses of unilateral upper limb amputees and individuals with all limbs intact to temporally based sensory tactile testing of the fingertips to test the hypothesis that changes in sensory perception also have an effect on the intact limb. Upper extremity amputees were assessed for the presence of referred sensations (RSs)—experiencing feelings in the missing limb when a different body region is stimulated, to determine changes within the brain that occur due to an amputation. Eight of 19 amputees (42.1%) experienced RS in the phantom limb with manual tactile mapping on various regions of the face. There was no correlation between whether someone had phantom sensations or phantom limb pain and where RS was found. Six of the amputees had either phantom sensation or pain in addition to RS induced by facial stimulation. Results from the tactile testing showed that there were no significant differences in the accuracy of participants in the temporal order judgment tasks ( p = 0.702), whereby participants selected the digit that was tapped first by a tracking paradigm that resulted in correct answers leading to shorter interstimulus intervals (ISIs) and incorrect answers increasing the ISI. There were also no significant differences in timing perception, i.e., the threshold accuracy of the duration discrimination task ( p = 0.727), in which participants tracked which of the two digits received a longer stimulus. We conclude that many, but not all, unilateral upper limb amputees experience phantom hand sensation and/or pain with stimulation of the face, suggesting that there could be postamputation changes in neuronal circuitry in somatosensory cortex. However, major unilateral limb amputation does not lead to changes in temporal order judgment or timing perception tasks administered via the tactile modality of the intact hand in upper limb amputees.
## Introduction
Great debate ensues regarding the etiology of phantom limb sensations (PLSs) and associated pain. Almost all amputees experience PLSs ( , ). The minority that tends not to experience any phantom sensations typically includes congenital amputees ( , ), although one study has identified such experiences in this population ( ). More than 80% of all amputees will also experience phantom limb pain (PLP), characterized by electric shock, stabbing, and cramping sensations ( ). PLP is a debilitating condition for many amputees. Unfortunately, the mechanisms that create phantom experiences, including sensation and pain, are not understood. When an amputation occurs, the peripheral limb is removed from the body causing drastic changes not only in the peripheral but also in the central nervous systems. Muscles and nerves attempt to forge new connections in place of lost ones, causing reorganization within the residual limb and the brain ( ). Several imaging studies have shown that, after an amputation, cortical representations of adjacent remaining body parts take over the cortical area that once responded to the now amputated region. In particular, the face-representing somatosensory cortical region expands and takes over the arm area in upper extremity amputees ( , – ).
Determining the mechanisms and specific pathways within the brain that are affected by an amputation will lead to further insight regarding the experience of PLS and pain. This study aimed to investigate the changes that occur within the brain of upper extremity amputees, testing the hypothesis that upper extremity amputees will experience hand-to-face remapping. Through the utilization of temporally based tactile stimulation, we also examined potential sensory perception changes that could occur within pathways controlling the intact limb. This study aimed to confirm previous studies on hand-to-face remapping while also determining other factors that may play a role in such experiences.
## Participants and Methods
### Participants
Participants for this study included 19 unilateral upper extremity amputees (Table ) and 27 normal control participants. Control participants were recruited through the University of Tennessee Health Science Center from July 2015 through July 2016, and amputee participants were recruited at the National Amputee Coalition conferences in Tucson, AZ (July 2015) and Greensboro, NC (June 2016). The Institutional Review Board at the University of Tennessee Health Science Center gave approval for the study, and all participants provided written informed consent. Inclusion criteria, except for the presence of an amputation, were the same for all groups and included being between the ages of 18 and 65, not having brain injury, able to follow instructions, and normal or corrected-to-normal vision. Exclusion criteria included evidence or the history of a major medical, neurological, or psychiatric illness, any traumatic brain injury, a learning disability, and drug or alcohol abuse/dependence within the last 3 months, except nicotine, taking prescription drugs or supplements that might affect brain function, and having serious vision or hearing problems.
Unilateral upper extremity amputee participant information .
Data include participant number, location of the amputation (R, right; L, left; AE, above elbow; BE, below elbow), cause of the amputation, whether or not they experienced referred sensation (RS), phantom limb sensation (PLS), and/or phantom limb pain (PLP) .
### Hand-to-Face Remapping
Amputees were asked to complete a series of questions regarding their amputation and phantom experiences. Information regarding the time since the amputation, the experience of phantom sensations, and the experience of PLP were investigated. Nineteen upper extremity amputees participated in facial mapping in order to determine how the brain reorganizes sensations as a result of amputation. The facial responses experienced by each amputee were mapped by using a stimulus consisting of a Q-tip brushed over different areas of the face. Testing began over the forehead with short smooth brushes and then moved around the eye, down the cheek, and over the chin. Brushing was completed both contralateral and ipsilateral to the side of amputation. As the investigator was brushing the face, the amputee was instructed to verbally express where the location of the brushing was felt. If facial mapping caused sensations within the phantom limb, repeat testing with a Q-tip dipped in cold water was performed. Finally, participants attempted to map the sensations on their own. All verbal reports of sensation felt within the phantom limb were recorded and the location(s) identified.
### Tactile Testing
Tactile stimuli were delivered to two fingers with a custom-built tactile stimulator (Cortical Metrics, Carrboro, NC, USA). Control participants underwent a battery of testing conducted on the index and middle fingers of both right and left hands. Upper extremity amputees completed the testing with the intact hand . Testing included temporal order judgment and duration discrimination tasks. During the testing session, the participants were situated with their arm (right then left for controls, intact arm for amputees) on a wrist support and fingers positioned appropriately over the tactile stimulator. Mechanical stimulation was applied to the tips of the index and middle fingers. A computerized procedure guided participants through a series of questions, answered via verbal report and recorded by a research member, relating to what the participants perceived on the tips of each finger. In both of the tasks described below, a simple tracking paradigm was used to determine each participant’s difference limen, the amount that the stimulus must be changed in order for differences between finger perceptions to be detected. Visual cues on the computer screen informed participants about appropriate times to provide their response. Practice trials were performed before each test to allow the participants to become familiar with the test, and three consecutive correct responses to the training trials were required before data acquisition began. The participant was not provided with feedback or knowledge or response accuracy during data collection trials.
### Temporal Order Judgment
To assess temporal order judgment, two taps were delivered sequentially, one to each finger, with an initial interstimulus interval (ISI) of 150 ms. Participants were queried as to which of the two stimuli came first. Subsequently, as the result of the subjects’ response, the ISI was altered between each trial. The tracking paradigm employed resulted in correct answers leading to shorter ISIs and incorrect answers increasing the ISI. For each trial, the finger that received the first of the two pulses was chosen randomly. Subjects were required to report which finger was tapped first.
### Duration Discrimination
Duration discrimination is the minimal difference in durations of two stimuli at which an individual can successfully identify the stimulus that has a longer duration. Sequential stimulus vibrations of varying durations were delivered, one to each finger. Subjects were asked to report which of the two fingers received the longer stimulus duration. The “standard” stimulus lasted 500 ms and at the start the “test” stimulus lasted 750 ms. Discrimination threshold determination was assessed using the same tracking paradigm, which reduced the duration of the test stimulus when subjects answered correctly and increased the duration of the test stimulus when the responses were incorrect. The finger and order of the stimuli were chosen at random for each trial.
### Data Analysis
ANOVA was used to analyze results of tactile testing comparing between upper extremity subject groups and controls. Analyses conducted on the experience of referred sensation (RS) and the presence of PLP were also completed utilizing direct participant verbal reports, a 2 × 2 factorial ANOVA test and a Pearson’s correlation test. Significance was determined by a p value <0.05.
## Results
### Hand-to-Face Remapping
Eight out of 19 (42.1%) upper extremity amputees, including one congenital participant, experienced a mislocalization of touch when an area of their face was brushed. Similar to the results reported by Ramachandran and Rogers-Ramachandran ( ), points on the face of each participant who reported elicited sensations within the phantom limb were documented and marked on a forelimb and face diagram, indicating the appropriate body region (Figure ). The cheek area evoked the greatest number of RSs. Two participants experienced the feeling of their little finger when the cheek was brushed. Two participants also reported feeling the first finger when the cheek was stimulated. In addition, amputees reported feeling the thumb, back of hand, underside of the arm, and elbow through cheek stimulation. Three participants reported mislocalization of touch when the forehead was stimulated, expressing feelings within the third finger, palm, and thumb. When the chin was brushed, two participants experienced RSs of the thumb and palm. Four of the participants only experienced phantom sensations in the amputated limb when the ipsilateral side of the face to the amputation was stimulated. Two participants experienced sensations in the amputated limb when either side of the face was stimulated, and one felt sensations when more of the center of the face was stimulated.
Locations touched on the faces that were reported to also elicit sensations within the phantom limb . Each point marked by an “X” on the face corresponds to a location that was felt on the phantom limb, represented in the same color. (A) – (G) represent the individual experiences felt by different participants, labeled by subject numbers that correspond to Table .
After the identification of prominent mislocalization of touch, investigators attempted to remap the experiences with a Q-tip dipped in cold water. Only one participant felt that the sensations were more intense with the cold water, all other participants reported the same experiences as felt with the dry Q-tip. Once the cold-water test was completed, the subjects were then asked to conduct the facial mapping on themselves. Two participants reported being able to still feel RSs, while six participants no longer felt any RSs.
In addition to obtaining reports of the mislocalization of touch, investigators also asked participants to report the time since amputation and their experiences with phantom sensation or PLP. Statistical analysis conducted on the time since amputation and the presence of RS showed no correlation, with the average time since amputation being 211.26 months ( p = 0.507). A 2 × 2 factorial ANOVA determined that there was no significant correlation between any of the experiences and the presence of RS ( p = 0.134). A Pearson’s correlation test confirmed these results as well ( p = 0.134). Out of the eight upper extremity amputees who experienced RS, four had PLP and four reported no PLP. In addition, six regularly experienced phantom sensations prior to testing and only two did not. Also, 6 of the 11 amputees who did not experience RSs also experienced PLP, and 10 experienced PLS, with only 1 not experiencing the presence of the missing limb.
Amputees who do not experience PLSs tend to be congenital amputees (those born without a limb) ( , ). Three congenital amputees completed the hand-to-face remapping study and reported their experiences with phantom sensations and PLP. Initially, all three congenital upper extremity amputees self-reported no RSs, no phantom sensations, and no PLP. However, two of the three participants, when asked to graphically depict their phantom limb on a piece of a paper, completed the task, suggesting that they do, in fact, feel the presence of a phantom limb. The one congenital amputee who was unable to trace the phantom limb reported that they did not have phantom sensation, just a “fizzy” feeling, again implying the feeling of sensation within the missing limb. Additionally, while conducting the hand-to-face remapping task, one of the congenital amputees who was able to depict their phantom limb felt the brushing sensation within the palm of the phantom limb when the forehead and chin were stimulated. This information is important to note, considering the rarity of phantom sensation reported by congenital amputees.
### Temporal Order Judgment
In the temporal order judgment task, in which the participant was instructed to determine which digit experienced a test tap stimulus first, the mean threshold scores were 31.4 ± 19.5 and 34 ± 22.8 ms for the control and upper extremity amputees, respectively ( p = 0.702). Additional analysis was conducted to determine correlations between threshold scores and whether the left or right arm was amputated. When compared to controls, neither right nor left-arm amputees differed in the temporal order judgment task ( p = 0.668).
### Duration Discrimination
The threshold accuracy of the duration discrimination task was determined to investigate the potential changes in the accuracy of timing perception for upper extremity amputees. In the duration discrimination task, participants were asked to identify which digit received a longer stimulus. The mean threshold scores were 66 ± 25.5 and 69.1 ± 28.3 ms for control and upper extremity amputees, respectively ( p = 0.727). When the amputees were separated based on the side of their amputation, results showed no significant difference in the scores obtained on the duration discrimination task ( p = 0.204).
## Discussion
When an individual loses a limb, many changes occur, not only within the peripheral system but also within the central nervous system. Although descriptions of phantom sensations and phantom pain have been around since at least the sixteenth century ( ), the etiology of these experiences is still not understood. After an amputation occurs, the nerves and muscles attempt to build connections wherever possible, leading to reorganization within the residual limb. Whether this reorganization fuels the central nervous system reorganization or vice versa needs further investigation. Results from this study indicate that cortical reorganization may be confined to the contralateral somatosensory cortex and does not significantly affect other cortical areas or spread transcallosally to the somatosensory cortex in the opposite hemisphere.
Our investigation of the effects of an amputation on the cortex were conducted through the use of facial mapping. By using a Q-tip to brush areas of an amputee’s face and evoking phantom sensations in the missing limb, we were able to positively identify hand-to-face remapping in 42.1% of upper extremity amputees. Such results show that the removal of an upper extremity does indeed cause changes within the main cortical target of somatosensory input projections, the somatosensory cortex. Additionally, this study showed that cortical reorganization is not always directly linked to the experience of PLP, since half of those experiencing mislocalization of touch failed to report any PLP. The time since amputation also did not play a role in the experience of cortical reorganization. Results are encouraging, if not definitive, and provide an important first step for future studies involving the timing of the onset and overall plasticity of cortical reorganization. One very interesting finding arising from this study was the identification of a congenital amputee who experienced mislocalization of touch. Facial mapping caused sensation within the palm of the missing limb as the forehead and chin were brushed. Initially, this participant reported that they did not experience PLP or a phantom limb; however, when asked to depict the phantom limb on a piece of paper, they proceeded to trace around a limb that they still perceived, a phantom representation. The ability of this individual to feel the palm of the missing limb on their forehead and chin shows that the brain has undergone cortical reorganization. These findings raise questions about the cause of the congenital limb loss. Although this person was born without the limb, there is the possibility that the limb was formed in utero and then removed, such as from amniotic band syndrome. In this scenario, there was regression of the limb during development such that the limb representation developed within the brain and did not disappear when the limb was lost. If the limb never formed during development, it is possible that the cortex still maintains some innate representation of all body parts. Such findings go against multiple studies that indicate congenital amputees do not experience phantom limbs and/or RS due to cortical reorganization ( , , ). Furthermore, since there was no correlation between the presence of phantom pain and whether there was detectable hand-to-face remapping, these findings suggest that cortical reorganization alone is not the etiology of phantom pain as previously postulated ( – ).
Furthermore, temporally based tactile stimulation testing was completed on upper extremity amputees to determine the effects of amputation on the temporal processing in the CNS. Temporal order judgment task and duration discrimination task are timing tests that are controlled by areas of the brain other than the somatosensory cortex. As described by multiple studies, the ability to judge which finger receives the first test pulse is controlled mainly by the pre-supplementary motor area and posterior parietal cortex ( – ). Duration discrimination, the ability to determine which test pulse lasted longer, is thought to reflect activity predominantly centered in the cerebellum ( ). Results from tactile testing on the intact limb of upper extremity amputees and controls showed that there is no significant difference on timing perception tasks between the two groups. These findings suggest that amputations lead to remapping effects that do not have an impact on timing measures that take place outside of the denervated somatosensory cortex or changes within pathways controlling the intact limb.
For clinical purposes and the management of PLP, more effort into determining the utility of visualization and residual limb movement therapies is necessary, especially if cortical reorganization alone is not a key factor in the presence of phantom pain. Future research efforts should focus on the timing of cortical reorganization to gain more insight into whether the peripheral or central nervous systems cause and/or maintain the phantom experiences. Additionally, tactile testing targeting the somatosensory cortex contralateral to the amputated side will provide information regarding changes there and effects that therapies and treatments contribute to these changes. Determining the effects that an amputation has on the organization of the brain will enable researchers to gain further knowledge about the presence of PLSs. Finally, more research needs to be conducted on the experience of phantom sensations felt by congenital amputees. It is possible that determining the factor that causes a congenital amputee to experience phantom sensations may lend great insight to the understanding of overall phantom experiences.
## Ethics Statement
This study was carried out in accordance with the recommendations of the human research protection guidelines, University of Tennessee Health Science Center IRB, with written informed consent from all subjects. All subjects gave written informed consent in accordance with the Declaration of Helsinki. The protocol was approved by the University of Tennessee Health Science Center IRB.
## Author Contributions
KC assisted with running of participants, assisted with data analysis, and lead manuscript creation. DM oversaw and completed the running of participants and completed data analysis. KH assisted with running of participants and analyzing data. MT and OF designed the protocol, completed data analysis, and assisted with the manuscript. RW assisted with data analysis, manuscript creation, and editing of the manuscript. JT oversaw study design and execution and led manuscript editing.
## Conflict of Interest Statement
MT is cofounder of Cortical Metrics, the company that built the tactile stimulator used in the study. No other authors have a conflict of interest.
|
## Objective
Galvanic vestibular stimulation (GVS) delivered as zero-mean current noise (noisy GVS) has been shown to improve static and dynamic postural stability probably by enhancing vestibular information. The purpose of this study was to examine the effect of an imperceptible level noisy GVS on ocular vestibular-evoked myogenic potentials (oVEMPs) in response to bone-conducted vibration (BCV).
## Materials and methods
oVEMPs to BCV were measured during the application of white noise GVS with an amplitude ranging from 0 to 300 µA [in root mean square (RMS)] in 20 healthy subjects. Artifacts in the oVEMPs caused by GVS were reduced by inverting the waveforms of noisy GVS in the later half of the stimulus from the one in the early half. We examined the amplitudes of N1 and N1–P1 and their latencies.
## Results
Noisy GVS significantly increased the N1 and N1–P1 amplitudes ( p < 0.05) whereas it had no significant effects on N1 or P1 latencies ( p > 0.05). Noisy GVS had facilitatory effects in 79% of ears. The amplitude of the optimal stimulus was 127 ± 14 µA, and it increased the N1 and N1–P1 amplitude by 75.9 ± 15% and 47.7 ± 9.1%, respectively, as compared with 0 µA session ( p < 0.05).
## Conclusion
Noisy GVS can increase the amplitude of oVEMPs to BCV in healthy subjects probably via stochastic resonance. The results of the present study suggest that noisy GVS may improve static and dynamic postural stability by enhancing the function of the vestibular afferents.
## Introduction
The vestibular labyrinth, which is composed of three semicircular canals and two otolith organs in each ear, senses angular and linear movement of the head, thereby contributing to stabilization of body balance ( ). Bilateral dysfunction of the vestibular labyrinth results in dizziness, oscillopsia, and postural instability while moving the head or body.
Galvanic vestibular stimulation (GVS) modulates activity in vestibular hair cells and their afferents by delivering electrical current subcutaneously through electrodes placed over the mastoid bones ( , ). GVS has been used to probe the vestibular system and its effects on posture and gait ( ). It has been shown that GVS delivered as zero-mean current noise (noisy GVS) of an imperceptible magnitude improves various functions, such as the baroreflex function in healthy subjects ( ), and autonomic and motor functions in patients with multisystem atrophy and Parkinson’s disease ( , ). Recently, we have shown that noisy GVS can improve postural stability in healthy subjects as well as in patients with bilateral vestibular dysfunction ( ).
The rationale behind these ameliorating effects of noisy GVS is considered to be stochastic resonance (SR), which is a phenomenon wherein the response of a non-linear system to a weak periodic input signal is optimized by the presence of a particular level of noise ( , ). In this phenomenon, an optimal amount of added noise results in the maximum enhancement, whereas further increase in the noise intensity only degrades detectability or information content. Previous studies have shown that an appropriate intensity of noise can magnify detection of weak subthreshold signals in a variety of sensory information such as visual ( ) and auditory perception ( , ). However, it has not been tested whether noisy GVS can really intensify vestibular information.
In the present study, we investigated the effects of noisy GVS on ocular vestibular-evoked myogenic potentials (oVEMPs) to bone-conducted vibration (BCV). oVEMPs reflect the function of otolith-ocular reflex, especially originated from the utricle ( , ). The purpose of this study was to see whether SR-like phenomenon can be observed in the vestibulo-ocular reflex.
## Materials and Methods
### Subjects
Twenty healthy subjects (10 men, 10 women; age 25–60 years, mean age 42 ± 2.6 years) were recruited. None of the subjects reported any auditory, vestibular, neurologic, cardiovascular, or orthopedic disorders. All subjects gave written informed consent. All procedures were in accordance with the Declaration of Helsinki and were approved by the University of Tokyo Human Ethics Committee (no. 3379) and registered in UMIN-CTR (UMIN00000829).
### oVEMPs to BCV
The methods for recording oVEMPs to BCV have been described in detail elsewhere ( , ). In brief, with the subject in a supine position, EMG electrodes were placed on the skin 1 cm below (active) and 3 cm below (indifferent) the center of each lower eyelid. The ground electrode was placed on the chin. During testing, the subject looked up approximately 30° above straight ahead and maintained their focus on a small dot approximately 1 m from their eyes. The signals were amplified by a differential amplifier (bandwidth: 0.5–500 Hz), and the unrectified signals were averaged ( n = 50) using Neuropack Σ (Nihon Kohden, Tokyo, Japan).
The BCV stimuli were 4 ms tone-bursts of 500 Hz vibration delivered by a handheld 4810 mini-shaker (Bruel and Kjaer, Naerum, Denmark) fitted with a short rod terminated in a bakelite cap 1.5 cm in diameter, which was placed, without pressure, perpendicularly on the forehead at the hairline in the midline (Fz). The driving voltage was 8.0 V peak to peak, and it produced a peak force level of 128 dB re 1 μN. This BCV caused a linear acceleration in the interaural axis at the mastoids with a maximal acceleration of approximately 0.4 g peak to peak as measured by linear accelerometers placed on the skin over the mastoid. The stimuli were applied five times per second, and the time window for analysis was 50 ms. Responses to 30 stimuli, which was composed of 15 stimuli in the early part and 15 stimuli in the later part, were averaged (Figure ).
Protocols for application of noisy galvanic vestibular stimulation (GVS) and recording ocular vestibular-evoked myogenic potentials (oVEMPs) in response to bone-conducted vibration (BCV) . (A) Schema of GVS and triggers for oVEMP measurement. During the early part of GVS (from 0.5 to 4 s) band noise between 1 and 4000 Hz with a flat spectrum was employed. In this example, the amplitude was 200 µA root mean square (RMS). During the later part of GVS (from 5 to 8.5 s), the waveform was inversed. The black bars at the bottom indicate the 15 triggers in each part for averaging oVEMPs. The short gray bars indicate the eight triggers used for averaging background EMG in the absence of BCV or GVS. (B) Magnified waveforms of GVS in the neighborhood of the oVEMP trigger. Note that the GVS in the later part is inversed. The 4 ms period after the trigger corresponds to the BCV stimulus duration for evoking oVEMPs. (C) oVEMP responses without GVS. (D) oVEMP responses after 15 triggers in the former part of noisy GVS (200 µA RMS). (E) Averaged oVEMP responses after 30 triggers (15 triggers in the early part and 15 triggers in the later part) of noisy GVS (200 µA RMS).
### Galvanic Vestibular Stimulation
Noisy GVS was applied with electrodes on the right and left mastoids by a linear isolator (DPS-560P/DPA-50, Physio-Tech, Tokyo, Japan) with digital storage for GVS waveforms, which are digital-to-analog converted at 20 Hz. Waveforms of noisy GVS and triggers for oVEMPs measurement were synthesized using MATLAB (MathWorks, Inc., Natick, MA, USA). We used band noise GVS ranging from 0.02 to 10 Hz. The waveform of noisy GVS was composed of band noise with a flat spectrum between 1 and 4000 Hz.
To reduce the artifact caused by GVS on the waveforms of oVEMPs, we inverted the waveform of band noise in the later half of the stimulus (from 5 to 8.5 s) from the one which was used in the early half of the protocol (from 0.5 to 4 s) (Figure ). In each period, 15 triggers for generating BCV were applied at 5 Hz. By averaging all the responses of oVEMPs in the early and later periods, any GVS artifact contaminating oVEMP waveforms was reduced.
The root mean square (RMS) of the amplitude of GVS used was 0, 25, 100, 200, and 300 µA. The intensities were applied in a randomized order during oVEMP recording. During each stimulus, subjects were asked whether they feel any sensation or pain caused by noisy GVS.
### Data Analysis
We analyzed the first negative peak (N1) with a latency of around 10 ms and the second positive peak (P1). The latency was measured from the onset of the stimulus to the peak. The amplitude of N1 was measured from the baseline to the peak, and the amplitude of N1–P1 was measured between N1 and P1.
We judged that noisy GVS had facilitatory effects on oVEMP responses when it increased the amplitude of both N1 and N1–P1 simultaneously in at least two consecutive intensities of noisy GVS relative to the control (0 µA). The optimal noisy GVS stimulus was defined as that which led to the greatest increase in N1 amplitude.
Data are expressed as mean ± SEM. The ratio of each parameter during each intensity of noisy GVS to that without GVS (0 µA) was calculated [normalized ratio (NR)]. The NRs of oVEMP amplitude and latencies of the responses at each GVS amplitude were compared using a one-way repeated measures analysis of variance on ranks (RM ANOVA) followed by the Tukey post hoc test. The amplitudes and latencies of oVEMP responses measured during the control and optimal stimulation trials were compared using the Wilcoxon signed-rank test. A difference was considered significant at p < 0.05.
## Results
By inverting the waveform of noisy GVS in the later half of stimulus to that in the early half, artifacts generated by GVS in the responses of oVEMPs to BCV were successfully erased in 34 (from 17 subjects) of the 40 ears (85%) in our 20 healthy volunteers (Figure ). Six ears (from three subjects) in which artifacts generated by GVS could not be erased by this method were excluded from the analysis (Figure ).
An example of oVEMPs to bone-conducted vibration in which artifacts generated by noisy galvanic vestibular stimulation (GVS) could not be erased successfully by inverting the waveforms of noisy GVS in the later half of the stimulus from the one in the early half . (A) oVEMP responses without GVS. (B) oVEMP responses with GVS.
Figure shows oVEMPs to BCV under various intensities of noisy GVS in a typical 58-year-old female subject. This subject showed an increase in the amplitude of both N1 and N1–P1 simultaneously under white noise GVS at an intensity of 50 µA, while a further increase in stimulus intensity resulted in a decrease in the amplitudes, suggesting the presence of an SR-like phenomenon (Figure ).
Changes in oVEMPs to bone-conducted vibration (BCV) during application of noisy galvanic vestibular stimulation (GVS) in a representative subject . (A) oVEMP responses to BCV during application of various intensities of GVS in a 58 year-old healthy subject. (B) Changes in the N1 and N1–P1 amplitudes of oVEMP responses during application of noisy GVS. In this subject, GVS at an intensity of 50 µA increased the amplitude of oVEMP responses, whereas further increases in intensity caused deterioration.
A comparison of NRs of N1 and N1–P1 amplitudes across each intensity of noisy GVS in 34 ears revealed that noisy GVS had significant effects on the amplitude of N1 as well as N1–P1 (RM ANOVA, p < 0.05; Figure A) and that there were significant differences between the control session (0 μA) and 100 µA GVS in both N1 and N1–P1 amplitudes (Tukey post hoc test, p < 0.05). On the other hand, noisy GVS had no significant effects on N1 or P1 latencies (RM ANOVA, p > 0.1; Figure B).
Changes in the amplitude and latency of oVEMPs to bone-conducted vibration during application of noisy galvanic vestibular stimulation (GVS) . (A) Changes in the normalized ratio of N1 amplitude (left panel) and N1–P1 amplitude (right panel) of oVEMP responses during application of noisy GVS in 34 ears. Mean ± 1 SEM is shown. * p < 0.05. (B) Changes in N1 latency (left panel) and P1 latency (right panel) of oVEMP responses during application of noisy GVS in 34 ears.
Among the 34 ears, noisy GVS had facilitatory effects in 27 ears (79%). The intensity of the optimal stimulus was 127 ± 14 µA, which was significantly smaller than the threshold of sensation (283 ± 21 µA; paired t test, p < 0.01). None of the subjects reported pain or unpleasant symptoms during or after the stimulus in this study.
The optimal intensity of the stimulus increased N1 and N1–P1 amplitude by 75.9 ± 15% and 47.7 ± 9.1%, respectively, as compared with the 0 µA session (Wilcoxon signed-rank test, p < 0.05; Figure A). On the other hand, there were no significant effects on the latencies of N1 or P1 responses ( p > 0.1; Figure B).
Effects of the optimal intensity of noisy galvanic vestibular stimulation (GVS) on oVEMPs to bone-conducted vibration . (A) Average (±1 SEM) of the N1 and N1–P1 amplitude without GVS (control) and with optimal intensity GVS [GVS (+)] across all ears in which noisy GVS had ameliorating effects ( n = 27). * p < 0.05. (B) Average (±1 SEM) of the N1 and P1 latencies during control and GVS (+) across all ears in which noisy GVS had ameliorating effects ( n = 27).
## Discussion
In the present study, we have examined the effects of low-intensity noisy GVS on oVEMPs to BCV in healthy subjects and have shown that an appropriate intensity of GVS can significantly increase the amplitude of oVEMP responses while it has no significant effects on their latencies.
Noisy GVS has been shown to improve static and dynamic postural stability, probably via SR ( , – ). We have previously shown that noisy GVS can improve postural stability in healthy individuals as well as in patients with bilateral vestibulopathy ( ). Recently, Wuehr et al. have shown that noisy GVS is also able to improve dynamic postural stability during walking in healthy subjects ( ) as well as in patients with bilateral vestibulopathy ( ). However, it has not been tested whether noisy GVS can intensify vestibular function.
To examine the effect of noisy GVS on vestibular function, we employed oVEMPs to BCV. oVEMPs reflects the function of the utricle and its afferents mediated by a crossed-ocular pathway ( , ). oVEMPs have several advantages over other vestibular function tests such as caloric tests or cervical vestibular-evoked vestibular myogenic potentials ( ), one such advantage being that oVEMPs can be recorded easily and repeatedly without discomfort or fatigue. Since oVEMPs are excitatory myogenic potentials, they can detect subtle changes in amplification of vestibular responses as compared to cVEMPs in cases such as superior canal dehiscence syndrome ( ) or Meniere’s disease ( ).
However, we encountered a problem in recording oVEMPs under noisy GVS because GVS caused clear artifacts in the waveforms of the oVEMPs. We overcame this problem by inverting the waveform of noisy GVS in the second half of the stimulus. By averaging all the responses, we successfully reduced the GVS artifacts in the oVEMP waveforms in a majority of ears tested. The small number of ears in which artifacts could not be reduced might be due to insufficient lowering of skin resistance of the electrodes.
We applied various intensities of noisy GVS during the recording of oVEMPs to BCV and showed that an appropriate intensity of noisy GVS significantly increases the amplitude of the oVEMP responses in approximately 80% of healthy subjects. The hypothesized mechanism underlying this enhancement of oVEMP amplitude by noisy GVS is SR, in which an optimal amount of added noise results in enhancement of the information content whereas a further increase in the noise intensity degrades the content ( , ). It has been shown that an appropriate intensity of noise can improve detection of weak subthreshold signals in visual ( , ), auditory ( , ), and tactile perception ( , ). Since the vestibular system is fundamentally non-linear ( , ), an appropriate intensity of noisy GVS might increase the activity of vestibular afferents by lowering the threshold of excitation through small changes in transmembrane potentials ( ), leading to enhancement of oVEMP amplitudes. It is unknown why noisy GVS did not have facilitatory effects in approximately 20% of healthy subjects in this study. However, this result is consistent with our previous study that noisy GVS improved static postural stability in 76% of healthy subjects ( ). It is possible that the subjects who did not show facilitatory effects of noisy GVS might already have sufficiently high vestibular function, so there might be little room for improving vestibular function.
In the present study, the optimal intensity of noisy GVS for oVEMPs to BCV was approximately 127 µA in RMS, which is smaller than the optimal intensity of the stimulus for improvement of postural stability in healthy subjects (228 µA in RMS) in our previous study ( ). One possible explanation for this difference might be different sensitivity to noisy GVS between the vestibulo-ocular pathway and the vestibulo-spinal pathway. It has been shown that a higher stimulus intensity is necessary to evoke a vestibulo-spinal reflex in the leg than that in the neck ( ). Another possibility is that there was a difference in the basal activities of the vestibular afferents between the oVEMPs and stabilometry recording sessions. In oVEMPs to BCV, noisy GVS modulates the activity of vestibular afferents, which are strongly activated by BCV, whereas in posturography, GVS modulates the activity of vestibular afferents, which are weakly activated by small head movements during two-legged stance tasks.
Our study has some limitations. First, we examined only oVEMPs to BCV to investigate the effect of noisy GVS on vestibular systems. It is possible that noisy GVS may have different effects on other vestibular function tests such as caloric tests or cervical VEMPs. To examine the effect of noisy GVS on vestibular afferents precisely, direct recording of the activity of vestibular afferents in animals is required ( ). Second, artifacts caused by GVS could not be erased completely by reversing the noisy GVS stimulus during recording. Small artifacts of GVS which remained in oVEMP responses to BCV might have affected the precise measurement of the oVEMP responses. However, in oVEMP responses under noisy GVS up to 200 µA, the effect of artifacts was minimal in the present study. Third, we included healthy subjects only in this study. To confirm noisy GVS enhances the function of the vestibular afferents, it is favorable to include a group of patients with vestibular dysfunction. The experiment that examines the effect of noisy GVS on oVEMPs in patients with peripheral vestibular dysfunction will become the next step of our study.
In conclusion, we have shown that noisy GVS can increase the amplitude of oVEMPs to BCV in healthy subjects probably via SR. The results of the present study suggest that noisy GVS improves static and dynamic postural stability by enhancing the function of the vestibular afferents.
## Author Contributions
All the authors contributed the design of the work presented in this paper. SI, SK, and TK designed the experiment, gathered the data, performed the analysis, and wrote the manuscript. FT and CF designed the experiment, performed the analysis, supervised the writing, reviewed the manuscript, and edited the manuscript. YY and TY reviewed the manuscript and edited the manuscript. All the authors take full responsibility for the correctness of this paper and approved the final version.
## Conflict of Interest Statement
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be constructed as a potential conflict of interest.
|
## Introduction
Individuals suffering from cerebral palsy (CP) often have involuntary, reflex-evoked muscle activity resulting in spastic hyperreflexia. Whole-body vibration (WBV) has been demonstrated to reduce reflex activity in healthy subjects, but evidence in CP patients is still limited. Therefore, this study aimed to establish the acute neuromuscular and kinematic effects of WBV in subjects with spastic CP.
## Methods
44 children with spastic CP were tested on neuromuscular activation and kinematics before and immediately after a 1-min bout of WBV (16–25 Hz, 1.5–3 mm). Assessment included (1) recordings of stretch reflex (SR) activity of the triceps surae, (2) electromyography (EMG) measurements of maximal voluntary muscle activation of lower limb muscles, and (3) neuromuscular activation during active range of motion (aROM). We recorded EMG of m. soleus (SOL), m. gastrocnemius medialis (GM), m. tibialis anterior, m. vastus medialis, m. rectus femoris, and m. biceps femoris. Angular excursion was recorded by goniometry of the ankle and knee joint.
## Results
After WBV, (1) SOL SRs were decreased ( p < 0.01) while (2) maximal voluntary activation ( p < 0.05) and (3) angular excursion in the knee joint ( p < 0.01) were significantly increased. No changes could be observed for GM SR amplitudes or ankle joint excursion. Neuromuscular coordination expressed by greater agonist–antagonist ratios during aROM was significantly enhanced ( p < 0.05).
## Discussion
The findings point toward acute neuromuscular and kinematic effects following one bout of WBV. Protocols demonstrate that pathological reflex responses are reduced (spinal level), while the execution of voluntary movement (supraspinal level) is improved in regards to kinematic and neuromuscular control. This facilitation of muscle and joint control is probably due to a reduction of spasticity-associated spinal excitability in favor of giving access for greater supraspinal input during voluntary motor control.
## Introduction
For decades, whole-body vibration (WBV) has widely been applied in different areas of rehabilitative medicine, geriatrics, and as a training method for elite athletes ( ). More recently, the respective research has increasingly been focused on the application and potential benefits of WBV as a therapy in neuro-rehabilitation, such as in adolescent patient groups with spastic cerebral palsy (CP) ( ). With the advantage of great neuroplasticity in children, training regimes commonly achieve higher efficiency compared to adolescent patient groups ( , ). To date, scientific evidence about possible beneficial effects of WBV on spasticity in CP has only reported the outcome, such as functional parameters of movement control. This is, for example, assessed by the clinical diagnostic tool “(Modified) Ashworth Scale” ( , ). Apart from the fact that this scale has been characterized with significant limitations of reliability ( – ) and validity for children with CP ( ), objective neurophysiological diagnostics, which provide a deeper understanding of WBV and its underlying mechanisms, are lacking.
Depending on the respective origin, severity and position of the lesion, spastic CP as a neurological motor disorder leads to several motor impairments ( , ). Spasticity, as one of the most prominent spinal phenomena, is defined as a motor disorder with velocity-dependent elevated tonic stretch reflexes (SRs) and exaggerated tendon jerks ( ). Those include hyperexcitability of short-latency ( , ), but reduced long-latency reflexes ( ). Coexisting with spasticity, also muscle weakness ( , ) as well as diminished selective and voluntary supraspinal motor control ( ) are expressed in developmental disorders of functional movement: patients suffer from gross motor problems ( ), such as impaired mobility ( ) and postural control ( , ), as well as disturbances in gait ( – ). On a neuromuscular basis, reduced electromyographic activity ( ), a strong co-activation of antagonistic muscles during movement ( , , , ), diminished reciprocal inhibition ( , ), and changes in perception ( ) have been associated with those functional impairments and should be considered regarding therapy of movement disorders.
The benefits of WBV include its uniqueness as a passive training modality during which neuromuscular structures are modulated by mechanical oscillations of the support-surface ( , , ). Thus, its application in neuro-rehabilitation has emerged as a particularly valuable tool. The positive effects of WBV exercise for CP involve acute ( , ) and long-term training-induced adaptations ( – ). Those are well documented on a functional level: while acute modulations comprise enhancements in gait-related parameters ( , ) and joint mobility, mutually accompanied with decreased spasticity ( ), long-term adaptations also include improvements of strength ( , , ), increased ankle excursions ( ), gross motor functioning ( , ), and balance ( ). Nevertheless, those results predominantly focus on the effects of WBV on a functional level, but evidence about the underlying mechanisms is still deficiently researched for patients with neurological disorders. Based on evidence that WBV has an impact on the central motor control in a healthy population, such as a decrease of spinal excitability and Ia afferent reflex activation after WBV ( – ), it could have an impact on deficiently controlled neuromuscular structures in subjects with CP. Nonetheless, a transfer of investigations in a neuro-rehabilitation setting with objective assessment tools is still lacking.
For this purpose, the aim of the current study was to identify neurophysiological changes after WBV in children with spastic CP. In particular, it should be examined whether the phenomenon of reduced spinal excitability after WBV might lead to a reduction of spasticity-related parameters in neuromuscular control. Based on the deficient voluntary movement control in subjects with CP, our approach included three protocols with electrophysiological and functional assessments that are of relevance during everyday movements: besides (1) controlling SR activity, changes were examined including (2) voluntary neuromuscular activation and (3) active joint mobility in the lower limb.
We hypothesized that WBV reduces SR responses, and thus reduces exaggerated sensory input via Ia afferent pathways. We further expected that this release in the spasticity-related condition would produce a better movement control reflected by a reduction in co-contraction and enhanced joint mobility during voluntary activation.
## Materials and Methods
### Subjects
Over 9 months (June 2014–February 2015) subjects with spastic CP were medically examined by the attending physicians in terms of the inclusion criteria of the study, such as diagnosis of unilateral or bilateral spastic CP [gross motor function classification system (GMFCS) score 2–4], the ability to stand upright with support, and healthy cognitive performance, so that procedures were understood by the subjects. Exclusion criteria were neurosurgical procedure of nerve structures (rhizotomy) ( ), acute injuries, and anxiety. For the screening process, subjects underwent physical and medical examination so that 44 subjects could be identified to fit the abovementioned criteria (18 females, 26 males, age 8 ± 4 years, height 123.0 ± 20.2 cm, body mass 26.3 ± 14.1 kg, GMFCS score 2.6 ± 0.5). Based on reliability and task performance ability, subject numbers varied between protocols. The exact numbers are noted below for each single protocol. All subjects and parents gave written informed consent to the diagnostic procedure in accordance with the latest revision of the Declaration of Helsinki, which is approved by the ethics committee of the University Hospital of Cologne.
### Procedures
We used a single-group repeated-measures study design to evaluate acute effects of WBV on neuro-mechanical coupling and motor control within a population of patients suffering from CP. For that purpose, kinematic and neuromuscular activation during motor tasks were recorded prior and immediately after 1-min bout of WBV ( , ) in three different protocols: (1) the first protocol aimed to assess vibration-induced effects on the mechanically evoked SR in m. triceps surae (Figure ). In the second protocol, (2) the influence of WBV on the maximal voluntary muscle activation of shank and thigh muscles was investigated. The third protocol (3) quantified the influence of WBV on the active range of motion (aROM) in the ankle and knee joint. Thus, vibration was applied for each protocol and between protocols a short break was included (>10 min) to minimize fatigue. Measurements were documented, surveyed, and supervised by the authors of the study. Prior to the first measurement, tasks and machinery were demonstrated to the subjects and practiced to exclude the influence of habituation effects within results and to avoid subjects’ anxiety of the investigation’s procedure.
Illustration of the overall subject preparation and the instrumentation in Protocol 1 . The participant was prepared with surface electromyography of the lower limb muscles m. soleus (SOL), m. gastrocnemius medialis (GM), m. tibialis anterior (TA), m. rectus femoris (RF), m. vastus medialis (VM), and m. biceps femoris (BF) as well as electro-goniometry of the ankle and the knee joint. In Protocol 1 , stretch reflexes (SRs) were elicited mechanically by a dorsiflexion induced with the ankle ergometer (A,B) . Muscular responses (C) were recorded continuously; SDs are illustrated in gray.
Outcome measures were recorded twice prior to WBV under the same conditions with short breaks (>10 min) to ensure that changes were not based on habituation or learning effects and to exclude subjects from the data analysis who were unable to repeat the tasks reliably in the required paradigms. Subjects who demonstrated great variations (>40%) between two separated pre-measurements were excluded. In detail, maximal 12 subjects (of 37, Protocol 1 ), 11 subjects (of 23, Protocol 2 ) and 16 subjects (of 27, Protocol 3 ) had to be excluded. All other subjects were included to calculate mean values (as described in “Calculation of Outcome Parameters”).
### Whole-body Vibration
We used a side-alternating vibration platform (Galileo Sport, Novotec Medical, Pforzheim, Germany), which generates vibration by platform oscillations along the frontal plane ( ). Subjects were placed on the upper surface of the platform, centrally aligned over the axis of rotation, with their feet parallel about 8.5–17.0 cm apart from the axis of rotation, depending on the respective body height ( ). The given distances resulted in a range of vibration amplitude of 1.5–3 mm (peak-to-peak displacement 3–6 mm). Independently from vibration amplitude, the frequency was set individually for each subject, ranging between 16 and 25 Hz, based on the subjects’ abilities (peak acceleration ranging between 15 and 48 m s ). Exposure to WBV was set for 1 min ( , ), with subjects maintaining a static body position with a knee angle of 10°, forefoot stance and equal weight distribution on both feet ( ). Neuromuscular and kinematic assessments were conducted immediately following WBV exposure.
### Protocols
For all three protocols, subjects were prepared for kinematic and neuromuscular assessment: first, monoaxial electro-goniometers (Biometrics , Gwent, UK) were attached to lower limb joints, with the rotation axis being placed on the malleolus lateralis of the ankle and over the lateral knee joint center, respectively. The moveable arms of the goniometers were lined up with the longitudinal axis of the foot and shank (ankle) as well as with the longitudinal axis of the shank and femur (knee) ( ). In addition, an EMG of the three shank muscles m. soleus (SOL), m. gastrocnemius medialis (GM), and m. tibialis anterior (TA), and the three thigh muscles m. biceps femoris (BF), m. rectus femoris (RF), and m. vastus medialis (VM) was used to assess muscular activation. Therefore, bipolar Ag/AgCl surface electrodes (Ambu Blue Sensor P and N, Ballerup, Denmark; diameter 7 and 9 mm, center-to-center distance 25 mm) were attached onto the disinfected skin over the belly of the respective muscle according to surface electromyography (EMG) for the non-invasive assessment of muscles ( ). A reference electrode was placed over the tibia. Cables were connected to the electrodes and fixed to the skin. Signals were transferred to an amplifier, where data were band-pass filtered (10 Hz–1.3 kHz) and amplified (1,000×).
All signals were recorded synchronously with a sampling frequency of 1,000 Hz.
#### Protocol 1 —SRs
In the first protocol, SR responses in the m. triceps surae were evoked in 37 subjects by a motor-driven ankle ergometer (Figure ), dorsiflexing the foot passively ( ). SRs were recorded with SOL and GM EMG, while mechanical dorsiflexion was monitored with goniometry. Visually, TA EMG was monitored as well to ensure that TA was not stimulated simultaneously. The axis of the ankle joint coincided with the rotation axis of the ankle ergometer. The mechanically induced dorsiflexion at the ankle joint were applied with an amplitude of 8.7 ± 0.4° and a velocity of 108.7 ± 5.2°/s, evoking an SR with interindividual latencies ranging between 35 and 55 ms (Figure ; Table ). Subjects stood upright in the ergometer holding onto sidebars for body stabilization and extending their knees as much as possible considering the respective degree of spasticity. Standardized position was monitored with the assessment of background EMG in the shank muscles as well as ankle and knee joint position prior to the stimulus. Instructions were given and controlled as follows: standing still, looking straight ahead, and not counteracting the passive ankle rotation actively. In total, 2 × 15 stimulations with interstimulus intervals of 4 s were applied prior and after WBV, respectively ( ).
Results of stretch reflex (SR, Protocol 1 ), maximal voluntary muscle activation (VA, Protocol 2 ), and active range of motion (aROM, Protocol 3 ) normalized to baseline values (=100%).
Bold numbers demonstrate significant time effects from pre- to post-measurements with p < 0.05 .
#### Protocol 2 —Maximal Voluntary Muscle Activation
In 23 subjects, maximal voluntary muscle activation (VA) was assessed by EMG with movement instructions according to Daniels and Worthingham ( ). Subjects were seated in an upright body position with the head facing in a forwards position; hands were positioned next to the body. For 5 s, muscular activation was performed isometrically against manual resistance given by the operator. Instructions depended on the respective muscle recorded: shank muscles were measured with the knee joint extended, extending (plantarflexion—SOL and GM) or flexing the ankle joint (dorsiflexion—TA) as much as possible against manual resistance. To quantify RF and VM activity, from a flexed starting position of 90° in the knee joint, a knee joint extension was performed by the subject against manual resistance. Finally, for BF measurements, subjects were instructed to flex their knee against manual resistance given to the calf ( , ). Fatigue of the muscles was prevented by including recovery pauses between measurements.
#### Protocol 3 —aROM
In the same seated position as described in Protocol 2 , 27 subjects were asked to actively bend and extend their ankle and knee joint as much as possible ( ). In the end position of angular excursion, the maximal voluntary rotation around joint axes (aROM) was measured by goniometry. In addition, muscular activation was recorded with EMG to analyze the amount of activation of the agonist in comparison to the respective amount of activation of the antagonist (co-activation ratio) during aROM. Ankle joint excursions, implemented by plantarflexion and dorsiflexion, were therefore recorded in combination with activity of shank muscles, while knee joint excursion was combined with muscle assessment of the BF, RF, and VM ( , ).
### Data Processing
Protocol 1 : Peak-to-peak amplitudes and integrals of SOL SR and GM SR were averaged over 30 trials, each recorded before and after WBV. Characteristics of the SR were assessed between the initial deflection of the EMG signal from baseline to the second crossing of the baseline according to Petersen et al. ( ). SOL SR and GM SR latencies (ms) were determined visually by the time interval between stimulus artifact and the first slope of the averaged SR amplitude, according to Ritzmann et al. ( ).
As muscle pre-activity and the joint position could affect the SR beyond the WBV treatment, both items were strictly controlled for all participants. Muscular pre-activation was controlled by an assessment of the rectified and integrated background EMG [iEMG (mVs)] for the interval of 100 ms prior to dorsiflexion ( ). Ankle and knee joint deflections were controlled by the goniometric recordings (°) for the intervals of 20 ms prior to dorsiflexion ( ).
Protocol 2 : VA of the skeletal muscles was assessed according to Kellis and Katis ( ). Values were rectified and integrated [iEMG (mVs)].
Protocol 3 : Ankle and knee joint displacements during aROM were assessed based on the maximal amplitudes of goniometric recordings (°). Voluntary activation of the agonistic and antagonistic muscles involved in the flexion and extension of the ankle (TA, SOL, and GM) and knee joint (BF, RF, and VM) was analyzed within 50 ms before and 50 ms after peak joint excursion (Figure ). EMG values were rectified, integrated, and expressed as iEMGs (mVs).
Exemplary illustration of the co-activation ratio determination in the shank: EMG activity of agonists was evaluated in relation to antagonists 50 ms before and 50 ms after peak joint excursion during max. plantarflexion for m. soleus (SOL) EMG and m. gastrocnemius medialis (GM) EMG and during maximal dorsiflexion for m. tibialis anterior (TA) EMG.
### Calculation of Outcome Parameters
All values are expressed as mean values and SDs for the conditions before and after WBV. For pre-data, mean values were calculated from both pre-measurements. Data regarding voluntary muscle activation (VA— Protocol 2 ; co-activation ratio— Protocol 3 ) and joint excursions ( Protocol 3 ) were normalized to baseline values obtained before WBV and outcome parameters were expressed as percentage changes.
In addition, to quantify the relation between voluntary and reflex activity, VA integrals were divided by the corresponding SR integral (VA/SR-ratio). This ratio illustrates the direct ratio between voluntary (with input from a supraspinal level) and reflex-associated motor control (of spinal origin). To gain further insight into antagonistic muscle coordination, the co-activation ratio of antagonistic muscles was evaluated for the end position of the aROM obtained in Protocol 3 (Figure ): therefore, the iEMGs of the agonists in the lower limb were divided by their respective iEMGs of antagonists (SOL/TA, GM/TA, BF/RF, and BF/VM) according to the calculation of Duchateau and Baudry ( ). The higher the percentage changes from pre- to post-WBV, the better the muscle coordination and the smaller the passive and active counterforces produced by antagonists.
### Statistics
To test for time effects in Protocol 1 , paired Student’s t -tests were performed with the variable time (2, pre vs. post) concerning the SR amplitudes and latencies. For Protocol 2 and 3 , time effects were assessed by one factor repeated measures analysis of variance applied for aROM (goniometry) and muscular activation (VA, co-activation ratio). Within-subject factors with variables time (2, pre vs. post) and muscle activity (6, SOL vs. GM vs. TA vs. RF vs. BF vs. VM) (VA, Protocol 2 ) or muscle groups (8, agonists vs. antagonists) (co-activation ratio, Protocol 3 ) were defined. Greenhouse–Geisser correction was used in case of violation of the assumption of sphericity (tested with Mauchly’s test for sphericity). p < 0.05 was defined as the significance level. To determine changes between pre- and post-measurements of VA/SR-ratio, one-tailed paired Student’s t -tests with p < 0.05 were calculated.
Test–retest reliability estimates were computed by the intraclass correlation coefficient for Protocols 2 and 3 using a one-way random single measure model with two items as both pre-measurement time points. Outcomes were described by Cronbach’s α according to Fleiss ( ). For absolute indices, standard error of measurement (SEM) was calculated according to Harvill ( ).
Statistics were conducted and analyzed by using the software IBM SPSS Statistics 22 (SPSS, Inc., Chicago, IL, USA). Data are presented in mean values and SDs (mean ± SD) with post-values being normalized to baseline values.
## Results
Grand means and SDs are displayed in Table .
Protocol 1 : Mechanically evoked SOL SR were significantly reduced after acute exposure to WBV (−12 ± 16%, p < 0.01), changes observed for GM SR remained not significant (−5 ± 30%, p = 0.20, Figure ). Latencies increased in SOL SR and GM SR by +1% ( p = 0.01) corresponding to 0.5–1.0 ms, respectively. Muscular pre-activation prior to stretch and kinematic starting position (goniometry) in the ankle and knee joint remained unchanged with respect to different points in time (cf. Table , A). Reliability was assured with good to excellent Cronbach’s α at levels of 0.88–0.96 for muscular pre-activation and 0.91–0.99 for initial joint angles, respectively.
Changes in stretch reflex (SR) excitability in the m. triceps surae, induced by mechanical dorsiflexion of the ankle joint ( Protocol 1 ): representative SR amplitude [m. soleus (SOL)] of a single subject (A) , results of each subject [SOL, (B) ] and mean values for SR excitability [SOL and m. gastrocnemius medialis (GM), (C) ] before (pre) and after whole-body vibration (post). (A,B) Raw values of reflex amplitude and (C) percentage changes for the m. triceps surae are presented. Significant changes ( p < 0.05) are illustrated with a * symbol.
Absolute [standard error of measurement (SEM)] and relative test–retest reliability (Cronbach’s α) was evaluated with the initial position from pre- to post-WBV ( Protocol 1 , A) and between both pre-measurements ( Protocol 2 and 3 , B).
Absolute values are illustrated as means ± SD .
Protocol 2 : Significant pre- to post-effects were observed after WBV: iEMGs during VA were significantly elevated for all lower limb muscles ( p = 0.03). No interaction effects of time x muscle were observed ( p = 0.78). While VA/SR-ratio in SOL was distinctly increased (19 ± 41%, p = 0.04), no changes could be observed for GM (−8 ± 71%, p = 0.32) (Figure ). Cronbach’s α yielded excellent results of 0.90–0.99. SEM values are listed in Table (B).
This graph illustrates percent changes in neuromuscular and kinematic parameters. The red dotted lines serve as references and mark baseline values at 100% referring to data collected before whole-body vibration (WBV). (A) Modulations of maximal voluntary muscle activation for the antagonist muscles encompassing the ankle joint [m. soleus (SOL), m. gastrocnemius medialis (GM), and m. tibialis anterior (TA)] and (B) knee joint [m. biceps femoris (BF), m. rectus femoris (RF), and m. vastus medialis (VM)] for joint flexion and extension. The rmANOVA revealed a significant increase ( p < 0.05) in response to WBV. (C) WBV-induced modulations in co-activation ratios of the muscles surrounding the ankle joint and (D) knee joint and corresponding active ranges of motion (aROM). The rmANOVA revealed a significant increase in co-activation ratios ( p < 0.05) and knee joint excursion ( p < 0.05) in response to WBV. Data are normalized to control values obtained prior to WBV. Significant changes in aROM are illustrated by * p < 0.05.
Protocol 3 : No significant changes could be observed in angular excursion of the ankle joint (−1 ± 42%, p = 0.46). In the knee joint, however, active angular excursion was increased during flexion and extension (+15 ± 20%, p < 0.01), accompanied by significantly greater co-activation ratios over time reflected by values >1 for all recorded agonist–antagonist muscle pairs ( p = 0.01). No interaction effects were revealed for the variables time × muscle group ( p = 0.86). Cronbach’s α was excellent for kinematic (0.94–0.96) and acceptable for neuromuscular measures (0.73–0.96). Test–retest reliability was questionable for co-activation during plantarflexion (SOL/TA) and unacceptable for knee extension (RF/BF and VM/BF, cf. Table , B). SEM values are listed in Table (B).
## Discussion
The current study demonstrated acute modulations after a 1-min bout of WBV including (1) decreased SOL SR activation, (2) elevated maximal voluntary muscle activation in lower limb muscles, and (3) increased aROM in the knee joint only, accompanied by improved intermuscular coordination in subjects with CP. The results point toward modulations opposed to the characteristics of pathological movement disorders and indicate improved voluntary movement control.
Voluntary and Involuntary Movement Control : Changes of the muscle and its neuronal control are causal for impairments in CP children ( , ), including spasticity-associated hyperexcitability of spinal reflexes ( , ). In the current study, neurophysiological consequences following WBV were investigated in regard to spinal excitability: subjects displayed a reduction of reflex activity by −5 to −12%, with minimally prolonged latencies of 1% in the m. triceps surae—the muscle that is closest to the vibration platform, and thus most affected by the WBV stimulus ( ). These results are in line with those observed in healthy subjects ( – ) and demonstrate in general that modulations in SR sensitivity to WBV may be comparable to subjects suffering from CP. Vibration is known to affect the receptor organs and reduce the sensitivity of primary or secondary muscle spindle endings ( – ). Furthermore, the integration of muscle spindle input is changed as well, which is illustrated by changes of Ia afferent transmission. Spasticity is, among others, associated with a decrease of inhibition of reflex activity ( ), and thus by lowering the Ia afferent sensory input, spinal excitability might become comparable to that existing in healthy subjects ( ). A reduction of SR sensitivity is of functional relevance during simple movement, because the muscle does not overreact to small stretch loads, leading to better “access” for voluntary muscle activation. This is in accordance with results demonstrating corticospinal facilitation ( ) concomitant with spinal inhibition ( ) after local vibration applied in healthy subjects. Therefore, facilitated input from supraspinal and brain-descending (corticospinal) structures could be associated with enhanced movement control ( , ).
In spasticity, reciprocal inhibition is pathologically affected ( , – ). This can be reflected in pathological co-contractions of antagonistic muscles leading to augmented joint stiffness ( , ) as well as muscle weakness leading to deficient movement patterns in children with CP ( , ). In the current study, however, neuromuscular control during voluntary movement execution emphasizes improved neuromuscular coordination following WBV: the results clearly demonstrate an enhanced muscular activation of lower limb muscles (values up to +37%) as well as improved coordination between agonists and antagonists during maximal excursion of the ankle and knee joint (ratios up to +117%). Especially, modulations in the m. triceps surae are of great relevance, as this muscle is predominantly affected in children with CP ( ). It could point toward a possible involvement of reciprocal inhibition as being facilitated after WBV, which would be in line with evidence concerning modulations during local vibration ( ), involving a neuronal inhibitory modulation of the non-vibrated muscle ( ).
Functional Relevance and Application : From a functional point of view, current neuromuscular modulations clearly point toward improved voluntary motor control which is assumed to be guided by supraspinal structures after WBV. For instance, a reduction of reflex activity has previously been associated with greater functional performance such as postural control ( – ) or concerning walking ability in subjects with CP ( ). In accordance with the neuromuscular benefits, greater motor control is also displayed by a wider range of motion in the knee joint, which is in line with previous investigations ( ).On the basis of this interrelation between spasticity, joint angular velocity and the ability for gross motor functioning performance ( ), increased joint mobility contributes to a better functional performance in subjects suffering from CP. Another aspect deals with the reflex latencies, which are decreased in subjects suffering from spasticity ( ). WBV-induced minimally prolonged reflex latencies indicating a reduced transmission velocity over afferent and efferent nerve fibers, which may counteract the pathological symptoms in CP patients ( ). With an emphasis on leg muscles which are of great relevance for everyday life activities due to their involvement in locomotion and posture control ( , , ), these modulations provide a perspective for advantageous WBV training regimes applied in the field of neuro-rehabilitation.
Limitations : The main difficulty of the current investigation was to balance standardization and moral support to the young participants while securing ethical tenability during the assessments. As a result, effects were controlled by test–rest reliability, but not with a separate control group. For instance, in Protocol 3 , neuromuscular activation failed reliability regarding knee joint excursion, and thus results should be considered with caution. The demonstrated effects after WBV emphasize the beneficial application of vibration. However, without any comparison with other treatments, it cannot be concluded whether those effects are solely specific to vibration. In addition, even though variables (type, amplitude, and frequency) of the vibration protocol were chosen carefully, based on current evidence, immediate results following WBV cannot be generalized and information about the temporal maintenance of the current modulations needs to be addressed in future research. Therefore, the evaluation of vibratory effects on spasticity during the described protocols can solely be discussed on the basis of evidence described in the literature.
### Conclusion and Prospective
In conclusion, the current study demonstrates an acute modulation of motor control in spastic CP children after WBV. Reduced pathological reflex responses concomitant with an increased voluntary muscular activation, improved intermuscular coordination of antagonists and increased knee joint mobility might be interpreted as counteracting spasticity-associated deficits in children with CP. With the benefit of a passive training modality, it may be assumed that spasticity and weakness in CP patients may be acutely and favorably modulated by the vibration stimulus. In addition, as the current young subject group probably still underlies a reorganization and maturation process of the developing brain, this could be particularly beneficial for achieving neural adaptation, and thus preventing secondary structural changes. Based on the positive acute effects in children with CP, investigations should be accelerated to illustrate the effect of longer periods of vibration and long-term adaptations in this patient group. Nevertheless, by demonstrating improved voluntary movement execution after WBV, the time frame immediately after WBV may be used for targeted movement therapy: subjects might actually take advantage of increased supraspinal input by means of greater voluntary motor control which has to be investigated in future studies.
## Ethics Statement
The current study is in accordance with the latest revision of the Declaration of Helsinki, which is approved by the ethics committee of the University Hospital of Cologne. All subjects and parents gave written informed consent to the diagnostic procedure in accordance with the latest revision of the Declaration of Helsinki, which is approved by the ethics committee of the University Hospital of Cologne. Children with spastic cerebral palsy were involved in the current study. They were medically examined by the attending physicians regarding inclusion criteria of the study and both, children and parents, were informed about the procedure. Children and parents gave their consent to take part but could drop out at any time during the measurements without stating any reason.
## Author Contributions
All the authors AK, ES, AG, ID, AF-M, KF, and RR made substantial contributions to the conception or design of the work, the acquisition, analysis, and interpretation of data for the work. Further, they contributed drafting the work and revising it critically, they helped with the final approval of the version to be published, and made the agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
## Conflict of Interest Statement
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
|
Genetic generalized epilepsy (GGE) consists of several syndromes diagnosed and classified on the basis of clinical features and electroencephalographic (EEG) abnormalities. The main EEG feature of GGE is bilateral, synchronous, symmetric, and generalized spike-wave complex. Other classic EEG abnormalities are polyspikes, epileptiform K-complexes and sleep spindles, polyspike-wave discharges, occipital intermittent rhythmic delta activity, eye-closure sensitivity, fixation-off sensitivity, and photoparoxysmal response. However, admixed with typical changes, atypical epileptiform discharges are also commonly seen in GGE. There are circadian variations of generalized epileptiform discharges. Sleep, sleep deprivation, hyperventilation, intermittent photic stimulation, eye closure, and fixation-off are often used as activation techniques to increase the diagnostic yield of EEG recordings. Reflex seizure-related EEG abnormalities can be elicited by the use of triggers such as cognitive tasks and pattern stimulation during the EEG recording in selected patients. Distinct electrographic abnormalities to help classification can be identified among different electroclinical syndromes.
## Introduction
Genetic generalized epilepsy (GGE) encompasses several electroclinical syndromes diagnosed and classified according to clinical features and electroencephalographic (EEG) characteristics ( – ). The EEG hallmark of GGE is bilateral synchronous, symmetrical, and generalized spike-wave (GSW) discharges. Polyspikes and polyspike-wave discharges are also commonly seen in GGE. Fixation-off sensitivity (FOS), eye-closure sensitivity, photoparoxysmal response (PPR), epileptiform K-complexes/sleep spindles, and occipital intermittent rhythmic delta activity (OIRDA) are among the spectrum of abnormalities described in GGE ( ).
In this review, we will be discussing the ictal and the interictal EEG abnormalities in GGE. We will also focus on the electrographic differences among different GGE syndromes, factors affecting the yield of EEG, and diagnostic pitfalls.
## Interictal Versus Ictal Abnormalities
Interictal EEG abnormalities are defined as “epileptiform patterns occurring singly or in bursts lasting at most a few seconds,” whereas ictal rhythms consist of “repetitive EEG discharges with a relatively abrupt onset and termination and characteristic pattern of evolution lasting at least several seconds” ( ). Subclinical seizure activity refers to EEG seizure patterns not accompanied by clinical signs and symptoms ( ). However, in absence seizures, differentiating interictal from ictal epileptiform discharges can be difficult as those discharges demonstrate monomorphic rhythmicity with little evolution. Consequently, the distinction between ictal and interictal activity depends on how long it lasts and clinical features, particularly impairment of consciousness during the discharge. Researchers have used several testing methods, including reaction time and motor tasks to study cognition and the degree of consciousness during spike-wave discharges ( ).
Consequently, there is no consensus on the duration of the GSW paroxysm that defines an absence seizure. Sadleir et al. diagnosed absence seizures based on two criteria: (1) GSW activity of any duration when accompanied by clinical signs and (2) GSW lasting >2 s even if not accompanied by clinical correlates. Discharges of <2-s duration without clinical signs were identified as interictal fragments ( ). A more recent study considered GSW bursts lasting 3 or more seconds, with or without clinical signs, as an absence seizure ( ).
Conversely, myoclonic seizures and generalized tonic-clonic seizures demonstrate well-characterized EEG changes and the distinction from interictal EEG abnormalities is more unequivocal ( ).
## Interictal Abnormalities
### Spike-Wave Complex
#### Morphology and Amplitude
Gibbs et al. published the first detailed analysis of the spike-wave complex ( , ). Subsequently, a more detailed analysis has revealed 3 components of the spike (spike 1, positive transient, and spike 2) ( ). The surface negative spike 1 is of low amplitude (25–50 µV) and brief duration (10 ms). The second component is a positive transient of 100–150 ms. It is followed by spike 2 of negative polarity lasting 30–60 ms with frontal amplitude maxima. The dome-shaped wave of negative polarity, which follows the spike, lasts 150–200 ms (Figure ) ( ). However, spike 1 is seen less consistently than spike 2 ( ).
Typical interictal epileptiform discharges in genetic generalized epilepsy. Note bilateral, symmetrical, and synchronous spike-wave discharges (A) , polyspike-wave discharges (B) , and polyspikes (C) .
A recent study based on 24-h ambulatory EEGs found 96.4% of generalized epileptiform discharges to be symmetric. However, the typical morphology was observed in only 24% ( ).
#### Topography
Typically, the maximum amplitude is seen over the frontocentral region. With the use of 3-dimensional field potential maps, researchers were able to demonstrate that the amplitude maximum of spikes was over the frontal region involving anterior and midline electrodes ( ). Using quantitative EEG analysis, Clemens and co-workers were able to demonstrate increased activity over the prefrontal region in patients diagnosed with GGE ( ). The field maxima during absence seizures are usually detected at Fz electrode with lateral spread to F3, F4, and posterior spread to Cz electrode ( ). The amplitude maximum of the spike-wave complex is most frequently observed in the frontocentral region (96.3%), followed by frontopolar (2.4%), and occipital (1.3%) regions ( ).
Further insights into topography have been revealed in studies using quantitative EEG techniques. The source localization of epileptiform discharges on dense array EEG in juvenile myoclonic epilepsy (JME) detected activity in the orbitofrontal and medial frontopolar cortex ( ). Another study using three techniques of source imaging analysis found anterior cingulate cortex and medial frontal gyrus as the primary anatomical sources of GSW discharges in GGE ( ).
#### Regularity
In EEG, regularity is defined as “waves or complexes of approximately constant period and relatively uniform appearance” ( ). The classic electrographic feature in GGE is regular and rhythmic GSW discharges. Nonetheless, a recent study has reported that 60% of GSW paroxysms are irregular ( ).
#### Frequency of Discharges
The typical 3 Hz spike-wave activity characteristic of absence seizures was first described by Gibbs and collaborators ( ). The fast spike-wave activity of >3.5 Hz is usually seen in juvenile myoclonic epilepsy (JME) ( ). The spike-wave discharge frequency in juvenile absence epilepsy (JAE) (mean 3.25 Hz) is faster than childhood absence epilepsy (CAE) and slower than JME ( ). In spike-wave paroxysms, the frequency is not constant throughout. The initial frequency is slightly faster and then it becomes more stable, slower, and regular ( ).
#### Background
In GGE, typically, epileptiform discharges emerge from a normal background ( ). Generalized epileptiform discharges occurring on a slow and disorganized background raise the possibility of an epileptic encephalopathy ( , ).
### Polyspikes and Polyspike-Wave Discharges
Polyspikes are characterized by a run of two or more spikes, whereas the polyspike-wave complex consists of polyspikes followed by slow waves ( ). In GGE, polyspikes usually occur in the form of high-amplitude rhythmic bursts with synchronized and generalized distribution (Figure ).
### Photoparoxysmal Response
This is an abnormal response manifesting with the generation of spike-wave complexes, polyspikes, or polyspike-wave discharges during intermittent photic stimulation ( ). The PPR is under the influence of several confounding variables including age, sex, ethnicity, genetics, antiepileptic medication use, state of alertness (sleep vs wakefulness), sleep deprivation, and the stimulation technique. There are three grades of PPR: (1) posterior stimulus dependent response, (2) posterior stimulus independent response, and (3) generalized response ( ). The response to photic stimulation is defined as self-sustained when the epileptiform discharges outlast the stimulus by ≥100 ms ( ). It is most frequently detected in JME (83%) followed by CAE (21%) and JAE (25%) ( ). However, PPR can also be elicited in 0.3–4% adults without a history of epilepsy ( , ). It is detected more frequently (14.2%) in asymptomatic children ( ). The influence of various confounders including stimulation techniques may explain the wide range of results reported in the literature.
### Eye-Closure Sensitivity
Epileptiform discharges characteristic of eye-closure sensitivity emerge within 1–3 s of eye closure and last for 1–4 s. However, the discharges do not persist for the total duration when eyes remain closed (Figure ). Photosensitivity and eye-closure sensitivity are related phenomena ( ).
Eye-closure sensitivity and fixation-off sensitivity (FOS) in genetic generalized epilepsy. (A) Generalized spike-wave and polyspike-wave discharges appear after eye closure (C) and fades away after one second indicating eye-closure sensitivity. (B) Generalized epileptiform discharges appear with eye closure (C), continues as long as eyes are closed, and disappears on eye opening (O) indicating FOS.
### Fixation-Off Sensitivity
Epileptiform discharges, generalized or occipital, triggered by the elimination of fixation and central vision are the hallmarks of FOS ( ). This abnormality needs to be distinguished from photosensitivity and eye-closure sensitivity. In FOS, epileptiform discharges persist for the total duration of eye closure and disappear on eye opening (Figure ) ( ). To confirm FOS, central vision and fixation should be abolished with the application of spherical lenses, Frenzel lenses, or Ganzfeld stimulation technique ( ). FOS has been described in GGE and occipital epilepsy ( ). In some patients, photosensitivity and FOS coexist ( ).
### Epileptiform K-Complexes and Sleep Spindles
The overlap between generalized epileptiform discharges and K-complexes (epileptiform K-complexes) as well as sleep spindles (epileptiform sleep spindles) has been known to researchers for many decades ( , ). This overlap generates complexes with a very characteristic morphology and topography (Figure ) ( ). A recent study has found this to be common in GGE with 65% of patients demonstrating epileptiform K-complexes and 10% epileptiform sleep spindles ( ). These abnormalities probably indicate the link between microarousals and epileptiform discharges in GGE ( ).
Epileptiform K-complexes and sleep spindles in genetic generalized epilepsy. (A) Polyspikes overlap with a K-complex at X . (B) A burst of generalized spike-wave discharges ( Y ) in the midst of a sleep spindle.
### Occipital Intermittent Rhythmic Delta Activity
Occipital intermittent rhythmic delta activity is characterized by transient unilateral or bilateral occipital runs of 2–3 Hz, regular, rhythmic, and sinusoidal delta activity ( ). Deep stages of sleep, as well as eye opening, typically attenuate OIRDA, whereas drowsiness and hyperventilation make it more prominent ( ). It is detected in approximately one-third of patients diagnosed with CAE ( ). Though often reported as an EEG abnormality of CAE, OIRDA is not specific to epilepsy. It is occasionally seen in encephalopathies, particularly in children ( ).
## Ictal EEG Changes
### Myoclonic Seizures
High-amplitude, generalized, polyspike activity of 10–16 Hz with the frontocentral maximum is the EEG hallmark of myoclonic seizures ( , ). These typical discharges may be preceded by irregular 2–5 Hz GSW activity and sometimes followed by irregular slow waves of 1–3 Hz (Figure ) ( , , ). The EEG seizure may be several seconds longer than the clinical seizure ( , ).
Electroencephalography of a myoclonic seizure. A burst of generalized polyspike-wave activity is followed a few slow waves.
### Typical Absence Seizures
Bilateral, regular, symmetrical, and synchronous 3-Hz spike-wave activity (range 2.5–4 Hz) sometimes admixed with polyspike-wave discharges on a normal background is the hallmark of a typical absence seizure (Figure ) ( , ). There are some differences among syndromes.
Electroencephalography of a typical absence seizure. Note the paroxysm of generalized, symmetrical, synchronous, and regular 3-Hz spike-wave discharges of frontocentral maxima.
It is not unusual for the initial ictal discharge to be atypical. It could be non-generalized, spike-wave, polyspike-wave, or irregular discharges, with typical GSW activity appearing after an average of 0.7 s ( ).
### Myoclonic Absence Seizures
Myoclonic absence seizures are semiologically characterized by absences in association with tonic contractions resulting in progressive upper limb elevation and superimposed rhythmic myoclonic jerks ( ). The impairment of awareness is less pronounced in comparison to typical absence seizures. Both myoclonic absence and typical absence seizure have similar ictal EEG patterns (Figure ) ( , ). Polygraphic recordings are used to demonstrate the correlation between the ictal EEG and tonic as well as myoclonic activity. Often triggered by hyperventilation, myoclonic absence seizures are less frequently (14%) induced by intermittent photic stimulation ( ).
Electroencephalography (EEG) of a myoclonic absence seizure. Note the paroxysm of spike-wave discharges is similar to a typical absence seizure as illustrated in Figure (time-base of this EEG = 20 s/page).
### Absence Seizures with Eyelid Myoclonia
Eyelid myoclonia with absences, EEG paroxysms/seizures triggered by eye closure, and photosensitivity are the main features of Jeavons syndrome ( ). Sometimes, eyelid myoclonia may not be associated with an absence seizure ( ). The ictal EEG typically shows generalized high-amplitude polyspikes and polyspike-wave discharges of 3–6 Hz lasting 1.5–6 s (Figure ) ( ). The ictal discharges occur with or before the onset of eyelid myoclonia ( ). The EEG abnormalities are usually triggered by eye closure, intermittent photic stimulation, and hyperventilation ( ). FOS may coexist ( ).
Electroencephalography of an absence seizure with eyelid myoclonia in Jeavons syndrome. (A) The absence seizure was triggered by eye closure at X . Note the paroxysm of generalized, fast polyspike activity ( X – Y ). This seizure was semiologically characterized by eyelid myoclonus, hyperextension of the neck, and unresponsiveness. (B) Interictal generalized polyspike-wave discharges during sleep recorded from the same patient.
### Generalized Tonic-Clonic Seizures
Muscle and movement artifacts mask the EEG during GTCS unless muscle relaxants are used to paralyze the subject. Generalized polyspike-wave bursts usually mark the ictal onset. Generalized amplitude attenuation follows, with or without low voltage, generalized, 20–40 Hz fast activity superimposing for a few seconds. The onset of the tonic phase coincides with the voltage attenuation. Then, generalized rhythmic alpha activity (10–12 Hz) evolves with increasing amplitude and decreasing frequency accompanied by the ongoing tonic phase. When the decreasing frequency reaches 4 Hz, repetitive polyspike-wave complexes emerge accompanied by myoclonic and clonic jerking semiologically. With the progression of the seizure, periodic bursts of polyspike-wave discharges appear with background suppression in between. Generalized EEG suppression is seen for a variable period with the termination of clonic jerking. The gradual recovery is marked by the restoration of the background rhythm from irregular generalized delta slowing proceeding to theta, and finally alpha rhythm ( ).
## Atypical EEG Abnormalities
The typical EEG abnormalities in GGE are generalized, symmetrical, and bisynchronous epileptiform discharges. However, for many decades, atypical EEG abnormalities such as focal discharges, lateralized discharges, asymmetries, and irregular discharges have been reported in the literature ( – ). In absence seizures, the initial discharge has been found to be non-generalized in 50% ( ).
A recent study based on 24-h ambulatory EEGs has quantified the atypical epileptiform EEG abnormalities in GGE ( ). This study identified six atypical EEG abnormalities: (1) amplitude asymmetry, (2) focal onset of paroxysms, (3) focal offset of paroxysms, (4) focal epileptiform discharges, (5) abnormal morphology, and (6) generalized paroxysmal fast rhythm (Figures – ). It was found that 66% of GGE patients had at least one type of atypical abnormality in the 24-h EEG recording. Patients diagnosed with JAE and JME had those abnormalities most frequently, followed by epilepsy with generalized tonic-clonic seizures alone (GTCSA) and CAE. The most frequent atypical abnormality in the cohort was atypical morphology in 93.4% of patients. Other atypical EEG abnormalities were amplitude asymmetry (28%), focal discharges (21.5%), focal onset (13.1%), focal offset (8.2%), and generalized paroxysmal fast rhythm (1.9%) ( ). It is of practical relevance to note that atypical abnormalities may result in misdiagnosis and delayed diagnosis ( ).
Atypical epileptiform discharges: amplitude asymmetry. (A) Note asymmetric epileptiform discharges with higher amplitude in the left frontal region. Synchronous epileptiform discharges of low amplitude are evident on the right on careful inspection. (B) More symmetric generalized epileptiform discharges recorded from the same patient.
Atypical epileptiform discharges: focal onset and offset of paroxysms. A generalized spike-wave paroxysm in juvenile absence epilepsy. Note the focal onset and offset in the left frontal region.
Atypical epileptiform discharges: focal discharges. (A) Note focal discharges at right temporal region ( X ). (B) Generalized epileptiform discharges recorded from the same patient.
Atypical epileptiform discharges: abnormal morphology. (A) Waves without spikes. Note at the end of spike-wave paroxysms there are waves without preceding spikes. (B) Spikes overriding the waves. Note spikes on top of the wave at X . (C) Spikes overriding the waves. Note spikes on the descending limb of the preceding wave ( Y ).
Atypical epileptiform discharges: generalized paroxysmal fast rhythm. (A) A run of generalized fast activity in wakefulness. (B) Similar changes during sleep.
## Provoking Factors Affecting the EEG
### Arousals, Sleep, Sleep Deprivation, and Circadian Rhythmicity
There are circadian variations in seizures and epileptiform discharges in GGE. Generalized spike-wave (GSW) activity is seen more often in non-rapid eye movement (NREM) sleep, but rare in rapid eye movement sleep ( ). In GGE, sleep deprivation significantly increases the density of spike-wave discharges in both sleep and wakefulness ( ). In JME, routine EEGs (without sleep deprivation) done in the morning are more often abnormal than those done in the afternoon ( ). In JME, sleep EEG always shows epileptiform discharges ( ).
Epileptiform discharges in GGE appear to be closely related to sleep–wake cycle. A retrospective study based on 24-h ambulatory EEGs found that 4.6% of patients had epileptiform discharges correlating with awakening. All patients who had epileptiform discharges on awakening were diagnosed with GGE. The epileptiform discharges were detected between 20 and 50 min following awakening in JME ( ).
The interaction between circadian rhythmicity and the sleep–wake cycle in the generation of epileptiform discharges in GGE has been evaluated in a recent study ( ). Epileptiform discharges are significantly shorter in duration and more frequent during the NREM sleep compared with wakefulness. When quantified, 67% of epileptiform discharges are detected in NREM sleep whereas 33% occurs in wakefulness. The distribution of epileptiform discharges demonstrates two peaks (11 p.m. to 7 a.m. and 12 noon to 4 p.m.) and two troughs (6 p.m. to 8 p.m. and 9 a.m. to 11 a.m.) ( ). These findings highlight the variability in the diagnostic yield in relation to the time-of-day and sleep–wake cycle. The best time for the optimal yield of EEG abnormalities is from 11 p.m. to 7 a.m. Similarly, capturing natural sleep during the EEG recording significantly increases the diagnostic yield ( ). Hence, 24-h ambulatory EEG is a very useful diagnostic tool in GGE.
### Hyperventilation
Hyperventilation is routinely used as an activation method in EEG. Hyperventilation-induced EEG abnormalities seem to depend on the severity of hypocapnia and the reduction in cerebral blood flow ( ).
Hyperventilation often induces ictal and interictal abnormalities in children diagnosed with absence seizures ( ). Hyperventilation triggered absence seizures in 67% of patients in a pediatric cohort (mean age 9.3 years) diagnosed with JAE and CAE ( ). In untreated children, hyperventilation induces absence seizures more often in CAE and JAE (87% each) in comparison to JME (33%) ( ). In contrast, another study involving a predominantly adult cohort, during hyperventilation, no one with generalized epilepsy had seizures and only 12.2% had an increase in interictal epileptiform discharges ( ). Hyperventilation-induced GSW paroxysms were found in only 12.3% of adult patients with GGE on treatment ( ). These studies suggest that during hyperventilation absence seizures are more likely to occur in the younger age group with untreated CAE and JAE.
### Photic Stimulation
Intermittent photic stimulation is a routine induction technique during EEG recordings. The PPR is more often seen in generalized epilepsy than in focal epilepsy. It is under the influence of several variables including age, sex, antiepileptic drug therapy, level of arousal, sleep deprivation, and the stimulation technique ( ).
### Reflex Triggers
Reflex seizures on exposure to specific stimuli are sometimes encountered in GGE. The use of such stimuli as activating procedures during the EEG recording in selected patients is an option to improve the yield of EEG abnormalities.
Reflex seizures involving visual stimulation have been reported in several epilepsy syndromes including GGE symptomatic generalized epilepsy, and occipital epilepsy ( ). Flickering lights, patterns, video games, and television are among the common visual triggers. Both photosensitivity and pattern sensitivity are implicated in television and video game induced seizures. Around 90% of patients with electrographic pattern sensitivity also demonstrate PPR ( ). 3D television and movies do not pose a higher risk of reflex seizures than 2D television and movies ( ).
Non-verbal cognitive stimuli such as thinking and praxis may induce reflex seizures in GGE. In a study involving reflex epilepsy triggered by spatial tasks, card or board games, and calculation, 96% experienced generalized tonic-clonic seizures often preceded by myoclonic jerks, whereas 68% demonstrated generalized epileptiform discharges on EEG ( ). Another study involving 480 patients found that neuropsychological tasks provoked epileptiform discharges in 38 patients and 36 of those patients were diagnosed with GGE ( ). Mental arithmetic and decision-making may trigger “noogenic” (thinking-associated) seizures among susceptible individuals. Cognitive activity in conjunction with planned motor tasks usually with hands is implicated in praxis-induced seizures ( ). Praxis-induced reflex seizures are particularly common in JME ( , ). Reading, talking, and writing are examples of verbal cognitive stimuli that may trigger reflex seizures. Both generalized and focal epilepsies have been reported under this category ( ).
## EEG Differences among Syndromes
### Interictal EEG Abnormalities in Electroclinical Syndromes of GGE
Several electroclinical syndromes such as CAE, JAE, JME, and epilepsy with generalized epilepsy with tonic-clonic seizures alone (GTCSA) have been described in GGE. In this review, we will focus on the four main syndromes: CAE, JAE, JME, and GTCSA. It should be noted that apart from the electroclinical syndrome, epileptiform abnormalities in GGE are under the influence of many variables including sex, age, the state of alertness, activation procedures, techniques of EEG recording, and antiepileptic drug therapy ( ).
### Interictal EEG in CAE
Childhood absence epilepsy is typically seen in children and the EEG signature is “generalized, bisynchronous, and symmetrical 3-Hz spike-wave discharges emerging from a normal background” ( ). Fragments of GSW discharges are seen in >90% of cases, predominantly in drowsiness and sleep ( ). Interictal polyspikes usually occur in drowsiness and sleep ( ). Polyspike-wave discharges were detected in 26% of patients in a different series ( ). Among untreated children with CAE, only 21% demonstrate PPR, whereas hyperventilation-induced absence seizures are seen in 87% ( ).
Occipital intermittent rhythmic delta activity is seen in 20–30% of CAE subjects ( , ), and 40% of those have a notched appearance ( ).
### Interictal EEG in JAE
The onset of JAE is in teenage years (12–17 years). Absence seizures are less frequent but myoclonus is more common in JAE compared with CAE. In comparison to CAE, generalized tonic-clonic seizures more frequently precede the onset absence seizures in JAE ( ). Fragmented discharges and polyspikes are seen in all patients, mostly in drowsiness and sleep ( ).
### Interictal EEG in JME
Patients with JME typically experience their first seizure at puberty (12–18 years). The typical semiologic feature is myoclonic seizures predominantly involving arms. Generalized tonic-clonic seizures occur more frequently than absences ( ). Sleep deprivation and alcohol are potent seizure triggers. Seizures, particularly myoclonus, frequently occur after awakening from sleep ( ).
The classic EEG abnormalities in JME are generalized polyspikes and polyspike-wave discharges ( , ). The interictal EEG is characterized by 3–6 Hz spike and polyspike-wave discharges in an irregular mix ( ). Focal EEG abnormalities are common ( ). PPR is seen in the majority ( ). Both eye-closure sensitivity and FOS have been reported in JME ( ).
### Interictal EEG in GTCSA
This condition is characterized by GTCS occurring on awakening or at random times. The median age of onset is (18 years) significantly older than JME and JAE ( ). The interictal EEG demonstrates generalized polyspikes, polyspike-waves, and spike-wave discharges similar to other GGE syndromes. The mean spike-wave frequency is 3.6 Hz. The density of epileptiform discharges is significantly lower than CAE, JAE, and JME ( ).
## Characteristics of Absence Seizures in GGE Syndromes
### Frequency of GSW Discharges
In all GGE syndromes, the initial frequency of GSW activity is faster. In the next phase, the discharges become more regular and slower in frequency by 0.4–0.6 Hz. The frequency decreases again in the terminal phase of CAE and JAE ( ). The highest median frequency of GSW during the first second of an absence seizure is in JME (3.5 Hz). It is marginally slower in JAE (3.25 Hz) and CAE (3 Hz) ( ). In JME, the GSW activity often tends to be faster (>3.5 Hz) ( , , ). A more recent study based on 24-h EEGs found median GSW frequencies of 3.3 (CAE), 3.1 (JAE), 3.8 (JME), and 3.5 (GTCSA). But the differences were not statistically significant ( ).
### Epileptiform Discharge Morphology and Duration
Childhood absence epilepsy and JAE demonstrate similar morphologies of GSW discharges. Multiple spikes preceding or overlapping slow waves give rise to an appearance of compressed “W”s in absence seizures of JME ( ). The polyspike-wave activity is seen more often in JME and JAE than CAE ( ). CAE and JAE have longer EEG seizure durations than JME ( , ). The longest EEG absence seizure is seen in JAE, whereas the shortest is in GTCSA ( ).
### Organization of Discharges
Absence seizures typically demonstrate well organized regular and rhythmic ictal EEG pattern. In disorganized discharges, regular rhythmic activity is interrupted by, (a) brief (<1 s) and transient interruptions in ictal rhythm, or (b) waveforms of different frequency and/or morphology (Figure ) ( ). Disorganized ictal discharges are 110 times more likely to occur in JME than CAE and eight times more likely in JAE than CAE ( ). It is also influenced by provoking techniques, the state of arousal, and the age ( ). Irregular and disorganized paroxysms are also seen in GTCSA though less frequently ( ).
A disorganized (irregular) paroxysm of generalized epileptiform discharges in juvenile myoclonic epilepsy. Note this paroxysm has a mix of polyspikes and polyspike-wave discharges with varying frequency and morphology.
Table summarizes key EEG differences among the four main GGE syndromes.
Differences in electroencephalographic (EEG) features among syndromes.
CAE, childhood absence epilepsy; GSWD, generalized spike-wave discharges; GTCSA, generalized tonic-clonic seizures alone; JAE, juvenile absence epilepsy; JME, juvenile myoclonic epilepsy; NA, not available; A, awake; D, drowsy; S, sleep; density; duration of epileptiform discharges (in seconds) per an hour of EEG recording; +++, highest value; +, lowest value; +++, middle value; pure GSWD, fragments and paroxysms containing only spike-wave discharges (without any polyspikes or polyspike-wave discharges) ( , ) .
## Underpinning Network Mechanisms of GSW Complex
Currently, epilepsy is considered to be a disorder of network pathways. This concept is reflected in the current International League against Epilepsy terminology defining generalized seizures as those involving both cortical and subcortical bilateral networks ( ). Hence, in GGE, the seizure activity originates at a certain point within the epileptic network and then rapidly engages bilaterally distributed network pathways ( ).
Many animal and human experiments highlight the importance of frontal lobe and thalamus in the formation and propagation of GSW complexes. In the rat model, absence seizures originate from the somatosensory cortex rapidly spreading to the thalamus ( ). In their pioneering work, Bancaud et al. recorded GSW discharges with the mesial frontal cortex stimulation ( ). More recently, novel EEG techniques have provided intriguing insights into the underpinning epileptic network pathways in GGE.
### Dense Array EEG and Source Localization
A study based on dense array EEG in absence seizures has demonstrated spike-wave discharge onset in the dorsolateral frontal and orbital frontal regions followed by rapid and stereotypic propagation ( ). Electrical source analysis of dense array EEG data has revealed frontotemporal networks involving the slow wave and spike propagation through ventromedial frontal networks during absence seizures ( ).
### Magnetoencephalography (MEG)/EEG
A combined MEG/EEG study has described a prefrontal–insular–thalamic network in absence epilepsy ( ). In JAE, the spike-wave discharge onset is in focal cortical regions with subsequent involvement of the default mode network as demonstrated by synchronous MEG/EEG data ( ).
### Simultaneous EEG and Functional MRI (EEG-fMRI) Studies
Electroencephalographic and functional MRI is a non-invasive technique to measure regional brain activation during epileptiform discharges using blood oxygenation level-dependent contrast ( ). A recent critical review has elicited three key features among EEG-fMRI findings in GGE: (a) activation of the thalamus, (b) activation of cortical regions, particularly frontal, and (c) deactivation of default mode areas ( ).
### Combined Transcranial Magnetic Stimulation (TMS) and EEG Studies
Combined TMS and EEG is an emerging non-invasive technique with a potential to study the functional connectivity of the brain ( ). A protocol to study GGE patients with TMS-EEG has been recently described ( ). However, to date, changes in network connectivity have mostly been studied with TMS-EEG in focal epilepsy ( ), while evaluation in GGE remaining in its infancy ( ).
### Graph Theory and EEG
Graph theory is a mathematical concept to study brain connectivity. It describes networks in terms of interrelationship between nodes (brain regions) and edges (connections) ( ). Graph theory is increasingly being used as a tool to analyze epileptic networks. A recent study has reported increased local connectivity in the frontal regions with spike-wave discharges in JME ( ). Another study based on graph theory using EEG data found similarities in network topology between patient with GGE and their unaffected relatives ( ).
Conclusions should be drawn with care from these studies due to various limitations. There is wide variability in the methodology among studies. In particular, EEG-fMRI studies vary in terms of study paradigms, and methods of data acquisition as well as analysis. Additionally, most studies are based on GSW activity. In GGE, there are other EEG abnormalities and underpinning network mechanisms may be different in those. Despite such limitations, there is growing support for the hypothesis that spike-wave discharges originate from a cortical focus with rapid spread to the thalamus followed by entrainment of the cortico-thalamo-cortical loop resulting in the classic GSW activity observed in GGE ( ).
## Diagnostic Tools
Routine outpatient EEG, sleep-deprived outpatient EEG, short-term outpatient video-EEG, inpatient video-EEG, and 24-h ambulatory EEG are common tools used to diagnose and classify epilepsy in routine clinical practice. The yield is influenced by several variables such as age, AED therapy, pretest probability of epilepsy, provoking techniques used, the length of the recording, and the state of arousal ( ).
The yield of interictal epileptiform discharges in the routine outpatient EEG is around 28% ( ). After the first seizure, the average yield is 29% according to a systematic review ( ). Serial EEGs appear to increase the diagnostic yield ( ). One study based on outpatient short-term video-EEG found the yield to be 17.2% ( ). However, in this study, 22% of patients had the test with the clinical diagnosis of psychogenic non-epileptic seizures reducing the yield of epileptiform discharges. Inpatient video-EEG monitoring has a higher yield (epileptic seizures 43.5%; interictal epileptiform discharges 43%) ( ), but is an expensive test with limited availability.
Sleep EEG can be considered the most effective diagnostic tool as 67% of generalized epileptiform discharges occur in NREM sleep ( ). Sleep deprivation appears to increase this yield further. Following sleep deprivation, GSW discharge densities increase in both sleep and wakefulness with the highest densities recorded in NREM sleep stages 1 and 2 ( ). Though results in the literature are variable, sleep deprivation appears to increase the yield of epileptiform discharges (focal and generalized) by about 30% beyond the effect of sleep ( ).
The use of multiple provoking techniques increases the diagnostic yield of EEG. A recent study reported a video-EEG protocol incorporating several provoking methods such as sleep deprivation, neuropsychological activation (language and praxis), hyperventilation, eye closure, intermittent photic stimulation, sleep, and arousal ( ). The video-EEG was recorded for 4–6 h. Interictal epileptiform discharges were detected in 85.8% of patients, whereas 54.9% had seizures during the recording ( ). The high yield might have been influenced by the fact that all patients in the cohort had an established diagnosis of GGE. Yet, this study demonstrates the importance of combining multiple provoking techniques to enhance the diagnostic yield.
Recent research indicates 24-ambulatory EEG to be a very useful test to diagnose and classify GGE ( ). Its diagnostic sensitivity is 2.23 times higher than routine EEG ( ). Ambulatory EEG recordings are very effective for several reasons. First, two-thirds of epileptiform discharges appear on sleep EEG recording and ambulatory EEG the most practical method to capture the natural sleep and increase the diagnostic yield ( ). Second, epileptiform discharges in GGE demonstrates a time-of-day dependence with two peaks (11 p.m. to 7 a.m. and 12 noon to 4 p.m.) and two troughs (6 p.m. to 8 p.m. and 9 a.m. to 11 a.m.) ( ). Routine outpatient EEG is likely to miss the most significant first peak (11 p.m. to 7 a.m.) while the 24-h ambulatory will capture both peaks. Third, it is four times cheaper than inpatient video-EEG ( ). Finally, home-based ambulatory EEG is more convenient and acceptable to patients than hospital-based inpatient monitoring ( ).
## Diagnostic Pitfalls
### Misdiagnosis of People without Epilepsy As Generalized Epilepsy
Paroxysmal disorders ranging from syncope to psychogenic non-epileptic seizures can be misdiagnosed as epilepsy. The rate of misdiagnosis is as high as 20–30% in general practice and outpatient clinics ( , ). Misdiagnosis is likely to happen when an individual presenting with a non-epileptic disorder undergoes an EEG test yielding epileptiform abnormalities. It has been shown that 0.5% of healthy adults in the general population have epileptiform abnormalities in the EEG ( ). Among healthy school children, the prevalence of GSW activity in the EEG is 0.9% ( ). Epileptiform discharges are more frequently (37%) detected among the offspring of patients with epilepsy. 6% of healthy first-degree relatives of JME probands demonstrate typical generalized epileptiform discharges ( , ). Hence, we wish to emphasize the importance of clinical correlation of EEG abnormalities in establishing the diagnosis of epilepsy.
### Misdiagnosis of Generalized Epilepsy As Focal Epilepsy
Atypical features, including focal epileptiform discharges, can potentially result in delayed diagnosis and misdiagnosis of GGE. The rate of misdiagnosis can be as high as 91% and the mean delay to the diagnosis ranges from 6 to 15 years in studies ( ). As a result, many patients receive inappropriate antiepileptic drugs such as carbamazepine leading to paradoxical worsening of some seizures ( ).
### Misdiagnosis of Focal Epilepsy As Generalized Epilepsy
#### Secondary Bilateral Synchrony
Tukel and Jasper coined the term “secondary bilateral synchrony” while reporting a series of patients with parasagittal lesions in whom the EEGs demonstrated bilaterally synchronous bursts of spike-wave complexes ( ). Along with Penfield, they postulated that “a cortical focus can fire into subcortical structures and set off a projected secondary bilateral synchrony” ( ). Subsequently, a stereo-EEG study reported that stimulation of the mesial frontal region induced paroxysms of bilaterally synchronous and symmetrical spike-wave discharges ( ).
Blume and Pillay proposed three diagnostic criteria for secondary bilateral synchrony; (1) ≥2 s of lead-in time, (2) focal triggering spikes having a different morphology from the bisynchronous discharges, and (3) both triggering spikes and focal spikes from the same region having similar morphology ( ). This is a rare phenomenon occurring in 0.5% of patients undergoing EEGs and is most frequently seen in association with frontal lobe foci ( ).
#### Frontal Lobe Epilepsy: The Conundrum of “Pseudo Bilateral Synchrony”
In frontal lobe epilepsy, the interictal epileptiform abnormalities range from focal to bilateral synchronous discharges. In a surgical series of frontal lobe epilepsy, 9% had bifrontal independent interictal epileptiform discharges whereas bilaterally synchronous discharges were recorded from 37% of patients ( ). Epileptiform discharges recorded on the scalp EEG represent the summated activity of volume conduction and cortico-cortical propagation. Cortico-cortical propagation gives rise to asynchronous discharges with a time a lag. However, small time lags may not be appreciated by visual inspection and can be interpreted as synchronous discharges ( , ). Hence, it is conceivable that frontal foci, particularly located in the midline, can generate bifrontal epileptiform discharges with “pseudo bilateral synchrony” that can be mistaken for truly bisynchronous discharges of GGE. Computer-aided analysis ( ) or specific re-montaging (reference-subtraction montage) ( ) can be used to detect the time lag between the electrodes and demonstrate that bilateral discharges are not truly synchronous but generated from a single a focus. Expanding the time-base of digital EEG is also a useful manipulation to detect time differences between seemingly synchronous discharges on two separate channels (Figures and ) ( ).
Pseudo bilateral synchrony in frontal lobe epilepsy. This patient presented with seizures following the surgery for left frontal brain abscess in the past. (A) In this longitudinal bipolar montage, bifrontal polyspike-wave discharges ( X, Y ) appear synchronous. However, focal sharp wave discharges are evident involving F3 electrode at Z . (B) The MRI demonstrating left frontal encephalomalacia.
Pseudo bilateral synchrony in frontal lobe epilepsy. (A) This average referential montage demonstrates the same activity seen in (A) of Figure . The discharges appear bifrontal. Note focal discharges involving F3 and C3 (time-base of the electroencephalography = 10 s/page). (B) When the time-base is expanded to 5 s/page, it becomes clear that the epileptiform discharge emerges first on the left at Fp1 and F3 ( X ), followed by activity on the right ( Y ) confirming pseudo bilateral synchrony.
### Misdiagnosis of Normal Variants As Generalized Epileptiform Discharges
The 6-Hz spike-wave (phantom spike-wave) pattern consists of bursts of generalized symmetric spike-wave discharges with a very low amplitude spike component ( ). The bursts are typically very brief but can last up to 4 s on rare occasions ( ). The amplitude maxima can be anterior or posterior ( ). This variant, particularly the type with posterior maximum, usually emerges from drowsiness and disappears during deep sleep. Though the typical frequency is 6 Hz, it can range from 4 to 7.5 Hz ( ). The spike component is <25µV in the majority and >75 µV in 5% ( ). This is a benign variant of no clinical significance.
The 14- and 6-Hz positive burst pattern (14 and 6 Hz positive spikes or ctenoids) is a benign variant most often seen in early teens, which then becomes infrequent with advancing age. These spikes are surface positive in polarity, occurring in bursts of <1 s, with unilateral or bilateral distribution and posterior dominance during drowsiness and light sleep ( ). This can be mistaken for polyspikes, but careful analysis of the polarity, frequency, and distribution should help clarify the diagnosis.
Small sharp spikes (benign sporadic sleep spikes) are seen in adults during drowsiness and light sleep. It disappears in deep sleep. The sharp waves are usually diphasic with a low amplitude (<50 µV) and brief duration (<50 ms) without an after-going slow wave. The spikes occur in the form of isolated transients with a unilateral or bilateral widespread field most prominent in the temporal regions ( ).
## Gaps in the Literature and Future Directions
Drawing robust conclusions from the literature is challenging due to wide variability in methodology. Additionally, EEG abnormalities in GGE are influenced by numerous confounding variables affecting the results. Most studies are descriptive in nature limiting the option of drawing statistical conclusions. Finally, most studies are based on short-term EEG recordings.
To circumvent these shortcomings, prospective studies in drug naïve populations with GGE using standardized EEG recording protocols are needed. Longer (≥24 h) EEG monitoring is required to study both circadian and infradian rhythms. Additionally, there is a potential role for long-term EEG monitoring to evaluate seizure control and occupational safety including fitness to drive. The application of machine-learning technologies allows more rapid and accurate identification of abnormalities from large volumes of long-term recordings and permits accurate quantification that may provide new insights into classification, prognosis, and clinical outcomes.
More research should focus on the distinction and interrelationship between interictal and ictal epileptiform discharges. From the clinical perspective, more analytical studies are needed to delineate EEG differences among GGE syndromes. Beyond its routine clinical interpretation, EEG data can be used for computational modeling to study network dynamics ( ). Research on network analysis in GGE needs to focus on all types of epileptiform abnormalities and associated networks.
## Conclusion
As highlighted in this review, there are several typical EEG features of GGE. The occurrence of atypical features, in particular, focal changes, should be borne in mind to avoid misdiagnosis. The use of provoking stimuli such as sleep deprivation, intermittent photic stimulation, hyperventilation, FOS, and reflex triggers during EEG recording can help increase the diagnostic yield. Some EEG features help differentiation among electroclinical syndromes. However, it should be emphasized that such differences are also influenced by several confounding variables including sex, age, state of alertness, activation methods, technical factors, and antiepileptic drug therapy.
## Ethics Statement
This study was conducted with approvals from the Human Research Ethics Committees of Monash Health and St. Vincent’s Hospital, Melbourne.
## Author Contributions
US: study concept and design, literature search, drafting, and critical revision of the manuscript. MC and WD: study concept, critical revision of the manuscript.
## Conflict of Interest Statement
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
|
Microsaccade research has recently reached a critical mass of studies that allows, for the first time, a comprehensive review of how microsaccadic dynamics change in neurological and ophthalmic disease. We discuss the various pathological conditions that affect microsaccades, their impact on microsaccadic and other fixational eye movement dynamics, and the incipient studies that point to microsaccadic features as potential indicators of differential and early diagnoses of multiple clinical conditions, from movement disorders to attention-deficit hyperactivity disorder to amblyopia. We propose that the objective assessment of fixational eye movement parameters may help refine differential diagnostics in neurological disease and assist in the evaluation of ongoing therapy regimes. In addition, determining the effects of ophthalmic disease on fixational eye movement features may help evaluate visual impairment in an objective manner, particularly in young patients or those experiencing communication difficulties.
## Introduction
When we attempt to fixate our gaze on a target, our eyes are never still, but produce small “fixational eye movements,” which include tremor, drift, and microsaccades. Microsaccades (also called fixational saccades) occur at a typical rate of 1–2 Hz. Converging research points to a saccadic generation continuum, which extends from the smallest fixational microsaccades to the largest exploratory saccades ( – ). Drift is a slow (typically less than 2°/s) motion that occurs between microsaccades and saccades, and travels in an erratic pattern that has been modeled as a random walk ( ). Tremor (or ocular microtremor) occurs simultaneously with drift, during intersaccadic intervals. This is the smallest fixational eye movement, with amplitudes that approximate the width of a single photoreceptor and dominant frequencies between 70 and 103 Hz (averaging 84 Hz) ( , ). Tremor studies are much scarcer than those centered on microsaccades and/or drift, due to the technical difficulties inherent to measuring this tiny motion ( , ). Thus, we do not address tremor in this review.
Because we spend approximately 80% of our waking hours fixating our gaze [not only in a sustained way but also in transient fashion, between large saccades ( )], understanding fixational dynamics is critical to advance current knowledge of oculomotor and visual function. Fixational eye movement assessments may also help further our understanding of central and peripheral pathologies that result in impaired fixation.
Various neurological and ophthalmic disorders produce abnormal fixational eye movement patterns, with distinctive characteristics. Thus, establishing how neurological and ophthalmic disease affects fixational dynamics holds the potential to help in the early and differential diagnosis of such disorders, clarify their pathophysiology, and quantify their progression and response to treatment. Recent research efforts have set out to characterize fixational dynamics in a growing record of neurological and ophthalmological conditions, which we discuss in this review.
A classification of abnormal eye movements in different disorders of fixation was previously published [see Table 1 of Martinez-Conde ( )]. The intervening decade has seen an upsurge in fixational eye movement research, with an emphasis on microsaccade studies. In addition, cross-fertilization between fundamental and translational approaches to fixational dynamics has facilitated the identification of previously unknown links between (micro)saccadic eye movements and saccadic intrusions (the latter formerly relegated to the clinical literature).
Such recent developments have resulted in a critical mass of studies that allows us, for the first time, to offer a comprehensive review of how microsaccadic dynamics change in neurological and ophthalmic pathologies, from movement disorders to attention-deficit hyperactivity disorder (ADHD) to amblyopia.
## Microsaccades in Neurological Disease
The balance that fixational eye movement system must achieve in healthy oculomotor function is quite delicate: whereas insufficient eye motion can result in visual losses due to neural adaptation and visual fading, excessive eye motion leads to blurred and unstable vision. This fine calibration is disrupted in patients of various neurological and neurodegenerative disorders who display increased gaze instability during the attempt to fixate ( ). Recent research efforts aimed to characterize such fixation instability—with an emphasis on the dynamic of microsaccades and drift—in neurological disease seek not only to improve early and differential diagnosis and help evaluate the efficacy of concurrent treatments but also to gain a deeper understanding of the pathophysiology and pathogenesis of such disorders.
### Microsaccades, Saccades, and Saccadic Intrusions in the Healthy Brain and in Neurological Disease
Converging evidence from physiological and behavioral studies conducted over the last decade has led to the current consensus that microsaccades and saccades—though previously considered as two different eye movement types—share a common oculomotor generator [( ); for review see Ref. ( )]. More recently, the proposal of a microsaccade-to-saccade continuum has been expanded to saccadic intrusions ( , , ), defined as involuntary saccades that interrupt, or intrude on, precise fixation. Sacccadic intrusions are prevalent in certain neurodegenerative disorders, although healthy individuals also produce them. The most common saccadic intrusion is the square-wave jerk (SWJ), which consists of a small, horizontal saccade moving away from the fixation target, quickly followed by a corrective return saccade of equivalent amplitude and opposite direction. Though microsaccades and SWJs have most often been studied as two separate types of eye movements, recent work has put forward the notion that they, too, may be fundamentally the same kind of eye movement with different names ( , , , ).
### Progressive Supranuclear Palsy (PSP) and Other Movement Disorders
Pinnock and colleagues found larger and more frequent saccadic intrusions (including small intrusions due to microsaccades) in patients with Parkinson’s disease, multiple system atrophy, and PSP, than in healthy age-matched controls ( ). Otero-Millan et al. subsequently set out to study the characteristics of microsaccades and SWJs in patients with PSP—a parkinsonian disorder that affects the basal ganglia, mesencephalon, and frontal lobe—in which SWJs are a distinctive clinical feature ( ) (Figure ).
Square-wave jerks (SWJs) from three progressive supranuclear palsy (PSP) patients (left) and three age-matched controls (right). In both populations, (micro)saccades with amplitudes equal to or larger than half a degree of visual angle are paired as SWJs. Only the horizontal eye positions are shown. Modified from Otero-Millan et al. ( ).
Though normal microsaccades were found to be rare in PSP, microsaccade magnitude was linked to SWJ coupling in both PSP patients and in healthy participants, with large microsaccades being more likely to trigger return saccades (forming SWJs) than small microsaccades (Figure ). In addition, microsaccades and SWJs were slower in PSP patients than in controls, and they had a diminished vertical component, consistent with the vertical saccadic palsy that sets apart PSP from other parkinsonian patients. The results supported the hypothesis that a common mechanism may account for microsaccade and SWJ generation ( , ) and explained how the position error from a large first saccade could serve as the trigger for the return saccade in SWJs produced by both PSP patients and healthy participants ( ). The study concluded that the apparent distinction between microsaccades and SWJs could be due to two complementary mechanisms, underlying: (1) microsaccade production and (2) correction of gaze fixation errors due to oversized microsaccades ( ). These two factors, combined, could explain square-wave coupling, both for microsaccade pairs in healthy subjects and for saccadic intrusions in neurological patients suffering from PSP, Parkinson’s disease, and other movement disorders, including multiple system atrophy, corticobasal syndrome, and spinocerebellar ataxia ( , ).
Square-wave coupling takes place for large but not small (micro)saccades. In this eye position trace from a healthy participant, red arrows point to pairs of larger microsaccades forming square-wave jerks; green arrows point to smaller unpaired microsaccades. From Otero-Millan et al. ( ).
### Mild Cognitive Impairment and Alzheimer’s Disease
Kapoula and colleagues recorded the eye movements of Alzheimer’s disease patients, patients with mild cognitive impairment, and healthy age-matched participants during the attempt to fixate. Whereas most microsaccadic features, including magnitude, velocity, duration, and intersaccadic intervals were equivalent across the three groups, oblique microsaccade directions were more prevalent in mild cognitive impairment and Alzheimer’s disease patients than in healthy participants ( ). Layfield and colleagues wondered about potential links (positive or negative) between microsaccade dynamics and the amelioration of cognitive deficits in aging adults—following from targeted interventions known as “Speed Processing Training”—but found no relationship ( ).
### Attention-Deficit Hyperactivity Disorder (ADHD)
The neural system that controls attention and the system that generates (micro) saccadic eye movements overlap extensively. Thus, multiple research studies have examined the connection between microsaccades, attention, and distractors [for review see Ref. ( )]. The superior colliculus, which plays a central role in (micro)saccade triggering, has moreover attracted recent interest as a potential site of dysfunction in ADHD ( , ). A handful of studies have examined the connection between ADHD and gaze instability during fixation ( – ). Most recently, two studies have focused on the characteristics of microsaccades in individuals with ADHD ( ) and ADHD traits ( ). Fried et al. found a higher microsaccade rate in adult individuals with ADHD who were off medication than in control participants. Methylphenidate medication served to normalize microsaccade rates in the ADHD group. Panagiotidi and colleagues similarly found differing microsaccade rates in non-clinical participants with high and low levels of ADHD-like traits ( ), assessed with the Adult ADHD Self-Report Scale ( ). These combined results suggested that abnormal fixation behavior is a core deficit in ADHD, which could aid in the development of a biomarker for the disorder ( ). Another recent study set out to investigate the impairment of temporal expectations in ADHD, by examining the inhibition of microsaccades prior to the onset of predicted stimuli. The data indicated decreased microsaccade inhibition in participants with ADHD than in controls, suggesting that microsaccade characterization may help enhance current understanding of the range of cognitive deficits that affect ADHD individuals ( ).
### Autism Spectrum Disorder (ASD)
Recent research has found increased drift in autistic individuals ( ). Microsaccade sizes and rates during fixation of a small target were comparable in ASD and neurotypical participants ( , ), but those with ASD presented greater fixation instability, more microsaccades, and larger microsaccades when asked to maintain fixation on a blank screen with no target ( ).
### Tourette Syndrome
A recent study found patients with Tourette syndrome to have reduced microsaccade amplitudes and increased intersaccadic intervals, along with increased fixation instability and drift velocities ( ).
### Schizophrenia
Egaña and colleagues ( ) found that previously reported decreased oculomotor function—in terms of decreased saccade and fixation rate—in schizophrenic patients ( ) no longer differed from that of control participants once they included microsaccades in the analyses. In other words, schizophrenic patients made similar numbers of overall eye movements as healthy individuals, but produced fewer large, exploratory saccades to scan wide regions of the visual field. This study shows that fixational eye movement analyses in neurological and psychiatric disorders can be valuable not only to differentiate across populations but also to reveal previously unknown similarities between groups.
### Cerebral Palsy
Kozeis and colleagues proposed that microsaccadic impairment might complicate the acquisition of reading skills in children with cerebral palsy ( ), but no studies to date have directly characterized fixational eye movements in this disorder.
### Hemianopia and Cortical Blindness
Hemianopia, or blindness in one-half of the visual field, can result from any lesion impairing post-chiasmatic central visual pathways. Reinhard and colleagues found microsaccadic distributions in hemianopic patients to be asymmetrical, with microsaccade directions biased toward the blind hemifield ( ).
Gao and Sabel ( ) subsequently investigated the characteristics of microsaccades in hemianopic stroke patients, to determine their potential relationship with visual performance and to assess how microsaccadic direction might be related to visual defect topography. They found that hemianopia resulted in enlarged microsaccades with impaired binocular conjugacy. Alterations of microsaccade dynamics worsened over time, being most prominent for older lesions. The data also revealed a microsaccade bias toward the seeing field, which was associated with faster reaction times to super-threshold visual stimuli in areas of residual vision, and suggested greater allocation of attention. Visual acuity was highest in patients with more binocular microsaccades and lower microsaccade velocities. The authors proposed that microsaccades may help compensate visual impairment in hemianopia and provide a basis for vision restoration and plasticity.
Blindsight is a rare phenomenon in which patients who have cortical blindness (due to lesions to the primary visual cortex) produce appropriate behavioral responses to visual stimuli they do not consciously see. Though no studies to date have systematically characterized microsaccadic properties in blindsighted patients, researchers in a recent case report studied microsaccadic inhibition (i.e., the transient suppression of microsaccade production after the presentation of a peripheral stimulus) in a patient who suffered from blindsight due to traumatic brain injury. The investigators observed that the patient’s microsaccade rates dropped briefly after the presentation of high- and low-contrast peripheral stimuli, in both the left (blind) and the right visual fields. In the case of low-contrast stimuli, the release from microsaccadic inhibition was slower in the blind field than in the sighted field, however ( ).
### Short-Term Hypoxia
Di Stasi and colleagues found that saccadic velocity decreased and intersaccadic drift velocity increased, in connection with short-term hypobaric hypoxia in aviators. The finding that acute hypoxia diminishes eye stability, the authors proposed, may help to better understand the relationship between hypoxia episodes and central nervous system impairments ( ).
## Microsaccades in Ophthalmic Disease
Vision and eye movements are intrinsically linked. Whereas it may seem intuitive to consider vision primarily in terms of its spatial characteristics, the process of seeing is a spatiotemporal one, where many timing features and constraints that impact our visual experience derive from the timing of eye movement production and targeting. Eye movements shape what we see, and our visual perception, in turn, affects the way we move our eyes. Ophthalmic disease, due to its deleterious effects on visual quality, tends to result in measurable abnormalities in eye movement properties, which extend to the fixational domain.
Because human beings are typically unaware of their fixational eye movements, studying their characteristics in ophthalmic disease may help evaluate the extent of a patient’s visual impairment in an objective manner—particularly in very young patients or those experiencing communication difficulties.
### Amblyopia and Strabismus
Most studies of fixational eye movements in ophthalmic disease to date have centered on amblyopia and strabismus. Amblyopia is defined as underdeveloped vision of one eye due to any condition that interferes with focusing during early childhood, including strabismus (in which the two eyes do not align correctly during fixation) and uncorrected refractive error (with anisometropia, or unequal refractive power in the two eyes).
Starting in the late 1970s, Ciuffreda and his colleagues conducted a series of pioneering studies on how amblyopia and strabismus affected fixation behavior ( – ). They found that, whereas amblyopic patients produced normal fixational eye movements during binocular fixation (and during monocular fixation with the fellow eye), monocular fixation with the amblyopic eye resulted in increased drift (whether or not strabismus was also present) ( , – ). If the amblyopia was due to strabismus, or in cases of alternating strabismus, this increase in drift was accompanied by sizable and frequent saccadic intrusions ( – ). By contrast, amblyopic fixation in dark-adaptation conditions was found to be normal or close to normal ( , ).
More recently, Shi and colleagues found less frequent but larger microsaccades during monocular fixation with the amblyopic eye than with the fellow eye ( ) and proposed that the objective evaluation of oculomotor function in amblyopia includes a microsaccade assessment. Otero-Millan et al. ( ) noted that microsaccades produced during normal binocular fixation of large targets have similar features to those reported by Shi et al. during monocular fixation with the amblyopic eye, which might indicate a common lack of fixation precision in both scenarios. This possibility is consistent with work finding reduced fixation stability in the amblyopic eye, as compared to the fellow eye and to binocular viewing ( ) (Figure ).
Eye movements of a patient with strabismic amblyopia. Horizontal ( X ) and vertical ( Y ) positions are plotted for the left eye (gray, amblyopic eye) and the right eye (black, fellow eye). Amblyopic eye viewing results in larger microsaccades in both eyes. Monocular viewing with the fellow eye is tied to increased instability in the amblyopic eye. Monocular viewing with the amblyopic eye produces increased instability in the two eyes. From González et al. ( ).
Ghasia et al. found that fixational saccades and ocular drift were more disconjugate for patients with strabismus than for control participants. This disconjugacy was greater for patients with large-angle strabismus and impaired stereopsis (as a result of the misalignment of their eyes) than for patients with small-angle strabismus and preserved stereopsis (Figure ). This study also found that drift was faster in patients with strabismus than in control subjects ( ).
Visual fixation of a target for 5 s, under monocular viewing conditions. (A) Participant with normal vision: both microsaccades and intersaccadic drift are conjugate. (B) Participant with small-angle strabismus and stereopsis present: disconjugancy across the two eyes is mild for both microsaccades and drift. (C) Participant with large-angle strabismus and absent stereopsis: disconjugancy is pronounced for both microsaccades and drift. (A–C) Horizontal (red: right eye, gray: left eye) and vertical (blue: right eye, gray: left eye) eye positions are presented. Black arrows indicate microsaccades, and gray arrows represent intersaccadic drift. From Ghasia et al. ( ).
Fixation stability, usually measured as the eye position dispersion [i.e., bivariate contour elliptical area (BCEA)] during the attempt to fixate, combines the effects of microsaccades and drifts, without making a distinction between the two types of eye motion ( ). Subramanian et al. ( ) found that the BCEA was larger in the amblyopic eye than in the fellow eye, especially along the horizontal axis, and that patients with larger BCEAs tend to have lower visual acuities.
Increased drift and decreased microsaccade production in severe amblyopia may lead to perceptual fading of large portions of the visual field, including the “small fixation spot, small and large acuity targets, and even portions of the laboratory” during monocular fixation with the amblyopic eye ( , – ). One patient reportedly “made saccades to revive the faded or blanked-out portions” of the image in such situations ( ), suggesting that visual fading in amblyopia might be related to ordinary Troxler fading [i.e., the kind of visual fading that normally sighted individuals can experience during fixation in the absence of microsaccades ( , )].
Increased drift in amblyopia could also produce lower visual acuity—and increased variability in visual acuity measurements—by shifting retinal images to more eccentric positions ( , ). Such links exemplify the tight bond between the motor and sensory aspects of fixational eye movements ( ).
Aiming to increase fixation stability for the amblyopic eye (and thus produce bifoveal fixation), Raveendran et al. ( ) decreased the contrast of the image viewed by the fellow eye until it was equivalent to the contrast perceived by the amblyopic eye. Fixation stability in the amblyopic eye improved as a result, but bifoveal fixation was nevertheless temporary (due to the amblyopic eye drifting away from foveal alignment).
Loudon and colleagues used a binocularity score to assess how well subjects fixated a target with both eyes and proposed that the presence of fixation instability can help detect amblyopia at an early age (when it otherwise goes undetected up to a third of the time) ( ).
Successful orthoptics therapy tends to normalize fixational eye movements in amblyopia [though not all oculomotor and visual functions may improve concurrently ( , )]. Thus, Ciuffreda and colleagues have proposed that amblyopic therapies should not be interrupted when patients achieve normal visual acuity and centralized fixation, but should continue until they produced normal or stabilized fixational eye movements. Because critical periods for some aspects of oculomotor plasticity may extend into adulthood, lack of fixational eye movement normalization in amblyopic patients could be a factor in their reverting to the pre-treatment condition once therapy is discontinued ( ). Thus, fixational eye movement assessments may help establish the optimal duration of treatments.
### Central Scotoma due to Macular Disease or Dysfunction
Macular scotomas, and other pathologies producing prolonged monocular visual deprivation, have also been connected to increased drift, with comparable characteristics to drift in amblyopes ( , ). A recent study by Kumar and Chung ( ) found that patients with macular disease presented not only increased drift amplitudes but also larger microsaccadic amplitudes (Figure ) than healthy subjects, without a corresponding change in microsaccade rate. The authors concluded that an increase in drift and microsaccade amplitudes—as opposed to changes in velocity or rate—is the strongest predictor of overall fixation instability in macular disease. Moller et al. previously found, in a group of diabetic maculopathy patients, that microsaccade magnitude increased as visual acuity decreased ( ). A more recent study set out to determine if saccades in an eye affected by diabetic maculopathy were influenced by the other eye during binocular fixation. The results revealed that microsaccades during monocular fixation with the eye most affected by macular edema were larger, more frequent, and involved a larger retinal area than those produced during binocular fixation. A significant negative correlation was found between area of fixation and visual acuity during monocular, but not binocular, fixation. The authors concluded that binocular fixation can reduce the fixation area and microsaccade amplitude in the “worst eye” of diabetic maculopathy patients and advised that microsaccades in diabetic maculopathy are studied during monocular fixation ( ).
Eye positions during fixation in a patient with macular disease (left) and a healthy control subject (right). From Kumar and Chung ( ).
### Myopia
Recent work found increased microsaccade amplitude—without a corresponding change in microsaccade velocity or microsaccade rate—in myopic individuals ( ). As the severity of uncorrected refractive error increased, so did the sizes of microsaccades. This suggested that the control of microsaccade amplitude relies on the precision of visual information on the fovea, with blurred information leading to fixational instability.
### Retinal Implants
Here, we discuss current efforts to characterize fixational eye movements, including microsaccades, in patients with subretinal implants. Because subretinal implants are placed below the retina (unlike epiretinal implants such as the Argus II, where an external camera is used to capture an image), the eye movements of implanted patients have the potential to affect—and be affected by—their visual perception.
In recent research, patients with a subretinal Alpha IMS scanned their visual field to locate a fixation target that appeared at random locations. Upon target fixation, the patients produced fixational eye movements—including microsaccades (with and without square-wave coupling) and drifts—that were analogous to those produced by control participants ( ). A previous study by the same group made similar observations ( ). The properties of (micro)saccades moreover depended on the shape of the stimulus being viewed. For instance, both patients and control participants made more horizontal eye movements when viewing a rectangle than when viewing a square ( ). These data suggest that (micro)saccadic dynamics might help provide an objective measure of the success of an implant, especially in situations where subjective reports are questionable or inviable: if eye movements characteristics change in response to changes in the stimulus, it would indicate that visual inputs have been processed appropriately ( ).
One limitation of subretinal implants such as the Alpha IMS is that visible stimuli typically fade from perception within seconds ( – ). Understanding the relationship between fixational eye movement dynamics and image fading in prosthetic vision may prove key to improving future subretinal implants. Microsaccades have been shown to counteract, and to help prevent, perceptual fading in natural vision ( , – ). Similarly, microsaccade occurrence has been connected to fading prevention in patients with subretinal implants ( ). It has also been proposed that the characterization of microsaccade patterns in patients could help fine-tune the frequency of stimulation that results in optimal visibility in specific individuals. That is, depending on a particular observer’s fixational eye movement patterns, he/she may need higher or lower stimulation frequencies to maintain visibility ( ). Future research may investigate the translational value of this potential relationship.
It remains currently unknown why visual fading is more severe in patients with prosthetic implants than in healthy observers. One possibility is that fixational eye movements counteract and prevent perceptual fading less effectively in implanted patients than in natural vision ( ). Recent modeling work has suggested that increased fading in prosthetic vision might be due to a lack of OFF responses and to lower contrast sensitivity than in natural vision ( ); thus, the quality of the visual input may be too low for eye movements to refresh retinal images effectively. Fading may also be more or less prevalent depending on the size of electrodes used: stimulation from a single electrode may affect such a large visual region that even when microsaccades shift the stimulus to adjacent electrodes, they may not significantly change the activated neurons ( , ).
## Conclusion
We have reviewed the characteristics of fixational eye movements in neurological and ophthalmic disease, with an emphasis on microsaccades. Though studies addressing microsaccadic impairments in patient populations remain relatively scarce, this has recently become an area of active inquiry, with valuable implications for both clinical and basic research ( ). Converging studies have made significant headway vis-à-vis the potential of microsaccade and other fixational eye movement dysfunctions as indicators of ongoing pathologies beyond the oculomotor realm. Thus, the objective assessment of fixational eye movement parameters may help refine differential diagnostics and assist in the evaluation of ongoing therapy regimes (i.e., successful treatments should result in the normalization of previously impaired fixational eye movements). These measures will also help refine current understanding of the pathogenesis of neural disease, as well as place constraints on—and guide the development of—future saccadic generation models, especially with regards to the relationship of (micro)saccades to saccadic intrusions in neurological disease.
## Author Contributions
SM-C, RGA, and SLM wrote and edited the manuscript. SM-C and RGA conducted the literature review.
## Conflict of Interest Statement
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
|
Cerebral cortical microinfarct (CMI) is common in patients with dementia and cognitive decline. Emerging studies reported that intestinal dysfunction influenced the outcome of ischemic stroke and that vagus nerve stimulation (VNS) protected against ischemic stroke. However, the effects of intestinal dysfunction and VNS on CMI are not clear. Therefore, we examined the influence of colitis and VNS on CMI and the mechanisms of VNS attenuating CMI in mice with colitis. CMI was induced using a two-photon laser. Colitis was induced using oral dextran sodium sulfate (DSS). The cervical vagus nerve was stimulated using a constant current. In vivo blood-brain barrier (BBB) permeability was evaluated using two-photon imaging. Infarct volume, microglial and astrocyte activation, oxidative stress and proinflammatory cytokine levels were assessed using immunofluorescent and immunohistochemical staining. The BBB permeability, infarct volume, activation of microglia and astrocytes and oxidative stress increased significantly in mice with colitis and CMI compared to those in mice with CMI. However, these processes were reduced in CMI mice when VNS was performed. Brain lesions in mice with colitis and CMI were significantly ameliorated when VNS was performed during the acute phase of colitis. Our study demonstrated that VNS alleviated CMI and this neuroprotection was associated with the suppression of BBB permeability, neuroinflammation and oxidative stress. Also, our results indicated that VNS reduced colitis-induced microstroke aggravation.
## Introduction
Cerebral cortical microinfarct (CMI) has gained increasing attention because of its major role in cognitive impairment and dementia ( ). Recent autopsy studies reported that CMI were detected not only in patients with dementia (62% in vascular dementia and 43% in Alzheimer's disease), but also in older individuals without dementia (up to 33%) ( , ). However, effective therapies for CMI are lacking.
Inflammatory bowel disease (IBD), which includes Crohn's disease and ulcerative colitis, is a group of chronic relapsing intestinal inflammatory disorders. Recent evidence demonstrated that intestinal dysfunction aggravated poststroke neuroinfla- mmation and outcome in experimental stroke ( , ). A cohort study also demonstrated a greater than two-fold increased risk of atrial fibrillation (AF) during IBD flares and persistent activity ( ), and AF is an important risk factor of CMI ( ). Many studies reported that IBD was a contributor to an increased risk of ischemic heart disease and cerebrovascular accidents during periods of active disease ( , ). Therefore, we hypothesized that IBD would impact CMI, which, to our knowledge, has not been examined.
The vagus nerve is a link between the brain and gut. Previous studies demonstrated that vagus nerve stimulation (VNS) reduced infarct volume and improved neurological score and motor function recovery after ischemic brain injury ( – ), but the mechanism of these effects remains controversial. Recent evidence demonstrated that VNS conferred neuroprotection against ischemic brain injury via the cholinergic anti-inflammatory pathway (CAIP) ( ). Other studies reported that VNS reduced the blood-brain barrier (BBB) permeability and cerebral ischemia/reperfusion (I/R) injury ( , ). However, the effect of VNS on CMI is not known. The anti-inflammatory effects of VNS against intestinal inflammation are well documented ( – ).
The present study examined the effect of VNS on CMI in mice with or without colitis induced via dextran sodium sulfate (DSS) administration. We also investigated the mechanisms of protective role of VNS.
## Materials and methods
### Animals
The Sun Yat-sen University (Guangzhou, China) Committee on the Care and Use of animals approved all animal experiments. Experiments were performed in a blinded manner. Forty wild-type C57BL/6J female mice (8–10 weeks, 20–25 g) were used. We employed females only according to previous study, because males are more sensitive to the disruptive effects of DSS on colon epithelia and can develop severe inflammation and die ( ). All mice were purchased from the Animal Center of Sun Yat-sen University and housed at controlled temperature and humidity under a 12-h light/dark cycles. Free access to food and water was supplied. All animals were randomly assigned to five groups. The CMI group mice underwent occlusion of a single penetrating arteriole only. The DSS+CMI group mice received four cycles of DSS followed by occlusion of a single penetrating arteriole on day 29. The CMI+VNS group mice received VNS 30 min, 1, 2, and 3 days after CMI induction. The DSS+CMI+VNS group mice underwent four cycles of DSS followed by occlusion of a single penetrating arteriole on day 29, and VNS was performed 30 min, 1, 2, and 3 days after the induction of CMI. The DSS+VNS+CMI group mice underwent four cycles of DSS, and VNS was performed during the acute phase of colitis followed by occlusion of a single penetrating arteriole on day 29. Figure presents the study design.
Establishment of CMI and DSS-induced colitis models. (A) Timeline for different groups ( n = 8 per group). Group 1: CMI mice. Group 2: DSS+CMI mice. Group 3: CMI+VNS mice. Group 4: DSS+CMI+VNS mice. Group 5: DSS+VNS+CMI group. (B1) Candidate arteriole for clotting was identified, and microvessel diameter was measured. (B2) Red blood cell speed was measured prior to clot formation. (B3,B4) Microvessel diameter and red blood cell speed in each groups. (C1, C2) in vivo three-dimensional stacks of images of the target vessels through the cranial window before and after occlusion (white arrow). (C3) H&E staining of brain section in CMI model (200×). (D1, D2) Immunohistochemical analyses of intestinal inflammation (H&E, 100×). (E) Cumulative changes in body weight (BW) in each group. Scale bars, 200 μm.
### Experimental colitis
Experimental colitis was induced using multiple-cycle administration of 2% (wt/vol) DSS (molecular weight 30,000 to 50,000, MP Biomedicals, CANADA) in drinking water on days 1 to 5, 8 to 12, 15 to 19, and 22 to 26, as previously described ( , ). Drinking water with fresh DSS solutions was replaced daily. Control mice received drinking water without DSS. Bodyweights were monitored daily.
### Electric VNS
The stimulating electrodes were self-constructed based on the design of Smith et al. ( ). The electrodes were composed of two polyethylene-coated curved silver wires held 1.5 mm apart with a solid bar. Briefly, the mice were anesthetized via an intraperitoneal injection of pentobarbital (1%, 50 mg/kg), and an incision was performed in the ventral side of the neck to isolate the left cervical vagus nerve. Bipolar electrodes were gently wrapped around the vagus nerve using a microscope and sutured to the sternocleidomastoid muscle. The mice in the treatment group were subjected to VNS (0.5 mA, 5 Hz) using a stimulator (Model SDZ-II, Medical Appliance Factory, Suzhou, China). Stimulation was delivered for 30 s every 5 min for 1 h. The mice in the sham group underwent the same procedure but did not receive stimulation. Each mouse was given gentamicin (4 mg/kg) to prevent infection only immediately after completion of surgery. For CMI mice and DSS+CMI+VNS mice, VNS was performed 30 min, 1, 2 and 3 days after the onset of CMI. For DSS+VNS+CMI mice, VNS was performed during the acute phase of colitis (days 1 to 5) ( , , ).
### Surgical procedures of the CMI model
The creation of micro-lesions was done in a blinded manner, without knowledge of animal assignment to groups. The mice were anesthetized via intraperitoneal injection of pentobarbital (1%, 50 mg/kg) and positioned in a stereotaxic frame (RWD Life Science Company, Shenzhen, China). A 2 × 2 mm cranial window was created using a microdrill over the right parietal cortex (2 mm bregma, 1.7 mm lateral), and a metal plate was glued to the edge of the cranial window for in vivo two-photon imaging, as described previously ( ). Artificial cerebrospinal fluid (ACSF) was used to keep the window moist during the entire surgical procedure. The experiment was not performed if any bleeding was present in the cranial window. Body temperature was maintained at 36.8°C throughout the surgical procedure using a feedback-regulated heat pad (RWD Life Science Company, Shenzhen, China). Fluorescein isothiocyanate (FITC)-dextran (0.2 ml, 2,000 kDa, 2%; FD20, Sigma, Germany) was injected into the tail vein prior to mouse fixation on the stage of the two-photon laser scanning microscope (Leica, Germany). A 0.12 numerical aperture × 4 air objective was used to obtain three-dimensional stack images of the cerebral vessels through the cranial window. A 0.95 numerical aperture and × 25 water immersion objective was used for high-resolution imaging, measurements of vessel diameter and blood flow speed, and vessel occlusion. A penetrating arteriole, approximately 20–25 μm in diameter, was selected as the target vessel. The target arteriole was placed in the center of the screen prior to occlusion. A segment that coursed parallel to the cortical surface was magnified to allow the easy verification of clot formation. The bleach mode was used to damage the endothelium. These bleach points were located within the vessel lumen a few micrometers from the vessel wall. Irradiation was initiated using an 800 nm laser. The intensity (max. power 3.5 W) was controlled at 30% (1.05 W) in the EOM setting and was depth-dependent. The energy was gradually increased by 10% until extravasation of fluorescently labeled plasma was observed outside of the vessel lumen ( , ). Irradiation was continued until a clot was visualized and the motion of red blood cells ceased. We observed this area for approximately 1 h until the downstream flow was redistributed and stabilized. Irradiation was repeated until the target vessel stopped flowing if the occlusion recanalized. Models with hemorrhage or diffuse burning were discarded. The diameters of cortical CMI in post-mortem studies of patients with dementia are 0.1–1 mm ( ). The lesions in the present study were typically microscopic (<1 mm).
### Analysis of the BBB permeability
The BBB permeability was analyzed 1 week after occlusion of a penetrating arteriole. Rhodamine B (RD; 0.2 ml of 1% in saline; Sigma) was administered intravenously immediately prior to two-photon imaging to visualize the vasculature. Fluorescent intravascular dyes were used to measure leakage from the vasculature ( , ). The total fluorescence intensity in the extravascular compartment was analyzed ( ). Images (512 × 512 pixels) were acquired using a 0.95 numerical aperture and × 25 water immersion objective 0, 5, 15, 30, and 60 min after administration of fluorescent dextran, and XYZ stacks of images were obtained.
### Histology
Mice were perfused transcardially with saline followed by a 4% paraformaldehyde / 0.1 M PB solution (pH 7.4) after the BBB measurements. The right parietal cortex and colon were rapidly removed. The cortex was sandwiched between two glass slides separated by a distance of 2.5 mm to ensure a flat section. Sections (1 cm) of the distal colon were cut, cleared of feces, and washed with a 0.1 M PB solution. Tissues were postfixed overnight in the same fixative and cryoprotected in 20 and 30% sucrose/0.1 M PB solutions at 4°C. Brain and colon tissues were serially sectioned (10-μm thick) using a frozen microtome (Leica, Germany). Colon sections were stained with hematoxylin and eosin (H&E) for pathomorphological examination. Brain sections were treated for immunofluorescent and immunohistochemical staining. Slices for immunofluorescent staining were blocked in 0.3% Triton X-100 and 10% goat serum for 1 h at room temperature. Slices were incubated overnight at 4°C with the following primary antibodies: monoclonal mouse anti-mouse NeuN IgG (1:200, Millipore, USA), polyclonal rabbit anti-mouse NeuN IgG (1:500, Abcam, UK), polyclonal rabbit anti-mouse Iba1 IgG (1:500, Wako, Japan), polyclonal rabbit anti-mouse GFAP IgG (1:500, Abcam, UK) and mouse anti-mouse mouse 3-nitrotyrosine (NT) IgG (1:100, Abcam, UK). Slices were subsequently incubated with species-specific secondary antibodies for 1 h at room temperature. All slides were stained with DAPI and sealed using a glass coverslip. Sections for immunohistochemical staining were treated with 3% hydrogen peroxide for 20 min and 0.3% Triton and 10% goat serum for 1 h at room temperature. The sections were incubated with primary antibodies (1:300 anti-TNF-α, Wako, Japan) overnight at 4°C, followed by secondary antibodies for 1 h and then by diaminobenzidine in PBS (1:100) for several seconds. The sections were incubated with hematoxylin for nuclear staining. The sections were immersed in ethyl alcohol and dimethylbenzene for several minutes, and the slices were mounted using Permount TM Mounting Medium under a cover glass. Image analysis was performed using ImageJ software.
### Statistical analysis
All data are presented as the means ± standard deviations of the means (SD). The 3D image overlays were visualized using Leica Application Suite (LAS) Advanced Fluorescence Lite software (LAS AF Lite, 2.4.1 build 6384, Leica). ImageJ software (National Institutes of Health, Bethesda, MD, USA) was used to analyze the immunohistochemistry and immunofluorescent results. An experimenter who was blinded to the experimental condition analyzed all sections. A two-way repeated measures ANOVA with multiple comparisons was performed to compare the BBB permeability measurements. An independent-samples t test was used for dual comparisons. Means were compared using one-way ANOVA analysis followed by a post hoc Tukey's multiple comparison test for multiple comparisons. Statistical analyses were completed using GraphPad Prism software (GraphPad Software, La Jolla, CA, USA). A P value < 0.05 was considered statistically significant.
## Results
### Occlusion of a single penetrating arteriole and DSS-induced colitis
We used two-photon microscopy to select target penetrating arterioles in mouse cortex and measured the microvessel diameter (Figure ) and red blood cell speed (Figure ) prior to clot formation. The average microvessel diameter (Figure ) and red blood cell speed (Figure ) did not differ statistically between each group (all Ps > 0.05). We used femtosecond laser ablation to trigger clotting in the selected penetrating arterioles (Figures ). The brain tissue injury caused by the lesion was visualized by H&E staining (Figure ). Histological changes in DSS-induced intestinal inflammation were analyzed using H&E staining of colonic sections (Figures ). All mice exhibited signs of intestinal inflammation as evidenced by infiltration of inflammatory cells, loss of crypt structure and depletion of goblet cells (Figure ). Body weight was monitored daily. A loss of 8 to 15% body weight was observed initially in DSS-treated mice, and normal weight was restored by day 28. Comparison between DSS+CMI group and DSS+VNS+CMI group indicated that VNS reduced the degree of body weight loss (Figure ).
### The effect of VNS on BBB disruption
A two-way repeated measures ANOVA indicated a significant interaction between the group and time factor ( P < 0.001), and significant main effects of the time factor ( P < 0.001) and group factor ( P < 0.001) in all groups. The average fluorescence intensity in the extravascular compartment increased in the DSS+CMI group compared to that in the CMI group ( P < 0.001) and decreased in the CMI+VNS group ( P < 0.001) 60 min after RD injection, which indicates that the colitis worsened the BBB disruption and that VNS improved the BBB integrity after CMI.
The BBB disruption was attenuated in the DSS+VNS+CMI ( P < 0.01) and DSS+CMI+VNS groups ( P < 0.01) at 60 min compared with that of the DSS+CMI group, which indicates that VNS alleviated the BBB disruption in mice with CMI and colitis. We compared the BBB permeability of the DSS+CMI+VNS group and DSS+VNS+CMI group to assess the timing of VNS intervention and found that the magnitude of the decrease in the BBB permeability was more significant in the DSS+VNS+CMI group than in the DSS+CMI+VNS group at 5 min ( P < 0.05), 15 min ( P < 0.001) and 30 min ( P < 0.05). However, there was no significant difference between the two groups at 45 min or 60 min ( P > 0.05) (Figures ).
Analyses of two-photon microscopy data capturing fluorescent dye leakage of the BBB. (A) XYZ stacks of brain vessels 5, 15, 30, 45, and 60 min after dye injection (250×), indicating dye permeation. (B) Linear graph of comparison of average fluorescent intensities in the extravascular compartment at different time points ( n = 8 per group). Scale bars, 200 μm.
### The effect of VNS on infarct volume
Experimental colitis significantly enlarged the infarct volume in the DSS+CMI group compared to that of the CMI group (223.658 ± 9.430 μm vs. 524.843 ± 14.262 μm , P < 0.001). VNS treatment significantly reduced infarct volume in CMI mice (223.658 ± 9.430 μm vs. 105.567 ± 8.275 μm , P < 0.001). We compared the DSS+CMI, DSS+VNS+CMI and DSS+CMI+VNS groups to evaluate the effect of VNS on CMI mice with colitis. We found a significant decrease in infarct volume in the DSS+VNS+CMI group (305.425 ± 48.406 μm vs. 524.843 ± 14.262 μm , P < 0.001) and DSS+CMI+VNS group (461.825 ± 42.469 μm vs. 524.843 ± 14.262 μm , P < 0.05). VNS treatment produced a greater improvement in the infarct volume in the acute phase of colitis than after the CMI onset (305.425 ± 48.406 μm vs. 461.825 ± 42.469 μm , P < 0.05) (Figures ).
Measurement of CMI volume. (A) Total infarct volumes in the CMI, DSS+CMI, CMI+VNS, DSS+CMI+VNS, and DSS+VNS+CMI mouse groups. (200×) (B) Histograms comparing the infarct volume in the cortex ( n = 8 per group). P ≤ 0.001 vs. CMI group. # P < 0.05 and ### P ≤ 0.001 vs. DSS+CMI group. Scale bars, 200 μm.
### The effect of VNS on activation of microglia and astrocytes
The DSS+CMI group exhibited greater numbers of Iba1+ cells in the infarct core ( P < 0.001) and GFAP+ cells in the peri-infarct regions than those in the CMI group ( P < 0.001), which indicates that colitis enhanced neuroinflammation in the acute phase of CMI. Our data suggest that VNS reduced Iba1+ cell infiltration and GFAP+ gliosis in CMI ( P < 0.001 for Iba1+ cells and P < 0.01 for GFAP+ cells). Similar effects were observed in mice with colitis (DSS+CMI+VNS and DSS+VNS+CMI groups). Our results revealed that stimulation of the cervical vagus nerve was more effective during the acute phase of colitis than after the CMI onset (DSS+VNS+CMI group vs. DSS+CMI+VNS group, P < 0.05) (Figures – ).
Measurement of activated microglia and astrocytes. (A) Immuno- fluorescent staining of ionized calcium-binding adapter molecule-1 (Iba-1)-positive microglia in the cortex of the CMI, DSS+CMI, CMI+VNS, DSS+CMI+VNS, and DSS+VNS+CMI mouse groups (200×). (B) Immunofluorescent staining of glial fibrillary acidic protein (GFAP)-positive astrocytes in the cortex of the CMI, DSS+CMI, CMI+VNS, DSS+CMI+VNS, and DSS+VNS+CMI mouse groups. (200×) (C) Histograms comparing the numbers of Iba1-positive microglia in the cortex ( n = 8 per group). P ≤ 0.001 vs. CMI group. # P < 0.05 and ### P ≤ 0.001 vs. DSS+CMI group. (D) Histograms comparing the numbers of GFAP-positive astrocytes in the cortex ( n = 8 per group). P < 0.01 and P ≤ 0.001 vs. CMI group. # P < 0.05 and ### P ≤ 0.001 vs. DSS+CMI group. Scale bars, 200 μm.
### The effect of VNS on deposition of NT
DSS-induced colitis enhanced NT expression ( P < 0.001), and VNS reduced NT expression compared to that of CMI mice ( P < 0.01). VNS prior to CMI reduced NT deposition in DSS+CMI mice ( P < 0.001), and VNS following the ischemic injury produced a mild effect on NT expression ( P < 0.05). Comparison of the DSS+VNS+CMI and DSS+VNS+CMI groups revealed that NT expression decreased more in the former group, which suggests that VNS was more effective in the DSS+VNS+CMI group ( P < 0.05) (Figures ).
Measurement of NT deposition. (A) Immunofluorescent staining of NT deposition in the cortex of the CMI, DSS+CMI, CMI+VNS, DSS+CMI+VNS, and DSS+VNS+CMI mouse groups (200×). (B) Histograms comparing the numbers of 3-NT deposition in the cortex ( n = 8 per group). P < 0.01 and P ≤ 0.001 vs. CMI group. # P < 0.05 and ### P ≤ 0.001 vs. DSS+CMI group. Scale bars, 200 μm.
### The effect of VNS on proinflammatory cytokine levels
TNF-α levels increased significantly in the DSS+CMI group compared to those in the CMI group ( P < 0.01), and the levels were significantly reduced in the CMI+VNS group ( P < 0.05). Decreased TNF-α expression was observed when VNS were performed during the acute phase of colitis in mice with colitis and CMI. However, our data demonstrated no significant difference between TNF-α levels between the DSS+CMI+VNS and DSS+CMI groups ( P > 0.05) (Figures ).
Measurement of TNF-α levels in brain. (A) Immunohistochemical staining of TNF-α in the cortex of the CMI, DSS+CMI, CMI+VNS, DSS+CMI+VNS, and DSS+VNS+CMI mouse groups (100×). (B) Histograms comparing the area percentage of TNF-α in the cortex ( n = 8 per group). P ≤ 0.05; P ≤ 0.01 vs. CMI group. # P < 0.05 vs. DSS+CMI group. Scale bars, 200 μm.
## Discussion
The current study found that VNS reduced CMI volume in mice, in the process of which involved decreased BBB permeation, microglia and astrocytes activation, oxidative stress and proinflammatory cytokine expression. DSS-induced colitis significantly exacerbated microstroke in mice, and VNS alleviated colitis-induced CMI aggravation. Notably, the DSS+VNS+CMI group exhibited smaller infarct volume, less BBB permeation and lower neuroinflammation and oxidative stress than the DSS+CMI+VNS group did, which indicates that in mice with colitis and CMI, VNS treatment during the acute stage of colitis was more effective than that following the CMI onset.
A previous study demonstrated that VNS reduced the infarct volume by 56.3% in transient middle cerebral artery occlusion (MCAO) and by 38.4% in permanent MCAO rat model of focal cerebral ischemia ( ). Our results demonstrated that VNS achieved a 53% reduction in the infarct volume in a mouse model of CMI, which indicates that VNS-induced neuroprotection extends to CMI.
The BBB is a key component of the neurovascular unit, and its integrity is critical for maintaining brain homeostasis and function. Breakdown of the BBB is an important contributor to CMI pathology and outcome ( ), which was supported in our study. VNS exhibited a protective effect on the BBB integrity in our CMI model. Alpha-7 nicotinic acetylcholine receptors (α7 nAChRs) are an essential regulator of the anti-inflammatory effect of VNS ( ). Previous studies in traumatic brain injury models revealed that VNS conferred neuroprotection on the BBB integrity ( ) and that stimulation of α7nAChRs on splenic macrophages decreased the synthesis/release of proinflammatory molecules, which reduced the BBB permeability ( ). Another study used an intracerebral hemorrhage model and suggested that the stimulation of α7 nAChRs on brain endothelial cells exerted protective effects on the BBB integrity via phosphatidylinositol 3-kinase-Akt–induced inhibition of glycogen synthase kinase-3β and β-catenin stabilization ( ). Proinflammatory cytokines, such as TNF-α, are released from endothelial cells, leukocytes and resident cells in the brain following cerebral ischemia injury, and inflammatory cytokines are implicated as mediators of the BBB permeability ( , ). However, a previous study reported that VNS regulated several I/R-related pathways and suppressed inflammatory cytokine synthesis ( ). Our study also observed a marked downregulation of TNF-α levels in the brains of CMI mice. Therefore, VNS may preserve the BBB integrity in our mouse CMI model via the modulation of α7nAChRs and inhibit the expression of inflammatory cytokines. However, the exact mechanism requires further investigation.
Inflammation is a key contributor to ischemic cerebral injury, including CMI and Alzheimer's disease. CMI contributes to the cognitive decline in individuals at high risk for Alzheimer's disease ( ). Our study found that reactive microglia infiltrated the ischemic core and that reactive astrocytes existed in the peri-infarct area in mice with CMI. However, many studies reported that VNS could alleviate ischemic cerebral lesions and Alzheimer's disease via anti-inflammatory effects ( , , ) involving several possible mechanisms. First, the VNS could activate α7nAChRs expressed on microglia and astrocytes in a rat model of acute cerebral I/R injury for ischemia insult to cause a significant reduction in α7nAChRs on the surfaces of microglia and astrocytes, thus suppressing the expression of inflammatory cytokines ( , , ). The stimulation of α7nAChRs also conferred neuroprotection via inhibition of the activation and infiltration of inflammatory cells ( ). Second, VNS could reverse the morphological signs of microglial aging and activation in a murine model of Alzheimer's disease ( ). Third, VNS could regulate the neuroinflammatory response following cerebral I/R injury via upregulation of the expression of peroxisome proliferator-activated receptor γ and subsequently suppress the synthesis and secretion of proinflammatory cytokines ( ). Our results consistently suggest that VNS influenced the activation of microglia and astrocyte and the expression of proinflammatory cytokines. Therefore, anti-inflammatory effects may be involved in the neuroprotection of VNS in CMI model.
Oxidative stress, such as the deposition of NT, is also a primary event leading to brain damage after cerebral ischemia, including CMI, and inhibition of oxidative stress is neuroprotective ( , ). Oxidative stress was high in the ischemic core and surrounding penumbra area in the present study, which is consistent with a previous report ( ). Recent studies suggest that VNS plays an important role in the inhibition of oxidative stress. For example, VNS exerts neuroprotective effects against ischemia/reperfusion (I/R) via modulation of miR-210 activity, which is an important microRNA that is regulated by hypoxia-inducible factor and Akt-dependent pathways ( ). Furthermore, α7nAChR agonist treatment attenuated oxidative stress in mouse models of MCAO and tibia fracture via modulation of anti-oxidant gene expression ( ). However, our results demonstrated that VNS significantly modulated the oxidative stress response, which was associated with the downregulation of NT deposition.
Previous reports found that intestinal dysfunction aggravated poststroke neuroinflammation and infarct volumes ( , ). We also found that DSS-induced colitis significantly exacerbated the infarct volume of CMI in mice, which may be due to acceleration of BBB permeation and aggravation of the inflammatory response and oxidative stress. On the other hand, previous studies suggested that the levels of circulating Th17 and Th1 cells were significantly increased in patients with ulcerative colitis ( ), which aggravated the inflammatory damage and outcome in stroke model ( ). That would be a alternative mechanism how colitis aggravated the ischemic stroke, However, whether similar mechanism exists in CMI yet remains to be studied. In addition, our data showed that DSS-induced colitis aggravated CMI without the influence of blood flow. Proinflammatory cytokines are key mediators in the pathological process of IBD ( ). Proinflammatory factors, such as TNF-α, enter the brain through the disrupted BBB after the onset of CMI and subsequently cause the activation of more microglia to produce more inflammatory factors, which may further trigger secondary reactions, thus beginning a self-propelling and vicious cycle of neuronal damage ( ). Previous studies demonstrated that VNS protected against intestinal inflammation via inhibition of the synthesis/release of inflammatory cytokines ( – ). The amount of TNF-α was reduced significantly in the DSS+VNS+CMI group compared to that of the DSS+CMI group, which protected against the colitis-induced CMI aggravation and included BBB disruption, neuroinflammatory glial overreaction and oxidative stress. However, TNF-α levels were not reduced significantly in the DSS+CMI+VNS group compared with those of the DSS+CMI group because VNS did not act effectively against the colitis at this time point, and the excess of inflammatory mediators produced from colitis entered the brain via the disrupted BBB after CMI onset to cause a serious of secondary ischemic brain injury. Therefore, VNS exhibited a weaker protective role in CMI than that in the DSS+VNS+CMI group. Our data indicate that VNS reduced the exacerbation of colitis-induced CMI, and therapy for colitis was critical in this condition.
There are several limitations in our study. We did not monitor the effect of colitis and VNS on the circulating factors and cells, which will be studied in our future study. Also, it was reported that estrogen provides neuroprotection against cerebral ischemic injury by activating estrogen receptors ( ) and female mice developed less severe colitis than male mice in DSS-induced colitis, and this protection seems to be mediated by estradiol ( ), which might be a potential effect in our study. It was a limitation that the stage of estrous cycle in individual mice was not determined here. Besides, we only used one VNS parameter following previous studies, we will explore the protective effect of different parameters on the ischemic stroke.
In conclusion, our results indicated that VNS alleviated CMI and this neuroprotection was associated with the suppression of BBB permeability, neuroinflammation, oxidative stress. Also, we concluded that VNS alleviated colitis-induced microstroke aggravation. Therefore, VNS may provide a therapeutic opportunity to improve the outcome of microstroke patients, and VNS performed in the acute phase of IBD may improve the therapeutic treatment of patients with IBD and CMI.
## Ethics statement
This study was carried out in accordance with the guidelines of Sun Yat-sen University (Guangzhou, China) Committee on the Care and Use of animals.
## Author contributions
XC, XH, ZL, ZP, and RH: designed the experiment; XC, XH, YF, FL, and TS: performed the experiment; XC, XH, SL, ZL, and ZP: analyzed the data; XC, XH, ZL and ZP: wrote the manuscript.
### Conflict of interest statement
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
|
Autoimmune encephalitis (AE) is one kind of encephalitis that associates with specific neuronal antigens. Most patients with AE likely suffer from seizures, but data on the characteristics of seizure and antiepileptic drugs (AEDs) utilization in this patient group remains limited. This study aimed to report the clinical status of seizure and AEDs treatment of patients with AE, and to evaluate the relationship between AEDs discontinuation and seizure outcomes. Patients with acute neurological disorders and anti-N-methyl-D-aspartate receptor (NMDAR), γ-aminobutyric acid B receptor (GABA R), leucine-rich glioma inactivated 1, or contactin-associated protein-like 2 (CASPR2) antibodies were included. As patients withdrew from AEDs, they were divided into the early withdrawal (EW, AEDs used ≤3 months) and late withdrawal (LW, AEDs used >3 months) groups. Seizure remission was defined as having no seizures for at least 1 year after the last time when AEDs were administered. Seizure outcomes were assessed on the basis of remission rate. The factors affecting the outcomes were assessed through Spearman analysis. In total, we enrolled 75 patients (39 patients aged <16 years, male/female = 39/36) for follow-up, which included 67 patients with anti-NMDAR encephalitis, 4 patients with anti-GABA R encephalitis, 2 patients with anti-voltage-gated potassium channel encephalitis, and 2 patients with coexisting antibodies. Among the 34 enrolled patients with anti-NMDAR encephalitis who were withdrawn from AEDs, only 5.8% relapse was reported during the 1-year follow-up, with no significant difference in the percentage of relapse between the EW and LW groups ( P = 0.313). Fifteen patients (an average age of 6.8, 14 patients with anti-NMDAR encephalitis and 1 patient with anti-CASPR2 encephalitis) presented seizure remission without any AEDs. Seventy five percent of patients with anti-GABA R antibodies developed refractory seizure. Other risk factors which contributed to refractory seizure and seizure relapse included status epilepticus ( P = 0.004) and cortical abnormalities ( P = 0.028). Given this retrospective data, patients with AE have a high rate of seizure remission, and the long-term use of AEDs may not be necessary to control the seizure. Moreover, seizures in young patients with anti-NMDAR encephalitis presents self-limited. Patients with anti-GABA R antibody, status epilepticus, and cortical abnormalities are more likely to develop refractory seizure or seizure relapse.
## Introduction
Autoimmune encephalitis (AE) is kind of encephalitis which associates with humoral or cellular responses against specific neuronal antigens ( , ). The clinical characteristics of these patients include seizure, abnormal behavior, speech dysfunction, movement disorders, and autonomic dysfunction ( ). With the development of biochemical assays, several antibodies, such as the anti- N -methyl-D-aspartate receptor (NMDAR), anti-γ-aminobutyric acid B receptor (GABA R), anti-leucine-rich glioma inactivated 1 (LGI1), and anti-contactin-associated protein-like 2 (CASPR2) antibodies, have emerged as the leading causes of AE. Therapeutic regimens included first-line immunotherapy (steroids, intravenous immunoglobulin, plasmapheresis), and second-line immunotherapy (rituximab, cyclophosphamide). Despite the severity of the disease in acute phase, most patients recover after proper immunotherapy and intensive support ( ).
In acute phase, seizure is a highly prevalent symptom, and parts of patients develop status epilepticus (SE) ( – ). Hence, multiple anti-epileptic drugs (AEDs) are often necessary to control the attacks. However, based on the previous studies and data from our center, most patients likely recover completely after adequate immunotherapy, and seizure is rarely reported in the chronic phase ( , , ). Considering the adverse events of AEDs, some patients stop taking AEDs at the early stage. However, an instructional database describing the long-term use of AEDs with AE is lacking.
This retrospective study aimed to report the seizure characteristics and long-term use of AEDs in outpatients with AE. Our secondary goals included assessing the outcomes between patients with early and late AEDs withdrawal, and determining the probable risk factors for seizure relapse and refractory seizures.
## Materials and Methods
### Study Population
This study was conducted in compliance with the ethical standards of Guangxi Medical University. Written consents were obtained from the patients.
The antibodies, including NMDAR, GABA R, LGI1, and CASPR2, were detected in patients' cerebrospinal fluid and serum samples. The anti-NMDAR antibody was detected by specific staining against NMDAR isolated from rat' hippocampus and cerebellum, and positive cell-based assay with HEK293 cells transfected with NR1. Other antibodies were detected using transfected HEK293 cells with the respective target proteins.
Patients with acute neurological disorders of either sex or any age were considered eligible for this study if they presented any positive antibodies from January 2012 to May 2017. The exclusion criteria included (1) patients diagnosed with epilepsy, cerebral infarction, cerebral trauma, cerebral tumor, and other nervous system disease prior to the onset of encephalitis, (2) patients with evidence of infectious encephalitis, for example, viral, bacteria, mycobacterium tuberculosis, or fungal, (3) patients in the acute phase of autoimmune encephalitis and still required hospitalization. Immunotherapies were used in the acute phase ( Figure 3A ). The decisions about the type and duration of immunotherapy were based on the clinical symptoms, curative, and side effects. The available onset medical data (seizure characteristics, AEDs utilization, and electroencephalogram/neuroimaging findings) were recorded. The electroencephalogram and neuroimaging findings were recorded in the acute phase of the disease.
### Definitions
The chronic stage of AE was defined by 3 months after the onset of AE symptoms. SE was defined as continuous seizure activity lasting >5 min or recurrent seizures without regaining consciousness between seizures for >5 min ( ). Refractory seizure was defined as an uncontrolled seizure after treatment by more than three standard therapeutic schedules ( ). Seizure remission was defined as having no seizure for at least 1 year after the last time when AEDs were used ( ). Seizure with focal characteristics was defined as a partial seizure or a patient with seizure and hemiplegia or hemianesthesia.
### Outcome Assessment and Grouping
AEDs utilization and seizure outcomes were assessed through outpatient services and/or telephone interview. AEDs utilization in the chronic stage (time of continuation/withdrawal) and outcomes (refractory, relapse, or remission) data were collected on patients. Before evaluating the outcomes, the patients who discontinued AEDs were observed for at least 1 year after the last AEDs use.
Patients who withdraw AEDs were divided into two groups based on the duration of AEDs use. The early withdrawal (EW) group had AEDs withdrawn within 3 months, and the late withdrawal (LW) group had AEDs withdrawn after 3 months. Outcomes were assessed based on seizure remission rate and the modified Rankin Scale (mRS) ( ).
### Statistical Analysis
Data analysis was performed using IBM SPSS Statistics 22. The skewness and kurtosis coefficients were used to evaluate whether the quantitative data fit a normal distribution. Data were regarded as a normal distribution if the skewness and kurtosis coefficients <1. For the data that did not fit this criterion, we used the Mann–Whitney test to evaluate significance. Other data was compared using a t -test or a Fisher's exact test. The factors affecting the outcomes were assessed through Spearman analysis.
## Results
We identified 83 patients with AE. Figures , show a summary of these patients. In the charts reviewed, 5 patients were lost and 3 patients died of severe pneumonia. As a result, 75 patients were enrolled to follow-up. Among the 75 patients, 12 continued taking AEDs, 15 were not given any AEDs, and 37 patients who withdrew from AEDs were followed up for at least 1 year (Figure ). The group with AEDs withdrawal included 34 patients with anti-NMDAR encephalitis, 1 with anti-LGI1 encephalitis, 1 with anti-GABA R encephalitis, and 1 patient presented coexisting antibodies of anti-LGI1 and CASPR2 (Figure ).
Flow diagram of patient inclusion and grouping.
Summary of the presenting patients and outcomes in accordance with the involved antibodies. No AEDs, No AED was given and no relapse was reported. Early withdrawal (EW), AEDs weaned within 3 months and no relapse was reported. Late withdrawal (LW), AEDs weaned after 3 months and no relapse was reported.
### Anti-NMDAR Encephalitis
Among the patients with anti-NMDAR encephalitis, 14 were not given any AEDs, in which 7 suffered from one-time seizure attack at the onset of the encephalitis. Compared to the patients with AEDs, the majority of this group was considerably young (6.8 ± 3.26, P = 0.004, Table ) and less likely to have repetitive seizures ( P = 0.038, Table ). The median duration of follow up was 20 months (range: 14–36 months), and no relapse was reported. Furthermore, 12 patients (85.7%) had good outcomes with a 0 mRS score; the remaining 2 patients presented with cognitive dysfunction.
Clinical characteristics and outcomes of patients with anti-NMDA encephalitis.
AED, Antiepileptic drug. P1, P-value among No AED, Early withdraw, and late withdraw group. P2, P-value between Early withdraw, and late withdraw group .
Among the 34 patients (20 men and 14 women, 23 in EW and 11 in LW) who were discontinued from AEDs, 7 patients (20.6%) did not report seizure, and 5 patients (14.7%) reported a one-time seizure at the onset. Two patients in the EW group reported hemianesthesia before presenting seizures. The occurrence of SE was 32.4% in total. Eighteen patients had an magnetic resonance imaging (MRI) scan and abnormalities were found in 33.3% of the patients, including 4 patients with white matter changes and 2 patients with cortical mass (Table ). The patterns of AEDs selection and discontinuation were variable (Figure ). Monotherapy was the most common selection, with 69.5% in the EW group and 63.9% in the LW group. At the early stage, carbamazepine ( n = 11) and oxcarbazepine ( n = 16) were the most chosen AEDs. Valproic acid was among the most commonly continued therapies over the course of follow-up (Figure ). Seven patients without seizure were treated with AEDs upon onset. Five of these discontinued AEDs within 1 month, and the remaining 2 patients underwent LW because of frequent subclinical discharge.
(A) Immunotherapies of patients with autoimmune encephalitis (Sort by outcomes). (B) Clinical patterns of AED discontinuation of patients with anti-NMDA encephalitis. CBZ, carbamazepine; OXC, oxcarbazepine; TPM, topiramate; CZP, clonazepam; LTG, lamotrigine; LEV, levetiracetam; VPA, valproate.
The patients' data were compared between the EW and LW groups (Table ). No statistically significant difference was observed between the two groups in terms of age ( P = 0.935), sex ( P = 0.458), seizure characteristics ( P = 0.359), antibody titers ( P = 0.727), SE ( P = 0.259), MRI findings ( P = 0.329), or AEDs selection ( P = 0.934). The medium durations of follow up were 36 months (range: 15–50 months) and 32 months (range: 17–62 months) for the EW and LW groups, respectively. 2 patients in the EW group relapsed in the first month after drug discontinuation. No remarkable difference in the percentage of relapse was observed between the two groups.
Detail of patients with anti-NMDAR encephalitis was presented in Supplementary File .
### Other AEs
A 44-year-old woman who was diagnosed with anti-CASPR2 encephalitis presented lethargy and headache without seizure at the onset. She did not take any AEDs. No relapse was reported during her 16-month follow up. A 72-year-old man with anti-LGI1 encephalitis and a 35-year-old woman with anti-GABA R encephalitis presented frequent seizures at onset and underwent an AED withdrawal 3 and 6 months later, respectively. No relapse was reported during their 1-year follow up. The other two elder patients (50 and 64 years-old, respectively) with anti-GABA encephalitis presented refractory seizures (Figure ).
We also reviewed 2 coexisting AE. One is a 30-year-old female who presented with anti-NMDAR and GABA R encephalitis. She had a seizure 10 years ago before she was diagnosed with AE through CSF detection. Her EEG presented δ brushes with generalized paroxysmal θ activities. The brain MRI was unremarkable. She developed refractory seizures after being treated by oxcarbazepine, carbamazepine, and clonazepam. The other patient was a 43-year-old woman with LGI1 and CASPR2 antibodies. She presented repeated generalized tonic-clonic seizure and paroxysmal speech dysfunction. Bilateral paracentral lesion was found in the MRI scan. She was given levetiracetam and valproic acid. The AEDs were withdrawn 2 years later, and no relapse was reported during her 1-year follow up (Figure ).
### Risk Factors
Among the 12 patients who remained on AEDs, 9 patients presented refractory seizure, including 6 patients with anti-NMDAR encephalitis, 2 patients with anti-GABA R encephalitis, and 1 patient with anti-NMDAR and GABA R encephalitis (Figure ). Among patients with anti-NMDAR encephalitis, refractory seizures occurred more often in the patients younger than 30 years of age (Figure ), and who presented repetitive seizures and SE, moreover, 4 patients (66%) showed cortical abnormalities on the MRI scan.
By combining the data of the 2 relapse cases, we evaluated the risk factors that contributed to the worse outcomes of anti-NMDAR encephalitis. The patients with relapse or refractory seizure were more likely to be accompanied with cortical lesions on MRI ( P = 0.028) and SE ( P = 0.004) than those who reached seizure remission (Table ). Moreover, the seizures with focal characteristics ( P < 0.001), SE ( P < 0.001), and MRI abnormalities ( P = 0.010) were significantly associated with refractory seizure (Table ). For other kinds of AEs, we found 3 patients with anti-GABA R encephalitis (75% in all, including 1 patient with concomitant disease) developed refractory seizure; this number is much higher than those of others.
Spearman analysis of factors associated with outcomes.
## Discussions
Parts of AE have been linked to cell surface antigens, which included voltage-gated potassium channel (VGPC, e.g., LGI1 and CASPR2), NMDAR, and GABA R ( ). To evaluate the AEDs utilization associated with AE, we focused on seizure in a cohort of patients with anti-NMDAR, anti-GABA R, anti-LGI1, and CASPR2 encephalitis. This study demonstrated low recurrence rates in young patients with AE who experienced first unprovoked seizures and highlighted an overall remission rate of 94% after the patients discontinued AEDs therapy. No difference was noted between the EW (≤3 months) and LW (>3 months) of AEDs. Moreover, a higher number of patients with anti-GABA R antibody, SE, cortical abnormalities, and focal neurological dysfunction experienced refractory seizure or seizure relapses compared to those who did not.
### Seizure Remission in Anti-NMDAR Encephalitis
Since 2005, the characteristics and long-term outcomes of anti-NMDAR encephalitis have aroused public attention due to the high incidence in young patients with serious neurological dysfunctions ( ). According to a previous study, we found that the probable predicted factors for poor outcomes in the acute phase included older age, altered consciousness, and SE, and the process of terminating SE was particularly important for anti-NMDAR encephalitis ( ). By evaluating patients through the mRS score, the other multi-institutional study which included clinical data from 577 patients with anti-NMDAR antibodies observed that 81% of the patients responded to immunotherapy ( ). Seizure occurs as a prominent feature in AE ( ), whether long-term AEDs are necessary after patients achieving good outcomes has not been established ( , ).
AEDs withdrawal after a successful seizure control may prevent adverse side effects and excessive cost. However, the studies which have evaluated the safety of AEDs weaning in patients with anti-NMDAR encephalitis are rare. One study demonstrated that no difference in seizure recurrence between 1 and 2 years of AEDs therapy in children with viral encephalitis ( ), and the relative risk factors might have included EEG abnormalities or HIV infection ( ). However, the duration of AEDs in anti-NMDAR encephalitis tended to be shorter ( ). Among the 34 enrolled patients with AEDs withdrawal, only 5.8% suffered from relapse during the follow up. Moreover, no difference was found between the EW (≤3 months) and LW (>3 months) groups, which is consistent with that of a previous study ( ).
Information regarding AEDs use in adolescents and adults with anti-NMDAR encephalitis has been reported previously, but data from younger children remain limited ( ). In the present study, we enrolled 14 young patients with average age of 6.8 (range: 3–12 years). Compared with the older age group, the young group was more likely be seizure free during the long-term follow-up without AEDs, even if they suffered one-time seizure attacks at the onset. The key treatment decisions after the first unprovoked seizure are a controversial issue in children ( , ). In some epilepsy syndromes, such as in benign childhood epilepsy with centrotemporal spikes or childhood absence epilepsy, remission is a regular feature of the natural history, whereas juvenile myoclonic epilepsy or other symptomatic epilepsies have long been considered to present in AEDs continuation ( ). By using the statistical methods for survival analysis, one study indicated that the cumulative risk of repetitive seizure in children was 29% in the first year and elevated to 45% within the 10-year follow up ( ). However, the factors associated with recurrences after the first seizure are complex. The probable risk factors may include age ( ) and etiology ( ). For anti-NMDA encephalitis, our data indicated that seizure in young patients tended to be self-limited. Moreover, some scholars indicate that a cognitive comorbidity likely accompany with the initial seizure if AEDs are not used ( ). However, evaluation of mRS score revealed that the majority of children in this group presented normal daily activities.
Although our data supports that seizure remission is common and that long-term use of AEDs may not be necessary, the significance of immunotherapy cannot be ignored to control the seizure. Numerous studies demonstrated that it is the immunotherapies that control the symptoms in anti-NMDAR encephalitis ( , , ). Moreover, one retrospective study which focused on the outcome of AEDs alone in controlling the seizure of patients with anti-VGPC-complex antibodies indicated that only 23.5% of patients became seizure free compared with 61.5% of patients with immunotherapy ( ).
### Seizure Remission in Anti-GABABR, LGI1, and CASPR2 Encephalitis
Anti-GABA R, LGI1, and CASPR2 antibodies have been recently detected in patients with limbic encephalitis ( ). Seizures are frequently reported in limbic encephalitis, but autonomic and psychiatric symptoms are more highlighted. In these antibody-mediated seizures, remission rate is variable, and may be related to complications, immunotherapy, and ICU management ( ).
Although rare, seizure with additional auto-antibodies may be a other probable risk factor. LGI1 and CASPR2 are the extracellular domains of VGPC. The coexistence of anti-LGI1 and anti-CASPR2 encephalitis may contribute to seizure, cognitive disturbance, movement disorders, and pain ( ). Besides AEDs use, the empirical approach to seizure control is corticosteroid treatment ( , ). A GABA R antibody is rarely accompanied with other antibodies in a same patient, and a probable reason may involve different genetic predispositions ( , ). In our study, the presented patients developed refractory seizure and severe cognitive dysfunction. One possible mechanism of AEDs resistance may be associated with different interaction sites, as anti-NMDAR antibodies have been suggested to decrease the synaptic levels of receptors, whereas the anti-GABA R antibody would further alter the synaptic function ( ).
### Risk Factors for Refractory Seizure and Seizure Relapse
As AEDs are supposedly unnecessary to seizure outcomes in AE after appropriate immunotherapy, we evaluate the other probable independent risk factors that contribute to refractory seizure and seizure relapse. In addition to involving the anti-GABA R antibody, our result shows that patients with SE, cortical abnormalities in MRI, and focal neurological dysfunction are more likely to develop worse outcomes than those who did not accompany these findings. SE has been proven to be an independent risk factor in major types of epilepsy ( , ). The complications of SE, such as severe pneumonia and ICU admission, have also contributed to poor outcomes ( ). Cortical abnormalities seem positively correlate with refractory seizure. By using structural MRI scans, one study demonstrated a strong association between incomplete recovery and superficial white matter lesions ( ). This observation indicates that the injured connection between adjacent cortices may play a crucial role in seizure control after AE.
## Limitations
This study has some limitations. First, we tried to discuss the risk factors of seizure outcomes for AE after appropriate immunotherapy. However, as antibodies were not measured at later time points, the direct relationship between persistent antibody levels and seizure outcomes remained unclear. A previous study presented that the neurological recovery accompanied with reduced antibody titer ( ), while the correlation between antibody titer and refractory seizure showed unremarkable in our study. One probability which caused the difference might involve the time when antibody was measured. Though the seizure outcomes were assessed based on the data of outpatients, the patients who developed refractory seizure might have persistent higher titer level than those who did not. Second, as the relapse rate was altered with the follow-up period ( ), long follow-up times are necessary. However, 54–100% of seizures after AE occurred within the first year, and new-onset seizure after 1 year of follow up was rare ( – ). Third, the results were limited because of the relatively small cohort. Combined with the previous data ( ), a probable correlation between seizure remission and AED utilization was noted, and further trials and meta-analysis are needed to confirm these results.
## Conclusion
Patients with AE have a high rate of seizure remission after proper immunotherapies. The long-term use of AEDs appears not be necessary to control their seizures. Compared with adults, young patients are more likely to become seizure free without AEDs. The risk factors that contribute to refractory seizure or seizure relapse may include anti-GABA R encephalitis, SE, and cortical abnormalities.
## Author Contributions
QH is the first author of this manuscript. YuaW is the corresponding author of this manuscript, they contributed to the study design, data collection and analysis, and draft writing of this work. MM, XW, and YL have revised and improved the manuscript. HQ and YueW contributed in the data collection and figures. All authors have seen and agreed on the finally submitted version of the manuscript.
### Conflict of Interest Statement
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
|
Persisting post-concussive symptoms are challenging to treat and may delay return-to-work (RTW). The aims of this study were to describe a multidisciplinary and holistic vocational rehabilitation (VR) program for individuals with mild traumatic brain injury (mTBI) and to explore course and predictors of employment outcome during VR. The VR program was described using the Standard Operating Procedures (SOPs) framework. Further, a retrospective, cohort study on individuals with mTBI receiving VR was conducted based on clinical records ( n = 32; 22% males; mean age 43.2 years; 1.2 years since injury on average). The primary outcome was difference in hours at work per week from pre- to post-VR, and the secondary outcome was change in a three-level RTW-status. Time since injury, age, sex, and loss of consciousness were investigated as predictors of the outcomes. The VR intervention is individually tailored and targets patients' individual needs. Thus, it may combine a variety of methods based on a biopsychosocial theoretical model. During VR, hours at work, 17.0 ± 2.2, p < 0.001, and RTW-status, OR = 14.0, p < 0.001, improved significantly with 97% having returned to work after VR. Shorter length of time since injury and male sex were identified as predictors of a greater gain of working hours. Time since injury was the strongest predictor; double the time was associated with a reduction in effect by 4.2 ± 1.4 h after adjusting for working hours at start of VR. In sum, these results suggest that individuals facing persistent problems following mTBI may still improve employment outcomes and RTW after receiving this multidisciplinary and holistic VR intervention, even years after injury. While results are preliminary and subject to bias due to the lack of a control group, this study warrants further research into employment outcomes and VR following mTBI, including who may benefit the most from treatment.
## Introduction
Traumatic brain injury (TBI) is a significant cause of morbidity and mortality worldwide and constitutes the third largest health expense in the USA ( , ). The vast majority of cases (70–90%) are categorized as mild TBI (mTBI), or concussion ( ). Individuals with mTBI tend to show considerable variation in post-concussive symptoms, which may include headache, fatigue, vestibular, and vision dysfunctions, increased sensitivity to light, noise, and pain, vertigo, sleep disturbances, cognitive deficits such as reduced concentration and poor memory, or mental health issues ( , ). Most individuals sustaining mTBI recover spontaneously during the first week, however, a small subset continue to experience persisting symptoms beyond 3 months post-injury ( ) with long-term implications for vocational, recreational, and social activities ( ). Some individuals may even experience symptoms for more than 1 year after the incident ( ). Persisting symptoms may delay return-to-work (RTW), reduce work productivity, adversely affect quality of life, and result in additional social and economic costs.
Evidence on vocational outcomes following mTBI is limited, and rates of RTW vary widely between studies ( , ). Results of a systematic review suggest that most workers RTW within 3–6 months after mTBI, however, 5–20% continue to experience work limitations for 1–2 years post-injury ( ), and possibly even longer ( ). RTW is often associated with increased psychological well-being and quality of life ( ), and is thus often identified as a major goal of recovery. However, even when returning to work, some individuals still experience distressing post-concussive symptoms, suffer from comorbid psychiatric conditions such as depression and anxiety, and work with functional limitations and reduced productivity ( ). Further, individuals with mTBI may experience challenges maintaining employment over time.
Employment outcomes following mTBI can be complicated by multiple factors, including personal, injury-related, and environmental factors. Research regarding specific predictors of outcome such as age, sex, or various injury-related factors is mainly inconclusive ( ). Some evidence suggests that a lower level of education, nausea or vomitting on hospital admission, extracranial injuries, severe pain early after injury, and limited job independence and decision-making latitude predict delayed RTW ( ). Wäljas et al. ( ) identified age, multiple bodily injuries, intracranial abnormality, and fatigue as predictors of delayed RTW, and Vikane et al. ( ) reported psychological distress, global functioning post-injury, and being sick-listed 2 months after and the last year before mTBI as predictors. Looking more specifically at productivity loss, Silverberg et al. ( ) found that residual symptoms and comorbid psychiatric conditions were predictors, and regarding long-term outcomes, Theadom et al. ( ) reported that cognitive complaints at 1 month post-injury were predictive of work limitations 4 years post-injury. A recent systematic review supports the role of cognition in predicting and facilitating RTW ( ). Thus, a range of factors, including demographic, physical, cognitive, and emotional as well as environmental and societal, may impact the course of employment outcomes after mTBI in a complex interaction, which is yet unclear.
Treatment of persistent symptoms after mTBI is based on limited evidence ( ), and so is vocational rehabilitation (VR) more specifically ( ). VR can broadly be defined as “whatever helps someone with a health problem to stay at, return to and remain in work” ( ) and may require a combination of healthcare and workplace interventions. Regarding mTBI, there is clinical concensus that recommendations should be individually tailored and based on a multidisciplinary evaluation of personal, environmental, and occupational factors ( ). Thus, VR constitutes a combination of individually tailored approaches; from initial assessment through intervention to evaluation of the patient's progress. Examples of means of promoting RTW and improving employment outcomes may be to reduce and in turn, if possible, gradually increase weekly working hours, to modify job demands, tasks, and the work environment, and to introduce rest breaks during the work day ( ).
VR, like other interventions within rehabilitation, lacks definitions of treatment approaches. Definition and development of treatment manuals within neurorehabilitation have been debated comprehensively in the literature for more than a decade. However, there is still no clear-cut recipe or right or wrong way of how to develop an efficient treatment manual in this complex and multidisciplinary field of treatment, where interventions involve a variety of different methods. In designing a manual, one has to balance between how rigid vs. flexible, how long vs. short, and how detailed vs. broad to make the manual, all depending on the context, in which it is to be used, and the nature of the treatment itself ( – ).
Previous research has primarily investigated the course of RTW following mTBI, and only few studies investigated the course and predictors of more detailed employment outcomes in individuals with mTBI undergoing VR. Further, contents and strategies of VR for mTBI are seldom described in detail. This study aimed to describe a multidisciplinary and holistic VR program for individuals with persisting post-concussive symptoms. Further, the study aimed to compare employment outcomes in individuals with mTBI before and after completing the VR program. It was hypothesized that participants work more hours per week following the VR program. Finally, the study aimed to investigate a panel of four baseline characteristics as predictors of employment outcomes in an exploratory analysis. Apriory, time since injury was considered the most influencial factor, then secondary age, sex, and loss of consciousness in parallel.
## Materials and Methods
### Design and Setting
This study was conducted at the specialized brain injury center BOMI in Denmark. BOMI offers multidisciplinary and individually tailored VR for individuals with brain injury, including mTBI and comorbid conditions. First, the Standard Operating Procedures (SOPs) framework was used to describe the VR program for mTBI. Second, a retrospective cohort study was conducted based on clinical records.
### Development of Standard Operating Procedures
Aims, contents, and procedures for each module of the VR program were described in an intervention protocol using the SOPs framework ( ). SOPs are specific standardized procedures that regulate the routine actions of individuals in specific positions and assign roles and responsibilities. SOPs within neurorehabilitation can act as a local adaptation of clinical guidelines (if such exists), based upon evidence-based practice. Implementation of guidelines in clinical practice often requires adaptation by the local workplace, where the guideline recommendations are combined with expert knowledge and routines. SOPs will help bridge the gap between evidence-based medicine (clinical guidelines) and the local circumstances and possibilities for carrying out rehabilitation. The SOP guides both the experienced and the inexperienced therapist through the same decision making processes to support a goal-oriented manner of practice.
For development of the SOPs, two representatives of each professional group in the multidisciplinary team providing VR at BOMI were recruited. That is, two occupational therapists, two physiotherapists, and two neuropsychologists. To be included, professionals had to be skilled with VR; hence, they had to have at least 2 years of experience with VR at BOMI. The staff members participated in workshops to discuss theory, goals, effective components, and practical approaches of VR.
### Cohort Study
#### Participants
BOMI Center for Rehabilitation and Brain Injury receives individuals with acquired brain injury from a large number of Danish municipalities, primarily from the Capital and Zealand regions of Denmark. Since 2011, one municipality from the Capital region has consistently referred all individuals, who require treatment for persisting symptoms following mTBI, to receive multidisciplinary treatment and VR at BOMI. For this study, we included all individuals with an mTBI diagnosis from this municipality, who had received VR at BOMI between 2011 and 2018. Individuals are referred to BOMI as soon as they report problems that involve sick leave from work for more than 1 month or a need to take a sick leave after struggling with symptoms for several months. Consequently, time since injury may vary among referred individuals. Individuals have not necessarily been hospitalized for their mTBI. In the beginning of this collaboration, the municipality did not identify as many individuals with mTBI as in the later years. The identification procedures needed to be implemented throughout different levels in the organization of the municipality where moderate to severe TBI previously were prioritized. However, during the years, the procedures of how to identify individuals with persisting symptoms after mTBI became more clear and the number of referred individuals with mTBI increased.
Clinical records at BOMI were screened to confirm that participants of this study had been exposed to a trauma involving a direct blow to the head or involving a coup-contrecoup movement. Further, participants had to fulfill at least one of the following criteria: Loss of consciousness (max. 30 min), post-traumatic amnesia for a period of max. 24 hours, disturbance of consciousness (confusion or disorientation in time, place, or personal data), or transient neurological symptoms. In addition, participants had to have a Glasgow Coma Scale score above 13 after 30 min. All participants completed the planned rehabilitation program.
#### Measures
Data was collected from clinical records and chart reviews. Pre-injury data was self-reported retrospectively at start of VR.
##### Demographics and injury-related factors
Demographics were recorded, including sex, age, educational level, living arrangement, and number of children. The following injury-related data was recorded: Time since injury, the event causing injury, loss of consciousness at injury, and earlier incidents of concussion. Finally, duration of VR was recorded. The duration of VR depended on a variety of factors, including the patients' progress and needs and the financial frame granted by the municipality.
##### Employment outcomes
Four indicators of employment outcome were evaluated: Hours at work per week, RTW, full-time vs. part-time work, and employment status. The number of hours at work or education (high school, college, or university level) was recorded for three time points: At time of injury (T ), at start of VR (T ), and at completion of VR (T ). RTW was evaluated at pre- (T ) and post-VR (T ). RTW was divided into complete and partial RTW by comparison with working hours at time of injury (T ). That is, complete RTW corresponds to returning to the same (or an increased) amount of hours per week compared to pre-injury, and partial RTW corresponds to returning to a reduced amount of hours. Full-time work was defined as ≥30 productive hours per week and part-time as 0 <30. Finally, employment status was evaluated as competitive employment, supported employment, or sick leave.
#### Intervention
All participants received individually tailored, face-to-face, multidisciplinary VR. Details of the program are described in the Results section.
#### Analyses
Demographics, injury-related variables and employment outcomes were explored using descriptive statistics. The primary outcome was defined as the difference in working hours before and after VR. This outcome was evaluated by linear models. The secondary outcome was RTW with three levels (i.e., complete RTW, partial RTW, and no RTW) and was treated as an ordinal outcome. This outcome was evaluated by ordinal regression. For both outcomes, four variables were investigated as predictors: time since injury, age, sex, and loss of consciousness. They were investigated univariately using simple linear models with either categorical or continuous variables as predictors, reporting relevant effect sizes. Initial inspection of data revealed that the distribution of data for “time since injury” differed from being normally distributed, and this variable was hence log2 transformed before the main analyses. The statistical analyses were conducted in R version 3.4.2 ( ) using describe() and stat.desc() from the packages psych and pastecs, respectively, for descriptive statistics, ggplot2 for plotting, base lm() and glm() for linear models and clm() from the ordinal package for ordinal regression. For mixed effect longitudinal models, lmer() from lme4 was used.
#### Ethics
The study was conducted in concordance with the Declaration of Helsinki, and the database was approved by the Danish Data Protection Agency (J.no. 2017-41-5256).
## Results
### A Multidisciplinary Vocational Rehabilitation Program
In this section, the SOPs for VR of concussion are described in headings offering an overview of its content. The SOP theoretical foundation is based on a biopsychosocial theoretical model and the hypothesis that post-concussion symptoms probably represent the cumulative effect of multiple variables such as trauma severity, genetics, mental health history, current life stress, general medical problems, chronic pain, depression, social problems, and personality. Thus, a large variety of cause-effect interactions may contribute to the symptoms, and a full description is therefore not included in this paper.
The SOPs are nested in a circular process, aiming at a continuous evaluation of a patient's progress and responses to treatment. The therapist begins by setting goals for the patient based on an initial analysis of the patient's symptoms and a hypothesis on the underlying causes. Then the therapist chooses a strategy of how to reach the goals, by reanalyzing the patient's state according to goals and treatment. The therapist relates the choices of action according to the hypothesis of underlying causes to the patient's problems, and adjusts the goals and intervention according to the continuous observation.
The VR program for mTBI is individually tailored for each patient and consists of different modules that address the patient's symptoms. Each module has the overall purpose of supporting the patient's workability, either in a direct or more in-direct manner. The combination, length, and intensity of the modules are determined based on the patient's situation, goals for the intervention, and the financial frame granted by the municipality.
The purpose of the concussion VR intervention is:
To delineate a holistic understanding of the patient's functioning and disability, and the individual factors involved, including assessment of fatigue, sleep disorders, headache, cognitive difficulties, visual and balance problems, mental health and coping strategies.
Supporting that the patient achieves a balance between home life, family life, leisure life, and working life so the patient can participate in necessary and desirable activities and roles.
To support the improvement of individual workability, and to allow the patient to RTW as soon and at as many hours per week as possible.
#### Ad1: Assessment and Analysis
First step in the VR intervention is to set goals for the intervention process and patient progress. Typical goals may consist of: Increased insight into different aspects of brain injury and its implications, goals of handling fatigue, incorporation of positive everyday routines to increase energy level throughout the day and prioritize desirable activities, scheduling and planning activities, goals of how to handle cognitive difficulties, monitoring own progress, and reflection of achieved functions.
Throughout this process, therapists collaborate with a neuropsychologist in order to continuously adjust the strategies to each patient's individual cognitive and psychological state.
#### Ad2: Individually Tailored Intervention
Second step is to plan intervention by setting up a hypothesis of the desired change in patient's physical, cognitive, mental, and/or behavioral state in order to reach the goal based on previous evaluations. Thus, the treatment must be somehow broad in methodology to incorporate an approach matching each patient's needs, goals, and circumstances.
Most of the intervention involves change of behavior and adapting compensational strategies. These strategies contribute to teaching the patient to manage different symptoms and daily living in a more appropriate way and initiate a positive lifecycle. The choice of modules, including the length and intensity of modules, all depend on the patient's symptoms and response to intervention. Modules may include:
##### Energy management (EM)
The therapist supports the patient in testing and implementing strategies of how to change routines and amount of daily activities so the patient's energy level will remain stable throughout the day. EM is a personal process where the therapist acts as a facilitator and coach. This involves supporting the patient to set up realistic goals for the energy management process involving that the patient works with: Habits, routines and ways of thinking, life values, family roles and identity, how to interact with others, and more.
Specific approaches in EM may be: Small breaks, breaks at fixed time points, midday nap, ensuring a good night sleep by introducing good sleep hygiene, testing need for ball blanket, use of mindfulness techniques, relaxation techniques, analyzing eating habits and implementing a healthy diet, performing exercise, and achieving positive experiences.
The therapist continuously follows the patient's energy level throughout the day, to help the patient adjust working hours, activity planning, adjusting according to surroundings and other personal or environmental factors both at work and at home. The occupational therapist is in charge of the EM approach in close collaboration with the neuropsychologist.
##### Neuropsychological intervention
The focus of the neuropsychologist is psychoeducation, involving reflection on the patient's thinking patterns regarding new life circumstances, depreciation of the symptoms, and anxiety and depression management. The neuropsychologist conducts an assessment of the psychological status, including symptoms and severity of depression, post-traumatic stress disorder, and anxiety. Furthermore, an evaluation of subjective cognitive level of functioning is conducted using an interview. Based on the psychological evaluation, the patient is offered individually adapted psychotherapy consisting of 3–30 sessions in which the patient is informed about the psychological and cognitive level of functioning, the interrelations of cognitive and psychological functions, thinking and behavioral patterns, and emotional reactions. Different compensation strategies are discussed and developed. Furthermore, existential dilemmas regarding new life circumstances such as health anxiety, relations, being in the world with new physical circumstances, altered time and space, and financial concerns are addressed.
##### Visual and balance training
Another key component of concussion VR is visual and balance training. This training is provided by a team of optometrists and physical therapists. The training involves individualized sensory integration, vestibular and proprioceptive exercises in combination with binocularity, fixation, tracking, vergence, and eye-hand coordination ( ). Typically, the patient receives 16–23 weekly sessions (or every other week), depending on the severity of symptoms and responses to the exercises. This training also involves instructions of how to implement exercises and symptom management strategies in everyday activities and work.
##### Physiotherapy
This treatment focuses on dizziness, balance problems, neck problems, pain, and headache. The training is performed individually and is often supported by home-exercises. The principles revolve around graduated exercise training, e.g., focusing on vestibular rehabilitation, active treatment on cervical spine, dynamic stability, adjusted according to pain and progress. The training always involves instructions of how to implement exercises and training in everyday activities. If severe neck problems are suspected, the patient is referred to a physical therapist specialist with a certification in neck problems. Further, if vision problems are suspected, the patient is referred to neuro-optometrist for visual assessment followed by interdisciplinary visual and balance training.
##### Mindfulness
The approach of mindfulness at BOMI is primarily based on “Mindfulness Based Stress Reduction” and, in addition, is inspired by “Mindfulness Empathy and Cohesion.” The purpose is to help the patient gain increased focus on sensitivity, indulgence, self-care, and awareness. As for the other modules, specific techniques are individually planned according to the needs of patients. Exercises may include “body scan,” sitting and/or walking meditation, breathing exercises, and gentle yoga with mindfulness of movements and bodily sensations.
#### Ad3: Vocational Support
In VR, it is recommended that the patient start on a low amount of working hours and a minimum amount of tasks. Thus, the patient typically commences with a few hours at work a day, few days a week, and with a low complexity in work tasks.
There is a close monitoring process of the patient's symptoms, and adjustment of hours at work and work tasks, to ensure that the total work load matches each patient's condition and energy level at work and at home. The therapist will usually see the patient once a week in the beginning, depending on the complexity and patient needs, whilst the frequency and intensity of contact decreases over time. The therapist may also act as a safety net for the patient. Thus, the patient is encouraged to contact the therapist outside of scheduled sessions if needed. The therapist has the authority to contact other relevant personnel, if necessary.
The therapist visits the patient's workplace to analyze and assess compensational strategies and need of work place adjustments. The assessment consists of the combination of subjective information (what the therapist is told by the workplace) and objective information (what the therapist observes at the workplace), and is continuously revised during workplace meetings and during individual contact with the patient. Relevant compensational strategies vary from patient to patient and depend on the patient's difficulties and resources. Compensational strategies involves support related to: When and how the patient should take breaks during work, how the patient compensates for difficulties in forming and maintaining an overview of work tasks, as well as planning different work tasks.
Based on the compensational strategy analysis, the therapist and the patient have reflective conversations in order to help patients evaluate their difficulties and resources. This involves discussions of the linkage between difficulties at the work place and the brain injury, how to use selected compensational strategies, the purpose of incorporating positive working routines and reflection on the individual goals.
### Cohort Study
#### Characteristics of the Cohort
Thirty-two participants were included in the cohort. Mean age at start of VR was 43 years ( SD = 11; range 18–65 years), and 78% of the participants were female. The majority of participants were living with a partner (59%), 28% were living alone, and 9% were living with parents. Most participants (77%) had children with a median amount of 2 (IQR = 1).
Median number of days since injury was 195 ( IQR = 273; range = 77–2,030) at start of VR. Duration of VR varied from 97 to 778 days with a median amount of 366 days ( IQR = 218). Incidents of injury included a fall (34%), a traffic accident (34%), sports-related injuries and injuries due to a blow to the head (31%). The minority of participants had been unconscious following the incident (22%). Please see for an overview of participant characteristics and for an overview of employment outcomes at pre-injury, pre-VR, and post-VR, respectively.
Characteristics of the cohort.
M, mean; Mdn, median; VR, vocational rehabilitation .
Employment outcomes at time of injury, at start of VR, and after VR.
Complete RTW represents working the same (or an increased) amount of hours compared to pre-injury, partial RTW represents working fewer hours compared to pre-injury, and no RTW represents not working any hours per week. VR, vocational rehabilitation; M, mean; Mdn, median; RTW, return-to-work .
#### Differences in Employment Outcomes Before and After Vocational Rehabilitation
From pre- to post-VR, mean hours at work per week increased significantly by 17.0 ± 2.2, p < 0.001. Each participant either remained or increased the amount of working hours from before to after VR (see ). That is, no participant worked fewer hours after VR.
Trajectories of hours at work per week. The graph illustrates each participants' amount of working hours per week on the y-axis at the time of injury (pre-injury; T ), at start of VR (pre-VR; T ), and after VR (post-VR; T ). Time points are distributed on the x-axis by the number of days (log2 transformed) from injury to pre-VR and from pre- to post-VR. Colors indicate RTW-status at post-VR. VR, vocational rehabilitation; RTW, return-to-work.
In terms of RTW, the levels of RTW changed significantly, OR = 14.0, 95% CI [3.5, 55.1], p < 0.001, from before to after VR (see ). Over the course of VR, no participant regressed in RTW-status (e.g., from complete RTW to partial RTW or from partial RTW to no RTW). On the contrary, RTW-status improved for 16 participants (50%) and remained stable for 16 participants (50%). As depicted in , the difference in RTW-status was larger between no RTW and partial RTW, p < 0.001, than between partial RTW and complete RTW, p < 0.51.
Trajectories of return-to-work status. The graph illustrates participants' development in RTW-status from pre- to post-VR. Streams ending at a higher-level color (0 = red/no RTW; 1 = blue/partial RTW; 2 = green/complete RTW) represent improved RTW-status, streams ending at its own color represent stable RTW-status, and streams ending at a lower-level color would represent regressed RTW-status (no cases of this). Complete RTW represents working the same (or an increased) amount of hours compared to pre-injury, partial RTW represents working fewer hours compared to pre-injury, and no RTW represents not working any hours per week. RTW, return-to-work; VR, vocational rehabilitation.
#### Predictors of Outcome
Time since injury and sex were significant predictors of change in working hours during treatment (see ). More specifically, double the time since injury was associated with a reduced gain of 5.8 ± 1.4 h, p < 0.001. That is, an individual receiving VR at day 100 since injury is observed having 5.8 more working hours per week from treatment compared to an individual receiving VR at day 200. However, participants starting VR in later phases of injury have more time to get back to more hours of work before starting VR, and may thus benefit less from VR, which could explain this association with time. Consequently, we introduced hours at start of VR as a covariate in the model, and the effect of time since injury attenuated from 5.8 to 4.2 ± 1.4, but remained significant, p = 0.006. Regarding sex, males had 11.2 ± 5.1 h better effect of treatment compared to women, p = 0.035. Age and loss of consciousness were not significant predictors. In terms of RTW, similar but weaker effects of predictors were observed compared to hours at work.
Predictors of difference in hours at work per week from pre- to post-VR.
Model parameters were estimated by simple linear regression. Response of the linear models was specified as the difference in hours at work from pre- to post-VR. M, mean; Mdn, median; VR, vocational rehabilitation .
## Discussion
This study described a holistic VR program for mTBI and found that individuals with mTBI had improved employment outcomes after completing the VR program. Time since injury and sex were statistically significant predictors of increase in working hours during treatment.
### Developing a Vocational Rehabilitation Program Within Neurorehabilitation
The holistic approach of this VR intervention can be a complex treatment to learn and conduct, particularly for inexperienced therapists. Thus, a program based on SOPs can act as a tool guiding the clinical reasoning process, by describing the different ways to assess and treat symptoms and the hypothesis of the cause-effect interaction. Moreover, the SOPs will make it easier to disseminate the program to professionals.
The combination of interventions in a multidisciplinary VR program differentiates in nature from more focused interventions such as UPFRONT for mTBI, described by Scheenen et al. ( ). UPFRONT is a short intervention involving five sessions of cognitive behavioral therapy aiming at facilitating RTW by enhancing the individual's feeling of competency. Such programs have a clear advantage of being more easily defined, and thereby more easily replicated and adjusted if needed. The Individual Placement and Support (IPS) model, used in other VR studies ( ), is another manual-based VR intervention for individuals with brain injury. However, this method involves vocational support only and does not consider other aspects that might influence workability. Given the complexity of mTBI symptoms, it may be important to consider the interaction of biological, psychological, social, and environmental factors ( ) in a VR program such as the one described here. LeBlanc and McLachlan ( ) further support this view in a study that found an early individualized educational approach to be more effective for employment re-engagement than a general group-based intervention in a cohort of individuals with mTBI.
### Vocational Rehabilitation and Employment Outcomes Following mTBI
During the course of VR, the cohort increased significantly in productive hours per week and improved in RTW-status. Following VR, 97% of participants had RTW compared to 63% before VR. Cancelliere et al. ( ) estimated that about 5–20% of workers with mTBI face persisting problems with regard to RTW 1 to 2 years post-injury, and found that research on the prognosis of RTW beyond 2 years of injury is limited. A recent study of 245 adults at 4 years post-mTBI reported that 17.3% had exited the workforce or reduced their working hours compared to pre-injury ( ). Another study reported that 59.1% of individuals with mTBI ( n = 110) returned to work-related activity following a specialized post-mTBI intervention, which was initiated a median of 3.3 months post-injury ( ). Comparisons with the present study are challenging, not least due to the variation in time since injury and other baseline characteristics. However, with an average of 2.2 years since injury at completion of VR, these preliminary results indicate that individuals receiving VR has the potential to RTW and improve workability, even 2 years after mTBI. Although not investigated quantitatively in this study, returning to work and resuming former work capabilities may have a substantial impact on the sense of well-being, social integration, and quality of life ( ). Thus, further research is warranted on long-term employment outcomes and the effects of VR for mTBI.
Not all participants, who had RTW, returned to pre-injury levels of employment (full RTW). Further, although being a statistically significant change, only half the cohort improved their RTW-status. In some cases, it may be necessary to recommend a reduction in working hours in order to maintain employment and daily life functioning on the long term. In fact, our clinical experience is that returning to pre-injury levels of employment too soon may worsen symptoms and thus be a barrier for maintaining employment to some individuals. Furthermore, the recommendation of graded RTW has been supported in other patient groups ( ).
Due to the heterogeneity and complexity of problems after mTBI, we suggest that an important element for outcome success may be the multidisciplinary, holistic, and flexible approach of this VR program, in which the specific contents, intensity and length of the intervention is continuously adjusted to the individual needs of patients. However, this approach makes the program less easily defined and harder to replicate compared to more focused programs such as UPFRONT ( ). Thus, a goal for future research could be to investigate the significance of flexibility in VR programs for outcome success after mTBI.
#### Predictors of Employment Outcome
First, results indicated that those starting VR earlier after injury gained more working hours during VR, even when adjusting for working hours at start of VR. There could be several plausible explanations to this relationship. For instance, “the sooner the better” could be a rule with regard to treatment effect or, alternatively, those starting VR later could have more severe or entrenched problems than those starting earlier and thereby benefit less from treatment. How to interpret this relationship is not evident from this study and would possibly require research involving dubious ethical protocols.
Second, we found that men improved significantly more in working hours than women. The reasons for this relationship are unclear, and the results are in conflict with a systematic review finding that sex did not predict RTW following mTBI ( ). However, previous research has suggested that women report more post-concussive symptoms than men, although this finding is not consistent ( , ). Given that women experience more symptoms, e.g., mental health issues such as depression and stress, this could influence RTW and explain why VR was less beneficial for women compared to men in this study. However, we did not have indicators of symptom severity, and further research is needed to provide insight into this matter.
### Generalizability
Since all individuals included in the study were recruited from a single municipality, it is relevant to consider potential biases related to demographics. The included municipality is characterized by a relatively high average income with 55.7% of the population having a higher income than the country's average, and the citizens are highly educated compared to the national average. Thus, the recruitment design of this study introduces a risk of selection bias, and demographics could be influencing the results positively. Further studies would have to investigate whether this VR design applies to individuals from other municipalities with work and education levels closer to and below the country's average.
## Limitations
This study had several limitations. First, the study design was retrospective in nature, the cohort was relatively small, and we did not have a control group. Although results are promising, they are preliminary and do not allow for specific inferences regarding effect of intervention. Although, we adjusted for working hours at start of VR in predictor analyses, the effect of time since injury could reflect spontaneous recovery, and not necessarily a more beneficial effect of VR, in earlier phases of injury compared to later. Second, the cohort was a selected group of individuals with mTBI from one minor community in Denmark. Thus, results should be interpreted with respect to these selection procedures and the fact that results may differ for another population referred to VR under different circumstances. Third, we did not investigate whether participants were able to maintain employment beyond VR. Fourth, no data was available on amount and severity of symptoms.
## Conclusion
In this study, we have initiated the work of defining a multidisciplinary and holistic VR program for individuals suffering from post-concussive symptoms using the SOPs framework. This program will be updated on an ongoing basis in line with its use in clinical practice; however, defining interventions in rehabilitation is an important step toward evidence-based practice and standardized methods. While results of this study are preliminary, both working hours and RTW-status improved significantly with 97% having RTW following VR. Time since injury and male sex were identified as predictors of outcome. In particular, double the time since injury was associated with a reduction of 4.2 h per week. Overall, these results suggest that individuals with persistent post-concussive symptoms may still improve employment outcomes, even years after mTBI. However, further research is needed for any firm conclusions to be drawn regarding the effect of VR, including predictors of effect.
## Author Contributions
FD wrote the first draft of the paper, organized data, performed descriptive analyses, and finalized the paper. TS conceived most part of the data, initiated the study, and had the primary role in describing the content of intervention in the results and discussion sections. MR discussed statistical methods, performed a substantial part of the data analysis, and contributed to description of the methods and results sections. LS is part of the research group and contributed with literature review and supported writing the final manuscript. EF is part of the research group and contributed with discussing the research design and population group, and with writing the manuscript. All authors critically read, improved, and approved the final manuscript.
### Conflict of Interest Statement
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
|
The possibility of sex-related differences in mild traumatic brain injury (mTBI) severity and recovery remains a controversial subject. With some studies showing that female subjects suffer a longer period of symptom recovery, while others have failed to demonstrate differences. In this study, we explored the sex-related effects of mTBI on self-reported symptoms and transcranial Doppler ultrasound (TCD) measured features in an adolescent population. Fifty-eight subjects were assessed—at different points post-injury—after suffering an mTBI. Subjects answered a series of symptom questions before the velocity from the middle cerebral artery was measured. Subjects participated in breath-holding challenges to evaluate cerebrovascular reactivity. The Pulsatility Index (PI), the ratio of the first peaks (P2R), and the Breath-Hold Index (BHI), were computed. Linear mixed effects models were developed to explore the interactions between measured features, sex, and time since injury while accounting for within subject variation. Over the first 10 days post-injury, the female group had significant interactions between sex and time since injury that was not present in the TCD features. This is the first study to compare sex-related differences in self-reported symptoms and TCD measurements in adolescents suffering an mTBI. It illustrates the pitfalls clinicians face when relying on subjective measures alone during diagnosis and tracking of mTBI patients. In addition, it highlights the need for more focused research on sex-related differences in concussion pathophysiology.
## Introduction
The presence of sex-related differences in mild traumatic brain injuries or concussions (mTBI), is a contentious subject. Several studies have found increased symptoms in females ( – ), as well as increased length of recovery ( – ). In the case of Preiss-Farzanegan et al. ( ), a difference existed in adults (18 years-old or older), but not in minors (17 years-old and younger). Conversely, others have failed to demonstrate increases in female symptoms at all ( – ). In general, the current state of the literature suggests that the existence of gender dependence in concussion recovery and severity is still an open question ( , ).
One commonality between these studies however, is that they all rely on neurocognitive evaluations, patient symptoms, or physical performance testing. Although these have been shown to provide insight into concussion severity and prognosis, they do not objectively measure physiological changes resulting from concussive injury. Here, we present a study comparing features of the cerebral blood flow velocity (CBFV) as measured with Transcranial Doppler (TCD) ultrasound in addition to self-reported symptoms to investigate sex-related differences in concussion.
With recent research demonstrating abnormalities in cerebral blood flow after a mTBI, it is clear that the microvasculature is affected ( – ). In Thibeault et al. ( ), the cerebral hemodynamic changes in adolescents between 14 and 19 years old after suffering a clinically diagnosed mTBI were assessed using TCD. In that study two distinct phases of hemodynamic alterations after a concussive injury were identified. In the initial phase, beginning within an hour of injury and lasting through the first 48 h, Pulsatility Index (PI), and peak ratio (P2R), showed a significant difference from controls. After 48 h however, these differences in pulsatile features were no longer observable. At this point in their recovery the breath-holding index (BHI), a measure of the cerebral vascular reactivity (CVR), was significantly increased when compared to controls. This lasted through day seven. After which, the population level increase was no longer significant.
Although Thibeault et al. ( ) was the first study to suggest the presence of multiple phases of hemodynamic dysfunction, there have been others demonstrating measurable alterations in mTBI subjects using TCD. Utilizing a hypercapnia challenge, Len et al. ( ) found significant changes in a population of concussed subjects. A subsequent study found significant differences during hypocapnia ( ). Similarly, the study from Albalawi et al. ( ), found vasoreactivity was linearly related to both severe headaches and cognitive symptoms. Baily et al. ( ), found lowered CVR in a population of subjects suffering from chronic symptoms. The present study, however, appears to be the first to explore sex specific abnormalities in mTBI subjects with both self-reported symptoms and an objective physiological measure.
## Methods
### Patient Population
Participants in this study consisted of adolescents between 14 and 19 years old from the Los Angeles, California metropolitan area. Subjects classified with an mTBI were diagnosed by independent physicians and were scanned at different times post-injury. For this analysis these longitudinal measurements were restricted to 13 days post-jury from 58 unique subjects. The population was comprised of 37 male and 21 female participants, with 81 and 57 total exams for each group, respectively. Within the male group, 17 subjects had more than one scan during the course of recovery and a median number of scans of 1.0 with an IQR of 2.0. In the female group, 13 subjects had more than one scan and there as an overall median 2.0 scans with an IQR 3.0. The control group consisted of 109 age-matched subjects, 89 male and 12 female, who had no reported head-injuries in the preceding 12-months. The control group was only scanned a once. The study was approved by Western Institutional Review Board (IRB #20141111). This data was previously used in Thibeault et al. ( ).
### Data Collection
The TCD signals were acquired from the middle cerebral arteries (MCA) transtemporally by ultrasonographers utilizing 2 MHz probes held by an adjustable headset. End-tidal CO was collected concurrently through a nasal cannula. The exam protocol, illustrated in , began with a 5-min baseline period of normal breathing. This was followed by a series of 4 breath-holding challenges as an estimate of CVR. Each of these consisted of a 25-s period where the subject was instructed to hold their breath, followed by 35-s of normal breathing.
Experimental Protocol and TCD Analysis. (A) The individual pulses are extracted before the systolic peak (P1), diastolic trough (D), second peak (P2), and the mean velocity (V ) are identified. (B) The CVR protocol consists of a 5-min baseline, left of the first vertical bar, followed by the four breath-holding challenges, between the vertical bars. The low-frequency component of the global signal (solid gray line), is used to compute the baseline mean velocity (V , dashed line) and the largest peak velocity (P ) used to calculate the BHI.
### Symptom Reporting
Before each of the data collection session, subjects were asked to answer a number of questions similar to the graded symptom scale checklist. presents the list of questions where subjects were asked to numerically rate their current symptom state. The ratings were used both individually and summed together as an estimate of severity.
Subjects were asked score themselves on the following symptoms based on how they feel now–None (0), Mild (1,2), Moderate (3,4), Severe (5,6).
### Analysis
The TCD features found to correlate with mTBI in Thibeault et al. ( ), were used to compare with the self-reported symptoms. The first pulse level feature, extracted from the baseline section, was the PI. This is generally believed to be related to distal resistance however, it appears to be more modulated by a number of physiological processes ( ), PI is found by
Where P , D , and V are defined in .
The second, P2R, is the ratio of P , and P , as illustrated in . This has been hypothesized to be related to distal bed compliance ( ). This is found by
These features were individually averaged across all the extracted pulses from the baseline section.
The CVR was estimated using the BHI. This was found by first finding the highest peak of the low-pass filtered CBFV waveform between the four breath-hold sections as illustrated in . This is then related to the baseline mean velocity by
### Statistical Modeling
Linear mixed-effect models were developed to explore the interactions between effects of time and sex on the measured variables while compensating for the unbalanced groupings and the potential individual subject variation. The models were developed in R using the lme4 package ( ). Summary statistics and significance values—using the satterthwaite method of degrees of freedom and t -test—were computed with the lmertest package ( ). Additional model analysis was completed with the Psycho library ( ). Effects were considered significant if p < 0.05, and the reported beta was at least twice the standard error (SE).
The models were developed for each of the three TCD features as well as the summed symptom scores, as dependent variables. For symptoms and BHI, the random-effects were explored by fitting different models with the maximum likelihood method and comparing with the likelihood ratio test—the models with significant improvement were selected. The fixed-effects and interactions were similarly compared, and the final models were then fit with the restricted maximum likelihood method. The models for PI and P2R failed to converge with the maximum likelihood method, however, the restricted method did reach convergence. Because of this, the resulting models both used a similar structure, with days-post injury, sex, and their interactions as fixed effects, and subject specific intercepts as random effects. For the sex category, a contrast encoding of [0.5, −0.5] with males as the reference was employed. Similarly, a dummy encoding with the controls as the reference group was used for the days-post category. These were grouped similar to Thibeault et al. ( ). Correlations between features were evaluated using the Pearson correlation coefficient and the sex dependent interactions of the resulting regression lines were explored using the ANCOVA method with a set of linear models fit with the ordinary least squares method from lme4 ( ). A one-way ANOVA was conducted to compare the effect of sex and condition (case or control), on age using the StatsModels package ( ) in Python.
## Results
### Population
There was no significant interaction between the effects of sex and condition on age [ F = 0.08, p = 0.78], or main effects of either sex [ F = 1.86, p = 0.17] or condition [ F = 0.004, p = 0.96]. All subjects identified as athletes, with the majority of males, 26 (74%) subjects from the mTBI group and 77 (87%) from the control group, playing football as their primary sport. The other subjects were split between rugby, soccer, basketball, baseball, lacrosse, ice hockey, and quidditch. Although within the female population soccer was the most popular, 10 (45%) from the mTBI group and 7 (75%) subjects from the control group, the overall spread was more diverse and included volleyball, dance, track, swimming, basketball, softball and cheer. Within the mTBI population there was a slight difference in the reported mechanism of injury. For the male population 33 (89%) subjects reported being injured playing a sport, while 4 (11%) did not provide a mechanism. Within the female population 16 (76%) identified their cause of injury from a sport, whereas 5 (24%) reported another mechanism or did not provide a cause.
### Symptoms
The model for symptoms had an explanatory power (conditional R ) of 76.74%, in which the fixed effects explain 49.95% of the variance (marginal R ). Though there was no overall main effect of sex in the model (β = 2.91, SE = 3.94, 95% CI [−4.58, 10.51], t = 0.74, p > 0.1), there were significant interactions between sex and days-post groups for the first 11 days, see . This is illustrated by the increased self-reported summed symptoms scores in the female population in . In addition, the individual symptom averages in illustrate that it was not a small subset of symptoms dominating the summed score for the female population. Additionally, there were large main effects for all days-post groupings, see .
Mixed effect model results for symptoms.
The bold rows indicate significant effects or interactions .
Features for each sex grouped by days post-injury. (A) Summed symptoms. (B) BHI. (C) PI (D) P2R. Significance values are indicated where a large interaction between sex and days-post was found (** P < 0.01, *** P < 0.001).
Self-reported mean symptom scores. (A) Males. (B) Females. The shaded squares without a value are below 1.
### TCD Features
#### BHI
The BHI model had a total a total explanatory power (conditional R ) of 45.16%, in which the fixed effects explain 13.83% of the variance (marginal R ). There was no main effect of sex found in the model, (β = −0.03, SE = 0.03, 95% CI [−0.09, 0.04], t = −0.73, p > 0.1), but there was one large interaction between sex and days-post at the 0–1 days grouping (β = 0.15, SE = 0.07, 95% CI [0.01, 0.29], t = 1.98, p < 0.05), . However, within this grouping, all of the male subjects were collected on the day of their injury, while the female subjects were all collected the day after their injury occurred. In this instance, it seems more feasible that the interaction is a product of the female subjects being collected closer to the period of hyperreactivity found in Thibeault et al. ( ), as opposed to a sex-related disparity.
Mixed effect model results for BHI.
The bold rows indicate significant effects or interactions .
The overall longitudinal profile found in Thibeault et al. ( ) was also predicted here by main effects for days-post 2–3 (β = 0.14, SE = 0.03, 95% CI [0.08, 0.21], t = 4.44, p < 0.001), 4–5 (β = 0.09, SE = 0.03, 95% CI [0.04, 0.15], t = 3.17, p < 0.01), and 6–7 (β = 0.06, SE = 0.03, 95% CI [0.01, 0.12], t = 2.32, p < 0.05), see .
#### PI
The model for PI had total explanatory power (conditional R ) of 72.02%, in which the fixed effects explain 8.35% of the variance (marginal R ). Here, a main effect of sex was predicted (β = −0.13, SE = 0.04, 95% CI [−0.21, −0.04], t = −2.96, p < 0.01). Exploring the population results in suggest that this effect may be a product of an inherent difference between males and females in the control population, as opposed to a sex-related difference. This is supported by the lack of significant interactions between sex and days-post injury grouping, . There was a significant main effect found at days-post 8–9 (β = 0.07, SE = 0.03, 95% CI [0.01, 0.13], t = 2.14, p < 0.05), that cannot be fully explained.
Mixed effect model results for PI.
The bold rows indicate significant effects or interactions .
#### P2R
The model predicting P2R had a total explanatory power (conditional R ) of 74.42%, in which the fixed effects explain 10.32% of the variance (marginal R ). A main effect of sex was present (β = 0.05, SE = 0.02, 95% CI [0.01, 0.10], t = 2.21, p < 0.05). However, similar to PI, illustrates a difference between control groups. There were no significant interactions between sex and days-post found, see .
Mixed effect model results for P2R.
The bold rows indicate significant effects or interactions .
#### Correlations
The correlations between features provides additional information about the sex-related differences in this population, . Both sexes had significant negative correlations between PI and P2R ( r = −0.8, p < 0.001; r = −0.67, p < 0.001). For the male population there were significant correlations between BHI and PI ( r = 0.27, p < 0.001), as well as BHI and P2R (−0.18, p = 0.02), that were no present in the female population, . Conversely, the female population had significant correlations between symptoms and BHI ( r = 0.28, p < 0.01), as well as symptoms and PI ( r = 0.28, p < 0.01), that were not found in the male population, .
Feature correlations. (A) Male correlations. (B) Female correlations. Correlations that are not significant at the 0.05 level are set to zero.
Feature Correlations for the male and female groups.
Further exploring the sex-related correlation structure by an ANCOVA analysis reveals a significant difference in the slopes of the regression lines for symptoms and BHI between sexes (β = 58.55, SE = 19.07, 95% CI [20.98, 96.12], t = 3.07, p < 0.01). A difference in slopes was also found comparing symptoms and PI (β = 69.08, SE = 17.58, 95% CI [34.45, 103.70], t = 3.93, p < 0.001). For symptoms and P2R however, there was no significant difference in slope (β = −56.21, SE = 31.79, 95% CI [−118.82, 6.40], t = −1.77, p = 0.08), instead a difference in y-intercepts was found (β = 69.45, SE = 25.11, 95% CI [20.00, 118.91], t = 2.77, p < 0.01).
## Discussion
### Symptoms
Several studies have found a similar increase in self-reported symptoms for female subjects ( , ). In the study from Baker et al. ( ), the increased symptoms in the acute stage may have influenced recovery time—explaining the prolonged recovery for females. Although the difference between sexes here does appear more pronounced, comparing that difference to those other studies is not possible given the heterogeneity of the symptom collection.
The mechanism of injury presents a potentially confounding factor. In this study the majority of male subjects played helmeted sports (68%). The protection afforded by these helmets could have contributed to the overall lowered symptoms. However, in the study from Broshek et al. ( ), female subjects were more than twice as likely to experience cognitive impairments than males in unhelmeted sports—illustrating that a difference existed even when accounting for helmets. The sex differences in reported symptoms observed in the current study were not accompanied by evidence for corresponding concussion-related differences in the TCD features. Moreover, the main effects of time observed for BHI suggest the progression of vascular injury to be similar for both sexes. A more compelling explanation would be an inherent reporting bias in the female group. Other studies have shown that female athletes tend to report more symptoms than males ( , ). In addition, females are generally more focused on, and aware of, their health ( ), suggesting that there is more of a motivation to ensure a complete recovery. Conversely, male athletes have a number of societal and cultural motivations to perceptually diminish the magnitude of their injury and return to sport as soon as possible ( ). Similarly, it was shown in Kerr et al. ( ), that male athletes were significantly more likely to hide a concussive injury.
There have been several studies exploring the physiological differences between sexes that contribute to the susceptibility and recovery from concussion, many of which center on the possible role of estrogen. In more severe traumatic brain injuries, estrogen has been shown to have a neuroprotective effect in male rats, but a deleterious one in females ( ). In humans, the study from Gallagher et al. ( ), found that female subjects suffering from a sport-related concussion who used hormonal contraceptives reported lower symptom severity than those who did not, suggesting that hormonal contraceptives may play a role in modulating the collapsing neurometabolic cascade that is a hallmark of concussive injuries ( ). Another consistent theme in mTBI gender differences is decreased neck strength in women ( , ), which has been shown to be inversely related to concussion susceptibility. A similar confound of this study is the role physical maturity plays in how someone responds to an mTBI. The study from Krix et al. ( ) found that male subjects in early stages of puberty had increased odds of a prolonged recovery from a concussive injury. Although puberty clearly affects the adolescent brain ( ), it is still unclear how that would contribute to the results of this work.
### TCD Features
It is important to note that in this context BHI is not meant an exact measure of reactivity. Breath-holding can introduce other autonomic and sympathetic responses that can confound its use for directly quantifying reactivity. However, as illustrated in Thibeault et al. ( ), and confirmed by the main effects of days-post here, BHI as measured in this population, is a robust biomarker of mTBI. The difference in slopes of the regression lines between symptoms and BHI illustrate the vulnerability of relying on subjective measures alone.
The overall sex-related main effect for both PI and P2R is most surprising aspect of this analysis. For both features that effect did not appear to be based on the injury, but rather an inherent sex-related difference in this population. Previously, when sex was ignored both were altered immediately following an mTBI ( ). The alterations of these features here, as illustrated in , appear to only be present in the male population. PI is a complex metric that is influenced by the combinations of cerebral perfusion pressure, cerebrovascular resistance, arterial bed compliance, heart rate, and the pulse amplitude ( ). Similarly, It has proposed that P2R is associated with distal bed compliance dynamics ( ), however there is no established physiological correlation. Why either of these features would have a sex-related dependence is unclear and will need to be explored further in the future. Regardless, these results illustrate that that dependence is not due to the injury.
The study from Esposito et al. ( ) showed that women had higher Cerebral Blood Flow (CBF) compared to males. Although, in the population here there was no significant trend in mean velocity during injury recovery, that may be because TCD cannot measure CBF directly, only the velocity. In addition, a post-concussive change in mean velocity has not been demonstrated with TCD ( ).
## Conclusions
This is the first study to compare sex-related differences between clinical symptoms and TCD measurements in adolescent mTBI subjects. The objective measures highlight the need to mitigate patient heterogeneity when assessing concussion recovery and the discrepancy in clinical symptoms illustrates how difficult this can be for clinicians. In the case of males the possibility of under-reporting may need to be considered. A physiological measurement such as TCD may eventually help remove ambiguity and provide clinicians with an objective physiological measure of mTBI recovery.
## Data Availability
The raw data supporting the conclusions of this manuscript will be made available by the authors, without undue reservation, to any qualified researcher.
## Ethics Statement
This study was carried out in accordance with the recommendations of Western Institutional Review Board (IRB #20141111), with written informed consent from all subjects. All subjects gave written informed consent in accordance with the Declaration of Helsinki. The protocol was approved by the Western Institutional Review Board.
## Author Contributions
CT had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. RH: study concept and design. CT and ST: analysis. CT, ST, and RH: interpretation of data. CT and ST: drafting of the manuscript. RH, SW, and ST: critical revision of the manuscript for important intellectual content. CT and ST: statistical analysis. NC and SW: technical or material support. RH and CT: study supervision.
### Conflict of Interest Statement
At the time that this research was conducted, CT, ST, NC, SW, and RH, were employees of, and either hold stock or stock options in, Neural Analytics, Inc.
|
Current medications for neurodegenerative and neuropsychiatric diseases such as Alzheimer's disease (AD), Huntington's disease (HD), Parkinson's disease (PD), and Schizophrenia mainly target disease symptoms. Thus, there is an urgent need to develop novel therapeutics that can delay, halt or reverse disease progression. AD, HD, PD, and schizophrenia are characterized by elevated oxidative and nitrosative stress, which play a central role in pathogenesis. Clinical trials utilizing antioxidants to counter disease progression have largely been unsuccessful. Most antioxidants are relatively non-specific and do not adequately target neuroprotective pathways. Accordingly, a search for agents that restore redox balance as well as halt or reverse neuronal loss is underway. The small molecules, cysteamine, the decarboxylated derivative of the amino acid cysteine, and cystamine, the oxidized form of cysteamine, respectively, mitigate oxidative stress and inflammation and upregulate neuroprotective pathways involving brain-derived neurotrophic factor (BDNF) and Nuclear factor erythroid 2-related factor 2 (Nrf2) signaling. Cysteamine can traverse the blood brain barrier, a desirable characteristic of drugs targeting neurodegeneration. This review addresses recent developments in the use of these aminothiols to counter neurodegeneration and neuropsychiatric deficits.
## Introduction
Cysteamine, also known as 2-mercaptoethylamine or aminoethanethiol, is the decarboxylated derivative of the amino acid cysteine. It exerts radioprotective effects and is more effective than cysteine alone, although a combination of cysteamine and cysteine display synergistic effects ( , ). Although cysteamine reduced mortality in irradiated Drosophila and mice, mutagenic effects of radiation were not prevented ( , ). Cysteamine has been utilized for the treatment of cystinosis, a lysosomal disorder, and, more recently, has been evaluated for the treatment of neurodegenerative disorders. This review will summarize the current understanding of cysteamine and cystamine, its oxidized derivative.
In cells, the amino thiol is generated by the degradation of coenzyme A, which in turn, is generated from pantothenate (vitamin B5) and cysteine ( ) ( ). Coenzyme A degradation yields pantetheine, which is hydrolyzed by pantetheinase or vanin, generating cysteamine and pantothenic acid. Cysteamine is then oxidized to hypotaurine by cysteamine dioxygenase ( ). Hypotaurine can be converted into taurine by hypotaurine dehydrogenase. Taurine is eliminated in the form of bile salts such as taurocholate, either via the urine or feces ( ). Levels of cysteamine has been reported to be in the low micromolar range in tissues such as the liver, kidney and brain, which were measured after treating lysates with DTT to liberate free cysteamine ( ), indicating association with proteins via disulfide bonding. Similarly, another study measured cysteamine after reducing perchloric acid treated kidney and liver lysates with mercaptopropionic acid ( ). The presence of disulfide-bonded cysteamine with proteins was subsequently shown by Duffel and associates ( ), which could account for the effects of cysteamine and cystamine on the activity of several proteins.
(A) Biosynthesis of cysteamine and intersection with cysteine catabolism. Cysteamine is generated in mammals by the degradation of coenzyme A, which is required for the metabolism of fatty acids, carbohydrates, amino acids and ketone bodies. When coenzyme A is cleaved (cleavage at the dotted line), pantetheine is generated, which is acted on by pantetheinase or vanin to form cysteamine. Cysteamine is converted to hypotaurine by cysteamine decarboxylase. Cysteine, a component of coenzyme A, is acted on by cysteine dioxygenase to form cysteine sulfonate which is decarboxylated by cysteine sulfonate decarboxylase to form hypotaurine. Hypotaurine generated is further metabolized to taurine by hypotaurine decarboxylase. (B) Effects of cysteamine/cystamine. Both cysteamine and its oxidized form cystamine have protective effects in cells and tissues. Originally identified as radioprotective molecules, subsequently these aminothiols have been reported to mitigate cystinosis, a condition characterized by accumulation of cystine crystals in the body. Cystamine and cysteamine have a variety of other effects which include antioxidant effects (by increasing cysteine and glutathione levels), inhibition of transglutaminase 2 and caspase 3 (possibly by modifying reactive cysteine residues or cysteaminylation), modulation of mitochondrial function, immunomodulation. These molecules have also been reported to increase levels of brain derived neurotrophic factor (BDNF) and heat shock proteins, which affords neuroprotective benefits.
The metabolism of cysteamine, cystamine and cysteine are linked in cells. Both cysteamine and cystamine increase cysteine levels intracellularly in a temporal and dose-dependent manner ( ). As cysteine is a component of glutathione and a potent antioxidant itself, treatment of cells with these aminothiols can mitigate oxidative stress. Treatment of SN56 cholinergic cells causes an increase in cysteine levels in 30 min. Cystamine is first converted to cysteamine in the reducing atmosphere of cells, and treating cells with cystamine elicits an increase of cysteine in 3 h. N-acetylcysteine (NAC), 2-mercaptoethanesulfonic acid (MESNA) and mercaptopropionylglycine (MPG), on the other hand, elevate cysteine levels to a lesser extent (2-fold as compared to 6-fold in the case of cysteamine). The study also revealed the importance of these thiols in sequestering reactive aldehyde species in cells and bolstering the antioxidant capacity of cells. Thus, cystamine and cysteamine also act as antioxidants themselves. Consistent with these observations, cysteamine affords protection against acetaminophen- mediated liver damage, where the highly toxic unsaturated aldehyde acrolein, is produced ( , ). Cysteamine has also been proposed to replace homocysteine as the substrate for cystathionine β-synthase (CBS) in a reaction with serine to generate thialysine or (S-(2-aminoethyl)-L-cysteine) ( ). Consistent with these studies, thialysine levels increase in the brain after feeding cysteamine to rats ( ).
## Protective Effects of Cysteamine and Cystamine
### Therapeutic Applications of Cysteamine and Cystamine in Peripheral Tissues
Both cysteamine and cystamine, have been used for the treatment of several conditions ( ). These compounds possess radioprotective properties and were initially used to treat radiation sickness that arises in cancer patients after radiotherapy, but subsequently discontinued after unsuccessful clinical trials ( , ). One of the earliest uses of cysteamine in medicine, which is FDA-approved, is the treatment of cystinosis, an inherited autosomal recessive disorder in which the body accumulates cystine due a defect in the lysosomal cysteine transporter, cystinosin ( , ). Cystine crystals build up in many tissues and damage organs such as the kidney and the eye. One of the initial manifestations of juvenile cystinosis is renal Fanconi syndrome which manifests as dysfunction of the renal proximal tubule leading to polyuria, phosphaturia, glycosuria, proteinuria, acidosis, growth retardation, and rickets ( ). Cysteamine participates in disulfide exchange reactions to form cysteine and mixed disulfides of cysteine and cysteamine, which can then exit the lysosome.
Cysteamine also has anti-malarial effects preventing the replication of the parasite, Plasmodium falciparum in vivo and also potentiates the action of the anti-malarial artemisin ( , ). Cysteamine has also been reported to have anti-HIV-1 effects ( , ). Cysteamine elicits both beneficial and harmful effects, some of which included ulcer formation and anti-angiogenic effects ( ). Cystamine, the oxidized form of cysteamine, inhibits erythrocyte sickling in sickle cell anemia ( ). Incubating sickle cells with cystamine leads to the formation of an S-ethylamine derivative and a decrease in sickling under hypoxic conditions. Several other beneficial effects of the two cysteine derivatives are summarized in .
Neuroprotective actions of cysteamine/cystamine.
### Therapeutic Applications of Cysteamine and Cystamine in Brain Diseases
Cysteamine and cystamine appear to be promising in the treatment of certain mouse models of neurodegenerative diseases, such as Parkinson's disease (PD) and Huntington's disease (HD) ( ). Cysteamine can cross the blood-brain barrier, which makes it an attractive candidate for therapeutic applications ( ).
#### Huntington's Disease
Huntington's disease is a neurodegenerative disorder caused by expansion of polyglutamine repeats in the protein huntingtin, Htt, which causes it to aggregate and cause widespread damage in almost all tissues expressing it ( ). Initial studies on cystamine and its therapeutic effects on disease progression in HD focused on its inhibitory effects on the enzyme transglutaminase ( , ). Transglutaminases catalyze the formation of ε-N-(γ-glutamyl)-lysyl crosslinks between proteins and were proposed to contribute to neuropathology of HD ( – ). However, later studies revealed that ablation of the transglutaminase gene did not prevent neurodegeneration in HD ( ). Cystamine has also been beneficial in a fly model of HD, where photoreceptor degeneration was rescued in adult flies ( ). Cystamine treatment in mouse models of HD lead to increased cysteine levels, which was proposed to be neuroprotective ( , ). Cysteine is a potent antioxidant and dysregulated cysteine metabolism mediates neurodegeneration in HD ( – ). Cysteine is also the precursor of the gaseous signaling molecule, hydrogen sulfide, which participates in a myriad of physiological processes ( – ). Cystamine, in combination with mithramycin, was also shown to be protective in the R6/2 model of HD ( ). The beneficial effects of cysteamine led to clinical trials in HD ( ). In addition, cystamine can augment levels of brain derived neurotrophic factor, BDNF, in mouse models of HD ( ). More recently cysteamine was shown to counteract toxicity mediated by mutant huntingtin in vitro in primary neuron and iPSC models of HD although the exact molecular mechanism by which cytoprotection is conferred is still unknown ( ).
#### Alzheimer's Disease
Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder and the most common form of dementia ( , ). The molecular hallmarks of AD include increased load of amyloid plaques and neurofibrillary tangles, which affect multiple cellular processes. Numerous reports describe links between dementia and AD with amyloid deposits or tangles. Postmortem analysis of cognitively normal subjects have revealed increased amyloid plaques, a pathogenic signature of AD, but no dementia ( ). Conversely, several diagnosed AD patients have no signs of neuritic plaques ( ). Thus, the correlation between amyloid plaques and AD awaits further study ( ). Regardless of these inconsistencies, it is clear that the brain has corrective mechanisms that delay cognitive decline and if harnessed, may stall neurodegeneration. The search for small molecules that stimulate neuroprotective signaling cascades may be beneficial. Cystamine and its derivatives are being evaluated as possible therapies for the disease. Chronic cysteamine treatment (daily injections for a period of 4 months) resulted in improvements in habituation and spatial learning deficits in the APP-Psen1 mouse model of AD ( ). The APP-Psen1 model harbors the human transgenes for the Swedish mutation of the amyloid precursor protein (APP) and presenilin-1 (PSEN1) containing an L166P mutation, regulated by the Thy-1 promoter ( ). AD patients have elevated transglutaminase levels, which colocalize with the amyloid plaques ( ). Transglutaminases accelerate amyloid beta aggregation and toxicity. Accordingly, cystamine therapy is being considered for lowering the amyloid plaque burden in AD patients. In particular, Multi-Target Directed Ligands (MTDLs) or single compounds which may simultaneously act on different targets are being explored. Along these lines, a cysamine-tacrine dimer has been developed, which decreased acetylcholinesterase (AChE)-induced beta-amyloid aggregation ( ).
#### Parkinson's Disease
Aggregation of alpha-synuclein, leading to the formation of Lewy bodies, is a hallmark of Parkinson's disease (PD), which affects the substantia nigra of the brain causing motor deficits and multiple abnormalities. Existing therapies for PD largely target symptoms and do not mitigate neuronal loss observed. Several lines of evidence suggest the therapeutic potential of the aminothiol in PD ( ). Cystamine ameliorated mitochondrial dysfunction and oxidative stress associated with 6-hydroxydopamine and 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced models of PD ( ). In the MPTP-induced neurotoxicity model of PD in mice, independent studies revealed various effects of cystamine such as elevation in the levels of tyrosine hydroxylase and BDNF ( , ). Similarly, cysteamine, the reduced form of cystamine, also afforded neuroprotection. Similar to AD, elevated transglutaminase activity caused an increase in the formation of cross-linked alpha-synuclein and insoluble aggregates, which could be abrogated by cystamine ( ).
#### Amyotrophic Lateral Sclerosis
ALS, also known as Lou Gehrig's disease, is a neurodegenerative disease where selective degeneration of motor neurons in the brain and spinal cord occurs leading to paralysis of skeletal muscles and progressive weakness and atrophy of limbs ( ). Difficulties in speech and movement follow and patients are typically wheelchair-bound. Causes of ALS can be either genetic or sporadic (refers to patients without a family history). Among the best studied genetic mutations in familial ALS include mutations in superoxide dismutase 1 (SOD1), which misfolds, aggregates, and elicit toxicity by multiple mechanisms ( , ). Proposed reasons for SOD1 aggregation include crosslinking mediated by transglutaminase 2 (TG2). Studies with cell culture models of ALS reveal that cystamine prevents aggregation of SOD1 and improved cell survival ( ). Furthermore, inhibiting spinal TG2 by cystamine reduces SOD1 oligomers, microglial activation and delayed progression in the G93A SOD1 mouse model of ALS ( ). Thus, cystamine treatment may be beneficial in treating ALS.
#### Neurological Complications of Cystinosis
Although cystinosis was not considered to affect brain function, it is now known that cystinosis can result in neurocognitive deficits in adults as well as children. These include impaired visual spatial, visual memory, language problems, academic impairment, seizures, memory impairment, motor incoordination, and neuromuscular dysfunction and is often accompanied by structural abnormalities in the brain ( – ). Early treatment with cysteamine orally prevents several of these neurocognitive deficits. Patients with cystinosis treated at or after age 2 years (late-treatment group) score poorer than the early treatment group (before 2 years) on verbal, performance, and full-scale IQ tests and tests rating visual-spatial skills ( ). Similarly, adults with cystinosis who receive consistent chronic treatment with cysteamine fare better on visual learning and memory skills ( ).
#### Schizophrenia and Neuropsychiatric Diseases
Schizophrenia is a psychiatric disease, with complex genetic and neurological contributions of unclear origins, manifesting as a combination of symptoms which includes hallucinations, delusions, motivational and cognitive deficits ( ). Although treatments for schizophrenia target psychotic symptoms, most existing drugs do not relieve social and cognitive deficits. The neurochemical changes in schizophrenia typically occur well before formal diagnosis, and, thus, preventive therapies could be beneficial. Schizophrenic patients have lower levels of BDNF so that schizophrenic patients might benefit from use of cysteamine due to its BDNF-enhancing properties and effects on the dopaminergic system ( , ). In an amphetamine-induced psychosis model of schizophrenia, cysteamine prevents increased locomotor activity by decreasing dopamine release ( ). Cysteamine counteracts the BDNF-lowering effects of haloperidol ( ). The anti-depressant effect of cysteamine may also benefit other mental conditions ( ). These studies are consistent with an earlier study which demonstrated that cysteamine blocked amphetamine-induced deficits in sensorimotor gating in male Sprague-Dawley rats ( ). Similarly, cysteamine treatment increases BDNF levels in the frontal cortex and hippocampus and improved spatial memory in heterozygous reeler mice, which exhibit behavioral and neurochemical abnormalities similar to those in schizophrenia ( ).
Similarly, cystamine and cysteamine may be beneficial in other conditions involving low neurotrophin levels, such as autism spectrum disorders (ASD). Analysis of postmortem human brain samples revealed increases in TG2 mRNA and protein levels in the middle frontal gyrus of subjects with autism spectrum disorder. Thus, cysteamine may alleviate symptoms of ASD by inhibiting TG2 and increasing BDNF levels ( ). The same study demonstrated that ER stress induced TG2 expression and deficits in social behavior. Systemic administration of cysteamine attenuated these behavioral abnormalities. In mice lacking methyl-CpG binding protein 2 (MeCP2), a model of Rett syndrome, associated with decreased BDNF levels and obsessive compulsive phenotypes, cysteamine treatment improved lifespan, and improved motor function ( , ). In a similar vein, cysteamine counteracted anxiety, and depression-like behaviors in a mouse model of anxiety/depression induced by chronic glucocorticoid exposure ( ).
## Potential Side-Effects of Cysteamine and Cystamine
Although cysteamine and cystamine have several desirable effects in cells and tissues, some studies have reported side-effects. For instance, in the treatment of HD patients using cysteamine (Cystagon) in the CYTE-I-HD clinical trials, rashes, nausea, and motor impairment along with bad breath were observed in a few patients ( ). In phase II trials, asthenia or fatigue was more commonly observed ( ). Despite these side-effects, cysteamine appeared to be well tolerated by almost all of the patients.
## Concluding Remarks
Some therapies using antioxidants have not yielded satisfactory outcomes in clinical trials ( – ). Several reasons have been attributed to the failure of such trials. Certain antioxidants inhibit fundamental cellular processes such as autophagy, which is crucial to eliminate misfolded proteins and damaged organelles ( ). Most antioxidants utilized only target specific free radicals and thus may counteract only selected types of free radicals. Most clinical trials were initiated relatively late in disease progression, when most of the oxidative damage has already accrued. Doses of antioxidants utilized have also not been adequately tested. Durations of several of these trials have also been short, and longer term uses of redox active molecules have not been studied in detail. Thus, development of antioxidant molecules that have multiple targets, while not inhibiting basic cellular processes such as autophagy, is crucial. Cysteamine normalizes the proteostasis machinery by restoring BECN1/Beclin 1-dependent autophagy in cystic fibrosis in mouse models of the disease and also in patients ( ). Cysteamine dendrimers have been found to ameliorate autophagy deficits in cystic fibrosis ( ). It is evident that signaling pathways modulated by cystamine and cysteamine are diverse ( ), and knowledge of these cascades will yield information that can be harnessed to tailor treatments for diverse diseases. The tissue-specific effects and optimal concentrations of the thiol redox couple that would be beneficial for specific diseases has still not been elucidated. Although these aminothiols possess beneficial disease-modifying effects in several conditions, it is still unclear whether these molecules or their metabolites mediate the cytoprotection observed in neurodegenerative diseases. However, increase in cysteine levels can promote neuroprotection, and some of the beneficial effects can be attributed to increases in cysteine to mitigate oxidative stress as has been observed in HD ( ). Similarly, systematic studies measuring the concentration and metabolism of cysteamine and cystamine in pathological conditions have not been conducted and are areas of future investigation. Epigenetic effects of cystamine and cysteamine and cysteaminylation, the posttranslational modification mediated by cystamine and cysteamine await detailed investigation. The use of cystamine and cysteamine is another example of a repurposed drug, which has cytoprotective effects in the brain. Combination therapy of these aminothiols with other approved drugs offer attractive options to arrive at safe and effective drugs for these complex diseases.
## Author Contributions
BP conceptualized the review. BP and SS wrote the review.
### Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
|
Watching TV is a highly prevalent leisure activity among older adults and, in many cases, the only leisure option of those living in low-income communities. While engaging in leisure activities have proven to protect older adults from cognitive decline, the effects of watching TV on cognition of this population is controversial in the literature. This study investigated the impact of watching TV on global cognitive function, immediate memory, verbal fluency, risk of dementia of amnestic mild cognitive impairment (aMCI) in a cohort of older adults residents of socioeconomically deprived areas of São Paulo, Brazil. We used data from the São Paulo Aging & Health Study (SPAH). Participants aged 65 years or over, with no dementia diagnosis at baseline and who completed the 2-year follow-up assessment were included in this study ( n = 1,243). Multivariable linear regression models were performed to assess the effect of watching TV on global cognitive function, immediate memory and verbal fluency. Multivariable logistic regression models were used to evaluate the risk of developing dementia and aMCI. Models were controlled by cognitive performance at baseline, sociodemographic characteristics and functional status. Cognitive performance at baseline and follow-up were similar. Thirty-one participants were diagnosed with dementia, and 23 with aMCI 24 months after inclusion in the study. Watching TV did not show any positive or negative effect on global cognitive function, immediate memory, verbal fluency, risk of dementia and risk of aMCI. It is good news that watching TV did not predict the decline in cognition in elders. However, it is essential to increase opportunities for other leisure activities for low-income and low-educated older adults if we do consider that leisure activities protect cognition decline in older adults. In the coming decades, developing countries will experience the highest burden of dementia and more than fun, public policies to promote leisure activities might be a strategy to alleviate this burden shortly.
## Introduction
More than 28 million Brazilians -or 13% of the country's population- are aged 60+, and the size of this age group is projected to double in the coming decades ( ). The Brazilian elderly population has been exposed to socioeconomic adversities throughout their life course ( , ). Most of the Brazilian elders have minimum or no formal education, had semi-skilled or non-skilled occupations throughout their lives, are poorer than the average adult population of the country and were raised in rural areas before moving to large cities during the second half of the 20th century ( ). It means that they are at a higher risk of developing cognitive related health problems ( , ). Currently, as many as 6 million Brazilians older adults cannot read or write ( ). These characteristics influence the type of leisure activity performed by them.
By 2050, Latin America will host more than 17 million dementia cases ( ), and strategies to reduce the burden of cognitive impairment in the region are badly needed. One possible response is to reduce social inequalities by improving modifiable risk factors for cognitive decline, such as education, quality of occupation and socioeconomic status. Social, physical and intellectual leisure activities have shown to reduce the risk of cognitive impairment ( – ) and dementia in older adults ( , , , ), but the ability to perform these activities is affected by socioeconomic difficulties faced during the life course ( – ).
Although watching TV is an everyday leisure activity among older adults ( ) worldwide, the effects of this activity on their cognition are controversial in the literature. There is no consensus in the literature of to which extent watching TV is a cognitively demanding activity or what type of activity is watching TV (e.g., is it a recreational activity?) ( – ). Longitudinal studies in Europe and the US suggest that excessive hours watching TV impairs cognition somehow ( , ). Some studies didn't see a longitudinal effect ( , ). However, one large study in Asia found a protective effect ( ).
To date, little ( ) is known of the potential protective effects of leisure activities among Latin American populations, particularly from low-income communities, where leisure activities significantly differ from those performed by older adults in high income countries. The low purchase power, restricted mobility and low literacy of this population are factors that restrict their leisure options. In this context, watching TV becomes an essential and almost the only leisure activity available to most Brazilians older adults. In Brazil, watching TV is the most frequent leisure option of 93% of the adults aged 60+ ( ).
Given the importance of watching TV as a leisure activity among older adults from low-income communities and the lack of empirical data on its effect on cognition in these population, we examined the association between watching TV and incidence of amnesic mild cognitive impairment (aMCI) and dementia in a 2-year cohort study of older adults residents of socioeconomically deprived areas of São Paulo, Brazil. We also examined the association between watching TV and changes in global cognitive function, immediate memory, verbal fluency in the same population.
## Materials and Methods
This study is part of the São Paulo Aging & Health Study (SPAH), a large 2-year population-based cohort study of older adults from low-income areas of São Paulo, Brazil ( , ).
### Participants
SPAH enrolled 2,072 community-dwellings aged 65+ that lived in socioeconomically deprived areas of the district of Butantã, in São Paulo, Brazil. The recruiting method was knocking on the door of all households within the 66 pre-defined census sectors (the smallest administrative areas) between 2003 and 2005. The census sectors selected a priori were those with the lowest Human Development Index, including large informal settlements and shantytowns. Follow-up occurred 24 months after enrollment, between 2005 and 2007. Trained interviewers conducted the baseline and follow-up assessments at participants' home, using the SPAH protocol. More details of recruitment procedures are described elsewhere ( , , ).
Participants without dementia at baseline and who could complete the follow-up assessment were included in this study. Socioeconomic and demographic information (age, gender, marital status, schooling, occupation, and personal income) of all participants were collected at baseline assessment. We used the 12-item World Health Organization Disability Assessment Schedule (WHODAS 2.0) to assess functional status ( ). The WHODAS 2.0 is a questionnaire that evaluates six areas of life: mobility (moving and getting around), life activities (domestic responsibilities, leisure, work, and school), cognition (understanding and communicating), self-care (hygiene, dressing, eating, and staying alone), participation (joining in community activities), and getting along (interacting with other people). The score ranges from 0 to 48, and the higher the score, the more severe is the disability.
The study received ethics approval from the Brazilian National Committee for Ethics and Research (CONEP-Brazil), and all participants provided written informed consent before enrollment in the study.
### Outcomes
The five clinical outcomes in this study are global cognitive function, immediate memory, verbal fluency, risk of dementia and risk of amnestic mild cognitive impairment (aMCI).
#### Global Cognitive Function
We used thecognitive score (COGSCORE) validated by the 10/66 Dementia Research Group to evaluate global cognitive function. The score is based on the Community Screening Instrument for Dementia (CSD-I) ( ), an instrument developed for low educated and illiterate populations ( ) and validated for use in Brazil ( ). The global cognitive function includes memory, abstract thinking, language, praxis, space and temporal orientation dimensions. Higher scores indicate more severe impairments.
#### Immediate Memory
We evaluate immediate memory by asking participants to memorize a list of 10 words adapted from the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) battery ( ). The total score is based on the number of recalled words, thus maximum score possible is 10 representing the best immediate memory.
#### Verbal Fluency
We used animal naming to evaluate verbal fluency. This test is part of the CERAD battery ( ) and the CSI-D ( ). First, we asked the participant to name items from another category (dressing). After this test, the participant was asked to name all animals he/she could remember during 1 min. Each animal named equals one point. Higher scores indicate better performance on the task.
#### Dementia
The diagnosis of dementia followed the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM-IV) ( ). It was based on the protocol developed by 10/66 Dementia Research Group for population-based studies in developing countries ( ). Detailed description can be found elsewhere ( , ).
#### Amnestic Mild Cognitive Impairment
This study used two conditions to determine the Amnestic Mild Cognitive Impairment diagnosis (aMCI): absence of dementia and memory impairment ( ). We considered an individual with memory impairment if his/her total score in the memory test was at least 1.5 standard deviation (SD) below the study population's mean. The memory test score was based on four items of the cognitive dimension of the Community Screening Instrument for Dementia (CSD-I) ( ). Participants were required to recall the interviewer's name, repeat three standardized words, recall these words and repeat stories. Scores are positively associated with memory performance.
### Predictor Variable
The predictor variable was time spent watching TV assessed by asking “How many hours per day you watch TV?” We collected this information at baseline using the Brazilian version of the 'Involvement in Activities' questionnaire (IA). The IA was developed to evaluate engagement in activities in diverse communities and was used in the United States and Nigeria ( ). The Brazilian version of the IA was validated in Brazil by the SPAH group in collaboration with the authors who developed the questionnaire. The test-retest reliability of the IA included 70 participants. They were re-assessed with an interval of 3–4 weeks. The reliability of the question about hours spent watching TV was high (0.63).
### Statistical Analysis
We used multivariable linear regression to examine the association between watching TV at baseline and global cognitive function, immediate memory and verbal fluency at 2-year follow-up. Two models were built for each outcome. In the first models, we entered hours watching TV adjusting for the corresponding baseline score. Then, we extended these models by adjusting for sociodemographic characteristics (age, gender, marital status, schooling, occupation, and personal income) and function status (second models).
To investigate the impact of watching TV on the risk of dementia and aMCI, we performed two multiple logistic regression models for each outcome variable. We adjusted the first models for sociodemographic characteristics and functional status at baseline. We then extended the models for risk of dementia and the model for aMCI adjusting for global cognitive function at baseline amnestic aMCI at baseline, respectively. We present the 95% confidence interval (CI) for all analysis and set statistical significance at 5%.
Statistical significance was set at 5% for all analysis. We performed the analyses with the software STATA 9.0.
## Results
A total of 1,243 subjects without the dementia diagnosis at baseline who completed the 2-year follow-up assessment were included in this study. Participants were most females (61%), with a mean age of 72 years, mainly not employed (73%), low educated (89% with up to 3 years of schooling) and with a low socioeconomic background (only less than one third had a personal monthly income of more than US$246). Participants' characteristics at baseline are presented in . The median functional status score was 2.8 (IQR = 0–72.2). Watching TV was reported by 87% of participants with a median of 2 h per day (IQR = 0–12). Thirteen percent of the participants reported never watching TV ( n = 165). Among those whom reported watching TV, 22.2% watched up to an hour/day ( n = 276); 22.7% watched up to 2 h/day ( n = 282); 14.9% watched up to 3 h/day ( n = 185); 11.3% watched up to 4 h/day ( n = 141); 7.4% watched up to 5 h/day ( n = 92); and 8.2% watched 6 h/day or more ( n = 102).
Sociodemographic and functional characteristics of the participants of the study ( n = 1,243).
The cognitive performance of participants was similar between baseline and follow-up assessments ( ). At follow-up, mean global cognition function, immediate memory, and verbal fluency scores were 27.1 ± 4.3, 3.2 ± 1.3, and 12.8 ± 4.5. Watching TV showed no association with any cognitive outcome when controlling only by the cognitive performance at baseline or in combination with sociodemographic characteristics and functional status ( ).
Mean and standard deviation of the cognitive performance of the participants at baseline and follow-up ( n = 1,243).
CERAD, Consortium to Establish a Registry for Alzheimer's Disease; CSI-D, Community Screening Instrument for Dementia .
Multivariable regression models of cognition performance at follow-up on watching TV (hours/day) adjusting by the corresponding cognition metric at baseline (model 1); and adding sociodemographic characteristics and functional status at baseline (model 2).
Thirty-one participants were diagnosed with dementia during the follow-up assessment. No association between watching TV and the risk of developing dementia in 2 years was observed in the two models. Amnestic—was identified in 23 participants 2 years after the baseline assessment, and watching TV also showed no effect on preventing or increasing the risk of developing this condition ( ).
Logistic regression models of the risk of dementia and amnestic mild cognitive impairment on watching TV (hours/day), sociodemographic characteristics and functional status at baseline (model 1); and adding baseline global cognitive function for dementia relative risk and the corresponding baseline score for amnestic mild cognitive impairment (model 2).
## Discussion
In our study, hours per day watching TV did not impact positively or negatively the global cognitive function, immediate memory or verbal fluency of older adults after 2 years. Watching TV did not increase the risk or protect participants of developing dementia or aMCI after 2 years of the initial assessment.
Despite being such a common leisure activity among older adults ( )—and frequently the only leisure option for low-income and low-educated older adults-, not many studies investigated the isolated effect of watching TV on cognition ( , , , , ) and the findings are controversial.
Our results are in accordance with an English ( ) and a French study ( ). In the English cohort study ( n = 6,359 older adults aged 65 or over, 2-year follow-up) ( ), more time watching TV was associated with poorer global cognition at baseline, but not with changes after 2 years. There is no specific information about the educational and socioeconomic status of the population in the English cohort. Our study also enrolled 65+ individuals for the same follow-up period. The Frenchstudy ( ) enrolled 2,579 adults aged between 45 and 60 years old and found that more time watching TV was associated with worse executive function at baseline, but not with verbal memory. All participants in the French study were over 60 years old and no changes were observed after 6 years. Compared to the French study, ours enrolled adults with higher ages (65+ vs. 60+ years old) but a shorter follow-up (2 vs. 6 years).
Different from our results, three studies found that excessive hours watching TV impairs at least one domain of cognition ( , , ). One of the studies is a large English cohort ( n = 3,662, 6 years of follow-up) ( ), where more hours watching TV was associated with a decline in verbal memory but not in semantic fluency. A possible reason why our results differ from theirs are differences in the population studied. While the English study enrolled adults aged 50+, we enrolled adults aged 65+. Unlike the English study, ours enrolled low-income older adults for whom watching TV is the main leisure activity (more likely exposed). The elders in our study also have less formal education, resulting in fewer opportunities to be exposed to highly cognitive stimulus that would compensate the passiveness of watching TV. The follow-up period in the English study was three times larger. The other two studies that found an association between watching TV and cognitive impairment are less robust. One is a European cross-sectional study ( ), and the other is a case-control study (135 cases and 331 controls) ( ) that investigated the incidence of Alzheimer Disease in the US.
One study, however, found a protective effect ( ). The result of this study differs from the others in the literature and ours. It is a large Chinese cohort ( n = 6,586, 6 years follow-up), where watching TV was associated with a lower risk of cognitive decline ( ). These authors argue that the difference in their results might be because their population is low educated (<6 years of education), and watching TV could act as a cognitive stimulus for them. If this is true, we would expect that watching TV would protect the brain of our population, given that 89% of the population of our study have up to 3 years of schooling.
An open question in the literature is if watching TV in later life could be a cognitive stimulus. If this is true, more time watching TV would build resilience and protect the brain. However, this is not what we observed in our cohort of low-income elders exposed to watching TV as their main leisure activity in adult life. It is likely that the type of program watched, not measured by our study, plays a vital role in the effect of watching TV on cognition. For example, watching a soap opera result in different a more passive stimulus for the brain, compared to watching an educational documentary on a novel subject ( ). Indeed, preferring to watch soap operas and talk shows over documentaries, news, sports and other programs was associated with worse performance in cognitive tests in a cross-sectional study ( ). Not accounting for the type of program watched is a limitation of our study. However, all longitudinal studies investigating the impacts of watching TV on the cognition of older adults have the same limitation ( , , , ). They focus on the frequency of watching TV and do not account for the type of TV program. The only study that accounted for the kind of TV program is a smaller cross-sectional study ( ). Given the low-income and low educational level of the population in our study, we can speculate that the programs watched were more similar to the soap opera than the documentary. By the time of the research, cable TV and the internet were not widely available.
Our study is the first study to evaluate the impacts of watching TV on the cognition of older adults living in socioeconomically deprived areas of South America. Although our study has a shorter follow-up period (2 years) compared to most of the prospective studies on the topic literature [ranging from 2 to 9 years follow-up ( , , , )], it is the only prospective study investigating the incidence of dementia.
The average Brazilian older adult is poor, low educated and with reduced leisure options compared to elder populations in developed countries, where most of the studies on healthy aging take place. In developed countries, the most frequent leisure activities among older adults seem to be reading, enrolling in courses, visiting museums, theater, or movies ( – ). In Brazil, the most frequent leisure activities are less complex: watching TV and listening to the radio ( ). These activities are not likely to have the same protective effect on cognition as more complex activities might have. Another important reason for not engaging in other leisure activities is purchase power. Mobility might play a role given 35% of Brazilians aged 60+ reported difficulties walking in streets, and 4% reported never living the house ( ).
Watching TV is a highly popular leisure activity among the population studied and among other older adults living in socially deprived urban areas of Latin America and other underserved regions. For most of these older adults watching TV is the only leisure option. The good news supported by our data is that watching TV did not predict a decline in cognition, as pointed out by a Chinese cohort ( ). Thus, our data do not support advocacy against watching TV among low-income and low-educated older adults. However, in our study, watching TV did not protect from cognitive decline either.
The literature shows that leisure activities can prevent cognitive decline in older adults ( , ) and should be encouraged through public policies. Thus, it is imperative to increase other leisure activities beyond watching TV for low-income and low-educated older adults. Leisure activities that they can engage in and benefit from.
Performing different types of activities, rather than always the same activity, seems to play a role in preserving cognitive function ( ), another strong point to support the promotion of leisure activities for older adults of socially deprived areas. In the coming decades, developing countries will experience the highest burden of dementia and more than entertainment, public policies to promote leisure activities might be a strategy to alleviate this burden shortly.
## Data Availability Statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics Statement
The studies involving human participants were reviewed and approved by Brazilian National Committee for Ethics and Research (CONEP-Brazil). The patients/participants provided their written informed consent to participate in this study.
## Author Contributions
VD and MS designed the study, supervised data collection, planned and carried out the statistical analyses, and reviewed drafts the paper. MS drafted the paper. LF and CN contributed to the interpretation of the results and drafting of the paper. All authors approved the final version of the manuscript and agreed to be accountable for the content of the work.
## Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
|
Background: There are many methods to diagnose diabetic autonomic neuropathy (DAN); however, often, the various methods do not provide consistent results. Even the two methods recommended by the American Diabetes Association (ADA) guidelines, Ewing's test and heart rate variability (HRV), sometimes give conflicting results. The purpose of this study was to evaluate the degree of agreement of the results of the Composite Autonomic Symptom Score 31 (COMPASS-31), skin sympathetic reaction (SSR) test, Ewing's test, and HRV in diagnosing DAN.
Methods: Patients with type 2 diabetes were recruited and each received the COMPASS-31, SSR, Ewing's test, and HRV for the diagnosis of DAN. Patients were categorized as DAN(+) and DAN(–) by each of the tests. Kappa consistency tests were used to evaluate the agreement of diagnosing DAN between any two methods. Spearman's correlation test was used to evaluate the correlations of the severity of DAN between any two methods. Receiver operating characteristic (ROC) analyses were used to evaluate the diagnostic value and the cutoff value of each method.
Results: A total of 126 type 2 diabetic patients were included in the study. The percentages of DAN(+) results by HRV, Ewing's test, COMPASS-31, and SSR were 61, 40, 35, and 33%, respectively. COMPASS-31 and Ewing's test had the best agreement for diagnosing DAN (κ = 0.512, p < 0.001). COMPASS-31 and Ewing's test also had the best correlation with respect to the severity of DAN ( r = 0.587, p < 0.001). Ewing's test and COMPASS-31 had relatively good diagnostic values (AUC = 0.703 and 0.630, respectively) in the ROC analyses.
Conclusions: COMPASS-31 and Ewing's test exhibit good diagnostic consistency and severity correlation for the diagnosis of DAN. Either test is suitable for the diagnosis of DAN and treatment follow-up.
## Introduction
Diabetic autonomic neuropathy (DAN) is one of the most common, chronic complications of diabetes mellitus (DM) ( ), and DM is also the most common cause of chronic automatic neuropathy ( ). Patients with DAN may present with dry skin with poor nutrition, persistent scarring, diarrhea/constipation, erectile dysfunction, resting tachycardia/bradycardia, orthostatic hypotension, painless myocardial ischemia, myocardial infarction, malignant arrhythmia, and even sudden cardiac death ( ). The reported prevalence of DAN in diabetic patients is 17–73% ( – ), with the wide range attributable to factors such as different diagnostic criteria, age, and race. Since 2012, Ewing's test and heart rate variability (HRV) have been recommended by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD) for diagnosing diabetic cardiac autonomic neuropathy (DCAN) ( , ). However, in clinical practice, we have found that these two methods frequently provide different results in the same patient. In addition, Ewing's test and HRV are time-consuming and require advanced equipment to perform, which makes them relatively difficult to perform in practice.
The Composite Autonomic Symptom Score 31 (COMPASS-31) is a self-assessment instrument published by the Mayo Clinic in 2012 and includes 31 items assessing six domains of autonomic function ( ). It is more convenient than its predecessors, the Autonomic Symptom profile composed of 169 items (ASP 169) and the COMPASS-72, and it has been proven to be suitable for the assessment of DAN or other small fiber polyneuropathies (SFPNs) ( , ). Skin sympathetic reaction (SSR) is also a common and simple method to evaluate the function of sympathetic nerves and is a useful electrophysiological test for the early diagnosis of diabetic neuropathy ( ). However, it is unclear whether COMPASS-31 or SSR is consistent with Ewing's test or HRV in diagnosing DAN.
Thus, the purpose of this study was to evaluate the degree of agreement of COMPASS-31, SSR, Ewing's test, and HRV in diagnosing DAN.
## Materials and Methods
### Subjects
Patients with type 2 DM were recruited from the Department of Endocrinology, Nanfang Hospital, between September 2017 and August 2018. The inclusion criteria for this study were: DM diagnosed based on the 1999 World Health Organization (WHO) criteria ( ) and 18–80 years old. The exclusion criteria were: (1) peripheral neuropathy; (2) history of stroke; (3) history of heart disease; (4) loss of any extremities; and (5) unable to stand without assistance. Patient information collected included age sex, course of DM, family history, smoking history, drink history, medication, and body mass index (BMI). Laboratory testing included measurement of hemoglobin A1c (HbAlc) level and low-density lipoprotein (LDL) level. All patients received the four tests being evaluated: COMPASS-31, SSR, Ewing's test, and HRV. Patients were categorized as DAN(+) or DAN(–) based on the individual test results, as described below.
This study was approved by the Ethics Committee of Nanfang hospital (NFEC-2018-115), and all participants provided written informed consent.
### Composite Autonomic Symptom Score 31
As there was not a formal Chinese version and norm of the COMPASS-31, we recruited 84 healthy volunteers as a control group. We scored the COMPASS-31 results for each patient and healthy control subject by translating the English version into Chinese and inquiring every examinee rather than reading the scale by examinees. The original score for each domain and the weighted total score were recorded ( ). The upper 95% confidence interval (CI) of the weighted total score of the control group was defined as the cutoff value for diagnosing DAN, and patients with a total score above the cutoff value were considered DAN(+).
### Skin Sympathetic Reaction
SSR was performed following the standard procedure described in the literature ( ). The latencies and amplitudes of initiation were recorded with an electromyography machine (Dantec Keypoint 9033A, Copenhagen, Denmark). Abnormalities were defined by reference values established by the Peking Union Medical College Hospital for healthy Chinese people. An upper extremity latency >1,512 ms or an amplitude <484 μV was considered abnormal; a lower extremity latency >2,230 ms or an amplitude <364 μV was considered abnormal. The number of abnormal extremities was the total SSR score, which ranged from 0 to 4. Patients with a score ≥1 were considered DAN(+).
### Ewing's Test
Ewing's test has been used to evaluate the autonomic function of diabetic patients since the 1980s ( , ). It consists of five tests. Three are predominantly parasympathetic tests: mean max/min ratio during three Valsalva maneuvers; mean max/min heart rate (HR) difference during six deep breaths; and the 30:15 ratio after standing. Two are predominantly sympathetic tests: the systolic blood pressure (BP) decrease after standing and the diastolic BP increase during a sustained handgrip. As the handgrip test is difficult to perform, only the other four tests are usually performed in clinical practice. The four tests (excluding the handgrip test) were performed using an electromyography machine (Dantec Keypoint 9033A, Copenhagen, Denmark) and a non-invasive BP monitoring system (Task Force Monitor, Finometer PRO, Netherlands). All patients were asked to refrain from caffeine, to not take β-blocker or angiotensin-converting enzyme inhibitor (ACEI) medications on the day of the testing, and to eat only a light breakfast. The tests were performed between 9:00 a.m. and 11:00 a.m. in a warm, quiet room. There was a 2-min rest period after each individual test. The results for each test were classified as normal, borderline, and abnormal and scored as 0, 0.5, and 1, respectively ( ). Thus, the total Ewing's score ranged from 0 to 4. Patients with a score ≥2 were classified as DAN(+) based on the ADA guidelines ( ).
The scored standards of each item for Ewing's test and HRV.
HRV, heart rate variability; SDNN, standard deviation of the normal-to-normal interval; SDANN, standard deviation of the average NN interval; RMSSD, square root of the mean squared differences of successive NN intervals; pNN50, proportion of successive NN intervals >50 ms; LF, low-frequency power; HF, high-frequency power .
### Heart Rate Variability
HRV has been used to evaluate the autonomic function of diabetic patients for many years ( ). The standard deviation of the normal-to-normal (NN) interval (SDNN), the standard deviation of the average NN interval (SDANN), the square root of the mean squared differences of successive NN intervals (RMSSD), the proportion derived by dividing the number of interval differences of successive NN intervals >50 ms by the total number of NN intervals (pNN50) in the time-domain analysis, and the low-frequency (LF) and high-frequency (HF) power in the frequency-domain analysis are recommended as indicators for the diagnosis of DAN by the ADA ( ). The sequence of the NN intervals in an entire 24-h period was recorded after the Ewing's test for all patients using a Holter recorder (Diagnostic Monitoring Software 300-4AL, Nevada, USA). The six recommended items were classified as normal and abnormal and scored as 0 and 1, respectively ( ). The sum of the six scores is the total HRV score, which ranges from 0 to 6. Patients with a total HRV score ≥2 were classified as DAN(+), as recommended by the ADA ( ).
### Statistical Analysis
All statistical analyses were performed by using SPSS version 20.0 software (IBM Corp., Armonk, NY, USA). Data were expressed as the mean ± standard deviation or the median and interquartile range (IQR). Independent-samples non-parametric tests were performed to compare the results of diabetic patients and controls. Kappa consistency tests were performed to evaluate the consistency of diagnosing DAN between any two methods. The correlation of DAN severity between the different methods was evaluated with Spearman's correlation test. Receiver operating characteristic (ROC) analyses were used to evaluate the diagnostic value and the cutoff value of each method. A value of p < 0.05 was considered statistically significant.
## Results
A total of 126 patients with type 2 DM were included in the study. The demographic data, physical and biochemical characteristics, and autonomic nervous function evaluation of the DM group and the control group are summarized in . The cutoff value of the COMPASS-31 for diagnosing DAN in this study was 21.4, which was calculated from the control group. The numbers of DAN(+) patients diagnosed by HRV, Ewing's test, COMPASS-31, and SSR were 77 (61%), 51 (40%), 44 (35%), and 41 (33%), respectively. This result suggested that HRV had a higher diagnostic rate than the other three methods.
The baseline characteristics of the diabetic patient group and healthy controls.
The health control group was compared with the total diabetic patients group. Patients were divided into a DAN(+) and a DAN(–) group by COMPASS-31. The DAN(+) subgroup was compared with the DAN(–) subgroup .
SDNN, standard deviation of the normal-to-normal interval; SDANN, standard deviation of the average NN interval; RMSSD, square root of the mean squared differences of successive NN intervals; pNN50, proportion of successive NN intervals >50 ms; LF, low-frequency power; HF, high-frequency power .
Based on the COMPASS-31 results, the patients were divided into DAN(+) and DAN(–), and their characteristics are compared in . Sex proportion, course of DM, family history, smoking, drinking, medication, BMI, and the HbAlc and LDL levels were not different between the two groups (all, p > 0.05). However, the mean age of DAN(+) patients was greater than that of DAN(–) patients ( p < 0.05). With respect to autonomic nervous function evaluation, DAN(+) patients had significantly higher Ewing's test scores and SSR scores than DAN(–) patients, but not HRV scores.
The agreement of diagnosing DAN between any two methods is shown in . The 2 × 2 tables are shown on the bottom left corner, and the kappa consistency coefficients are shown on the top right corner. The results indicated that COMPASS-31 and Ewing's test had the best consistency for diagnosing DAN (κ = 0.512, closest to 0.75). The correlations of the severity of DAN scored by each of the methods are shown in . The results indicated that COMPASS-31 and Ewing's test exhibited the best correlation ( r = 0.587) with respect to diagnosing DAN severity.
The kappa consistency tests of the four methods for diagnosing diabetic autonomic neuropathy (DAN).
The kappa coefficients and p-values are shown on the top right corner. The original cases for the consistency of DAN between any two methods are shown in the 2 × 2 tables on the bottom left corner .
COMPASS-31, Composite Autonomic Symptom Score 31; HRV, heart rate variability; SSR, skin sympathetic reaction .
The correlations of diabetic autonomic neuropathy (DAN) severity between any 2 methods. COMPASS-31 and Ewing's test exhibited the best severity correlation. (A) Ewing's test versus COMPASS-31. (B) HRV versus COMPASS-31. (C) SSR versus COMPASS-31. (D) HRV versus Ewing's test. (E) SSR versus Ewing's test. (F) SSR versus HRV.
The ROC analyses of each method by using HRV as the dependent variable are shown in . Ewing's test and COMPASS-31 had relatively good diagnostic values [area under the curve (AUC) = 0.703 and 0.630, respectively] which were not inferior to the combined diagnosis. The best cutoff values were 1.75 for Ewing's test ( ) and 14.72 for COMPASS-31 ( ). The ROC analyses of Ewing's test and COMPASS-31 calculated by Bootstrap also had good AUC and confidence interval ( , ).
Receiver operating characteristic (ROC) analyses for the diagnosis of diabetic autonomic neuropathy (DAN). Model A ( black ) represents the ROC curve of Ewing's test; model B ( red ) represents the ROC curve of COMPASS-31; model C ( green ) represents the ROC curve of SSR; model D ( deep blue ) represents the ROC curve of Ewing's test combined with COMPASS-31 and SSR; model E ( light blue ) represents the ROC curve of Ewing's test combined with COMPASS-31; model F ( purple ) represents the ROC curve of COMPASS-31 combined with SSR; model G ( yellow ) represents the ROC curve of Ewing's test combined with SSR.
(A) Receiver operating characteristic (ROC) curve of Ewing's test to diagnose diabetic autonomic neuropathy (DAN). The optimal cutoff value for classification is indicated by a multiplication symbol annotating this threshold value followed by specificity and sensitivity. AUC , area under curve. Cutoff value = 1.750, AUC = 0.703, 95% CI = 0.613–0.794, specificity = 79.6%, sensitivity = 53.3%. (B) ROC curve of Ewing's test to DAN as calculated by Bootstrap. The blue shading denotes the Bootstrap-estimated 95% confidence interval with the AUC. AUC = 0.708, 95% CI = 0.627–0.793.
(A) Receiver operating characteristic (ROC) curve of COMPASS-31 to diagnose diabetic autonomic neuropathy (DAN). The optimal cutoff value for classification is indicated by a multiplication symbol annotating this threshold value followed by specificity and sensitivity. AUC , area under curve. Cutoff value = 14.72, AUC = 0.630, 95% CI = 0.531, 0.729, specificity = 63.3%, sensitivity = 64.9%. (B) ROC curve of Ewing's test to DAN as calculated by Bootstrap. The blue shading denotes the Bootstrap-estimated 95% confidence interval with the AUC. AUC = 0.632, 95% CI = 0.520–0.721.
## Discussion
There are many methods to evaluate autonomic nerve function in clinical practice ( ), but the results of the different methods are often inconsistent. Even Ewing's test and HRV results, the two methods recommended by the ADA guidelines for diagnosing DCAN, are often inconsistent in the same patient. Past studies have compared two different methods for assessing autonomic nerve function ( , ), but only Singh et al. ( ) compared the results of COMPASS-31, SSR, Ewing's test, and HRV. The authors divided the patients with diabetes into a definite DCAN, early DCAN, and a no DCAN group using the COMPASS-31 results and then compared the results of the four methods in these three groups. In our study, we also evaluated the autonomic nerve function of diabetic patients with these four methods, but we focused on evaluating the degree of agreement of the four methods in diagnosing DAN.
Because there was not a formal Chinese version and norm of the COMPASS-31, we recruited 84 healthy volunteers as a control group. We found that the COMPASS-31 scores of the control group were not normally distributed and had no correlation with age (data not shown). Furthermore, the average age of the control group was younger than that of the diabetic group. The control group was not large enough and not balanced across age groups. It was the main limitation of this study. In any case, there must be an existing cutoff value. Our cutoff value is slightly lower than that used in a prior study (21.4 vs. 28.7) ( ), but is closer to the cutoff value (14.72, seen in ) in the ROC analysis. Of course, more precise cutoff values of the COMPASS-31 for diagnosing DAN will require the recruitment of a large number of volunteers in the future.
We used the kappa consistency test to compare the degree of agreement of diagnosing DAN between any two methods. It is generally considered that if the kappa is >0.75, then the agreement between the two methods is good, while a kappa of <0.4 means that agreement is poor; a kappa between 0.4 and 0.75 is considered to indicate moderate agreement. Our results showed that the kappa value of the COMPASS-31 and Ewing's test was the greatest (0.512), indicating that these two tests had the best agreement. All of the other kappa values were <0.4. In addition, in the correlation analysis of DAN severity, the best correlation was found between the COMPASS-31 and Ewing's test ( r = 0.587). These results indicate that the COMPASS-31 results are consistent with those of Ewing's test, which is recommended by the ADA guidelines. Furthermore, the ROC analyses also showed that Ewing's test and COMPASS-31 individually had good AUC as well as combined for diagnosing DAN ( ). Importantly, the COMPASS-31 is simple and easy to perform in clinical practice, unlike the complicated Ewing's test.
Another advantage of the COMPASS-31 is that it has a continuous range from 0 to 100, which provides a more accurate evaluation of the severity and progression of DAN. Another study has validated that the COMPASS-31 is useful as an initial screening tool for SFPNs ( ). In that study, the average COMPASS-31 scores of SFPN(+) and SFPN(–) patients were 38.8 and 19.6, respectively ( n = 28 and 38, respectively). These scores were both slightly higher than the scores of our DAN(+) and DAN(–) groups ( ). This difference might be due to ethnic differences or differences in the methods of grouping. In addition, our translation of the COMPASS-31 may also have impacted the results ( ).
HRV is also recommended by the ADA for diagnosing DCAN, but it showed very poor agreement with the other methods in the present study. In , the patients were divided into a DAN(+) and a DAN(–) group by COMPASS-31. Theoretically, all six HRV items of the DAN(+) group should be lower than those in the DAN(–) group. But in this study, the RMSSD in the DAN(+) group was higher than that in the DAN(–) group, and the other five items showed no significant differences between the two groups. On the contrary, the Ewing and the SSR scores of the DAN(+) group were significantly higher than those of the DAN(–) group. These results also indicate that the agreement between HRV and COMPASS-31 is poor. There were 77 out of 126 diabetic patients (61%) diagnosed as DAN(+) by HRV, and thus the diagnostic rate was much higher than those of the other three methods. We thought that it was because of the poorer discrimination for the severity evaluation of DAN by HRV. We all know that the normal reference value for HRV decreases with age ( ). The HRV score has six items. When the HRV is used for diagnosing DAN, a value far below the normal reference value was chosen as a threshold for each item, and so each item is classified as normal or abnormal. Thus, the HRV score ranges from 0 to 6 (seven grades), which is less than the nine grades of the Ewing's test score. In this situation, some diabetic patients with only slight autonomic nervous abnormalities may be over-diagnosed with DAN. In our study, most DAN(+) patients (62/77) diagnosed by HRV had a score of 2 ( ), which was the lowest threshold for diagnosing DAN. We can speculate that if the six items of HRV were also classified as normal, borderline, and abnormal and scored as 0, 0.5, and 1, respectively, like the Ewing's test, some DAN(+) patients might be scored 1.5 or 1 and would then be categorized as DAN(–). With this method, the diagnosis of DAN with HRV might be improved and the HRV results would be more accurate.
SSR has a similar defect as HRV as the SSR score ranges from 0 to 4 (five grades); SSR exhibited a worse differentiation of DAN than did HRV. Moreover, the disagreement between SSR and Ewing's test and other methods might also be due to anatomical differences of the peripheral autonomic nerves and cardiac autonomic nerves ( , ). SSR is a measure of peripheral sympathetic function, while three of the four Ewing's test items are a measure of vagus function. However, in the present study, we did not find that DM would tend to involve the peripheral autonomic nerve or the cardiac autonomic nerve first.
There are also some other limitations of the current study that should be considered. Skin biopsy and determination of the intra-epidermal nerve fiber density (IENFD) is the gold standard for the diagnosis of DAN ( ), and this was not done. As such, we could not calculate the sensitivity and specificity of each method in the diagnosis of DAN and, thus, not directly evaluate which method is the most accurate. Other methods or questionnaires for the evaluation of autonomic nerve function, such as quantification of nerve fibers in corneal confocal microscopy (NF-CCM) ( ), the small fiber neuropathy and symptoms inventory questionnaire (SFN-SIQ), and the small fiber neuropathy screening list (SFNSL) ( , ), were not evaluated in this study. A comparison of their results with those of COMPASS-31 would be useful. Lastly, creation and validation of a formal Chinese version and normal references of COMPASS-31 should be done to examine the results of this study.
In conclusion, in the present study, the results of COMPASS-31, SSR, Ewing's test, and HRV were compared with respect to the diagnosis of DAN in diabetic patients. Of the four methods, COMPASS-31 and Ewing's test exhibited the best diagnostic agreement and severity correlation, and they had good diagnostic values in the ROC analysis. As COMPASS-31 is a simple, economical, and practical clinical questionnaire, and is much easier to perform than the Ewing's test, it can be used for the diagnosis and follow-up of diabetic patients with DAN.
## Data Availability Statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.
## Ethics Statement
The studies involving human participants were reviewed and approved by Ethics Committee of Nanfang Hospital (NFEC-2018-115). The patients/participants provided their written informed consent to participate in this study.
## Author Contributions
YP and Y-sL contributed equally to the paper and wrote the first draft. YP, S-yP, and L-lX conceptualized the study design. Y-sL, M-yW, C-nC, C-qL, A-qJ, C-xL, YW, and GT participated in the literature search, data acquisition, performed the data analysis, and statistical analysis. S-yP and L-lX finalized the manuscript. All authors participated in the discussion and approved the final version of the manuscript.
## Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
|
Combining functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) enables a non-invasive investigation of the human brain function and evaluation of the correlation of these two important modalities of brain activity. This paper explores recent reports on using advanced simultaneous EEG–fMRI methods proposed to map the regions and networks involved in focal epileptic seizure generation. One of the applications of EEG and fMRI combination as a valuable clinical approach is the pre-surgical evaluation of patients with epilepsy to map and localize the precise brain regions associated with epileptiform activity. In the process of conventional analysis using EEG–fMRI data, the interictal epileptiform discharges (IEDs) are visually extracted from the EEG data to be convolved as binary events with a predefined hemodynamic response function (HRF) to provide a model of epileptiform BOLD activity and use as a regressor for general linear model (GLM) analysis of the fMRI data. This review examines the methodologies involved in performing such studies, including techniques used for the recording of EEG inside the scanner, artifact removal, and statistical analysis of the fMRI signal. It then discusses the results reported for patients with primary generalized epilepsy and patients with different types of focal epileptic disorders. An important matter that these results have brought to light is that the brain regions affected by interictal epileptic discharges might not be limited to the ones where they have been generated. The developed methods can help reveal the regions involved in or affected by a seizure onset zone (SOZ). As confirmed by the reviewed literature, EEG–fMRI provides information that comes particularly useful when evaluating patients with refractory epilepsy for surgery.
## Introduction
Localization of the epileptic generators is one of the striking topics in the treatment of epilepsy. It is still a challenge to find the precise brain regions of epileptic foci. Simultaneous EEG and fMRI data recordings are two modalities that can expose the brain regions with changes in metabolism and blood flow in response to epileptic spikes seen in the EEG, which are presumably accordant to the origin of epileptic discharges. fMRI which has a relatively poor temporal resolution but excellent spatial resolution is proper for localizing the brain regions with neuronal activity changes compared to the sham. This change is accompanied by a modification of the ratio of the concentration of oxy- and deoxy-hemoglobin in the blood, measured through the blood oxygen level-dependent (BOLD) effect ( , ). In contrast, EEG has a high temporal resolution that makes it capable of measuring the neuronal currents directly from the scalp in the range of milliseconds but poor spatial resolution, which causes difficulty in determining the exact location of the current sources. The limitations of EEG are the deficiency in precise information of individual geometry and conductivity and the limited number of recording channels. Therefore, simultaneous recording of EEG and fMRI data provides a useful tool in using the two techniques' complementary features and overcoming the spatial limitations of EEG and fMRI's temporal boundaries.
An area where EEG and fMRI modalities have considerable clinical relevance is the pre-surgical evaluation in patients with epilepsy. In many patients with drug-resistant focal epilepsy undergoing surgery, standard magnetic resonance imaging (MRI) scans cannot visualize an exact source of epileptic seizures. Therefore, an invasive stereo-EEG analysis is required. However, simultaneous EEG and fMRI recordings offer a non-invasive alternative that can be a valuable approach for the localization of brain regions generating interictal epileptiform activity. This recording approach has become a useful tool for exploring ictal and interictal epileptic activity to reveal the epileptic foci and specify the relationship between hemodynamic changes and epileptic activity ( – ). EEG and fMRI are complementary for the localization of epileptic spike areas, but they can indicate different activity regions. Also, SEEG measures confirm EEG and fMRI results, although the concordance of simultaneous EEG–fMRI is not as good as the concordance between either one and SEEG ( ). Unlike the general fMRI studies involving sensory, motor, and cognitive functions, the control and experimental conditions are determined based on the task. In epilepsy studies, these conditions are determined based on the absence and presence of epileptic discharges on the baseline of the EEG signal. So, in this context, the EEG signal is necessary for the analysis of fMRI data. The epileptic analysis of EEG–fMRI data is conventionally based on the identification of IEDs on EEG to create a regressor representing the effects of interest for a GLM analysis. Also, the model of epileptic activity is generally obtained by the convolution of EEG events as the stick functions of unitary amplitude with a predefined model of the event-related fMRI response, represented by the HRF. Finally, the activity maps showing the regions of significant IED-related change are obtained through the voxel-wise fitting of the model and application of appropriate statistical thresholds ( , , – ). Generally, BOLD responses are much less visible in patients with focal epilepsy compared to patients with generalized epilepsy ( , ). Also, the posterior head regions are almost as involved as frontal regions in the BOLD response of patients with generalized epilepsy ( ).
This paper reviews majority of the interictal studies presented with the aim of epileptic focus localization. For this purpose, the articles were classified based on their analysis method and reviewed in each part sorted by their publication date to reveal the trend of works in all the covered methods. First, we will present the primary concepts of epileptic source localization and analyze EEG inside the MRI scanner covered by associated studies. We will then review the localization methods and their clinical results obtained from patients with various types of epilepsy, showing the capability of each method for the pre-surgical evaluation of patients with epilepsy in comparison with the other methods. Finally, we discuss the complex issue of interpreting the result of EEG–fMRI in epilepsy studies. In this review, we tried to strike a balance between method-based studies and clinical outcomes. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flowchart below shows the organization of the extracted articles ( ). This review includes all EEG–fMRI studies focused on epileptic focus localization, which we have found by searching the related keywords in Google Scholar, PubMed dataset, Scopus, and ResearchGate. All studies are done interictally.
PRISMA flowchart showing the classification of the extracted articles related to the epileptic focus localization through simultaneous EEG–fMRI recording. Web of Science, Google Scholar, Pub Med, Scopus dataset, and ResearchGate websites and the references of the research and review articles. Not relevant to research question, aims and objectives, or old articles. Intracranial or ictal studies.
## Primary Concepts
### Signal Quality and Pre-processing
Recording EEG in the MR scanner requires non-magnetic electrodes and an MR-compatible amplifier system that transmits the amplified EEG outside the scanner. The patient must be as immobile as possible during the session. The magnetic gradient of the MR scanner induces large artifacts in the EEG. After the scanning session is finished, the artifacts will be removed by software to retrieve an EEG of reasonable quality (to retrieve a “clean” EEG); consequently, it allows us to mark the time of epileptic events. The next step is to build a mathematical model of what the BOLD signal should be at the voxels involved in the event. Voxels that took part in the event should have changes in their time courses as a result of each event in a predictable manner (in concordance with the HRF). Finally, the time course of every voxel is analyzed, and the voxels that have a correlative time course with the model are identified. Such voxels are either involved in the generation of the marked epileptic events in the EEG or a consequence of the event ( – , – ).
Although the simultaneous recording of EEG–fMRI is one of the most valuable non-invasive tools for studying brain activity, it remains challenging to reach a high-quality signal of EEG and fMRI recorded simultaneously. Generally, simultaneous EEG–fMRI data are affected by various confounding factors and artifacts. The most important effective factor on the quality of the EEG and MRI recording is the immobilization of the head and electrode wires that can be reached by a plastic bag full of small polystyrene spheres. Besides, MRI-compatible sandbags are well-suited to immobilize the electrode wires on the way to the amplifiers ( ).
Among the various artifacts, the artifact of MR gradient switching and ballistocardiogram (BCG) remain the major challenges in simultaneous EEG–fMRI study that make the EEG signal hard to interpret. Removing the fMRI scanner artifact is essential for the successful EEG–fMRI analysis. On the other hand, the presence of the BCG artifact does not necessarily lead to a complete failure in identifying epileptic events ( – ). Yet, eliminating the BCG artifact improves the readability of the EEG and is useful for detecting subtle events like small epileptic discharges ( , ).
To reduce the MR artifacts, one of the effective ways is the blind source extraction (BSE) algorithm followed by the averaging-and-subtraction method ( ). Also, Amini et al. ( ) proposed an approach based on generalized eigenvalue decomposition (GEVD) and median filtering, which demonstrated a considerable improvement in reducing MR artifacts compared to the conventional methods.
For eliminating BCG artifacts, two well-known methods are independent component analysis (ICA) and principal component analysis (PCA) which keep the spikes intact. However, ICA usually makes a better distinction between artifact and non-artifact components and performs stronger in artifact removal while preserving the spikes ( ). Also, for a significant number of events, the subtraction filter is better than the Fourier filter in producing distortion but impairs the readability of EEG because of leaving large remaining artifacts inside the frames ( ). Another approach for BCG artifact correction is multiple-source correction (MSC) ( ). First, the source of IEDs is extracted from the EEG data collected outside the scanner to avoid the distortion of EEG data during the correction of BCG artifacts. Then, the topographies of the BCG artifacts defined based on the EEG data acquired inside the scanner are added to the alternative model of IED sources. The combined source model is applied inside the EEG data. Lastly, the artifact signal is subtracted from the EEG without considerable distortion of the IED topography. Compared with the traditional averaged artifact subtraction (AAS) method, the MSC approach has improved the ability of IED detection, especially when the BCG artifact is correlated and time-locked with the EEG signal produced by the focal brain activity of interest ( ).
In the study of Körbl et al. ( ), 18 patients with epilepsy were studied with the common methods of BCG removal and the conventional method of using marked IEDs to perform event-related analysis. Besides, nine patients used the moiré phase tracking (MPT) marker to discard suspicious IEDs synchronous with the BCG before the event-related analysis. The results demonstrated no significant difference between the two groups. However, the IED timing distribution was significantly related to the cardiac cycle in 11 of the 18 patients recorded without the MPT marker, but only two of the nine patients with the marker. In patients recorded without the marker, failing to discard suspicious IEDs led to more distant activations and more inaccurate fMRI maps.
In some of our previous works ( – ), the MRI gradient switching artifact was removed by using the fMRIb algorithm ( ), which first increases the sampling rate to 20 kHz and then applies a low-pass filter at 60 Hz. The fMRIb toolbox also removed the BCG artifact associated with cardiac pulsations. shows the EEG signals inside the scanner before and after the artifact removal procedure.
EEG signal recorded inside the MR scanner: (A) before and (B) after the elimination of gradient and BCG artifacts ( ).
One of the other factors that affect the quality of the BOLD images is the signal loss due to variations in magnetic susceptibility, which alters the local magnetic field experienced by the subject's brain. For reducing this signal loss and increasing the ability to detect significant regions of BOLD signal changes, z-shimming is a practical technique. However, the question is whether this signal loss will be a limiting factor to identify the spike-related BOLD signal changes in patients with epilepsy. To find the actual effect of z-shimming in the results of identifying the spike-related BOLD responses, Bagshaw et al. ( ) designed an experiment in which eight patients with temporal lobe epilepsy (TLE) underwent an EEG–fMRI session, and z-shimming was applied to their BOLD images. After comparing the intensities between z-shimmed and standard images and creating BOLD activation maps from the two sets of functional images using the times of spikes extracted from the EEG, it was found that the mean signal of the temporal lobes (TLs) increased 45.9 ± 4.5% as a result of z-shimming. Also, the percentage of the TL voxels above the brain intensity threshold increased from 66.1 ± 7.6% to 77.6 ± 5.7%. However, this increase in the signal did not make any significant differences in the statistical maps. So, the signal loss is not a limiting factor for identifying the spike-related BOLD responses in patients with TLE.
The magnetic field strength of the MRI scanners could be an effective factor for the reproducibility of the EEG–fMRI results, which makes the results reliable as a clinically valuable method. This issue was addressed by Gholipour et al. ( ). Fifteen epilepsy patients, including seven who had one 1.5T and one 3T EEG–fMRI scans and eight who had two 3T EEG–fMRI scans were studied. Then, the IED-related BOLD responses acquired from equal numbers of the IED events were compared between the scans of each patient. In four of the 15 patients, the results of the comparison between two sets of scans acquired from 1.5T and 3T scanners showed more significant responses in 3T scans just because of the higher magnetic field strength. Also, for the eight patients, the results of comparisons between two consecutive 3T scans showed reproducible responses in five cases with similarity in the visual pattern of activation and partly differences in terms of maximum t -score and cluster size in some cases.
Reduction of motion interference has been considered in some studies. In a study of Klovatch-Podlipsky et al. ( ), a method based on MR-compatible dual-array EEG (daEEG) was proposed to reduce the motion interference in the EEG–fMRI recordings. The EEG electrodes were organized into two sets of nearly orthogonally intersecting wire bundles, and virtual bipolar measurements were obtained both along and across the bundles. By applying ICA on the EEG data and using the fact that only motion interference is influenced by the cable orientation and is more prominent in across-bundle measurements, daEEG allows suppression of both BCG and non-BCG interference from the data. Testing this method in 10 patients with epilepsy and comparing the results with those of the Optimal Basis Set (OBS) ( – ) showed more detected spikes after using daEEG than after OBS in nine of the 10 patients.
In the GLM analysis, settings and preprocesses are also important for the localization of the epileptic sources and can be optimized. For instance, considering some video-EEG physiological confounds like eye blinks and swallowing as additional regressors can reveal further IED-related BOLD clusters which might be part of the epileptic networks ( ). Mikl et al. in ( ) used the EEG–fMRI data of 13 patients with pharmacoresistant epilepsy and an excellent surgical outcome and performed 240 statistical analyses for each patient including all possible combinations of the used preprocessing and GLM settings. The results showed that preprocessing type, i.e., mainly the basic pipeline, or cardiac artifact correction does not affect GLM-based analysis results. The IED stimulation time course shifted 2 s earlier than positions from the EEG description, and also the massive filtering of artifact (24 movement regressors, signals from white matter and CSF, and global signal) are considered as the optimal preprocessing pipeline. Also, they reported that the canonical HRF as the basis function led to the best results of GLM analysis in agreement with some previous studies like ( , ). However, its superiority over more flexible basis functions may be due to the used concordance measure. It is noticeable that in another study, Lemieux et al. ( ) used a more flexible model of the event-related response, a Fourier basis set, to identify regions of activation corresponding to non-canonical responses associated with individual IED in 30 experiments of patients with focal epilepsy. They reported that non-canonical activations were almost always remote from the presumed generator of epileptiform activity. Thus, the BOLD response to IED is primarily canonical and the non-canonical responses may represent a number of phenomena, including artifacts and propagated epileptiform activity.
### HRF and Spike Characteristics
In the common methods of EEG–fMRI analysis, a particular HRF is usually used for all patients. For example, the GLM framework models a prior knowledge of hemodynamic response in the design matrix and then explains the measured data by parameter estimation ( , – ). However, the real BOLD response to IEDs for each patient can be significantly different from the healthy controls ( , , ). Even in a specific patient, the shape of HRF varies with different brain areas and also is time-variant in each area ( , ). The delay of the estimated function in a patient is different from those of the common theoretical models ( , ). Using patient-specific HRF increases the detection sensitivity of epileptic spikes in EEG–fMRI ( ). For instance, van Houdt et al. ( ) used a finite impulse response approach for estimating the HRF from a dataset including 42 IED sets acquired in 29 patients and observed that more brain regions were active consistent with the EEG focus compared to the classical approach supposing a fixed HRF for each voxel in the brain (26 vs. 16).
Using multiple HRFs with peaks ranging from 3 to 9 s increases the BOLD response compared with using the standard HRF alone ( , ). It was shown that the standard HRF that peaked at 5.4 s was more proper in detecting positive BOLD responses, and the HRFs that peaked later than the standard were more accurate for negative BOLD responses ( ).
It has been observed that the results of EEG–fMRI analysis are influenced by the evaluation of the EEG signal and the scanning techniques more than the HRF model. Thus, although the HRF model influences the results of EEG–fMRI analysis, it may not be the main parameter in clinical practice ( ).
Regarding the epileptic spikes in the EEG signal, it is revealed that the activation in BOLD response from EEG–fMRI analysis depends on the number of IEDs occurring during data acquisition ( , ). However, the spiking rate is not the only influencing factor in the presence of the BOLD response. BOLD responses were seen in patients who had very few spikes, and a lack of response was noted in patients who had a high spiking rate ( ).
Another issue is the spike identification that can be done automatically ( , ) or by an expert. According to the study of Pedreira et al. ( ), the automated spike-sorting algorithms for the classification of IEDs increase the value of EEG–fMRI analysis and mapping of IED-related BOLD responses ( ). However, there is uncertainty in the results of spike identification because of the false detections and missed events. Huiskamp et al. ( ) evaluated the impact of these two errors on the significance of the expected fMRI activation and revealed that the effect of missed events is larger in deteriorating the expected results. According to this study, although the uncertain spikes cause errors in IED-related BOLD responses, if they are considered as the events and included in the analysis, the responses will be closer to the expected results.
A sample of visual and algorithmic classification of the IEDs. (A) The result of visual classification from the bipolar montage (64 channels) of EEG recorded inside the scanner is performed by an expert. (B) The results of EEG–fMRI analysis, based on visual-IED labeling. (C) The seven classes identified using the algorithmic classification. (D) The result of EEG–fMRI analysis, associated with class 7 of identified IEDs. All the fMRI results are overlaid on the subject's T1-weighted image ( ).
In the same direction, the aim of Flanagan et al. ( ) was to find the influence of inexact or unreliable marking of EEG epileptiform events on the result of statistical parametric mapping (SPM) analysis in EEG–fMRI studies of patients with epilepsy. In this paper, the EEG–fMRI data of 10 patients with epilepsy were analyzed, and epileptiform events were marked. Then, the effect of omitting, mislabeling, and inconsistent timing of events was observed separately, considering the numbers of voxels above the threshold in the resulting SPM analysis. The results showed that omitting true epileptiform events decreased the number of above-threshold voxels. Mixing epileptiform and non-epileptiform events usually (but not always) caused a similar decrease. Inconsistent timing of events for small (<200 ms) and large (>500 ms) inconsistencies had small and large effects on the results, respectively. This suggests that accurate marking up of epileptiform events in EEG is still one of the most important factors for obtaining reliable results from EEG–fMRI analysis.
Besides, multiple fast fMRI sequences have been recently developed, one of which is magnetic resonance encephalography (MREG). Comparing MREG with the traditional sequence of echo-planar imaging (EPI) has revealed that MREG gives higher maximum t -values than EPI ( ). However, Safi-Harb et al. in ( ) reported that EPI yielded a better true positive rate and larger cluster size than MREG using a proper threshold. Also, it was shown that the HRF shape had a larger effect on MREG detection than EPI. Additional studies are needed to make a definitive judgment on their relative sensitivity. In terms of localizing the epileptic network, Jäger et al. ( ) state that high-density EEG and fast fMRI seem to improve EEG–fMRI analysis results.
### Pre-spike BOLD Signal Changes
Hemodynamic changes that are time-locked to spikes may reflect the propagation of neuronal activity from a focus, or conversely the activation of a network linked to spike generation ( ). That is why pre-spike concordant BOLD signal changes may contain information about the epileptic networks. In a study of Jacobs et al. ( ), five patients with idiopathic focal epilepsy and six patients with symptomatic focal epilepsy were studied. Spike timing was identified, and HRFs were calculated as the most focal BOLD response to model the regressors of statistical analysis with the timing of spike events convolved to HRFs peaking at −9 to +9 s around the spike. The results showed pre-spike BOLD responses in 11 of the 13 studies which were more focal and related to the spike field than post-spike responses ( ).
Projected HRFs illustrated according to a sequence of patients. The bottom row shows HRFs calculated over the area with the highest t-statistic. The HRFs which presented with dotted lines are those that did not pass the SNR criterion of 4.5. The Blue scales had to be adjusted to obtain visibility of the HRF and thus differ from the rest. The HRF shape differences and their sporadic early peak times are obvious ( ).
The question of whether these pre-spike BOLD responses were the result of a synchronized neuronal discharge was yet to be investigated. In another study ( ), four patients with pharmacoresistant focal epilepsy were selected by showing both pre- and post-spike BOLD responses concordant with the EEG focus during the session of EEG–fMRI recording. Then, they underwent stereo-EEG (SEEG) as part of their pre-surgical evaluation to specify the origin of pre-spike BOLD signal changes. Pre-spike BOLD signal changes in the spike field area were analyzed using HRFs with peaks ranging from −9 to +9 s around the spike. After that, SEEG signals were analyzed for detecting electrographic changes consistent with the time and location of the early HRF responses. The results showed that only one of the patients had a consistent SEEG interictal discharge. No electrographic changes were detected in the rest of the patients, consistent with the early HRF responses in period and location. Therefore, the early BOLD signal change usually reflects a metabolic event that does not seems to be the result of a synchronized neuronal discharge.
### BOLD Response to IEDs
There may be a direct relationship between the BOLD signal changes and overall synaptic activity ( , ). The generation mechanisms of interictal discharges are unknown in humans, but the cortical development abnormalities have distinctive interictal discharges ( ). For a reliable localization of epileptic foci using EEG–fMRI, we need IEDs correlated with the BOLD signals recorded simultaneously. However, the epileptogenic regions with correlated signals have not yet been thoroughly understood ( ). Thus, a considerable part of the literature is centered on the behavior of BOLD signal changes associated with interictal discharges.
Federico et al. ( ) focused on the BOLD signal changes associated with interictal discharges in six patients with malformations of cortical development and seizures using spike-triggered fMRI 3T. They revealed four positive changes in the lesion and four negative changes surrounding the lesion, five changes at distant cortical sites, and three subcortical sites (basal ganglia, reticular formation, or thalamic). Waites et al. ( ) studied two patients with frequent epileptiform events and concluded that interictal discharges result in BOLD responses distinctly different from those obtained by examining random events. Besides, Bonaventura et al. ( ) investigated BOLD responses related to epileptic EEG abnormalities in 31 partial and 12 generalized epilepsy patients and revealed that there are obvious associations between BOLD results and EEG abnormalities in 21 cases with 18 concordant to electro-clinical findings.
Another strand of literature focuses on idiopathic generalized epilepsy (IGE). For instance, Briellmann et al. in ( ) analyzed the data from 17 patients with IGE and frequent, stereotypical generalized discharges that were present in 14 of them during scanning. As reported, the cortical changes were found in all patients, and subcortical changes were found in only seven of the patients who had bursts of rhythmic discharges during scanning. Fifty-five percent of the patients showed deactivation in the posterior cingulate, and two of the patients who had marked activation and electro-clinical absences during scanning showed thalamic signal change.
Tyvaert et al. ( ) analyzed the EEG–fMRI data from 10 patients with IGE during generalized spike-and-wave discharges (GSWDs). The HRFs were calculated in four ROIs related to the left and right thalamic structures and were compared within and between them. The results pointed to an activation of the centromedian and parafascicular (CM-Pf) nuclei and then of the anterior nucleus during GSWDs. This suggests that the early propagation and maintenance of epileptic discharges may belong to the posterior intralaminar nuclei and anterior nucleus, respectively.
In another study ( ), the EEG–fMRI data of 83 patients with medication-refractory IGE (R-IGE) were analyzed, and statistical parametric maps concerning the BOLD response were generated. Thirty-six patients were identified as cases with absence seizures. It was inferred that when thalamic BOLD changes peaked at ~6 s after the onset of absence seizures, the other areas, including the prefrontal and dorsolateral cortices, showed brief and non-sustained peaks at ~2 s earlier than the thalamic peak. Also, TL peaks occurred at the same time as the thalamic peak, with a cerebellar peak occurring ~1 s later. Thus, the origin of absence seizures may be the widespread cortical (frontal and parietal) regions and sustained in subcortical (thalamic) areas, representing the cortical onset of epileptic seizures with propagation to the thalamus.
In a study of Benuzzi et al. ( ), 18 patients with IGE and absence seizure (AS) were studied, and the event-related analysis was performed using the onset and duration of GSWD as one regressor and GSWD offset as another. The results pointed to a thalamic activation and a deactivation in pre-cuneus/posterior cingulate related to the GSWD onset and a BOLD signal decrease over the bilateral dorsolateral frontal cortex GSWD termination.
For a 28-year-old focal epilepsy patient with left frontal seizures who were treated with oxcarbazepine (1,200 mg/d), two sessions 1 month apart of continuous EEG–fMRI with two different runs for each session were held. The IEDs were extracted using the location of the electrodes with the maximum amplitude of the epileptiform activity, and the colocalization of fMRI clusters was established based on the anatomical lesion and IEDs. In both runs of the first session, a unique left frontal main cluster was identified in the left opercular region colocalized to IEDs and near the posttraumatic lesion. However, in the second session, two main clusters were detected in the inferior frontal gyrus of both hemispheres. Therefore, EEG activity did not considerably change within each session, whereas the spatial distribution of interictal events showed significant variations between the sessions ( ).
In another study, Flanagan et al. ( ) reviewed the EEG–fMRI data of 27 patients with focal epilepsy in terms of the location and extent of the IEDs and the resulting pattern of significant BOLD responses. This study characterized important features of the BOLD responses associated with the IEDs and confirmed that the piriform cortex is a common node underlying IEDs and suggests a purpose for further study and potential therapy.
Fahoum et al. ( ) studied 32 patients with TLE, 14 patients with frontal lobe epilepsy (FLE), and 20 patients with posterior quadrant epilepsy (PQE) and acquired the patterns of cortical and subcortical BOLD responses related to focal IEDs using a group analysis. The patients with TLE showed activations in the midcingulate gyri bilaterally, ipsilateral mesial and neocortical temporal regions, insula, and cerebellar cortex, and also the most widespread deactivations in the default mode network (DMN) areas. The patients with FLE showed activations in the midcingulate gyri bilaterally, ipsilateral frontal operculum, thalamus, internal capsule, and the contralateral cerebellum, and also deactivations in the DMN areas. Lastly, the patients with PQE showed only deactivations in the DMN area.
### Negative BOLD Signals
For the cases of negative BOLD signals, the epileptogenic regions with correlated signals are not also completely understood ( ). For explaining the negative BOLD signals, three different scenarios could be as follows: An overcompensating cerebral blood flow decrease could accompany a decreased metabolism as a “normal” negative BOLD response; the epileptic activity could produce an increased metabolism without adequate blood flow change resulting in a negative BOLD effect; and the oxygen consumption could stay constant throughout the IED event while at the same time, a reduced local blood flow is induced ( ).
The study of Rathakrishnan et al. ( ) aimed to explain the negative BOLD responses seen in the source of epileptiform discharges by the undershoot of an antecedent positive response. In analyzing the EEG–fMRI data of 82 patients with focal epilepsy, only eight patients showed a focal negative BOLD response in the spike field area using models with HRFs peaking from −9 to +9 s around the spike. Thus, the origin of negative BOLD responses in the epileptic foci is not an initial positive BOLD response and remains unexplained in most patients.
To determine the origin of BOLD negative response to the IEDs, Pittau et al. ( ) studied two groups of patients, each including 15 patients with significant positive and negative BOLD responses within the IED region, and explored the relationship between the type of response (activation/deactivation) and several IED characteristics. The results denoted that the IEDs of patients with deactivation were more frequently of long duration with larger involved cortical areas and more focused in the posterior quadrant. Also, the IEDs accompanied by a slow wave were present in 87% of the deactivation group and only in 33% of the activation group which is the critical feature reliable for focal deactivations.
## Localization Approaches
### Localization of Epileptic Focus Using EEG–fMRI
The EEG–fMRI analysis has been widely used for the localization of epileptic foci. However, the respective approaches need more refinement to be reliable for pre-surgical decision making ( ). In the following section, the results of applying various analysis methods are reported.
#### Conventional Analysis
Simultaneous EEG and fMRI recordings can reveal the source of spiking activity that is highly correlated with epileptic foci and epileptogenic lesions in a large number of patients. However, many of the patients have no significant activation for unknown reasons ( ). In the study of Al-Asmi et al. ( ), the EEG–fMRI data of 38 patients with focal epilepsy and frequent spikes were analyzed in terms of fMRI activation using two methods: (1) the significance of the t-statistic value at every single voxel and (2) the significance in the clusters of contiguous voxels based on random field theory ( ). The concordance between the spike location of EEG and anatomic abnormalities of MRI and other EEG and clinical measures were taken into consideration. From the analyzable ones, activation regions were obtained in 39% that were concordant with EEG source localization in nearly all of them. Forty percent showed activation without any MRI lesion, and 37.5% showed activation near or inside the lesion.
In a study of Zijlmans et al. ( ), the EEG–fMRI data of 29 patients with epilepsy were studied, and 46 sets of IEDs were identified in the agreement between two experts. The BOLD response related to each type of IEDs was modeled in an event-related design using a canonical HRF with a temporal derivative, and statistical maps of activity were created. The results showed an improvement in the localization of epileptic focus and opened new prospects for surgery. For instance, at least one significant positive BOLD response topographically concordant with the IEDs was found in eight patients who were rejected for surgery due to reasons like unclear focus or multifocality. This is, therefore, a valuable tool in the pre-surgical evaluation of patients with epilepsy.
Besides, in the study of De Tiège et al. ( ), the IEDs were extracted and segregated into separate regressors applying a half-maximum amplitude cutoff in six children with pharmacoresistant focal epilepsy. The regressors were then convolved with the canonical HRF and its temporal derivative for an event-related fMRI analysis. The results showed significant activations in four children, colocalized with the presumed epileptic focus, activation and deactivation in one child, and a widespread deactivation in another.
In another study, Grova et al. ( ) evaluated the level of consistency between EEG source localization and BOLD responses using two comparison strategies: (1) MEM concordance, which is the comparison between EEG sources detected using Maximum Entropy on the Mean (MEM) and fMRI clusters of significant BOLD response and (2) fMRI relevance: if sources located in an fMRI cluster could explain some scalp EEG data, the assessment of the fMRI-relevance index α would measure when this fMRI cluster was used to constrain the EEG inverse problem. For this purpose, seven patients with focal epilepsy underwent EEG–fMRI and an EEG recording outside the scanner. The results of combining two mentioned strategies to report the concordance between BOLD response and EEG sources showed that from 62 fMRI clusters assessed by standard event-related analysis, 15 were highly concordant with EEG according to both strategies, five were concordant only according to the fMRI-relevance index, 30 were not concordant, and 10 clusters had a significantly negative α index suggesting EEG–fMRI discordance.
Avesani et al. ( ) analyzed the EEG–fMRI data of a patient with symptomatic epilepsy to find the linkage between the “epileptogenic” zone and the “irritative” zone, which is the meticulous cortical distribution of spikes. They used EEG signals as paradigms in the fMRI study and compared the EEG interictal slow-spiked wave with the normal EEG conditions. The results showed a BOLD signal increase around the epileptogenic area in the left neocortical temporal region, laterally and posteriorly to the porencephalic cavity, representing a connection between “epileptogenic” and “irritative” areas.
In a study of Jackson ( ), Jackson extracted 46 IED sets from 29 patients with epilepsy who were excluded for surgery on unclear foci. Also, he analyzed the fMRI data to identify BOLD, significant responses, and topographical concordance with IEDs. Fifteen patients showed significant positive or negative BOLD responses. Eight patients showed IED-related positive BOLD responses. Four of the five patients with presumed multifocality showed multiple epileptic foci. Four of six patients with unclear foci showed a confined focus, opening new predictions for surgery.
Besides, in the study of Liu et al. ( ), the EEG–fMRI analysis for the localization of partial epilepsy includes extracting and convolving the spike times with a two gamma-variate canonical HRF and adding the result as a task regressor to the SPM design matrix. This approach was applied to the data of eight EEG–fMRI sessions acquired from six patients with partial epilepsy and showed six with activation and deactivation, one with activation only, and one with deactivation only. Seven of the observations corresponded to electroclinical localization of epileptic focus. As reported in this study, the concordance seems to be more associated with positive BOLD responses, and the response to deactivation seems less associated with IEDs. Such studies generally demonstrate that IEDs may be revealed in the brain regions well beyond the presumed area in which they are generated ( ). In the study of Moeller et al. ( ), the EEG–fMRI data was acquired from nine patients with non-lesional frontal lobe epilepsy (FLE). Using four HRFs, IED-related BOLD responses were obtained and compared to the spike topography determined by BESA as a voltage activation map. The results showed a concordance between the positive BOLD response and the spike localization in eight of nine patients.
Borelli et al. in ( ) studied a patient of focal cryptogenic epilepsy with speech arrest seizures and bilateral synchronous spike and wave scalp EEG pattern (secondary bilateral synchrony). Following the conventional analysis of EEG–fMRI data, the IEDs were identified, convolved with a two-gamma canonical HRF, and added to a single-subject GLM. The statistical map of significantly activated voxels showed an explicit BOLD response over the left supplementary motor area (SMA) and, to a lesser degree, over the homolateral motor strip. Forty-three patients with focal epilepsy were studied in ( ), and BOLD responses associated with IEDs, including at least five significant contiguous voxels, were extracted and labeled as consistent and inconsistent with the EEG spike field and contributory or not contributory, based on whether or not they provided additional information to EEG about the epileptic foci. The main analysis included convolving a regressor developed using the time and duration of each IED-type event with four HRF peaking at 3, 5, 7, and 9 s and adding all the regressors to GLM. Thirty-three patients who had more than two IEDs during recording were shown to have significant BOLD changes, among which 29 were considered consistent, and 21 were contributory. The BOLD responses were validated in 12 of 14 patients having intracerebral EEG or a focal lesion on MRI.
Ten patients with atypical benign partial epilepsy (ABPE) underwent simultaneous EEG–fMRI, and several types of IEDs were extracted from their data in ( ). The analysis of BOLD signal changes associated with each IED type showed distant significant responses in cortical and subcortical structures for 31 cases out of 33 among which 21 were concordant with the spike field. Also, to find the responses across the patients, group analysis was performed and showed a thalamic activation. It is noteworthy that the revealed activation in ABPE was analogous to the outlines showed in studies of rolandic epilepsy and continuous spike-wave during slow sleep (CSWS). Zhang et al. ( ) investigated the results of pre-surgical EEG–fMRI analysis and iEEG monitoring in a patient with seizure recurrence after epilepsy surgery. They suggested that EEG–fMRI is a useful tool for pre-surgical evaluation but requires caution. Also, the intact seizure foci in the remaining brain may cause the non-seizure-free outcome.
In previous studies of improvement in the localization of epileptic foci, Tousseyn et al. ( ) used the conventional GLM-based approach for the localization of epileptic focus in a semi-automated manner by proposing a spike identification method as an alternative for the challenging and time-consuming visual spike detection. In this method, a patient-specific spike template was generated by averaging the spikes observed on the EEG outside the scanner, and the cross-correlations were calculated between the template and the EEG inside the scanner. Then, the result was binarized by a threshold determined from healthy controls and convolved with a canonical HRF to be used as the regressor of GLM. Examining this semi-automatic method on the EEG–fMRI data of 21 patients with refractory focal epilepsy yielded a good performance with the optimal area under the ROC curve of 0.77.
Sandhya et al. ( ) studied three patients with drug-resistant reflex epilepsy, including eating, startle myoclonus, and hot water epilepsy using conventional analysis. The results showed frontoparietal network activation pattern in the patient with startle myoclonus epilepsy concordant with SPECT, fronto-temporo-parietal involvement in the patient with eating epilepsy concordant with SPECT, and fronto-parietal-occipital involvement in the patient with hot water epilepsy. In research conducted by Tousseyn et al. ( ), 28 patients with refractory focal epilepsy underwent EEG–fMRI and subtraction ictal SPECT co-registered to MRI (SISCOM). Comparing the perfusion changes during seizures obtained from SISCOM and spike-related BOLD signal changes obtained from EEG–fMRI revealed a concordance between the BOLD responses and EEG spikes in 27 cases, a significant spatial overlap between hyperperfusion on SISCOM and hemodynamic changes on EEG–fMRI in 20 cases, and significant overlay between ictal hypoperfusion and interictal deactivation in 22 cases.
#### Dipole-Based Analysis
The spike source reconstruction of EEG is generally consistent with the BOLD localization ( ). It can therefore be used for the localization of epileptic focus. Some of the source localization methods are fixed dipoles, moving dipoles, LCMV (linearly constrained minimum variance), spatial filtering, MUSIC (multiple-signal classification) dipole scans, and LORETA (low-resolution tomography) ( ).
Lemieux et al. ( ) recorded a 12-channel EEG inside a 1.5T MRI scanner in six epilepsy patients with partial seizures. A T1-weight volume scan and a 64-channel scalp EEG outside the scanner were obtained from each patient. Having extracted spikes from the EEG signals, they performed the source reconstruction using three generator models consisting of multiple moving dipoles, MUSIC dipole scan, and current density reconstruction (Curry 3.0 software) to localize spike generators and compared its results with the spike-triggered fMRI activation maps (SPM96 software). They concluded that the spike generator was located inside or in the same fMRI activation lobe. Therefore, source reconstruction was generally consistent in EEG generator models and fMRI individual clusters.
Bagshaw et al. ( ) showed that EEG–fMRI results should not constrain MEG and EEG inverse solutions for equivalent current dipole approaches in epilepsy and that the use of distributed source modeling would be a more appropriate way of combining EEG–fMRI results with source localization techniques. They analyzed the EEG–fMRI data from 17 patients with focal epilepsy and compared the results of spatiotemporal dipole modeling with the peak and closest EEG–fMRI activations and deactivations. They reported that, generally, the distance from the dipoles to the voxel with the highest positive t -value and nearest activated voxel was 58.5 and 32.5 mm, respectively, and also that for deactivations was 60.8 and 34.0 mm, respectively. It is obvious that these values are significantly higher than what is generally observed with ERPs, possibly due to a comparatively broad field that could lead to deep artificial dipoles and also the prevalence of EEG–fMRI responses away from the focus of the epileptic activity hypothesis.
Recently, a new method has been proposed for measuring the physical distance between the BOLD clusters and selected component dipoles to improve the identification of epilepsy-related components in the EEG–fMRI analysis ( ).
In a study of Secca et al. ( ), two patients with idiopathic occipital lobe epilepsy (OLE) were studied in terms of the source analysis using instantaneous regional dipoles at the peak of averaged detected spikes with a three-layer boundary element model (BEM) of volume conduction. Relating the BOLD effect with interictal spikes using a standard Gamma HRF with derivatives, the authors were able to detect BOLD clusters and compared them with the malformative lesion and diagnosed seizure symptomatology, which was moving the right hand, which yielded a very good concordance for each patient between the BOLD clusters, malformative lesion, and the seizure symptomatology.
In another study ( ), three patients with idiopathic childhood occipital lobe epilepsy (OLE) underwent EEG–fMRI. EEG source analysis was conducted using prompt moving dipoles at the peak of averaged spikes, which were detected visually, with a standard three-layer boundary element model (BEM). Next, the BOLD activation map was acquired coupled with the incidence of EEG spikes. The results showed no changes in the BOLD activation in the cortex adjoining to the source analysis dipoles. Deactivation analysis showed several clusters with more consistency to the localization of EEG source analysis over the right parietal area. Therefore, the spatial overlap between EEG source analysis results and the BOLD activation map was not quite acceptable. However, the fMRI results were more consistent with the clinical advents.
In our works, we used a dipole-based method for the evaluation of our localization method ( ). This study revealed that BOLD responses were related to epileptic spikes in various brain regions of patients with refractory focal epilepsy ( ). So, dipole-based analysis can help in the localization of epileptic focus in patients with focal epilepsy and is comprised as part of the pre-surgical evaluation for patients with pharmacoresistant epilepsy.
Dipole-related BOLD response showed a focal activation in the left frontal lobe. (A) Raw EEG data acquired inside the MR scanner. (B) Cleaned EEG after removing the gradient artifact. (C) Identified component time series. (D) The component identified on scalp EEG located in the left lateral frontal lobe. The active area is marked by yellow-red color. (E) Dipole localization of the identified generator in deep brain structures. (F) Localization of the generator applying simultaneous analysis of EEG–fMRI ( ).
#### Component-Involved Analysis
Besides the SPM that is a hypothesis-driven method, ICA is a data-driven method ( ) that can be used to find independent components of epileptic sources and add them to the simultaneous EEG–fMRI analysis. The component-involved approaches can also corroborate a negative decision concerning surgical candidacy in some cases ( ).
In the study of Penney et al. ( ), the EEG–fMRI data of a patient with refractory right TLE were studied. Applying spatial ICA (sICA) to the BOLD fMRI measurements, a hemodynamic response model signal derived from the task-related spatial ICs and used as a regressor in SPM to generate the significant BOLD activity maps. The results of this approach were compared to the same results using a conventional regressor generated from a canonical hemodynamic model and revealed a concordance between the activated regions. So, this sICA-based model may improve the accuracy of localizing epileptic focus.
Also, in the study of Rodionov et al. ( ), the findings of sICA compared to the EEG-based GLM analysis in eight patients with focal epilepsy. The spatiotemporal concordance was assessed between the BOLD-related ICs and GLM-derived results to find one IC related to IED-based GLM results. So, the remaining candidate BOLD-related ICs may include the IEDs which were not apparent on the EEG. So, the sICA-based approach can be used to recognize the SOZ and may be helpful when the epileptic activities are not evident on the EEG signal.
Sercheli et al. in ( ) used the EEG dipole modeling analysis to ICA components for the localization of epileptic focus in a patient with right mesial TLE before and after a successful resecting of the epileptic region. With this aim, the same dipole source localization of ICs was performed within a three-shell boundary element model of MNI standard brain using DIPFIT2 plug-in of the EEGLAB toolbox. The conventional approach was also performed to evaluate the results of ICA dipole modeling analysis, which used the fMRI statistical analysis with a regressor of IEDs convolved to a gamma HRF. The results of the conventional analysis showed a right hippocampus induction of the large interictal activity in the left hemisphere. However, the results of dipole modeling analysis showed a widespread distribution of activity, and almost only a quarter of the dipoles were near the right hippocampus region. Using just the EEG analysis to precisely identify the epileptic sources is too weak even by a sophisticated method like ICA.
Marques et al. ( ) suggested a technique based on the ICA and applied it to the EEG–fMRI data of nine patients with epilepsy. In this method, after using ICA on the EEG data, the candidate ICs were one or two components that were most powerfully related to IED activity considering only the signal, which is over three standard deviations from the mean of the respective channel. The candidate components were convolved with the canonical HRF and added as the regressor to GLM of the BOLD signals. The results of this method were compared with the conventional method and showed concordance in six patients with more significance and extent in most of them, compared to the conventional method results. The rest of the three patients showed no significant activation using the conventional method to be comparable.
In another study ( ), various IED types were classified using ICA and temporal correlation of ICs with the raw EEG channel. Then, the time pulse of each IED type was convolved with a canonical HRF and added separately to GLM for finding the focus of each identified IED type. This method was used in 10 patients with epilepsy including two cases with unknown sources of activity using the conventional method. The results of the proposed method on two patients with unknown source of activity showed some foci consistent with electroclinical data, and those on the rest of the eight patients showed significant activity from at least one type of IED consistent with the conventional method that proves the efficacy of this method for the localization of epileptic focus.
In a study of LeVan ( ), 15 patients with focal epilepsy underwent simultaneous EEG–fMRI, and ICA was applied to each of their fMRI data. Then, matching a canonical HRF to the ICs time series in the IEDs' time, the components associated with the seizures were found, and the matched HRFs were used to regulate the sign and delay of the actual HRF peaks. HRFs with an obvious peak were used to create the activation maps of significant BOLD signal changes and compared with the results of a common GLM method. Evaluating the concordance of results with the presumed epileptic foci determined by clinical history, EEG, and MRI abnormalities revealed that the ICA maps were correlated with the GLM maps for all the patients with an activation network that always included the presumed epileptic foci, but more widespread, as much as 20.3% of the brain volume averagely.
Besides, in the study of Leite et al. ( ), five metrics including total power, un-normalized root mean square frequency, un-normalized mean frequency, root mean square frequency, and mean frequency were calculated and added to GLM using the performed ICA on the EEG data. For calculating these metrics, the power spectrum was acquired from time-frequency analysis using Morlet wavelets. The metrics were calculated for only the component spectrums presenting spectral alterations during the events identified by the neurophysiologist. In a practical case, applying this method to the EEG–fMRI data of one patient with epilepsy produced wider and more significant activation maps compared to the conventional method using a standard square waveform regressor. Furthermore, the EEG metrics with a frequency content were better predictors of the BOLD signal than global power metrics, supporting previous theoretical predictions and experimental evidence. This method was also tested in ( ) for four patients with epilepsy and again revealed more significant activations compared to the conventional analysis.
In another experiment ( ), a 10-year-old male patient with epilepsy underwent simultaneous EEG–fMRI for investigating the dynamic responses of epileptic networks. ICA was used in fMRI data, and IED-related ICs were detected fitting an HRF to their time courses at the time of the IED event. Then, the epileptic source of the EEG signals was identified by convolving a canonical HRF with the time pulse function of IEDs as a regressor of a GLM analysis. Comparing IED-related ICs with the EEG source imaging of IEDs in terms of HRF peak delay and spatial consistency using minimum norm estimation (MNE), the fMRI ICs were classified into spatially consistent and inconsistent ones. So, the spatially compatible ICs with early HRF peaks which resulted from spatial-temporal EEG–fMRI fusion (STEFF) would be the possible indicators of the epileptic focus.
Formaggio et al. in ( ) presented a novel automatic approach for simultaneous EEG–fMRI to identify the epileptic focus based on ICA and wavelet analysis. This method consists of four steps: (1) applying ICA and selecting components related to IEDs based on their power using a wavelet time-frequency representation because of higher amplitude in IED activity than background activity and the non-stationarity of the signal; (2) eliminating unselected components and reconstructing the EEG signal with only the IED-related components; (3) calculating the cross-correlation between the reconstructed EEG and the original signal to compare and find the IED channel with the highest correlation coefficient, and also building the power signal using a partial maximum of the estimated time-frequency power spectrum of IED channel for each epoch of 3.7 s by wavelet analysis; and (4) convolving the power time series with the canonical two-gamma HRF as the regressor of GLM. After validating this method on simulated data and applying it on real EEG–fMRI data, including five patients with partial epilepsy and two normal subjects, the results showed an extension in current knowledge on epileptic focus localization and suggested that BOLD activation related to slow activity might contribute to the localization of epileptic foci even in the absence of clear interictal spikes.
Franchin et al. ( ) presented a method to classify the ICs of fMRI using an elevated algorithm to distinguish the sources of interest from noisy signals. Applying this method for estimating the BOLD activations related to epilepsy and comparing its results with the conventional GLM approach showed that the activations resulted using this method comprised subareas of the those resulted from the conventional analysis, even with partial discordant patterns of the activated areas, and also consists of additional negative regions implicated in a default mode of brain activity, and not clearly identified by GLM.
In our previous study ( ), we attempted to localize the focus of epileptic seizures by identifying the neural behavior of the seizures and detecting the related components as a regressor and the input of a GLM model. For this aim, 28 sets of IEDs from nine patients who were excluded for surgery because of unclear focus in four, presumed multifocality in three, and a combination condition in two cases were analyzed. The result of localization showed an improvement in localization of foci using the component-based approach, which includes five of six patients with unclear foci, advocating one of the foci in five patients with assumed multifocality, confirming multifocality in one of them, opening new prospects for surgery in seven of the patients. Also, in two of the patients, intracranial EEG supported the EEG–fMRI results.
In a study of Hunyadi et al. ( ), the ICA was used in the fMRI time series collected from 28 patients with refractory focal epilepsy. For reducing the number of ICs to an optimal number by the minimum description length (MDL) criteria, the temporal dimension of the time series was reduced using principal component analysis (PCA). Then, the component activation maps were generated with Z -scoring the component voxel values and using the threshold of Z > 5. The results showed that the selected ICs, regardless of the spike presence during EEG recording, truly correspond to the epileptic activity. Considering only one of the ICs as the epileptic IC according to the overlap with the already known SOZ, the component activation maps were ordered. The average overlap between the epileptic IC and the SOZ was 10.6% ± 7.2.
Rummel et al. ( ) analyzed the ordinal patterns and revealed that the BOLD responses to EEG-ICA predictors involved the brain region whose resection led to seizure freedom. In the study of Panda et al. ( ), the EEG microstates were considered as the regressor in the GLM design to reveal the epileptic resting-state network. The EEG microstates were obtained from the maxima of the global field power (GFP) due to the stability in topography around the peaks of the GFP using sLORETA software. Considering each EEG microstate as an event, an input function was modeled based on the timing of each microstate and convolved with three columns customized gamma HRF. This model was added as the regressor in the GLM design for the ICA of fMRI data. The results of this method on five patients with epilepsy showed that using EEG microstate and ICA of fMRI data may examine the brain areas involved in resting-state brain discharge.
In another study ( ), eight patients with epilepsy and known epileptogenic zone from the outcome of surgery were studied for the association between the ICs of fMRI epochs during the presence and absence of the IEDs. The fMRI data were divided into two epochs according to the EEG signal with visible IEDs and without IEDs. Then, spatial ICA was applied to each epoch separately, and IC maps were compared to the resection area and the EEG–fMRI correlation pattern by calculating a spatial correlation coefficient for identifying the epilepsy-related IC. The results showed a high similarity between the epilepsy-related ICs of the epochs with IEDs and those without IEDs. So, the epilepsy-related components are not contingent on the existence of the IEDs in the EEG signals.
Hunyadi et al. ( ) studied 12 patients with refractory epilepsy and good surgical outcomes. The epilepsy-related independent components (eICs) were obtained from temporal ICA applied to EEG and spatial ICA applied to fMRI. After convolving the time courses of EEG ICs with the canonical HRF and upsampling the time courses of fMRI ICs to match the sampling rate of the EEG, Pearson's correlation coefficient was calculated for all possible pairs of EEG–fMRI ICs and labeled as matched for the correlation coefficient > 0.1. The results showed matching EEG-eIC for a single fMRI-eIC in four patients with three overlapped to the epileptic zone and matching EEG-eIC for at least two fMRI-eICs in six further patients.
Carnì et al. ( ) compared two data-driven methods based on sICA and semi-blind ICA with the conventional GLM-based method using the EEG–fMRI data of 10 patients with epilepsy. A cross-correlation analysis was then completed between the epilepsy-related ICs and a GLM regressor. The results showed a concordance of the BOLD activation areas in response to synchronized epileptic activity obtained from sICA and semi-blind ICA with the GLM analysis and presumed electroclinical hypothesis. Semi-blind ICA showed more power against the noise and a higher correlation with the GLM regressor.
In our study ( ), to measure the physical distance between BOLD clusters and selected component dipole location using patient-specific high-resolution anatomical images, we recommended a component-based EEG–fMRI method. The EEG–fMRI data of 17 patients with refractory focal epilepsy underwent this method for the localization of epileptic focus, determination of quantitative concordance, and comparison of the maximum BOLD cluster with the recognized component dipole. For the concordance level, the distance from the voxel with maximal z-score of maximum BOLD response to the center of the extracted component dipole was measured. This improved the localization accuracy to 97% that marks a significant rise compared to conventional works. illustrates a graphic illustration of the recommended technique in ( ) to identify the components. The results of the implementation of the proposed method are shown in .
The model of the proposed approach in ( ) to identify the components.
Summary of IED studies which indicated a significant component-related BOLD response to consensus IEDs ( ).
In the superscript, the topographical concordance between clinical localization and BOLD response is given: (++): same area, ipsilateral; (+): same area, contralateral; (*): ipsilateral but a different area; (–): no concordance ( ) .
Also, in our recent work ( ), we found and obtained the time series of components associated with epileptic foci from EEG and added them to the GLM analysis. Twenty patients with refractory epilepsy and 20 age- and gender-matched healthy controls were studied, and the identified components were examined statistically to find the epilepsy-related components. The threshold of localization accuracy was determined as 86% using receiver operating characteristic (ROC) curve analysis, and the accuracy, sensitivity, and specificity were found to be 88, 85, and 95%, respectively. Also, the contribution of EEG–fMRI and concordance between the location of maximal BOLD response and the spike field were evaluated. The result confirmed the concordance in 19 patients and contribution in 17. Besides, considering the spatial correlation between the spike template and candidate components as well as the patients' medical records makes it possible to predict the behavior of epileptic generators. shows the results of the method proposed in ( ), comparing three different methods. In this study, the epileptic focus localization can be viewed through the ICA algorithm, dipole, and on the MR images.
A sample of component-related BOLD response illustrates a neocortical activation in the first occipito-temporal cortex concordant with the spike field. Also, the marked events are in P8, PO8, and TP8 with referential montage. Top: The identified epilepsy-related component located in the right occipito-temporal lobe. Middle: The result of dipole-based localization of the identified component. Bottom: The localization of the epileptic generator acquired from simultaneous EEG–fMRI analysis ( ).
#### Dynamic Causal Modeling Analysis
Dynamic causal modeling (DCM) is another useful tool that can be used for estimating the synaptic drivers of cortical dynamics during an epileptic seizure. However, it has a costly computation in the requisite Bayesian inversion procedure ( ).
In the study of Hamandi et al. ( ), the EEG–fMRI data was acquired from a 23-year-old patient with refractory TLE. The EEG spikes were detected and convolved with an HRF and its temporal derivative to be used as the onsets of GLM. The results showed activation related to the left anterior temporal interictal discharges, in the left temporal, parietal, and occipital lobes. For determining the functional relationship between the IED-related activation areas, DCM was used and the deployment of neural activity from the focus of temporal to the region of occipital activation was suggested. Also, for tractography analysis, the probabilistic index of connectivity (PICo) algorithm was used to detect the anatomical connections of TL activation and showed connections from this origin to the site of occipital activation, which delineate the pathways of deployment of epileptic activity.
In a study of Murta et al. ( ), the EEG–fMRI data from five patients with focal epilepsy were analyzed for detecting the focus of epileptic seizures. For this purpose, three different methods were used, and the outcomes were compared with the clinical outlook: (1) the classic method based on GLM at different neurophysiology regressor lags (LasgM) considering 19 regressors by lags ranging from −16 to +20 s in 2-s steps around the events which were convolved with four types of HRF including single gamma with its temporal derivative, canonical HRF with its temporal derivative, gamma bases functions, and FIR basis functions. In this method, the activation map was obtained using GLM analysis for each lag, and the lags with the maximum number of activated voxels for each VOI were selected to detect the focus of activity propagation; (2) the DCM method, which is a suitable model-based method for studying effective connectivity and has been used several times in the fMRI data of epilepsy patients ( , ); and (3) the Granger causality (GC) which is a data-driven statistical hypothesis test to analyze effective connectivity in fMRI data with the primary precondition of stationary covariance for the data variables ( , – ). Evaluating the results of three methods revealed that DCM analysis, although suffering from generally poor SNR, provides meaningful results in a sufficient number of seizure events. Also, the LagsM results were concordant with the clinical anticipation as much as to be a useful complementary approach. However, the CG results showed that this method seems to be not appropriated to use in the cases like this effective connectivity analysis, at least with the situation of SNR and time resolution of the data used in this study.
In epilepsy associated with hypothalamic hamartomas (HH), although the origin of seizures is known to be in HH, diffusion pathways are not known specifically. Murta et al. in ( ) employed the DCM approach to estimate these diffusion pathways from the fMRI data acquired from an HH patient. Examination evaluating a set of clinically possible network connectivity models of discharge diffusion, the most likely model to explain the data showed a diffusion pathway from the HH to the temporal–occipital lobe followed by the frontal lobe. Therefore, this method makes it possible to find the diffusion pathway of seizures, which is helpful in the surgical procedure of epilepsy treatment ( ).
Model space tested with DCM. Each row contains eight models consistent with each propagation hypothesis. Each column corresponds to a different latent connectivity structure. For each latent connectivity structure, the linear model is presented with solid arrows and the bilinear model is presented with solid arrows (intrinsic connections) and dashed arrows (connections' modulation). Seizure activity is fed into the HH network node ( ).
Vaudano et al. in ( ) studied a patient with reading epilepsy (RE) to identify the network of seizures. The BOLD, significant changes were obtained in 21 s around each seizure corresponding to various linear combinations of a set of Fourier basis functions to find a range of possible HRF shapes. Then, using the results of this analysis, four ROIs were selected, and four linear models were constructed using DCM to analyze the effective connectivity between ROIs. It was eventually revealed that the dominant premotor cortex (BA6) is the origin of seizures in RE, but also an area in the left deep PFC is closely linked to the beginning of the epileptic activity.
For the localization of epileptic focus, IED-related fMRI maps acquired from common analysis methods often show a network including multiple regions of the signal change instead of a highly focal region that drives the generation of seizures within the epileptic network. Vaudano et al. in ( ) used the DCM approach to identify the SOZ on the EEG–fMRI data of one patient with FLE. Although pre-surgical EEG–fMRI showed two distinct clusters of IED-related BOLD activation in the left frontal pole and the ipsilateral dorsolateral frontal cortex, the DCM approach revealed the left dorsolateral frontal cortex as the driver of changes in the frontopolar area, and An et al. in ( ) generated the BOLD activation maps and linearly registered them to postoperative anatomic MRI images for 35 patients with focal epilepsy who later had a surgical resection. The results showed 10 fully concordant patients with maximum t -value inside the resection area, nine partially concordant patients with maximum t -value near to resection area and overlapped results, five partially discordant patients with a less significant cluster inside the resection area, and 11 fully discordant patients with no response related to the resection area.
#### Functional Connectivity Analysis
Functional connectivity is a perfect technique for epilepsy to detect the complex brain effects because of dysfunctional and maladaptive networks produced by seizures ( ).
Preti et al. in ( ) recommended a new way to reveal the connectivity changes associated with an epileptic activity using the information of EEG and dynamic functional connectivity (dFC). Applying this method to the EEG–fMRI data of two patients with epilepsy revealed the specific patterns of connections and disconnections successfully associated with the epileptic activity.
Omidvarnia et al. ( ) studied seven patients with focal epilepsy who underwent EEG–fMRI to identify the relationship between the interictal EEG power and local fMRI connectivity. The wavelet coherence was developed between dynamic regional phase synchrony (DRePS, calculated from fMRI) and band amplitude fluctuation (BAF) of a target EEG electrode with dominant IEDs. This approach revealed the regions with a concordance between EEG power and local fMRI connectivity that were near the suspected SOZ in some of the cases. Also, the found regions had a little overlap with the results of conventional EEG–fMRI analysis more in medial posterior cortices, perhaps because of reflecting different aspects of the epileptic network.
In a study of Dong et al. ( ), 18 patients with juvenile myoclonic epilepsy (JME) were studied to identify discharge-affecting networks using eigenspace maximal information canonical correlation analysis (emiCCA) and functional network connectivity (FNC) analysis ( ). emiCCA is a data-driven method to detect the linear and non-linear relationships between two datasets, which can be the EEG discharges and fMRI networks in JME, and tackle the multivariate problem in the comparison of two datasets ( ). Also, the FNC is an approach to identify the interactions between resting-state networks (RSNs) and the effects of epileptic discharges on them ( – ). The results showed a relationship of the epileptic discharges with the discharge-affecting networks in the DMN, self-reference (SRN), basal ganglia (BGN), and frontal networks. Also, a significant increase was found in FNCs between the salience network (SN) and resting-state networks.
The framework of discharge-affecting network analysis using emiCCA. (A) Dataset Y was defined by applying group ICA to fMRI data and concatenating the ICs across the patients. Also, after identifying the onsets of GSWDs by neurologists and convolving with four SPM canonical HRFs peaking at 3–9 s, one Glover HRF, and one single Gamma HRF, a design matrix containing all of them formed the dataset X. (B) The emiCCA was applied for identifying significant linear and non-linear discharge affecting ICs with weights (α) exceeding the 1.5 standard deviations of weight values corresponding to the significant maximal information Eigen coefficients (MIECs). (C) For examining the possible functional network connectivity between the networks identified by emiCCA, the maximal time-lagged correlation method was used ( ).
In the study of Siniatchkin et al. ( ), the EEG–fMRI data recorded from 33 children with focal and multifocal epilepsy during sleep and resting-state functional connectivity were acquired using 15 ROIs. For the focal epilepsy patients, some strong correlations were found between the corresponding interhemispheric homotopic regions with a short-distance and weak long-distance functional connectivity similar to the healthy children. However, for the multifocal epilepsy patients, significantly stronger correlations were found among several regions of DMN, thalamus, and brainstem with longer-distance functional connectivity and not dependent on the presence of Lennox-Gastaut syndrome in patients.
In another study ( ), a total of 261 IED events from 21 patients with unilateral left and right TLE were identified, and a 20-s period around them was used in the dynamic FC analysis for left and right hippocampus and amygdala separately. The results showed that the left IEDs had more effect on the hippocampus-seeded networks and caused FC changes in the reward–emotion network (more of the prefrontal-limbic system) and visual network, but the right IEDs had more effect on amygdala-seeded networks and caused a coactivation in the reward-emotion network (more of the reward system).
Su et al. ( ) identified the different types of IEDs according to the spatial distributions from 38 patients with focal epilepsy and were used separately in the analysis of IED-related BOLD responses. The concordance between the maximal BOLD responses and the SOZ was found using iEEG, and then the functionally connected zone was determined for each one using the maximal BOLD as a seed ( ). Lastly, IED rates in iEEG channels inside and outside the functional connectivity zone (FCZ) were examined. The results of 36 studies from 25 patients revealed that IED rates inside the FCZ were considerably greater than outside in concordant cases.
The general pipeline of the research ( ). (A) Functional MRI data were preprocessed through realignment, slice timing, outlier detection, coregistration, segmentation, spatial smoothing, and noise regression. Then the maximal BOLD response and its neighboring 26 voxels were used as seed regions to calculate the seed-based functional connectivity maps. One-sample t -test was applied to determine regions with significant functional connectivity. (B) Preimplantation 3D-T1 images were segmented to obtain the brain region. The postimplantation 3D-T1 images were first coregistered to the pre-implantation images, and then the location of electrodes was determined. (C) The iEEG data were resampled to 200 Hz, band-pass-filtered between 10 and 60 Hz, and notch-filtered at 60 Hz to eliminate noise. Spike detection based on signal envelope distribution modeling was applied afterward. (D) The number of IEDs in each channel was normalized by the median number of IEDs in each subject. Statistical analysis was performed to determine group difference of IED rates between channels inside and outside the FCZ ( ).
In a study of Iannotti et al. ( ), 10 patients with pharmacoresistant focal epilepsy were studied, and the regions involved in epileptic network generation were identified by GLM analysis using the time course of fMRI-defined focus acquired from the IED-related BOLD maps as the main regressor. Then, using a sliding-window approach, the dFC time courses were assessed between the involved regions and correlated with the sliding-window variance of the IED signal (VarIED) to identify connections whose dynamics related to the epileptic activity. This method's results revealed the epileptic network in nine patients with dynamic subnetwork connections proximate to the epileptic focus ( ).
Visualization of dynamic epileptic subnetwork. For each patient, the dynamic epileptic subnetwork is shown in the form of a brain graph in axial, coronal, and sagittal views. Green spheres of equal size represent fROIs, labeled with a number indicating their statistical relevance in the epileptic network. The strength of significant connections is color-coded according to a global color bar scaled in the range [−1, 1]. The dynamic epileptic subnetwork is also reported in the form of a lower triangular correlation matrix with equivalent color-code. The lightning bolt indicates the epileptogenic hemisphere for each patient. L, left; R, right ( ).
#### Electrical Source Imaging
Electrical source imaging (ESI) is a non-invasive, low-cost method of localizing the sources of the EEG signals recorded with scalp electrodes ( ). So, it also can be used in the EEG–fMRI analysis of localizing the epileptic sources.
In the study of Vulliemoz et al. ( ), 13 IED types detected from nine patients with focal epilepsy were used as the separate regressors in the GLM to obtain the map of IED-related BOLD signal changes. Also, in 12 cases, the electrical source imaging (ESI) could be performed successfully on the IEDs using a realistic head model (SMAC) and a distributed linear inverse solution (LAURA). The results showed that in 10/12 studies, ESI at IED onset (ESIo) was anatomically close to one BOLD cluster in which, for 4/12, it was most relative to the maximally significant positive BOLD cluster, and for 4/12, it was closest to the negative BOLD responses. Furthermore, in 6/12, ESI at a later time frame (ESIp) revealed a diffusion to remote sources co-localized with other BOLD clusters. So, this study showed that analyzing ESI and EEG–fMRI simultaneously can discriminate areas of BOLD response related to the initiation of IED from propagation areas.
In another similar study of Vulliemoz et al. ( ), the maps of BOLD responses explained by continuous activity of the estimated IED sources (cESI) were compared to the results of the conventional IED-related analysis. The comparison showed a concordance between the results in 13/15 different types of IED. The cESI model showed other major BOLD alterations in the concordant regions for 10/15, better detection of the IED-related BOLD responses in 4/7, and contaminated diffusion pattern due to the incompletely corrected artifacts of the source signal in four IED types.
Brodbeck et al. ( ) performed the ESI using LAURA on the IEDs of 10 operated patients with non-lesional MRI, and at postsurgical follow-up of at least 1 year five had extratemporal lobe epilepsy. The results showed localization of the SOZ in eight patients correctly, and it means that ESI reflects the definite source of the epileptic activity. However, the spike peak comprises the diffusion areas.
In another study ( ), nine children with refractory focal epilepsy undergoing pre-surgical evaluation were studied. The resected area was compared with three analyses for the localization of epileptic foci, which were, respectively, the conventional method, the analysis of IED-related BOLD changes using spike-specific voltage maps of average IED acquired from long-standing monitoring outside the scanner, and the ESI approach using LAURA. The concordant results of activation within the resection area using the mentioned analysis were revealed in three, four, and all the nine patients, respectively. Therefore, the ESI method is a more valid approach to localize the epileptic foci in children with refractory focal epilepsy.
Also, Centeno et al. ( ), studied 53 children with drug-resistant epilepsy, and the localization map of the epileptic focus was performed using BOLD responses, ESI, and the combination of both maps. Comparing the results with the presumed epileptic focus and the postsurgical outcome revealed significant maps in 52 patients, which included 47 for EEG–fMRI, 44 for ESI, and 34 for both. Also, the epileptogenic zone was concordant with the results of 29 patients, which included 11 for EEG–fMRI, 17 for ESI, and 11 for both ( ).
Localization extraction procedure from left to right. For the individual map of EEG–fMRI, the result of localization based on global maxima is shown. Also, for the individual map of ESI, the same result based on maxima in the map from the 50% rising phase of the IED is shown. For the combined test, the spatial conjunction of both maps was used for the localization. Only for the concordant maps, the result of combined localization was extracted from the region encompassing the ESI max and the closest significant EEG–fMRI cluster located in the same sub-lobe ( ).
### Long-Term EEG Recording
In conventional methods, an experienced neurophysiologist reviews the EEG obtained from within the scanner and identified and marked the timing of epileptiform discharges. Spikes were modeled as zero-duration events, convolved with a standard HRF, and used as a regressor for the GLM model and fMRI analysis ( ). Given that it is difficult to detect spikes inside the scanner due to artifacts, many studies have suggested automatic detection methods. These methods require long-term EEG recording outside the scanner ( , ). In many studies, to extract the spike pattern inside the scanner, it is necessary to identify the spike pattern of the same subject outside the scanner in order to extract the spikes inside the scanner through computational methods and detection algorithms ( , , ). To this end, IED-related spikes distinguished on the EEG collected outside the MRI scanner are averaged to build a patient-specific spike template, and their similarity is then examined through methods such as cross-correlation ( , , ). In these studies, all patients undergo a preoperative assessment at the hospital, including long-term monitoring ( ). To evaluate the extracted results from source localization algorithms, the results obtained need to be compared with the medical results obtained from different modalities. For the localization of SOZ and irritative zone (IZ) in the pre-surgical evaluation of each patient, all the available data such as the comprehensive clinical record, full neurological examination, long-term video-EEG monitoring ( ), structural MRI ( ), neuropsychological assessment, and other non-invasive investigations such as PET and ictal SPECT ( ) are usually reviewed.
An important study by Grouiller et al. ( ) benefited from long-term EEG recording to localize seizure foci in patients without inside scanner IEDs. To this end, the correlation of epilepsy-specific EEG voltage maps with the hemodynamic changes was investigated in 23 patients with focal epilepsy. An epilepsy-specific EEG voltage map was built by averaging IEDs acquired from long-term clinical EEG recording outside the scanner. Then, for each time frame, the correlation between the voltage maps of the EEG signals outside and inside the scanner was calculated. Next, the time course of the correlation coefficient convolved with a standard HRF was used as a regressor for fMRI analysis. The results of this technique were like those of the conventional analysis in all five patients who had significant BOLD changes associated with IEDs. More importantly, the method correlated BOLD responses with the scalp maps of epileptic activity in 14 out of the remaining 18 patients who had inconclusive simultaneous EEG–fMRI study using conventional analysis due to the absence of IEDs in the inside scanner EEG recording.
In another study ( ), 30 patients with drug-resistant TLE and undergoing TL resection were monitored. The IEDs were visually identified by experts on the intra-MRI EEG, and the average topography map of IEDs recorded during long-term video-EEG outside the scanner was computed. Then, both of them were used as the regressors of a GLM analysis, and the results of BOLD responses in TL were divided into two groups of Concordant and Discordant compared to the surgical resection areas. So, it was revealed that 13 of the patients with good surgical outcomes were in the concordant group (16 patients), and only three of them were in the Discordant group (14 patients).
In our previous study ( ), we extracted the IED template from the outside of the scanner for computing the correlation. To this end, IED-related spikes were detected in the outside of the scanner and were averaged to build a patient-specific spike template. After band-pass filtering, the template was ultimately outlined by a significant spike deflection on the EEG channels, beginning from the onset at baseline to the negative peak of the following slow wave. The objective was to identify the neural behavior of epileptic generators by detecting the components-of-interest and using the GLM analysis substituting in the classical linear regressor. The general pipeline of this study is shown in . This method applied 28 IED sets from nine patients who were excluded for surgery because of the unclear focus in four, presumed multifocality in three, and a combination of the two conditions in two of them. The results revealed at least one BOLD response, which was significant, positive, and topographically related to the IEDs in eight patients.
Graphic illustration of the suggested method for identification of components ( ).
### Localization of Epileptic Focus Using Other Approaches
#### EEG Slow-Wave Discharges
In the study of Laufs et al. ( ), a patient with refractory epilepsy was studied using continuous EEG–fMRI, characterizing the seizures by head turning to the left and clonic jerking of the left arm that suggests a right mesial frontal onset zone. The routine interictal EEG showed symmetrical post-central alpha rhythm and occasional runs of independent, non-lateralized slow activity in the delta band with right frontocentral dominance. Although long-term scalp EEG, structural MRI, and the EEG during simultaneous EEG–fMRI showed no clear significance, the observed slow activity suggests a role for seizure localization with EEG–fMRI even in the absence of clear interictal discharges.
Manganotti et al. in ( ) compared the BOLD signal changes on fMRI in two states of rest and activation in terms of EEG focal interictal slow-wave discharges. In all the eight volunteered patients with partial epileptic seizures, the EEG activation of focal slow-wave discharges caused a significant BOLD activation in the related brain region. This significant concordance showed that focal BOLD activation provides useful information for the pre-surgical process even in partial epilepsy patients whose standard EEGs demonstrate focal interictal slow-wave discharges without spikes.
#### Additional EEG Measures
In the recent EEG–fMRI studies for identifying epileptic focus, some patients have shown poor sensitivity and inconsistency between EEG epileptic foci and BOLD activation patterns. That said, using additional measures may be helpful for better localization of epileptic focus. Moehring et al. ( ) studied 11 children with focal epilepsy. Then, the sleep-specific activities such as sleep spindles, k-complexes, and vertex sharp waves were extracted, characterized as a twig function, convolved with a canonical HRF peaking at 6 s, and considered in the GLM as the additional separate regressors. The results showed that considering these regressors increased the significance of activated voxels inside the anticipated IED source area and decreased the number of significantly activated voxels outside of it. So, using the sleep-specific activities in the statistical model is useful to achieving better sensitivity and results of identifying seizure foci in epilepsy.
Also, in the study of R. Abreu et al. ( ), the phase synchronization index (PSI) and global field synchronization (GFS) within the frequency bands of 1–45 and 3–10 Hz along with the root mean square frequency (RMSF), total power (TP), and conventional unitary regressors were computed and used to reveal the associated epileptic networks on nine EEG–fMRI datasets including IEDs. After cross-validating the results through ESI, the best performance was revealed using the average PSI within 3–10 Hz across several measures in all datasets ( ). Also, testing the PSI in three patients with no IEDs during EEG recording showed partially reasonable networks in all patients.
The results of epileptic network mapping for a patient. (Top–Left) The epileptic networks obtained using the EEG regressors UR, TP, RMSF, GSF , and PSI , together with the number of voxels (Nvox); the color codes red-yellow and blue-green depict positive and negative BOLD responses, respectively. (Top–Right) The BOLD signal measured at the maximum Z-score voxel (black trace), the average BOLD signal within the activation cluster (blue trace), and EEG regressor (thicker red trace), together with the maximum Z-score (Zmax) and the variance explained by the motion parameters (VEMP). (Bottom–Left) ESI solution maps at IED onset and propagation, obtained at half the maximum of the first rising phase of GFP and its associated peak, respectively, for validation of the GLM-derived epileptic networks. Consistent results with the ESI solutions were obtained for all patients with clear IEDs only when using the PSI metric. (Bottom–Right) Correlation matrix between all metrics of interest ( ).
#### Mutual Information Maps
The most outstanding feature of using mutual information (MI) for the EEG–fMRI analysis is the balance of involving both imaging modalities, not requiring any prior model of HRF or relationship between EEG spikes and BOLD responses ( ).
In the study of Caballero Gaudes et al. ( ), five patients with epilepsy underwent EEG–fMRI and electroclinical localization of epileptic focus. For each IED onset, a period with TR duration was defined, and the result was downsampled to the temporal resolution of BOLD signals. Then, the voxel-wise MI was computed between the EEG–fMRI score and the fMRI data, and MI maps were thresholded using a non-parametric wavelet resampling approach. Comparison of the results with the electroclinical localization and conventional GLM-based analysis revealed a concordance of focal BOLD responses in four patients.
Caballero-Gaudes et al. ( ) investigated the MI between the IEDs on EEG and BOLD signal on fMRI to generate the MI maps and validate its performance for the localization of epileptic focus ( ). The EEG–fMRI data of 14 patients with pharmacoresistant focal epilepsy were used to generate the MI maps based on the four-dimensional wavelet packet resampling method. Comparing the results with the statistical maps obtained from two conventional GLM methods showed the same concordance of ~57% with the epileptogenic area defined electro-clinically or surgically.
Schematic diagram of the information-theoretic approach. The EEG recorded in the MR scanner is corrected for gradient and pulse artifacts. The time of occurrence of IED peaks is marked, and the EEG score indicating the existence of epileptic activity is created and finally downsampled to the temporal resolution of fMRI (TR) to generate the EEG–fMRI score (top gray-shaded square). The fMRI data is first preprocessed (rigid-body registration for motion correction, spatially smoothed, high-pass filtered, and z-normalized). The MI between the fMRI voxel time series and the EEG–fMRI score is computed based on the entropy and conditional entropy (bottom-right red-shaded square) at multiple latencies by shifting the EEG–fMRI score, resulting in an MI time course. The shape of the HRF is deconvolved based on the IED timing. Significant MI statistics are those exceeding a thresholded th , which is chosen according to the non-parametric statistical procedure where 19 surrogate datasets created with a 4D wavelet resampling approach are analyzed in the same way as the original dataset and the PDF of the MI statistics under the null is estimated (bottom gray-shaded square). To summarize the results, three maps are generated: a maximum MI map, a latency map showing the latency at which the maximum MI occurs, and a map plotting the amplitude of the HRF at the latency of the maximum MI ( ).
#### Voxel-Based Morphometry
In the study of Salek-Haddadi et al. ( ), nine patients with reading epilepsy underwent simultaneous EEG–fMRI with an extra recording of voice, electromyography (EMG), and electrocardiography (ECG), and six of them experienced reading-induced seizures during recording. Also, 30 neurologically normal control subjects with a similar age range and gender distribution were scanned for comparison. Voxel-based morphometry (VBM) was used for the structural brain analysis. However, as the result of VBM analysis, no significant differences in gray matter density were detected comparing the epilepsy patients with the control group.
#### Non-linear Hemodynamic Responses
Pouliot et al. ( ) studied the EEG–fMRI data recorded from three patients with refractory focal epilepsy for quantifying non-linear hemodynamic responses using the second-order expansion of the Volterra kernel. In the Volterra expansion, which is a functional Taylor expansion, the time-dependent inputs were epileptic spikes, and the outputs were BOLD, oxyhemoglobin (HbO), and deoxyhemoglobin (HbR) time series at a certain fMRI voxel. The results showed significant non-linearities in all the patients with a good concordance to the epileptic focus and negative BOLD response regions. Furthermore, this method identified the epileptic focus in one patient who had shown nothing while common analyses.
#### Two-Dimensional Temporal Clustering Analysis
The two-dimensional temporal clustering analysis (2dTCA) is a data-driven approach for the localization of epileptic networks using fMRI data. Maziero et al. in ( ) used the EEG–fMRI data of 14 patients with epilepsy as inputs to the 2dTCA for generating the histograms and adding to GLM as predictors. The results showed success in eight patients, not confined to the presence of IEDs, while the conventional analysis identified coherent maps in only six patients who had at least one IED during recording.
Maziero et al. ( ) also used the 2dTCA to map the seizure onset zone in 18 patients with focal epilepsy (12 presenting IEDs). The results of this method, along with the conventional method, were compared to the region of surgical resection. The concordant results showed that 2dTCA was successful in localizing the EZ in 13 patients (3 of the cases with no IEDs), but the conventional method was successful in only five of the patients who presented IEDs.
#### Lateralization Index
Mangalore et al. in ( ) used the EEG–fMRI data of 10 patients with refractory epilepsy who showed well-formed IEDs in a proposing method to lateralize the seizure focus in an ROI with the aid of the peak BOLD signals. For each patient, the lateralization index was computed from the significant clusters of different ROIs using the following formula: the number of activated voxels multiplied by the Z-scored intensity of activation in the given ROI. Then, the seizure focus was determined by thresholding the lateralization index. Compared with the output of other modalities, the results of this method were successful in temporal and extratemporal lobe epilepsy, reflex epilepsy, and lesional epilepsy. The only disadvantage of EEG–fMRI in this work was if irrelevant BOLD changes were correlated with the specified IED or not.
#### Adapted Directed Transfer Function
In the study of Qin et al. ( ), 18 patients with juvenile myoclonic epilepsy (JME) underwent simultaneous EEG–fMRI. Between EEG electrodes, the adapted directed transfer function (ADTF) values were computed to describe the time-varying network, and its information within sliding windows were used as a temporal regressor in GLM analysis ( ). The outcomes demonstrated that BOLD activations allied with high network variation were mostly placed in the thalamus, cerebellum, precuneus, inferior TL, and sensorimotor-related areas, including the middle cingulate cortex (MCC), supplemental motor area (SMA), and paracentral lobule. Also, the deactivations related to medium network alternative were originated in the frontal, parietal, and occipital areas.
An overview of the suggested EEG–fMRI analysis. (A) After preprocessing the EEG signal, the time-varying scalp network was constructed using ADTF. (B) The variation of the ADTF information flow between electrodes in each 2-s time window was extracted for the generation of network variation time series. (C) The significant values of network variation time series exceeding one standard deviation and the mean were selected as the regressors and added to the GLM analysis, respectively. (D) The results were acquired from the GLM analysis ( ).
#### Four-Stage Localization Method
Wan et al. ( ) proposed a four-stage method for the localization of SOZ that includes identifying events of interest using Hilbert transform, acquiring channels of interest (CoIs) using the Shannon-entropy-based complex Morlet wavelet transform (SE-CMWT)-based power spectral density, detecting high-frequency oscillations (HFOs) on CoIs with the combination of adaptive-genetic-algorithm-based matching pursuit (AGA-MP) and Morlet wavelets, and localizing SOZs based on the half-maximum method using characteristics of HFOs. This approach showed the highest sensitivity and specificity compared to the four existing methods of SE-CMWT, AGA-MP, RMS, and CMWT.
### Ancillary Issues
#### The Relation Between rCBF and Epileptogenic Areas
Studies have shown that seizures induced by musical stimulation, especially in temporal epilepsy, cause a rise of regional cerebral blood flow (rCBF) in putative epileptogenic foci and the other brain regions. However, this is a virtual temporal relation between epileptic discharges and rCBF changes due to the offline EEG recordings ( ). In the study of Marrosu et al. ( ), simultaneous EEG–fMRI recording of musicogenic elicited seizures was studied in a patient with partial epilepsy. The statistical maps obtained from the GLM technique showed that EEG features extracted from epileptogenic areas are largely coupled with rCBF increase. Also, the rCBF changes in other areas may suggest further aspects of musicogenic seizures. For instance, this physiological activation induced by music in several brain areas may initiate musicogenic seizures in predisposed subjects.
#### Validation of EEG–fMRI Results Using a Gold Standard
For the validation of EEG–fMRI outcomes with a gold standard to figure out the actual role of this multimodal approach in pre-surgical evaluation, Houdt et al. ( ) compared the correlation patterns of EEG–fMRI data acquired from 16 surgical candidates with the involved brain areas of ECoG IEDs, the SOZ, resected area, and degree of seizure freedom ( ). The results of the comparison revealed a concordance between at least one of the EEG–fMRI areas and an interictally active ECoG area for all patients. Also, the EEG–fMRI areas covered the whole SOZ in 83% and resected area in 93% of the dataset.
Flowchart of ECoG analysis consisting of two steps: estimation of interictally active ECoG areas (steps 1–6) and the estimation of an onset area (steps 7–9) ( ).
#### The Relations Between IEDs and SOZ
Regarding the relations between IEDs and SOZ, Yamazoe et al. ( ) hypothesized that the number of IEDs and their spatial extent could contribute to revealing the SOZ. To test this hypothesis, 157 types of IED grouped by spatial distribution were extracted clinically from the EEG–fMRI data of 64 patients with refractory localization-related epilepsy. Then, each IED was convolved with four HRFs peaking at 3, 5, 7, and 9 s to construct four regressors, and a combined t -map was created with the most significant t -value at each voxel. Two levels of significance were defined to observe reliable activation in the combined t-maps. The first level was defined by any set of five contiguous voxels with the t -value ≥ 3.1, and the second level was the t -values being higher than the whole-brain topological false discovery rate (FDR) of 0.05 for multiple-cluster comparisons. For each type of IED, the primary cluster was referred to as the cluster with the highest absolute t -value at a peak located in the cerebral cortex compared to the thresholds defined in significance levels. Finally, the presumed seizure onset zone (pSOZ) of the patients that were determined using SEEG findings or the other comprehensive evaluations ( ) was compared to the primary cluster in EEG–fMRI to measure their concordance at the sublobar level. The result of this study confirmed the initial hypothesis and revealed the significance in the number of IEDs in the types with t -value above FDR that was higher than below FDR and in the extent of IED types concordant with the SOZ that was larger than IED types discordant with the SOZ. The complex pathophysiology of epileptic cerebral structures, types of seizures, and frequency features have not been studied as the authoritative factor for precise detection of epileptic foci using EEG–fMRI ( ).
## Conclusions
Recording EEG and fMRI simultaneously is a non-invasive method identifying cerebral hemodynamic changes related to IEDs on scalp EEG. Several studies revealed the capacity of EEG–fMRI to distinguish various forms of generalized and focal epilepsy. In patients with epilepsy, especially those who are pharmacoresistant and surgical candidates, the significant clinical matter of how BOLD changes relate to IEDs can contribute to localizing the epileptic focus. The BOLD signal usually rises in regions causing focal IEDs, but often in the context of more extensive, or even distant, responses.
The simultaneous EEG–fMRI recording is an effective non-invasive method to study the brain regions associated with the epileptic discharges. The neuronal discharges that occur through interictal spikes or spike-wave bursts cause an increase in metabolism and blood flow, redirected in the BOLD signal measured by fMRI. Although this increase has the highest intensity in generating discharges, it can be revealed in areas only affected by the discharges. Also, the epileptic discharges can lead to a decrease in metabolism that the origin of which is not completely understood. It has been shown that EEG–fMRI applied to patients with focal epilepsy results in maxima of the BOLD signal most often concordant with other localization methods and helped to localize the epileptic focus in non-lesional frontal-lobe epilepsy. It has also been revealed that the thalamus is an active region in generalized epileptic discharges. These can be used to investigate the location and extent of the brain regions intricate during epileptic discharges and evaluate the disease progression.
Simultaneous recording of EEG and fMRI provides a great potential to find the pathophysiological mechanisms of the discharges ( ). The most capable method of acquiring data is probably continuous scanning followed by EEG artifact removal. Some cases have shown inconsistent fMRI results with EEG. However, we cannot imagine a one-to-one correspondence between EEG and fMRI findings. These inconsistencies may be due to the fMRI data analysis problems. Some of the responses shown in the fMRI results are “noises” caused by practical artifacts such as movement, an erroneous HRF model, or inappropriate statistical methods. Despite the noise, most responses can be considered valid since they make sense in the context of our understanding of an epileptic condition.
It is also essential to consider the natural differences between the two modalities. First, in fMRI, the BOLD response is measured everywhere, but EEG records only superficial cortical layer activity. Secondly, two different types of activities are evaluated: one is electrical, and the other is based on the changes in deoxyhemoglobin in the veins. EEG and fMRI are considered complementary since each measures an activity that the other one does not.
Although the ideal approach of data analysis remains undefined, the majority of focal and generalized epilepsy patients had a consistent BOLD effect with the spikes. Instead of using techniques developed for functional activation in the future, there should be a focus on adapting fMRI analysis techniques to the specific requirements of the epileptic activity. Friston et al. ( ) proposed a method that does not depend on linear assumption. Other approaches such as temporal clustering try to analyze the BOLD signal independently of the EEG event ( , ). The deconvolution approach makes assumptions with regard to HRF ( , ). Finally, the ICA approach decomposes the data sets into spatially independent components. Using some of these methods, we may be able to discover epileptic discharges anywhere in the brain, regardless of seeing spikes on the scalp EEG.
The importance of the diverse BOLD response is another issue that should be assessed. In epilepsy studies, the fact that we see both activation and deactivation is considered perplexing, where it is expected to see activation (increased BOLD) as a result of extreme neural activity. Moreover, it is important to assess particular responses in different types of epileptogenic structural abnormalities such as mesial temporal sclerosis, brain tumors, and malformations of cortical development (MCDs), which are commonly complicated by intractable focal epilepsy ( ).
The presence of both positive and negative BOLD responses in generalized epilepsy patients may be interpreted differently, also indicating the explanation of deactivation. Bilateral activations were observed in the thalamus, mesial mid-frontal region, insulae, and cerebellum. Deactivations were found bilaterally in the anterior frontal and parietal regions, in a global pattern resembling the default state of the brain ( ). This finding suggests that the default state of the brain is suspended during an epileptic discharge. Deactivation occurs as a result of the indirect effect of the discharges on attention mechanisms. Performing these studies on experimental animals provides further insight into human results ( , ).
When the BOLD responses are found in multiple regions, particularly in focal epilepsy, this possibility arises that the regions are related to the propagation of the interictal discharge, or distant sites particularly sensitive to the effect of epileptic discharges. However, the temporal resolution of fMRI is not able to measure the propagation times of a few milliseconds. So, the EEG source modeling can help to assess the propagation of epileptic discharges if the model includes EEG sources in the same regions as BOLD responses. BOLD response patterns may be different in the primary epileptogenic region and in the region in which the activity propagated ( ). It would be interesting to assess functional connectivity using the fMRI data ( ).
In the past, most studies used a 1.5-T scanner, although a few studies used 3 T. Using a 3-T scanner may create the expectation of better recognition of hemodynamic changes and deteriorating some difficulties such as higher signal loss as a result of susceptibility artifact, the pulse artifact, and movements that cause worse artifacts in the EEG. Fortunately, with suitable artifact removal methods, studies in a 3-T scanner would be more efficient ( ).
Finally, an important study of Markoula et al. ( ) assessed the impact of EEG–fMRI on the clinical decision-making process and showed the actual capability of this approach to be applied prospectively in localization of seizure focus during the pre-surgical evaluation. They studied 16 patients with refractory extra-temporal focal epilepsy, referred for pre-surgical evaluation in a period of 18 months. Interpretable EEG–fMRI results which were available in 13 patients made a modification of the initial surgical plan in 10 (77%), suggesting a significant influence of EEG–fMRI on epilepsy surgery planning.
In conclusion, combining EEG and fMRI seems to be a potential method in the source localization of epileptic foci. This complicated technique is quite practical and offers a new view in the study of epileptic disorders. Although applying it to individual patients (subjects) to localize epileptic foci is not yet justified, it can present potential areas for further research, for instance, focused anatomical MRI analysis or electrode implantation.
All in all, the works reviewed in this paper can bring us closer to the localization of focal epileptic activity and, afterward, to real-life applications. Applying simultaneous EEG–fMRI for combining EEG temporal resolution and fMRI spatial resolution recommends more excellent diagnoses of precise epileptic source localization. This allows for providing more patients with the option of surgery while increasing the likelihood of a successful and life-improving operation.
## Author Contributions
SS, EE, and HS-Z jointly designed the study. SS, EE, and MSh did the literature survey and wrote the initial version of the manuscript. HS-Z edited the draft and submitted the manuscript. All authors participated in the revision process and approved the final version of the manuscript.
## Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
|
Objectives: Autoradiography on brain tissue is used to validate binding targets of newly discovered radiotracers. The purpose of this study was to correlate quantification of autoradiography signal using the novel next-generation tau positron emission tomography (PET) radiotracer [ F]PI-2620 with immunohistochemically determined tau-protein load in both formalin-fixed paraffin-embedded (FFPE) and frozen tissue samples of patients with Alzheimer's disease (AD) and Progressive Supranuclear Palsy (PSP).
Methods: We applied [ F]PI-2620 autoradiography to postmortem cortical brain samples of six patients with AD, five patients with PSP and five healthy controls, respectively. Binding intensity was compared between both tissue types and different disease entities. Autoradiography signal quantification (CWMR = cortex to white matter ratio) was correlated with the immunohistochemically assessed tau load (AT8-staining, %-area) for FFPE and frozen tissue samples in the different disease entities.
Results: In AD tissue, relative cortical tracer binding was higher in frozen samples when compared to FFPE samples (CWMR vs. CWMR : 2.5-fold, p < 0.001), whereas the opposite was observed in PSP tissue (CWMR vs. CWMR : 0.8-fold, p = 0.004). In FFPE samples, [ F]PI-2620 autoradiography tracer binding and immunohistochemical tau load correlated significantly for both PSP ( R = 0.641, p < 0.001) and AD tissue ( R = 0.435, p = 0.016), indicating a high agreement of relative tracer binding with underlying pathology. In frozen tissue, the correlation between autoradiography and immunohistochemistry was only present in AD ( R = 0.417, p = 0.014) but not in PSP tissue ( R = −0.115, p = n.s.).
Conclusion: Our head-to-head comparison indicates that FFPE samples show superiority over frozen samples for autoradiography assessment of PSP tau pathology by [ F]PI-2620. The [ F]PI-2620 autoradiography signal in FFPE samples reflects AT8 positive tau in samples of both PSP and AD patients.
## Introduction
Many neurodegenerative diseases are still lacking options to reliably diagnose the causal neuropathology in vivo . Facing the huge and growing number of patients suffering from those diseases, it will be important for global health care systems to improve diagnosis and to stratify individuals at risk in order to provide the best patient management and offer possible inclusion to therapy studies. In addition to characterization of the clinical phenotype, supportive in vivo biomarkers have been introduced to many diagnostic schemes to describe the neuropathological correlates of the diseases ( ).
Tauopathies form the major group of adult-onset neurodegenerative diseases including Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD-tau) spectrum diseases including some atypical Parkinsonian syndromes. In vivo visualization of tau deposits is now facilitated by positron emission tomography (PET) using different tau-targeting tracers ( – ), but reliable quantification of intracerebral tau burden remains difficult. First-generation PET tau tracers suffered from large inter- and intra-case variability due to off-target binding ( – ). Therefore, it is crucial to carefully validate novel next-generation tau tracers in vitro on human postmortem brain tissue of neuropathologically confirmed tauopathies. While the mixed three-/four-repeat (3R/4R) tau pathology of AD has been shown to be detectable with various tau-specific radiotracers ( – ), detection of 4R tau in progressive supranuclear palsy (PSP) is more challenging and only few examples of detectable binding have been presented so far ( , ). In a subsample of those cases, blocking with excessive nonlabeled [ F]PI-2620 indicated specific binding in PSP ( ).
Although many neuropathology departments aim to preserve both frozen and formalin-fixed paraffin-embedded (FFPE) samples, the preservation of brain samples is not standardized due to different resources and available procedures. The formalin-fixation is broadly available, does not require expensive equipment and the storage is convenient. Furthermore, it is tissue-conserving and does not interact with the primary structure of cells, but long storage of tissue in formalin leads to denaturation of proteins and deoxyribonucleic acids ( ). Many studies suggest freezing as a better way to preserve molecular targets for analyses requiring very high resolution, such as examination of protein structures ( ). However, the applied freezing procedure including the reachable freezing time has a great impact on the quality of frozen tissue samples and can diminish the integrity of proteins. Also, different antibodies have different binding characteristics in FFPE/ frozen samples ( ). Taken together, the superiority of freezing over formalin fixation could not be confirmed in a head-to-head comparison using various immunohistochemistry antibodies ( ).
In this regard, autoradiography (ARG) aiming to detect aggregated tau has been successfully conducted on paraffin-embedded ( ) and frozen brain tissue ( , ). ARG with β-amyloid radiotracers was also successfully performed with both types of tissue samples ( – ). However, head-to-head comparisons of ARG in FFPE and frozen tissue are rare. A small subsample of six patients with AD showed that despite a good correlation between both techniques, the binding [pmol/mg] of the radioligand ([ H]PiB) to the FFPE samples was only 43 ± 24% of that observed in the corresponding frozen tissue samples ( ). In a study investigating the β-amyloid PET tracer [ F]florbetapir, frozen and FFPE brain sections were analyzed head-to-head, both showing strong quantitative correlations in the gray matter between radiotracer binding (optical density of the signal) and β-amyloid (% area) to immunohistochemistry ( ). Supportive in vitro ARG data of our recent [ F]PI-2620 investigation in PSP showed a detectable and blockable tracer signal in FFPE sections of the basal ganglia and the frontal cortex ( ). However, other groups were not able to see elevated [ F]PI-2620 binding in frozen PSP tissue ( , ).
Therefore, the objective of this study was to systematically investigate the impact of tissue sample preparation on radiotracer binding in [ F]PI-2620 ARG of PSP and AD samples. We aimed to compare ARG quantification of both tauopathies and healthy controls (HC) between FFPE and frozen tissue. Furthermore, ARG quantification of [ F]PI-2620 binding to both tissue types was correlated with the immunohistochemically assessed tau load.
## Materials and Methods
### Brain Tissue Samples
Tissue samples of all autopsy cases investigated were provided by the Neurobiobank Munich, Ludwig-Maximilians-University (LMU) Munich. They were collected according to the guidelines of the local ethical committee and usage of the material for this project was additionally approved (application number 19-244). Autopsies and subsequent analysis of brain pathology were performed according to standardized protocols ( – ). During autopsy, one hemisphere of the brain is fixed in formalin and the other hemisphere is sliced and frozen at −80°C. Samples of frozen and formalin-fixed tissue of the frontal cortex (Gyrus frontalis medius) from six patients with AD, five patients with PSP and five HC were included in the analysis. Only cases without relevant co-pathology in the cortical target region (negative for α-synuclein and Aβ in PSP/ HC) were selected from the database. Cortical tissue was chosen to evaluate binding in a brain area with limited off-target sources and to be able to directly compare AD and PSP tissue. Furthermore, collection of frozen basal ganglia material can be accompanied by destruction of sample material in the surrounding brain areas. Therefore, we compared both techniques in frontal cortex material which is affected in both diseases and is relatively easy to obtain in both FFPE and frozen material (stored at −80°C) from the same patients by collecting the respective samples from both hemispheres.
### Immunohistochemistry and Tau Load Quantification
Immunhistochemistry was performed on 4 μm thick paraffin sections of formalin-fixed tissue and on 10 μm thick brain sections of frozen brain tissue using standard techniques. The immunohistochemical tau-staining was performed semi-automatically on a BenchMark device (Ventana, now Hoffmann-LaRoche, Basel, Switzerland) with mouse monoclonal AT8 antibody raised against hyperphosphorylated tau (Ser202/Thr205, 1:200, Invitrogen/Thermofisher, Carlsbad, CA, USA) on adjacent sections of those used in the ARG. The immunostained sections were digitized at 20x magnification with a Mirax Midi scanner (Zeiss, Carl Zeiss MicroImaging GmbH, Jena, Germany). Five cortical gray matter regions of interest were drawn manually and the tau load (in %) was quantified using the Pannoramic Viewer (1.15.2) software with HistoQuant (3DHISTECH, Budapest, Hungary) based on color threshold values (see ). All brain sections included are shown in the .
Tau load quantification in immunohistochemistry (IHC) samples with AT8-staining based on color thresholds on the left (upper image: original AT8 image, bottom image: all parts within the threshold marked in pink) and quantification of autoradiography signal (ARG) in corresponding frontal cortex brain areas (on the right). The subcortical white matter (WM) was used as a reference region.
### In vitro ARG
[ F]PI-2620 was synthesized as previously described ( ). For each subject and sample type (FFPE, frozen), ≥6 sections (consecutive to immunohistochemistry) were prepared for ARG and ≥4 artifact-free sections (no freezing artifacts, no artificial tracer retention due to insufficient washing, intact brain tissue) were used for analysis (see for all used and excluded brain sections). The sections (both FFPE and frozen) were incubated with [ F]PI-2620 (21.6 μCi/ml after dilution to a volume of 50 ml with phosphate buffered saline solution, pH 7.4, specific activity 480±90 GBq/μmol) for 45 min. Washing was performed by 30% ethanol/PBS for 1 min, 70% ethanol/PBS for 2 min and PBS for 1 min. After drying at room temperature for 60 min, the sections were placed on Fujifilm BAS cassette2 2025 imaging plates. The plates were exposed for 12 h and then scanned at 25.0 μm resolution with the Elysia-raytest equipment (CR-35 BIO, Dürr Medical, Bietigheim-Bissingen, Germany). Resulting images were analyzed with a dedicated software (AIDA image analysis, V4.50, Elysia-raytest, Straubenhardt, Germany). Five cortical gray matter regions of interest were drawn manually on each sample with AT8 staining of adjacent sections serving for precise anatomical definition (but blinded to quantification of AT8). AT8 negative white matter (assessed visually) served as reference region (circle area) and for calculation of tracer uptake ratios between target and reference regions. The background of the photo plate was subtracted before quantification. Analysis resulted in ARG binding ratios between frontal cortex and white matter (CWMR) for both FFPE (CWMR ) and frozen samples (CWMR ). For illustration of the ARG signal quantification see . All brain sections included are shown in the .
### Statistical Analysis
GraphPad Prism (version 8.4.3, GraphPad Software Inc., San Diego, CA, USA) was used for statistical analysis and illustration of results. Demographics of patient groups (age, postmortem delay, fixation time) were compared using a Kruskal-Wallis test for multiple comparisons). Immunohistochemical tau load and ARG binding ratios were compared between AD, PSP, and HC for both tissue types by a Welch analysis of variance (after testing for homogeneity of variances by Brown-Forsythe) and Dunnett's T3 multiple comparisons test. ARG signal quantification of FFPE and frozen samples were correlated with corresponding immunohistochemical tau load in the same tissue type samples by a linear regression with error bars of ARG ratio quantification results (separately for AD and PSP). A significance level of p < 0.05 was applied in all analyses.
## Results
### Demographics
summarizes demographic, clinical, and neuropathological characteristics of patients included. The cohort consisted of six subjects with AD (mean age 75 years, range 63–88, 4 female), five subjects with PSP (mean age 76 years, range 67–85, 4 female) and five HC (mean age 57, range 46–85, 2 female). The mean age was not significantly different between groups (AD: 75 ± 8 y, PSP: 76 ± 9 y, HC: 58 ± 16 y). The postmortem delay was equal in all groups (AD: 27.8 ± 15.0 h, PSP: 18.4 ± 12.9 h, HC: 20.6 ± 2.2 h). When compared to HC, the fixation times were longer in AD ( p = 0.025) and PSP patients ( p = 0.066), but not between AD and PSP (AD: 135 ± 77 d, PSP: 117 ± 68 d, HC: 8 ± 4 d).
Patient characteristics.
AD, Alzheimer's disease; PSP, progressive supranuclear palsy; HC, healthy control; CBS, corticobasal syndrome; PD, Parkinson's disease; FTD, frontotemporal dementia; NNPD, no neurological/psychiatric disease; CHD, coronary heart disease; DM, diabetes mellitus; MI, myocardial infarction; CAA, cerebral amyloid angiopathy; AGD, agyrophilic grain disease; NNPF, no neuropathological findings; ABC-score, Amyloid Braak CERAD (Consortium to Establish a Registry for Alzheimer's Disease); Aβ, amyloid-β; α-syn, α-synuclein; TDP-43, Transactive response DNA binding protein 43 kDa; FUS, fused in sarcoma; n.s., not specified .
All cases were lacking relevant copathology in the cortical target region (negative for α-synuclein and Aβ for PSP/HC). All AD patients had high Alzheimer's disease neuropathologic change (ADNC) levels (A3, B3, C3) ( ).
### Visual Assessment of in vitro [ F]PI-2620 Binding and Immunohistochemical AT8 Staining
Visual comparison between in vitro [ F]PI-2620 binding and AT8 staining revealed high concordance for PSP and AD cases in FFPE samples. In vitro [ F]PI-2620 binding in the cortex of PSP tissue was consistently lower when compared to AD tissue. Exemplary FFPE and frozen samples of one HC, PSP, and AD patient each are shown in .
Exemplary tau-immunohistochemistry (left corresponding sections) and autoradiography (right corresponding sections) of frontal cortex sections of both tissue types (FFPE on the left, frozen on the right) in a healthy control (both no. 15), progressive supranuclear palsy (both no. 8) and Alzheimer's disease (FFPE: no. 4, frozen: no. 3). Upper row: section overview, lower row: zoom. FFPE, formalin-fixed paraffin-embedded; IHC, immunohistochemistry; ARG, autoradiography.
### Comparison Between FFPE and Frozen-Tissue Samples
provides an overview of immunohistochemistry and ARG results. The immunohistochemically determined tau load (AT8 staining, separately for FFPE and frozen tissue) was significantly higher in AD tissue when compared to PSP (AT8 : 9.7-fold, p < 0.001; AT8 : 7.4-fold, p < 0.001) as illustrated in . For both FFPE and frozen-tissue samples and in accordance with the immunohistochemical tau load, cortical [ F]PI-2620 binding ratios were significantly higher in AD tissue when compared to PSP (CWMR : 2.1-fold, p < 0.001; CWMR : 6.8-fold, p < 0.001) and HC (CWMR : 2.8-fold, p < 0.001; CWMR : 6.8-fold, p < 0.001).
Immunohistochemistry and autoradiography results.
Quantitative comparison of immunohistochemistry (% tau load) and autoradiography (cortex to white matter ratio) between HC, PSP and AD in FFPE (A,C) and frozen (B,D) tissue samples. Violin plots represent the distribution of data with the median and quartiles. HC, healthy controls, PSP, progressive supranuclear palsy; AD, Alzheimer's disease; FFPE, formalin-fixed paraffin-embedded; IHC, immunohistochemistry; ARG, autoradiography; CWMR, cortex to white matter ratio; *** p < 0.001; n.s., not significant.
In PSP, significantly higher cortical [ F]PI-2620 binding ratios compared to HC were only evident in paraffin-embedded samples (CWMR : 1.3-fold, p < 0.001) but not in frozen-tissue samples (CWMR : 1.0-fold, p = n.s.) ( ).
Comparing FFPE and frozen samples, relative [ F]PI-2620 binding was higher in frozen AD samples when compared to FFPE (CWMR vs. CWMR : 2.5-fold, p < 0.001), whereas in frozen PSP tissue the relative binding was lower when compared to FFPE tissue (CWMR vs. CWMR : 0.8-fold, p = 0.004). All ARG binding ratio differences between groups are illustrated in .
### Quantitative Correlation of in vitro [ F]PI-2620 Binding and Immunohistochemical Tau Load
In FFPE samples, significant correlations between immunohistochemical tau load and relative [ F]PI-2620 binding in ARG were found for both PSP ( R = 0.641, p < 0.001) and AD tissue ( R = 0.435, p = 0.016) as illustrated in . In frozen tissue samples (see ), a significant correlation was only found for AD tissue ( R = 0.417, p = 0.014), whereas no significant correlation could be observed in PSP ( R = −0.115, p = n.s.).
Quantitative correlation of autoradiography and immunohistochemistry for FFPE (A) and frozen (B) tissue for all entities and AD/PSP patients (each symbol represents on patient), respectively. Simple linear regressions are expressed by R -values and regression lines with corresponding 95%-confidence intervals for AD and PSP. AD, Alzheimer's disease; PSP, progressive supranuclear palsy; HC, healthy control; FFPE, formalin-fixed paraffin-embedded; IHC, immunohistochemistry; ARG, autoradiography; CWMR, cortex to white matter ratio; * p < 0.05, *** p < 0.001.
## Discussion
Several next-generation PET tracers detecting tau pathology in the human living brain have been developed ( ) and were used in first clinical studies ( , – ). To ensure a reliable application as clinical diagnostics, it is essential to evaluate if the scan is representative of the underlying disease or suffers from off-target binding, which can lead to misinterpretation of the PET results. ARG is frequently used to investigate binding capacities of PET tracers in vitro , but tissue preparation prior to analysis is not standardized and might have an impact on ARG quantification. We present the first direct comparison of the next-generation tau PET tracer [ F]PI-2620 in both frozen and FFPE cortical brain sections from subjects with different tauopathies (AD and PSP) and HC. We show that the ARG signal correlates with the immunohistochemical tau load for both FFPE and frozen samples in AD. However, only FFPE but not frozen samples indicate a significant ARG correlation with the immunohistochemical tau load in PSP patients. This is also reflected by lacking detection of an elevated ARG binding in frozen PSP tissue when compared to healthy controls.
In our study, we were able to intra-individually compare both tissue preparation techniques, FFPE and freezing, in terms of their effects of concomitant ARG and immunohistochemistry. To our knowledge, our study represents the first direct comparison between FFPE and frozen sections with a tau radioligand. The only former study including a similar direct comparison of a β-amyloid ligand ([ F]florbetapir) between FFPE and frozen sections in AD samples found 43% lower binding ratios in FFPE sections when compared to corresponding frozen tissue ( ). In line with this finding, binding ratios of FFPE sections were 41% lower when compared to binding ratios in frozen sections in our AD samples. Thus, lower binding ratios in FFPE sections with high target abundance seem to be independent of the tracer target (i.e., tau or β-amyloid) and may indicate higher background binding in the target-free reference tissue or less preserved binding sites of the target in FFPE sections when compared to frozen tissue.
For in vivo differential diagnosis of patients with suspected tauopathies, radiotracer binding to aggregated tau needs to exceed background binding of HC and other neurodegenerative diseases lacking tau pathology. Although the limited resolution of PET naturally leads to higher binding ratios of ARG sections in vitro when compared to relative binding of a PET tracer in vivo ( ), ARG can still be used to predict in vivo PET results. In AD, [ F]PI-2620 indicated significantly elevated tracer binding in vitro (frozen sections) and in vivo ( , , ), and ARG binding ratios of our sample proved to be significantly higher than those of HC in both FFPE and frozen sections. Taken together, both FFPE and frozen sections appear to be usable to depict specific binding to 3/4R tau isoforms in AD with significant correlations for both techniques. However, higher binding ratios in frozen tissue samples need to be considered.
It has been shown for several β-amyloid and one first-generation tau PET radiotracer that FFPE and frozen tissue preparations provide significant correlations between the immunohistochemical amyloid, respectively, tau load and the ARG signal in AD patients ( , – ). In line, we also found significant correlations between ARG quantification and the immunohistochemically assessed tau load of corresponding brain sections with both FFPE and frozen AD brain sections, indicating that [ F]PI-2620 binding has an overall high agreement with the underlying tau pathology in AD. In a recent study with several tritium labeled next-generation tau radiotracers ([ H]PI-2620, [ H]RO948, [ H]MK6240, and [ H]JNJ067), all four radiotracers depicted AD-related tau inclusions (paired helical filaments) with high specificity ( ).
In the non-AD tauopathy PSP, elevated [ F]PI-2620 binding to PSP target regions has already been shown in a large multi-center investigation in vivo ( ), but in vitro results were discrepant for FFPE ( ) and frozen samples ( , ). FFPE samples (frontal cortex, basal ganglia) from two PSP patients lacking co-pathology indicated a blockable tracer signal ( ). Other studies with frozen samples only showed an elevated tracer signal when concomitant AD pathology was present ( ). Our current head-to-head comparison revealed results fitting to these preliminary findings, indicating that significantly elevated ARG binding of PSP tissue (in contrast to HC) was only present in FFPE sections, whereas frozen sections did not comprise discernible binding in patients with PSP. Furthermore, a significant correlation between the [ F]PI-2620 ARG signal and immunohistochemical tau load was only observed in FFPE samples, whereas frozen samples did not show any association between [ F]PI-2620 ARG binding and AT8 quantification. While findings using tissue with both preservation types are now confirmed at least at two independent sites, the question about the origin of this discrepancy remains to be solved. First, [ F]PI-2620 ARG binding ratios of PSP samples were significantly lower when compared to AD samples, which can be explained by the lower tau load of deceased patients with PSP when compared to the high tau load of most of the late-stage AD cases. Furthermore, we note that different [ F]PI-2620 binding affinities among the underlying 4R and 3/4R tau isoforms of PSP and AD could also contribute to the lower binding ratios in PSP ( ), again fitting to the observations in vivo ( ). In this regard, the correlation between AT8 staining and [ F]PI-2620 ARG binding ratios of FFPE tissue gave a satisfactory fit when considering PSP and AD samples together ( ). Thus, the resulting signal per amount of tau seems at least roughly comparable between 4R and 3R/4R tauopathies. Importantly, micro-ARG of PSP tissue confirmed that [ H]PI-2620 binding was co-localized with tau ( ), which makes a FFPE induced off-target source unlikely. Furthermore, the significant correlation between binding in FFPE samples of PSP patients and immunohistochemically assessed tau load supports the claim that the ARG signal is specific, but larger sample sizes are needed to confirm those assumptions.
Still, the lacking [ F]PI-2620 ARG signal of frozen PSP sections deserves further discussion. Since tissue preparation followed the same standardized protocol for all samples (HC, PSP, AD) and AD patients showed even higher binding in frozen samples, we conclude that the discrepant results cannot be explained by the preparation techniques themselves. We speculate that the higher density of tau in AD neurofibrillary tangles could contribute to a better preservation of binding sites in frozen tissue when compared to PSP. In PSP, the lower amount and the more diffuse type of tau, which is not only located in neuronal bodies but also in faint processes of astroglia might be less preserved in frozen tissue. Although we avoided to compare AT8 quantification between FFPE and frozen sections due to potential interhemispheric differences, our data would at least roughly support this explanation since AT8 quantification was consistently lower in frozen PSP tissue (mean load 4.6%) when compared to FFPE PSP tissue (mean load 7.2%). In conclusion, the fixation related preservation of faint tau aggregation may lead to detectable ARG binding only in FFPE but not in frozen tissue.
### Limitations
To avoid bias by co-pathology, we searched for samples without concomitant α-synuclein, TDP-43 or FUS in our brain bank. Thus, the resulting sample sizes of our study were limited, consisting of five HC, five PSP and six AD patients. Yet, all samples comprised both frozen and FFPE brain sections, which represents a strength of this head-to-head comparison. To enhance reproducibility and to increase the robustness of our data, multiple samples for both tissue techniques were used from each patient and five cortical regions-of-interest from each section were included in the analysis. PSP patients indicated significantly higher cortex to white matter binding ratios compared to HC, but lower cortex to white matter binding ratios when compared to AD. In contrast to AD, tau pathology of PSP is not limited to neurons but is also present in glial cells and can therefore also be present in white matter regions. Although we selected reference tissue devoid of a positive AT8 signal, we cannot fully exclude a remaining impact of subcortical tau pathology on our quantification.
Not all target regions known to be affected in AD/PSP are reflected in this study. As unspecific binding, e.g., to MAO-B, has been shown for first-generation tau radiotracers especially in subcortical brain regions ( ), frontal cortex samples were used for this head-to-head comparison. Further studies should also consider cortical and subcortical brain regions in order to expand the current results.
As the amount of tau pathology can vary between different hemispheres and FFPE/frozen samples are taken from one hemisphere each of the same donor, a direct quantitative comparison of immunohistochemistry and autoradiography results between both is hampered.
Another limitation is the concentration of tracer used. In the ARG experiments described here, a concentration of 1.6 nM of [ F]PI-2620 was used for comparability between AD and PSP samples. This concentration is in the range of the IC50 for AD, but seems to be lower than the IC50 described for PSP ( ). Especially in frozen samples with potential structural loss as hypothesized above, the lower concentration could contribute to the reduced binding in PSP samples. Higher concentrations should be tested in subsequent experiments and analyses.
## Conclusion
In postmortem tissue of AD patients, FFPE and frozen brain samples can be used for in vitro evaluation of the novel next-generation tau-radiotracer [ F]PI-2620. Frozen samples of PSP patients did not indicate specific cortical binding of [ F]PI-2620, whereas the ARG signal of FFPE samples significantly correlated with the immunohistochemical tau load. Therefore, FFPE samples should be favored for further investigation of binding capacities of [ F]PI-2620 in non-AD tauopathies by ARG.
## Data Availability Statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics Statement
The studies involving human participants were reviewed and approved by LMU Munich application number 19-244. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.
## Author Contributions
MW, SR, MB, and LB work was designed. MW, SR, AH, TA, MS, GR, and GH contributed to acquisition of data and/or analysis. MW, SR, AM, NK, MB, and LB data and results were interpreted. MW, SR and LB drafted the work. GR, OS, HB, MP, OM, AM, NK, ASc, ASt, GH, PB, JH, MB and LB substantively revised the work. All authors read and approved the final manuscript.
## Conflict of Interest
GH has served on the advisory boards of UCB and Biogen. JL reports speaker fees from Bayer Vital and Roche, consulting fees from Axon Neuroscience and Ionis Pharamceuticals, author fees from Thieme medical publishers and W. Kohlhammer GmbH medical publishers, non-financial support from Abbvie and compensation for duty as part-time CMO from MODAG, outside the submitted work. OS received research support from Life Molecular Imaging. MB received speaker honoraria from GE healthcare and LMI and is an advisor of LMI. AM, NK, and ASt are employed by Life Molecular Imaging. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
|
Nerve injury resulting in muscle paralysis from trauma or surgery is a major medical problem. Repair of such injuries with existing nerve grafting and reconstructive techniques often results in less than optimal outcomes. After previously demonstrating significant return of function using muscle-nerve-muscle (MNM) grafting in a rat facial nerve model, this study compares a variant of the technique, muscle-nerve-nerve (MNN) neurotization to MNM and interposition (IP) nerve grafting. Thirty male rats were randomized into four groups (1) control with no intervention, (2) repair with IP grafts, (3) MNM grafts and (4) MNN grafts. All groups had the buccal and marginal mandibular branches of the right facial nerve resected. Return of vibrissae movement, orientation, and snout symmetry was measured over 16 weeks. Functional recovery and muscle atrophy were assessed and quantified. All interventions resulted in significant improvement in vibrissae movement and orientation as compared to the control group ( p < 0.05). The MNM and MNN groups had significantly less time to forward vibrissae movement as compared to controls ( p < 0.05), and a large number of animals in the MNN group had coordinated vibrissae movement at 16 weeks. MNN and IP grafts retained significantly more muscle mass as compared to control ( p < 0.05). Thus, MNN grafting is a promising adjuvant or alternative technique for reanimation for patients with unilateral peripheral nerve injury who are not candidates for primary neurorrhaphy.
## Introduction
Nerve injuries resulting in muscle paralysis are usually a result of trauma or surgery and represent a major medical problem. Existing nerve grafting and reconstructive techniques for the repair of such injuries can result in less than ideal functional outcomes with synkinesis and unpaired movement. Immediate coaptation of the severed nerve is the optimal solution, but when this is not feasible, other strategies are necessary to induce restoration of muscle function. Such techniques include, nerve grafts, splitting nerves longitudinally to share fascicles with the denervated muscle, end-to-side grafting, nerve-muscle pedicles, and direct muscular neurotization by implanting the distal end of a nerve into denervated muscle ( – ).
Muscle-nerve-muscle (MNM) grafting uses an autogenous nerve graft that serves as a conduit pairing an innervated, normally functioning muscle with a denervated muscle. After interposing the harvested graft between the muscles, axonal sprouting is induced in the normal muscle and traverses the graft to innervate the denervated muscle. Thus, when the normal muscle is stimulated, simultaneous contraction of the paired, denervated muscle is observed. MNM grafting has the advantages of being relatively simple technically and having minimal associated risk or morbidity. This technique has been described to be effective in rat facial nerve and somatic nerve models, a dog laryngeal nerve model, and in a limited number of human facial nerve patients ( – ). We previously demonstrated the feasibility and comparable results of this grafting technique to other nerve grafting techniques, and the potential of using multiple grafts in order to try to “amplify” the nerve signal and improve results further ( , ).
This paper is the first to describe a variant of the MNM model in which one end of the nerve conduit is embedded into the normal muscle and the other end is anastomosed to the severed distal nerve that supplied the denervated muscle. We hypothesized that this new MNN group would have improved functional movement, decreased muscle atrophy, and histologic evidence of increased reinnervation compared to controls and MNM grafted groups. Further, the MNN group would have improved innervation by utilizing the original intact nerve-muscle junctions of the denervated muscle and would be more effective than the MNM technique of embedding the nerve into the affected muscle and awaiting new nerve-muscle junctions to form. We also compared to the MNN to the “gold” standard of direct nerve coaptation. The potential applications of such a technique in treating facial nerve paralysis, paralysis of the larynx, and other unexplored areas are great.
## Materials and Methods
### Animals
Thirty male Sprague-Dawley rats (200 g) from Envigo (Indianapolis, Indiana, USA) were housed under a 12-h light/dark cycle and received a standard rodent diet and water ad libitum. All surgical procedures were completed in accordance with the National Institutes of Health guidelines on care and use of laboratory animals for research purposes and approved by the institutional animal care and use committee at Edward Hines Jr. VA Hospital.
Animals were randomly assigned to one of four groups: no graft (CTL) (negative control), interposition (IP) graft, MNM graft, and MNN graft. All animals then underwent a right transfacial approach with concurrent parotidectomy. The buccal and marginal mandibular branches were immediately identified deep to the subcutaneous tissues. Retrograde dissection of these branches allowed for identification of the main trunk of the facial nerve. The buccal and marginal mandibular facial nerve branches were harvested from their initial ramification to their distal insertion into the muscles of the vibrissae, yielding ~2.0 cm segments, and were subsequently used as the nerve grafts. The incision was extended across the snout to expose the contralateral vibrissae muscle pad in all groups.
The procedures are schematically depicted in . Dashed lines represent nerves that were removed and the black “X” demonstrates where the nerve graft was sutured (adapted with permission from Braintree Scientific, Inc.) ( ). The buccal (orange) and mandibular (red) branches were removed from the control animals with no further intervention as previously depicted ( ).
Illustrations depicting of the MNM grafting technique with two grafts (A) , and the MNN (B) grafting technique are displayed. These images were adapted from drawings with permission from Braintree Scientific, Inc. ( ). The buccal branch is displayed in orange and the marginal mandibular branch is in red. The dashed nerve lines represent when the nerves was removed. The black X demonstrates all the places where the nerve grafts were sutured.
### Interposition (IP) Graft Repair
The buccal and marginal mandibular facial nerve branches were dissected and harvested as previously described ( ). The buccal nerve graft served as the graft for the mandibular branch and was sutured to the nerve stumps of the mandibular branch. The mandibular nerve graft served as the graft for the buccal branch and was sutured to the nerve stumps of the buccal branch with 9-0 nylon sutures through the epineurium as previously depicted ( ).
### Muscle-Nerve-Muscle Grafting Repair
In the group that had the MNM repair technique, the buccal and mandibular nerve branches were harvested and sewn from the denervated right levator labii superioris muscle pad into the innervated left levator labii superioris muscle pad as previously described ( ). The two nerve grafts were tunneled ~1–2 mm into superior portion of the right levator labii superioris muscle parallel to the muscle fibers, and secured with 9-0 nylon sutures through the epineurium ( ). An incision was then made in the innervated left levator labii superioris muscle bed where the graft was embedded to introduce trauma to the axons in the muscle pad to induce axonal sprouting into and across the grafts.
### Muscle-Nerve-Nerve Grafting Repair
In the group undergoing MNN repair, the buccal, and mandibular nerve branches were harvested and the distal ends of the two nerve grafts were implanted into the superior portion of the left unaffected levator labii superioris muscle, parallel to the muscle fibers, The proximal end of the buccal branch were sutured to the nerve stump of the mandibular branch, and the proximal end of the mandibular branch was sutured to the nerve stump of the buccal branch ( ).
### Functional Assessments
Animals were observed weekly for 16 weeks to assess functional recovery of vibrissae movement and orientation and snout symmetry. This time frame was chosen based on our previous study that demonstrated a large proportion of animals experienced some intervention dependent functional recovery by 16 weeks ( , ). Recovery of facial nerve function on the right denervated side was compared to the innervated left side. All functions were assessed by two laboratory technicians in a blinded manner. Vibrissae movement was assessed utilizing a 6-point scale as previously described ( ). Briefly, 1 represented no movement, 2 represents vibration, 3 represented the onset of whisking movement, 4 represented forward but delayed whisking, 5 represented forward coordinated movement with the innervated side of unequal intensity, and 6 represented a forward coordinated movement with equal intensity to the innervated side. Vibrissae orientation was assessed on a 3-point scale, where 1 represented vibrissae on the denervated side flattened against the face, 2 represented vibrissae that are less flattened and oriented more forward, but not matching the innervated side, and 3 represented vibrissae on the denervated side indistinguishable from the innervated side. Symmetry of the snout from the midline was quantified on a 4-point scale, where 1 represented minimal symmetry (~45-degree deflection from midline), 2 represented mild symmetry (30 degrees from midline), 3 represented moderate symmetry (15 degrees from midline), and 4 represented complete symmetry.
### Muscle Weights
At the end of the 16-week experiment, animals were euthanized by isoflurane overdose. The denervated and innervated mystacial vibrissae muscle pads, containing the levator labii superioris, dilator naris, nasolabialis profundus, and the maxiolabialis, were dissected out from the nasal bone through the premaxillary bone and weighed [anatomy described by Haidarliu et al. ( )]. Muscle atrophy was calculated as a standardized percentage of the denervated vibrissae muscle pad weight to the innervated vibrissae muscle pad weight.
### Statistical Analysis
Significant changes in vibrissae movement, orientation, and nose symmetry were determined using a two-way analysis of variance [ANOVA; factors = time (days post-operative) and treatment], followed by a Newman Keuls' multiple comparison post-hoc test. Significant changes in muscle weights amongst the groups were determined using one-way ANOVA followed by Tukey's multiple comparison test (GraphPad Prism). All data is represented as Mean ± SEM An a priori repeated measures ANOVA (within-between interaction) power analysis was run (effect size f = 0.24, α = 0.05, power = 0.95) using G Power 3.1 determined the total sample size for the 4 groups was 28.
## Results
### Functional Recovery
The effects of the grafting techniques on recovery of facial function was followed for 16 weeks following the surgical intervention. displays the significant improvement in vibrissae movement among all three intervention groups as compared to the CTL group. Significance in both grafting technique and time was shown by the two-way ANOVA. The multiple comparisons test revealed statistical significance amongst the three grafting techniques as compared to the CTL group ( p < 0.01 and p < 0.05 all groups compared to control, p < 0.05 MNN alone as compared to CTL, p < 0.05 MNM and MNN as compared to CTL, and p < 0.05 MNN and IP as compared to CTL). The MNN and MNM grafting groups had a significantly faster return of forward vibrissae movement (defined as a score of 4 or greater) when compared to the CTL group ( p < 0.01), with an average return of movement at 60.38 ± 1.84 and 60.38 ± 2.63 days as compared to 91.00 ± 13.28 days, respectively ( ). At the end of the 16 experiment, 50% of MNN animals, 38% of IP, and 13% of MNM, achieved coordinated vibrissae movement (defined as a score of 5 or greater) ( ). However, none of the CTL animals achieved coordinated vibrissae movement. All interventions significantly improved vibrissae orientation compared to the CTL ( p < 0.05) ( ). In terms of snout symmetry (assessed on a 4-point scale), all three intervention groups reached a mean score of ~2.5 while the control group reached a score of 2. Although not statistically significant at 16 weeks, all three intervention groups reached their final symmetry scores sooner than the control group ( p < 0.05) ( ).
Functional recovery results: weekly vibrissae movement scores (A) , number of days until each group achieved forward vibrissae movement (defined as a score of 4 or above) (B) , weekly vibrissae orientation scores (C) , weekly snout symmetry scores (D) . Data represented as Mean ± SEM (** p < 0.01 as compared to CTL, * p < 0.05 all groups compared to control, p < 0.05 MNN alone as compared to CTL, p < 0.05 MNM and MNN as compared to CTL, and p < 0.05 MNN and IP as compared to CTL).
Percentage of animals per group achieving coordinated forward vibrissae movement (defined as a score of 5 or above) at 16 weeks.
### Muscle Atrophy
To determine the effect of the grafting techniques on muscle atrophy, the muscle pads were dissected and weighed. The muscle pads of the CTL group, with no attempt at reinnervation, weighed 252.6 ± 28.5 milligrams (mg) or 55.86 ± 0.63% the size of the uninjured muscle pad weight at 16 weeks. A significant effect of the grafting techniques was determined by the ANOVA [ F = 1.990, p = 0.0076]. The multiple comparisons test revealed that he IP and MNN groups retained significantly more muscle pad weight at 346.8 ± 23.5 and 400.5 ± 14.4 mg or 72.69 ± 4.35 and 70.94 ± 2.78%, respectively ( p < 0.05 as compared to CTL) ( ). Although the weight of the muscle pads from the MNM group increased to 428.7 ± 43.2 mg or 69.54 ± 3.49% of the uninjured muscle pad, this change was not significant.
Mean vibrissae muscle pad weight in each group. Muscle atrophy was determined by calculating the denervated muscle pad weight as standardized to that of the innervated muscle pad. Data represented as Mean ± SEM (* p < 0.05).
## Discussion
Neurotization, the implantation of a nerve directly into a denervated muscle, was first described in the early 1900's by Hacker. Direct neurotization was initially explored for use of denervated muscles in poliomyelitis ( ). This technique has proven successful in multiple animal studies, but its clinical use has only sporadically been described in the literature ( ). Clinically, more conventional methods for reconstruction, including nerve grafting and nerve transfer techniques, have been employed. These conventional methods require either the presence of both ends of a severed nerve, or they utilize unrelated motor nerves that potentially provide muscle tone or learned muscle contractions.
In previous papers, we explored the method of direct neurotization using a muscle-nerve-muscle graft ( , ). We demonstrated that an innervated muscle will sprout axons that enter and transverse the graft, and innervate a denervated muscle. In the current study, we describe the MNN grafting technique which is a variant to the MNM model in which one end of the nerve conduit is embedded into the normal muscle and the other end is anastomosed to the severed distal nerve that supplied the denervated muscle. Although IP grafting is typically the ideal surgical strategy, MNM and MNN grafting techniques have multiple advantages. IP grafting often times results in synkinesis and unpair movements. In paired, symmetrically functioning muscles such as in the face and larynx, the potential of symmetrical movement can be realized with the MNM and MNN grafting techniques. It can be highly advantageous to capitalize on the property of symmetric movements with unilateral deficits and use the uninjured side to supply the injured side with innervation or some form of signaling ( ). Although independent, unilateral functioning would not occur with neural pairing of these symmetrical muscles, for most purposes, the motor deficit would be minimized.
Poor axonal regeneration across the injury site had been well-established in multiple peripheral nerve injury models and many studies have explored therapeutic agents, including the use of electrical stimulation, to improve the efficacy of axonal regeneration across the injury site ( , – ). The IP grafting technique requires two sutured sites to be traversed by the sprouting axons. Although the MNM and MNN grafting techniques does not eliminate the need for a harvest nerve graft, they do reduce the need for surgical neurorrhaphy. The MNM grafting technique eliminates all surgical neurorrhaphy while MNN grafting technique reduces the number of suture sites for the regenerating axon to transverse to only one. However, one limitation of the MNN and MNN techniques is that their success depends on sprouting axons from the donor muscle to enter the graft.
This paper is the first to demonstrate the use of muscle-nerve-nerve grafting as an alternative option to interpositional grafting. In this study, animals that had the MNN grafting repair recovered facial behavioral function similar to the animals that had the IP and MNM grafting repair techniques. The behavioral results from the IP and MNM grafting groups in this study were consistent with our previous findings ( , ). These data suggest that in surgical situations when IP grafting may be challenging including trauma or tumor removal, the MNN grafting repair may be a promising alternative surgical option to gain symmetric axonal regeneration.
In our previous study, the lower zygomatic branch of the facial nerve was identified and noted to contribute to whisking outcomes ( ). When the lower zygomatic branch was transected, animals did not achieve any recovery of facial function. In the current study, we did not transect this branch in an attempt to have earlier and greater recovery in the intervention groups. Allowing the lower zygomatic branch to remain intact lead to some recovery in the control group, most likely as a result of axonal compensation. However, the improvement in movement in the MNN or MNM groups compared to the control group in this paper is considered the contribution of the nerve graft(s). Since it was hypothesized that earlier and potentially greater recovery would be observed because the lower zygomatic branch remained intact in these animals, the 16- week experimental time course remained consistent with our prior studies ( ). However, this study could have benefited from a longer experimental time course which to capture the overall potential of the grafting techniques.
Muscle atrophy was significantly decreased, and vibrissae movement was significantly improved in the IP and MNN grafting groups compared to the control group. However, most promising was the finding that the MNN grafting technique was comparable to the clinically widely accepted IP grafting technique in both attaining vibrissae movement as well as minimizing muscle atrophy. No difference was observed between the MNM and MNN grafting. We hypothesize that since the IP and MNN grafting techniques utilize the distal nerve as part of the nerve conduit, the original neuromuscular junctions have the potential to provide enhanced muscle reinnervation. Future studies will examine the histological differences in neuromuscular junction occupancy in all groups.
In some regard, the MNM and MNN techniques are new paradigms for reinnervation. These are the only models in which neural input for a denervated muscle is not coming directly from the nervous system. All other techniques utilize damaged, altered, or misfit inputs directly from the peripheral nervous system (PNS). Common otolaryngologic examples include the XII-VII grafting for facial nerve paralysis and the ansa hypoglossi-recurrent laryngeal nerve anastomosis for vocal cord paralysis ( – ). These nerve grafts normally innervate multiple, independently functioning muscles and thus bring misfit signals from the PNS to muscles whose function is completely different from the nerves' intended purpose. To the contrary, the MNM and MNN grafts bring signals directly from muscles, not from the PNS. Also, the signals they carry are simplified, only transmitting neural input from a single source that induces muscle contraction from a similar, single functioning muscle (the dilator naris muscle). Although this technique can lead to damage to the healthy, contralateral muscle pad, trauma to this area is minimal and no significant detrimental effects were noted in the contralateral whisking and snout function.
It may be reasoned that innervating a paralyzed muscle with the nerves from an intact muscle is essentially creating one functioning muscle from two. By doing so, contraction of the denervated muscle has the potential to be more specific, more natural, and stronger. Stimulation of the intact muscle would result in near simultaneous contraction of both muscles. Note that this idea is only an extension of what actually occurs when reinnervation of the distal denervated portion of a lacerated muscle transpires by ingrowth of nerves from the muscle's proximal intact nerve ( ). Thus, the MNM and MNN techniques may also be considered when repairing lacerated muscles or when attaching muscle flaps to partially resected muscles. It is important to recognize that these techniques are unique in that they can be utilized in scenarios as described above in which a proximal nerve stump is not present. The MNM or MNN technique could create a muscle flap that potentially becomes a functional extension of the muscle from which it receives its graft.
Many questions remain regarding the efficacy and potential applications of MNM and MNN grafts. Future studies are needed to determine how long after muscle denervation the grafts will be effective and whether there is the limiting length of the graft. We will also explore possible neurotherapeutic strategies to enhance axonal sprouting from the innervated muscle as well as enhance axonal regeneration across the sutured site. Lastly, we will explore whether artificial grafts will function as well as autogenous ones.
## Conclusion
This is the first study to evaluate the efficacy of MNN neurotization for facial nerve injury. Our results suggest MNN grafting is a viable technique for repair of unilateral peripheral nerve paralysis. For patients with unilateral peripheral nerve injury, particularly those who are not candidates for primary neurorrhaphy, this study provides a promising adjuvant or alternative technique for reanimation and reinnervation. Future studies may explore MNN grafting in the larynx, smaller facial nerve branches, extremities, and other areas of denervation.
## Data Availability Statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics Statement
The animal use protocol was approved by the Institutional Animal Care and Use Committee (IACUC) of Edward Hines Jr. VA Hospital.
## Author Contributions
SC, MH, and EF contributed to conception and design of the study. MH, SB, and JS performed the technical work and gather the data. EF performed the statistical analysis. SC wrote the first draft of the manuscript. MH, EF, and SC wrote sections of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.
## Funding
Funding support from the Department of Otolaryngology—Head and Neck Surgery at Loyola Medical Center, Maywood, Illinois.
## Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's Note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
|
## Objective
Non-vitamin K antagonist oral anticoagulants (NOACs) are proven alternatives to warfarin for preventing stroke in patients with non-valvular atrial fibrillation. We aimed to examine the treatment patterns and patient factors associated with the use of antiplatelet agents, warfarin, and NOACs in clinical practice.
## Methods
We conducted a retrospective cohort study using the Korean Health Insurance Review & Assessment Service database. Patients receiving antithrombotics were identified before and after the introduction of NOACs (from August 1, 2013 to December 30, 2014 and July 1, 2015 to November 30, 2016, respectively). Patients were included if they were aged ≥18 years, had an atrial fibrillation diagnosis, and had a CHA DS -VASc score ≥2. Treatment pattern was assessed by classifying patients into NOAC, warfarin, or antiplatelet users based on the first date of antithrombotic prescription. Clinical factors associated with the type of antithrombotics chosen were examined using logistic regression analyses.
## Results
We identified 129,465 and 196,243 patients before and after the introduction of NOACs, respectively. The proportion of antiplatelet users was 60.7 and 53.0% before and after the introduction of NOACs, respectively. The proportion of warfarin users was higher in patients with low HAS-BLED score, high CHA DS -VASc score, or stroke before the NOAC era. A similar trend was observed for NOAC and warfarin users after the introduction of NOAC. Compared with antiplatelets, warfarin and NOAC uses were significantly associated with CHA DS -VASc score and stroke, whereas presence of myocardial infarction (MI) and peripheral arterial disease were significantly associated with antiplatelets prescription. For comparisons between NOAC and warfarin, HAS-BLED and CHA DS -VASc scores showed significant associations with NOAC use, whereas comorbidities including MI were significantly associated with warfarin use.
## Conclusions
The treatment pattern of antithrombotics did not change with the introduction of NOACs. However, comorbidities served as an important factor in choosing treatment regardless of NOAC entry.
## Introduction
Large randomized controlled trials of patients with non-valvular atrial fibrillation (NVAF) have established that non-vitamin K antagonist oral anticoagulants (NOACs) are as effective as warfarin for preventing stroke/systemic embolism (S/SE) and are safer than warfarin regarding major bleeding (MB) and intracranial hemorrhage ( , ), making NOACs the recommended first-line drug for stroke prophylaxis in patients with NVAF; hence, their use has grown dramatically worldwide ( – ).
Many patients with NVAF have one or more comorbidities. Approximately 20–40% of patients with atrial fibrillation (AF) present with coronary heart disease (CHD), whereas ~5–10% of patients undergoing percutaneous coronary intervention (PCI) have AF ( ). In a pivotal trial of NOAC, one in four patients with AF was found to have had a prior PCI ( ). Antithrombotic treatment patterns may differ depending on the presence of comorbidities. Moreover, the presence of comorbidities, such as stroke, CHD, and peripheral arterial disease (PAD), may affect treatment patterns of antithrombotics in patients with NVAF ( ). Additionally, many patients with NVAF are prescribed multiple medications, and antiplatelet agents are widely used in clinical practice ( ). In patients receiving oral anticoagulant (OAC) treatment for prevention of stroke, concomitant treatment with antiplatelets was shown to be associated with an increased rate of MB ( ), which may affect treatment patterns of OACs in patients with NVAF.
For more appropriate use of OACs to prevent S/SE in NVAF patients, factors affecting treatment patterns of antithrombotics need to be evaluated. We hypothesized that comorbidity affects treatment patterns of antithrombotics in patients with NVAF, even after the introduction of NOACs. Therefore, we compared antithrombotic treatment patterns before and after the introduction of NOACs and examined the factors that affect treatment patterns, including comorbidities, such as stroke, myocardial infarction (MI), and PAD.
## Materials and Methods
### Study Design and Data Source
We conducted a retrospective cohort study using the Korean Health Insurance Review & Assessment Service (HIRA) database from January 1, 2007 to November 30, 2016. We explored the treatment patterns of antithrombotics and the clinical factors associated with the type of antithrombotics chosen for NVAF patients both before and after the introduction of NOACs in South Korea. We identified two separate groups of NVAF patients who received antithrombotics: during the first (from August 1, 2013 to December 30, 2014; before introduction of NOACs) and second intake periods (from July 1, 2015 to November 30, 2016; after introduction of NOAC). Patients were included in both groups if they had received antithrombotics during both periods. Antithrombotics included antiplatelets (aspirin, clopidogrel, ticagrelor, prasugrel, and ticlopidine), NOACs (apixaban, dabigatran, and rivaroxaban), and warfarin. Index date was the first date of antithrombotics prescription during the intake period.
The HIRA database includes patient-level information on diagnosis, treatment, procedure, and medication of ~50 million beneficiaries, which corresponds to 98% of the total population of South Korea. This is owing to the universal coverage of the National Health Insurance program ( ). This study was exempt from ethical review from the Institutional Review Board of Pusan National University (PNU IRB/2019_101_HR).
### Study Population
Patients satisfying all of the following criteria were included in the study: (1) received antithrombotics during the intake period; (2) aged 18 years or older on the index date; (3) had more than one medical claim for AF within 6.5 years of the index date; and (4) had a CHA DS -VASc score ≥2 in the year before the index date. Patients were excluded from the study if they had medical claims for: (1) valvular AF or prosthetic heart valves within 1 year of the index date; (2) venous thromboembolism within 1 year of the index date; (3) hip/knee replacement surgery within 6 weeks of the index date; (4) end-stage chronic kidney disease, kidney transplant, dialysis, or pericarditis within 1 year of the index date; and (5) transient AF or cardiac surgery within 1 year of the index date. Patients who had been prescribed multiple OACs on the index date were also excluded from the study. Diagnosis, procedure, and medication codes used to define the study population are listed in .
### Outcome Measures
For each of the two groups identified before and after the introduction of NOACs, we assessed the treatment patterns of antithrombotics and clinical factors associated with the choice of antithrombotic. To assess treatment patterns, patients prescribed NOACs or warfarin on the index date were classified as NOAC or warfarin users, respectively, regardless of antiplatelet co-prescription. Patients prescribed antiplatelets without NOACs or warfarin on the index date were classified as antiplatelet users. We examined the proportion of patients prescribed each type of antithrombotic based on CHA DS -VASc score, HAS-BLED score ( ), and comorbidity. Comorbidities included stroke, MI, and PAD, which may affect the choice and pattern of anticoagulation therapy. Patients were regarded as having stroke if they had more than one diagnosis of ischemic stroke or transient ischemic attack (International Classification of Diseases, Tenth Revision code of I63, I69.3, or G45.9 as main or subdiagnosis codes) within 6.5 years of the index date. MI and PAD were defined by the diagnosis codes of the Charlson Comorbidity Index (CCI; ).
We examined the clinical factors associated with the choice of antithrombotics, such as age, sex, CHA DS -VASc score, HAS-BLED score, CCI, comorbidities (e.g., stroke, MI, and PAD), and medication use, such as non-steroidal anti-inflammatory drugs (NSAIDs), proton pump inhibitors (PPIs), H2-receptor antagonists, antiarrhythmics, digoxin, and statins.
### Statistical Analysis
Treatment pattern was analyzed descriptively and are presented as numbers and proportions. For sensitivity analysis, the change in antithrombotic treatment pattern was evaluated excluding overlapping patients in first and second intake periods. To examine the clinical factors associated with the choice of antithrombotics, we used a logistic regression to estimate odds ratios (ORs) and 95% confidence intervals (CIs). The SAS Enterprise Guide (version 6.1 M1; SAS Institute Inc., Cary, NC, USA) was used for statistical analyses. P < 0.05 was considered statistically significant.
## Results
### Baseline Characteristics of Study Population
We identified 129,465 and 196,243 patients for the first and second intake periods, respectively ( ). Baseline characteristics were largely similar between the two groups ( ). For the first and second intake periods, mean ages were 70.6 and 71.9 years, respectively, and 43.8 and 44.0% were women, respectively. Mean CHA DS -VASc scores were 4.0–4.2. More than 80% of patients had a HAS-BLED score ≥3, which indicated that patients were at an increased risk of bleeding. For comorbidities, 39.1–40.3, 5.2–5.8, and 21.1–22.9% of patients had stroke, MI, and PAD, respectively.
Patient selection flow diagram. AF, atrial fibrillation; NOAC, non-vitamin K antagonist oral anticoagulants.
Baseline characteristics of study population.
CCI, Charlson Comorbidity Index; COPD, chronic obstructive pulmonary disease; NOAC, non-vitamin K antagonist oral anticoagulants; NSAIDs, nonsteroidal anti-inflammatory drugs; PPIs, proton pump inhibitors; SD, standard deviation .
### Treatment Pattern of Antithrombotics
Before the introduction of NOACs, warfarin was preferred in patients with a HAS-BLED score of 0–1, and the proportion of warfarin users tended to increase with higher CHA DS -VASc scores ( ). Patients with stroke had a higher proportion of warfarin users than those without stroke, whereas the proportion of warfarin users was lower among patients with MI or PAD than those without MI or PAD ( ). Among warfarin users, 24.6% were co-prescribed antiplatelets. Among antiplatelet users, 11.8% were treated with more than one antiplatelet medication (i.e., dual or triple antiplatelet therapy).
Treatment patterns of antithrombotics before and after the introduction of NOACs. Proportions of patients prescribed each type of antithrombotics before the NOAC introduction (A) according to the HAS-BLED score; (B) according to the CHA DS -VASc score; (C) according to the comorbidities, and after the NOAC introduction (D) according to the HAS-BLED score; (E) according to the CHA DS -VASc score; (F) according to the comorbidities. MI, myocardial infarction; NOAC, non-vitamin K antagonist oral anticoagulants; PAD, peripheral artery disease.
The findings were relatively similar after the introduction of NOACs ( ). OACs (either warfarin or NOAC) were preferred among patients with HAS-BLED scores of 0–1, and the proportion of OAC users who used NOACs instead of warfarin numerically increased with increasing HAS-BLED score, from 30 to 46.4%. The proportion of OAC users tended to increase with CHA DS -VASc score, and the proportion of patients treated with NOACs among OAC users numerically increased with increasing CHA DS -VASc score, from 37.1 to 49.1%. The proportion of warfarin and NOAC users tended to be higher in patients with stroke and those without MI or PAD. Among NOAC and warfarin users, 20.3 and 21.1% were, respectively, co-prescribed antiplatelets, and 13.2% of antiplatelet users were treated with more than one antiplatelet.
When removing the patients included in both intake periods, treatment pattern was generally similar with the results of base case ( ).
### Clinical Factors Associated With the Choice of Antithrombotics
The change in ORs of clinical factors associated with the choice between warfarin and antiplatelets was small with the introduction of NOACs ( ). Age, female sex, HAS-BLED score, MI, PAD, diabetes, congestive heart failure, chronic obstructive pulmonary disease (COPD), PPI, H2-receptor antagonist, and antiarrhythmic were significantly associated with the use of antiplatelets, whereas CHA DS -VASc score, CCI score, stroke, bleeding, renal disease, digoxin, and statin were significantly associated with warfarin use, both before and after the introduction of NOACs.
Clinical factors associated with the choice of antithrombotics. Odds ratios for the association of clinical factors with the choice of (A) antiplatelets vs. warfarin before and after the NOAC introduction; (B) warfarin vs. NOAC and antiplatelets vs. NOAC after the NOAC introduction. CCI, Charlson Comorbidity Index; CI, confidence interval; COPD, chronic obstructive pulmonary disease; MI, myocardial infarction; NOAC, non-vitamin K antagonist oral anticoagulants; NSAID, non-steroidal anti-inflammatory drug; OR, odds ratio; PAD, peripheral artery disease; PPI, proton pump inhibitor.
After the introduction of NOACs, all clinical factors showed significant ORs with relatively small effect sizes when comparing prescription preference of warfarin and NOAC ( ). All comorbidities and digoxin use were significantly associated with warfarin use while all other clinical factors including HAS-BLED and CHA DS -VASc scores favored NOAC use. When compared with antiplatelets, NOACs were more likely to be used in patients with higher CHA DS -VASc score, higher CCI score, stroke, bleeding, NSAID, PPI, statin, or antiarrhythmics. Female sex, HAS-BLED score, MI, PAD, hypertension, diabetes, congestive heart failure, COPD, renal disease, and digoxin showed significant associations with the use of antiplatelets.
When comparing combined OAC and antiplatelet therapy with OAC monotherapy, MI showed the strongest association with combined therapy followed by statin, PAD, and HAS-BLED score ( ).
## Discussion
In our real-world population data, we found that patient treatment patterns of antithrombotics did not change significantly after the introduction of NOACs. OACs were not commonly used as recommended by the guidelines, and the proportions of antiplatelet users were 60.7 and 53.0% before and after the introduction of NOACs, respectively. Moreover, the factors affecting treatment patterns of antithrombotics remained the same. Stroke and CHA DS -VASc score were associated with the use of OACs, whereas female sex, MI and PAD were associated with the use of antiplatelets. When comparing NOACs with warfarin, higher HAS-BLED and CHA DS -VASc scores were associated with NOACs use, whereas comorbidities including MI were associated with the use of warfarin.
In this study, we revealed that OACs are still being underused despite the introduction of NOACs. Treatment patterns of antithrombotics and the clinical factors associated with the choice of antithrombotics were similar before and after the introduction of NOACs. The clinical factors associated with the choice between taking warfarin and antiplatelets were similar to those associated with the choice between taking NOACs and antiplatelets.
In the present study, comorbidities affected the treatment pattern of antithrombotics both before and after the introduction of NOACs. AF patients with stroke were more likely to be prescribed OAC compared to antiplatelets, whereas AF patients with MI and PAD were more likely to use antiplatelets than OAC. The results of the present study are in line with recent real-world data analyses of the American College of Cardiology PINNACLE registry, which showed that in a cardiology outpatient population of NVAF patients with moderate to high risk of stroke, more than one-third were treated with aspirin alone, without OACs ( ). In this study, the presence of CVD risk factors (e.g., hypertension and dyslipidemia) and CHD (e.g., prior MI/angina and recent coronary artery bypass graft) was associated with aspirin monotherapy, whereas prior stroke, TIA, and SE were associated with more frequent prescriptions of OACs. Therefore, based on these findings we can infer that treatment patterns are influenced by comorbidities of individual patients. The neurologists who take care of stroke patients are more likely to prescribe OACs because AF is associated with strokes with an increased risk of severe disability and mortality, and appropriate use of OAC is the most important modifiable factor of prognoses after stroke in patients with AF ( ). However, treatment at a non-neurological department has been shown to be one of the factors associated with reluctance in prescribing OACs in patients with AF who have suffered a stroke ( ). Ischemic events targeted by physicians may differ depending on patients' comorbidities. In several randomized clinical trials of ticagrelor, an antiplatelet agent that blocks the ADP (P2Y ) receptor, the most common type of recurrent ischemic event was reported to be stroke in patients with previous acute stroke/TIA, whereas CHD and limb revascularization were the most common in those with prior MI and PAD, respectively ( – ).
It is well-known that OACs—both warfarin and NOACs—are less likely to be used in women with AF ( , ). A recent cohort study enrolling 2.3 million U.S. patients with a new diagnosis of AF and CHA DS -VASc score ≥2 showed that women, compared to men, were less likely to receive OAC which mediated the increased risk of stroke and decreased risk of intracranial hemorrhage ( ). Low preference of OAC use in women by both physicians and patients ( ) may explain why women were more likely to be prescribed antiplatelet agents than OACs in our study. In sensitivity analysis, we stratified logistic regression models by gender to explore the difference in factors affecting the choice of antithrombotics between male and female patients. The results of sensitivity analysis were generally comparable to that of main analysis, suggesting the factors affecting the choice of antithrombotics are generally similar between male and female patients ( ). Statin, which was another significant factor associated with OAC use, is the cornerstone of secondary prevention for vascular events in patients with coronary artery disease and PAD. Therefore, it is likely that statin treatment as well as antithrombotic prescription were influenced by the patients' comorbidities.
The choice between taking NOACs instead of warfarin was influenced by the CHA DS -VASc and HAS-BLED scores, albeit at small effect sizes. This finding was expected because unlike warfarin, NOACs do not require anticoagulation monitoring and demonstrated a clear reduction of risk of stroke and bleeding ( , ). When compared with NOAC, warfarin use was associated with underlying renal disease in our study. With limited evidence of NOAC use in AF patients with chronic kidney disease, it is understandable that NOAC was less preferred in patients with renal disease. Several other factors were shown to have statistically significant association with either warfarin (MI, bleeding, hypertension, diabetes, congestive heart failure, COPD and digoxin use) or NOAC (age, CCI score, NSAID, PPI, H2-receptor antagonist, and antiarrhythmics use) prescription when compared to each other in our study, however these differences may not necessarily relate to clinical significance.
Further studies are needed to evaluate the role of combining NOACs with antiplatelets in patients with NVAF and the effect of comorbidities. Although the concomitant use of antiplatelets and OACs increases the risk of MB, a meta-analysis of randomized trials showed that it may be safer and more effective in preventing S/SE to use NOACs with concomitant aspirin therapy over warfarin in patients with NVAF ( ). Randomized trials on the use of NOACs with antiplatelets have been conducted in patients with AF who underwent PCI (PIONEER AF PCI for rivaroxaban ( ), RE-DUAL-PCI for dabigatran ( ), AUGUSTUS for apixaban ( ), and ENTRUST-AF PCI for edoxaban ( ). In the AUGUSTUS trial, patients with NVAF and acute coronary syndrome or PCI treated with apixaban and a P2Y inhibitor showed lesser bleeding and fewer hospitalizations than those treated with warfarin and dual antiplatelets ( ). Furthermore, a recent meta-analysis showed that NOACs were associated with less MB and fewer major cardiovascular adverse events, although warfarin was associated with lower rates of mortality and stroke ( ). With additional trials of combined antiplatelets and NOACs in patients with comorbidities, treatment patterns can be changed accordingly.
Our study has some limitations. First, we did not consider variables that were not included in the HIRA database but may be associated with treatment patterns, such as clinical laboratory data, over-the-counter medications, and antithrombotic treatment preferences of patients and physicians. In addition, we assumed that patients complied with their treatment as prescribed. Second, as AF was defined based on the diagnosis codes, there is a possibility that misclassifications occurred during the identification of the study population. However, the proportion of misclassified patients is likely to be negligible given that we also included antithrombotic prescription and CHA DS -VASc score as inclusion criteria. Third, comparing two patient groups identified during two intake periods (i.e., before and after the introduction of NOACs) may not have been appropriate because there could be duplicate patients in both groups. However, we allowed duplication in patients because the aim of this study was to explore the prevalence, not the incidence, of antithrombotic treatment. This also provides an information on whether the treatment pattern changes within the same patient group after the introduction of NOACs. Given that the results were robust when we removed the duplicate patients in both intake periods, the impact of allowing duplicate patients might be negligible in this study. Lastly, it may not be the optimal time to observe the change in treatment pattern of antithrombotics immediately after the introduction of NOACs. Further studies are needed on treatment pattern of antithrombotics with a more recent data.
## Conclusion
In a large, real-world population of NVAF patients with moderate to high risk of S/SE, more than half were not treated with OACs, regardless of the introduction of NOACs. The treatment pattern of antithrombotics did not change following the introduction of NOACs. However, comorbidities had a considerable influence on the treatment pattern during both the “warfarin era” and “NOAC era.” Further clinical trials of NOACs in patients with comorbidities are needed.
## Data Availability Statement
The data analyzed in this study was obtained from the Korea Health Insurance Review & Assessment Service (HIRA) claims database, the following licenses/restrictions apply: requests to access these datasets must first be approved by the HIRA Service. Requests to access these datasets should be directed to the HIRA Service, .
## Author Contributions
OYB, SKi, and HSS: conceptualization and writing—original draft preparation. OYB, SKi, YKO, M-YL, S-WJ, SH, HSS, and Y-HK: investigation. OYB, SKi, YKO, M-YL, S-WJ, SH, JR, SKa, HSS, and Y-HK: writing—review and editing. SKi and HSS: data curation, methodology, and formal analysis. JR and SKa: project administration. SKa, HSS, and Y-HK: supervision. All authors contributed to the article and approved the submitted version.
## Funding
This study was sponsored by Pfizer and Bristol Myers Squibb.
## Conflict of Interest
JR and SKa were employed by Pfizer Korea Ltd. This study received funding from Pfizer and Bristol Myers Squibb. The funders had the following involvement with the study: design, analysis, data interpretation, writing manuscript, and decision to submit for publication. OYB, YKO, M-YL, S-WJ, SH, SKi, HSS, and Y-HK were paid consultants to Pfizer and Bristol Myers Squibb in connection with the development of this manuscript.
## Publisher's Note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
|
## Introduction
A persistent vegetative state (PVS) can be caused by traumatic or non-traumatic brain injury. PVS is a complex clinical condition with numerous complications. Nursing care, medical treatment, and comprehensive rehabilitation are necessary to improve the outcomes of PVS. However, the prognosis remains unsatisfactory. Acupuncture therapy has been used as a rehabilitation strategy to treat patients with PVS in China, showing better results in the recovery of consciousness, intellectual capability, and motor function.
## Case description
We present the case of a 4-month-long PVS after herpes simplex virus encephalitis (HSVE) in a 3.5-year-old boy who underwent Tongdu Xingshen acupuncture integrated with Western medicine and rehabilitation. The patient regained consciousness post-treatment. His intelligence and motor function gradually recovered after seven treatment sessions.
## Conclusion
Tongdu Xingshen acupuncture is a potential complementary therapy to optimize clinical outcomes in PVS.
## Introduction
Prolonged disorders of consciousness (PDoC) are defined as any disorder of consciousness that has continued for at least 4 weeks following sudden-onset brain injury. PDoC includes vegetative state/unresponsive wakefulness syndrome (VS/UWS) and minimally conscious state (MCS) ( ). VS/UWS is defined as a state of unaware wakefulness in which there is a preserved capacity for spontaneous or stimulus-induced arousal—as evidenced by sleep–wake cycles and a range of reflexive and spontaneous behaviors.
Few studies have reported the epidemiology of VS. A systematic review reported that the prevalence of VS ranged from 0.2 to 6.1 patients with VS/UWS per 100,000 people ( ). Studies on children found a prevalence rate of 6–80/million children ( ).
The major causes of VS are trauma, vascular events, hypoxia or hypoperfusion, infection or inflammation, and toxic or metabolic disorders ( ). Central nervous system infections account for 5–10% of pediatric persistent VS (PVS) ( ). Viruses are responsible for 20–50% of all cases of encephalitis. Herpes simplex virus is the most common sporadic encephalitis worldwide ( ). Herpes simplex virus encephalitis (HSVE) is fatal in more than 70% of patients if untreated. Antiviral treatment has decreased mortality to 20–30% ( ), whereas most surviving patients continue to suffer from moderate-to-severe neurological sequelae including PVS. A previous study ( ) reported that HSVE survivors experience amnestic difficulties (75%), global cognitive decline (25%), and personality and behavioral abnormalities (40–60%). It has also been reported that nearly 1% of pediatric patients are found to be in a VS at long-term follow-up evaluation ( , ).
There are no established therapies for children with PDoC ( ). Currently, the clinical evidence for therapies for children with PDoC is inadequate. Drugs such as amantadine, pramipexole, donepezil, and zolpidem are sometimes used in clinical practice ( , ). Specialized neurological rehabilitation is recommended, and traditional Chinese medicine is used as a rehabilitation method ( ). The prognosis for regaining consciousness and subsequent survival is poor, and long-term survival from PVS among the pediatric population is poor ( ). Thus, the treatment of PDoC in the pediatric population deserves further research.
Here, we report the case of PVS after contracting HSVE in a young child. A combination of Tongdu Xingshen acupuncture therapy and Western medicine was adopted. The patient progressed favorably with respect to the level of consciousness and intelligent and motor function.
## Case presentation
A 3.5-year-old boy showed perturbed consciousness with movement and intellectual dysfunction after suffering from HSVE and secondary epilepsy ( ). He was administered antiviral and antiepileptic therapy, as well as immunomodulatory and neurotrophic agents. On admission, he was unsteady with his head upright and was able to roll over but unable to sit up independently. He was unable to actively or passively grasp objects. Although he was able to open his eyes, no visual tracking was observed. He was unable to follow any instructions. His left limb had involuntary activity sometimes accompanied by altered sleep–awake cycles. He was able to cry and make a “hum” sound through his nose. He required bolus nasogastric tube feedings due to dysphagia. His growth and development had proceeded normally until the onset of HSVE. Seizures were under control after taking levetiracetam tablets ( ).
On initial physical examination, his vital signs were normal. He presented deficits in upright head/neck control and inability to support himself with his elbows and hands. Although he could roll over, he had problems controlling the movement of all four limbs, making it difficult for him to crawl, stand, and walk independently. He could not easily control shifts in position, e.g., from lying down to sitting up. His muscle tone was generally normal, but limb weakness grade was 3/5 (MRC strength scale). Adductor angle, popliteal angle, and dorsiflexion angle of his foot was 150°, 150°, and 70°, respectively. Knee/Achilles jerk reflexes were normal. Ankle clonus was positive. Neither the Babinski sign nor the meningeal irritation sign was positive.
His PVS score ( ) was 5 (command execution = 0, body movement = 2, eye movement = 1, emotional reactions = 1, swallow = 0, and speech = 1). Gesell Developmental Schedules (GDS) scores ( ) indicated a severe defect [gross motor (developmental quotient (DQ) = 4.4, fine motor (DQ = 0), adaptive behavior (DQ = 0), language (DQ = 0), and personal–social behavior (DQ = 0)]. Infants-Junior High School Students' Social Development Screening Test ( ) (coarse score = 1 and standardized score = 6) indicated a severe abnormality, suggestive of movement and intellectual disorder. Gross Motor Function Measure-88 (GMFM-88) ( ) was as follows: A = 82.4, B = 0, C = 0, D = 0, and total score = 16.5, indicating significant retardation in GMF.
Brain magnetic resonance imaging (MRI) showed that the sulcus in the bilateral cerebral hemispheres was slightly deeper than before, and the lateral ventricles were slightly enlarged. Some abnormal signals were observed around the bilateral ventricles. There were perivascular lesions in the bilateral frontal lobe and pachymeningeal enhancement ( ). Video electroencephalogram (VEEG) demonstrated slow background activity. Numerous bilateral sharp waves and sharp wave complexes were observed in the posterior head and right rolandic area. Transcranial Doppler (TCD) indicated that the blood flow velocity of the bilateral middle cerebral artery was asymmetric, with its right side slightly slower. The anterior and posterior communicating arteries were unobstructed with compensatory capability. Visual evoked potential (VEP) and brainstem auditory evoked potential (BAEP) were normal.
Magnetic resonance imaging of the brain before receiving Tongdu Xingshen acupuncture showed mild brain atrophy and pachymeningeal enhancement associated to meningitis.
The results of routine biochemical tests were normal. Immunologic tests found a high concentration of immunoglobulin G (IgG), while the concentration of IgA was decreased.
## Diagnosis
Because the age of onset of PVS was >1 year after birth and the patient showed a normal development before HSVE, cerebral palsy and inherited metabolic disease were ruled out. According to the nervous system, physical examination, and the disease being unprogressive, progressive muscular dystrophy was also ruled out.
The following diagnostic criteria for PVS ( ), proposed at a meeting in Nanjing in April 1996, were applied: (1) no evidence of awareness of self or environment and inability to execute commands; (2) sufficiently preserved respiratory function and blood pressure; (3) intermittent wakefulness manifested by the presence of sleep–wake cycles; (4) no evidence of language comprehension or expression; (5)unconsciousness with eyes open; (6) no visual tracking; and (7) hypothalamic and brainstem autonomic functions sufficiently preserved to permit survival with medical and nursing care.
The patient's clinical presentations conformed to the abovementioned diagnostic criteria, and his PVS score of 5 indicated incomplete vegetative syndrome. VEEG activity demonstrated typically slow wave activity, and the patient had a history of HSVE. Therefore, he was diagnosed to be on PSV during convalescence from HSVE. Based on the case history, VEEG and MRI results, and medication history, a diagnosis of secondary epilepsy and brain atrophy was considered.
## Treatment and outcomes
### Routine treatment
The patient was given intravenous (IV) scopolamine (one time a day, 0.03–0.06 mg/kg; the IV was adjusted according to the patient's conditions in the first three treatment courses) compound Danshen tablets (two times a day with one tablet each time in the remaining four treatment courses) to improve brain microcirculation, and cattle encephalon glycoside and ignotin (CEGI) injection (one time a day, 2 ml, IV throughout the treatment period) to alleviate the nerve function injury. Regular rehabilitation therapy included exercise therapy, massage, and speech and cognitive training.
### Acupuncture therapy
The acupuncture method used was Tongdu Xingshen acupuncture comprising scalp acupuncture and body acupuncture ( ).
#### Treatment course
In the first four treatment courses, the selected acupoints were nine intelligent needles [Sishencong (EX-HN1) plus forehead five needles], temporal three needles, BaiHui (GV20), foot motor sensory area, motor area, balance area, second speech area, spirit-emotion area, YinTang (EX-HN3), Neiguan (PC6), Sanyinjiao (SP6), and Shenmen (HT7).
In the remaining three courses, acupoints were adjusted based on the previous four courses. The foot motor sensory area, motor area, and spirit-emotion area were removed. Areas of the heart and liver were added during these treatment courses.
#### Acupuncture manipulations
Acupuncture treatment was performed by an independent certified practitioner (acupuncturist) with 5 years of clinical experience.
##### Scalp acupuncture and Bai Hui (GV 20)
Disposable stainless steel needles (size 0.30 mm × 40 mm; Huatuo, Suzhou Medical Appliance, Suzhou, Jiangsu Province, China) were manually inserted at an angle of ~15° to a depth of 20–35 mm. For a total of 120 min, the needles were twirled and rotated at 180–200 revolutions/min for 3 min every 30 min.
##### Body acupuncture
a. Yin Tang : The 0.30 mm × 25 mm acupuncture needles were inserted obliquely in the direction of the nasal root at an angle of ~10–20° and an insertion depth of 10–15-mm.
b. Bilateral Neiguan and Shenmen : The needles were vertically thrust at depths of 10–15 (Neiguan) and 8–10 mm (Shenmen).
c. Bilateral Sanyinjiao : The 0.3 mm × 40 mm needles were vertically inserted at a depth of 15–20 mm.
d. Manipulation : The even reinforcing-reducing method was adopted by twirling the needles for at least 180 revolutions/min for 3 min every 10 min. The needle retention time was 30 min. “De qi” is an indication of effective needling. Acupuncturists will feel tightness around the needle when the qi arrives.
e. Treatment course : The patient received seven courses of acupuncture with an average of 19 days each course. Acupuncture was implemented every other day for 10 times per treatment course. If the patient caught a cold or other conditions that influenced acupuncture treatment, the course was prolonged.
The diagnosis and treatment process is illustrated in .
The timeline of diagnosis and treatment for this patient. NG tube, nasogastric tube; PVS, persistent vegetative state; GDS, Gesell Developmental Schedules; MRI, magnetic resonance imaging; FMSA, Foot motor sensory area; MTA, Motor area; BLA, Balance area; SCSA, Second speech area; SEA, Spirit-emotion area; GV 20, Baihui acupoint; EX-HN 3, Yintang acupoint; PC 6, Neiguan acupoint; HT 7, Shenmen acupoint; SP 6, Sanyinjiao acupoint.
### Evaluation of therapeutic effect
#### PVS score scale
The curative effect was evaluated using a PVS score scale proposed at the meeting in Nanjing in April 1996 ( ). The total score was calculated according to the sum of scores for six clinical features. The standard for the total score is as follows: complete vegetative state (PVS ≤ 3); incomplete vegetative syndromes (4 ≤ PVS ≤ 7); transitional vegetative syndromes (8 ≤ PVS ≤ 9); out-of-vegetative-state (10 ≤ PVS ≤ 11); and recovery of consciousness (PVS ≥ 12). A patient is in the out-of-vegetative-state if he can execute instructions.
#### Gesell developmental schedules
The Chinese version of the GDS ( ) is used to evaluate neurodevelopmental symptoms in children, such as gross motor skills, fine motor skills, adaptability, language, and personal–social activity. The degree of mental development is classified according to the average DQ score: normal (DQ ≥6), borderline (DQ: 76≤ –≤85), mild defect (DQ: 55 ≤-≤ 75), moderate defect (DQ: 40≤-≤54), and severe and extremely severe defect (DQ ≤ 39).
### Therapeutic effect
Approximately 1 week after the abovementioned treatment, the nasogastric tube was removed, and the patient was fed thick porridge and rice cereal. After 12 days of treatment, the patient smiled on his own. After completion of the first treatment course, the PVS score on the PVS rating scale increased to 8 (command execution = 0, body movement = 2, eye movement = 1, emotional reactions = 2, swallow = 2, and speech = 1) ( ), indicating that the patient evolved favorably. The patient showed the ability to control his head and neck when he was upright and could sit independently. Muscle strength in his upper limbs gradually recovered, and he demonstrated his ability to support the upper body with his hands and elbows. He had a normal crying and laughing reaction to stimulation ( ).
The therapeutic effect on PVS (A) and GDS score (B) of patient.
At the end of the second treatment course, the patient was out of VS (the PVS rating scale was 15) (command execution = 2, body movement = 3, eye movement = 3, emotional reactions = 3, swallow = 2, and speech = 2) ( ). He was able to perform simple tasks with continuous eye tracking and eye contact. He could speak simple words like “mum” and “dad,” sit up straight, stand up, and walk slowly by holding or touching a support surface. The grip strength of his hand increased. The urge to defecate gradually recovered. Four limbs muscle strength was gradually recovered (grade 4+/5) ( ).
In the third treatment course, GDS was assessed ( ). The score was 21.74, indicating a severe and extremely severe defect [gross motor (DQ = 17.9), fine motor (DQ = 21.9), adaptive behavior (DQ = 24.8), language (DQ = 23.2), and personal–social behavior (DQ = 20.9)]. MRI was rechecked after this course, demonstrating that mild atrophy-like changes in the bilateral cerebral hemispheres improved and the abnormal signal near the bilateral ventricles was basically absorbed. The abnormal signal in the white matter of bilateral frontal lobes indicated the possibility of poor myelination, and a follow-up review is advised to rule out focal demyelination. Bilateral temporal dura enhanced, indicating intracranial infection sequelae, for which short-term review is recommended ( ).
Magnetic resonance imaging of the brain after receiving Tongdu Xingshen acupuncture suggested mild brain atrophy and pachymeningeal enhancement associated to meningitis improved.
After five treatment courses, the patient was able to resume a full diet. He could sit and walk independently and grasp objects flexibly. He demonstrated hand–mouth–eye coordination. He was able to communicate with others by talking short sentences of 5–6 words and could play a simple imitation game. GDS scores increased to 53.98, suggesting a moderate defect [gross motor (DQ = 51.2), fine motor (DQ = 48.1), adaptive behavior (DQ = 55.1), language (DQ = 58.6), and personal–social behavior (DQ = 56.9)] ( ) ( ).
In the last treatment course, the patient was able to follow instructions and play games. He exhibited fluency in routine verbal communication. He could not only sit, stand, and walk independently, but also run and jump. He exhibited hand–mouth–eye coordination and was able to stand upright independently. Muscle strength in all four limbs was generally normal. Ankle clonus was positive ( ). No adverse or unanticipated events were reported throughout the treatment period. At the 1-year follow-up examination, ambulatory electroencephalography (AEEG) showed a paroxysmal complex of sharp–slow waves in bilateral brain areas. MRI demonstrated few bilateral lacunar ischemic foci in the subfrontal cortical white matter. The patient was studying in kindergarten and was able to actively communicate with other children and teachers.
### Preventive measures during acupuncture
To avoid unexpected situations, the acupuncturists selected a comfortable posture for the needling and paid due attention to the manipulation. Parents were asked to carefully observe the patient during acupuncture to monitor for emergency conditions. If any adverse event occurred, appropriate measures were taken immediately.
## Discussion
Evidence has shown that some of the factors involved in the prognosis of VS/UWS are etiology, age at the time of acute injury, and time spent in the same state. As for age, the recovery of consciousness and survival rates are higher in younger patients than in older ones; however, pediatric patients <1 year of age have been reported to show higher mortality ( ). Better consciousness and independence outcomes are observed in traumatic causes than in non-traumatic ones ( ). The correlation between time spent in VS/UWS and a better outcome is negative. Given that VS/UWS impairs neurological development among pediatric patients, many patients require long-term care. Therefore, effective and early intervention is indispensable for the long-term prognosis of VS/VWS.
Acupuncture is commonly used in various neurological conditions. The addition of acupuncture can alleviate consciousness disorders. It is reported that the addition of acupuncture can remarkably promote the recovery of the consciousness level ( ) and improve motor function of the limbs. Further, computed tomography (CT) demonstrated a reduction in the width of the third ventricle ( ) and apparently reduced the mean curing time ( ). Only a few adverse events were reported. The abovementioned studies suggested that the addition of acupuncture is safe and effective.
Scalp acupuncture is a modern acupuncture technique that combines the traditional needling method with modern medical knowledge ( ). It has been widely used to treat cerebral diseases in traditional Chinese medicine.
The mechanism underlying scalp acupuncture therapy in cerebral diseases remains elusive. However, the following are considered potential mechanisms: (1) increases cerebral blood flow; (2) improves cerebral oxygen metabolism; (3) reduces the deterioration of brain tissues as a result of free radicals and inflammatory factors ( ); (4) enhances synaptic plasticity via the regulation of neurotrophic factors ( ); and (5) regulates the brain microenvironment via nerve growth-related proteins ( ).
Tongdu Xingshen acupuncture is developed based on Lin's scalp acupuncture, Jiao's scalp acupuncture, and our clinical practice. Our previous studies reported that Tongdu Xingshen acupuncture improved intelligence, language ability, social adaptive ability, and motor function ( – ). It exerts stimulation on the corresponding scalp projection area of the cerebral cortex including the frontal lobe (precentral gyrus), parietal lobule (postcentral gyrus), paracentral lobule, temporal lobe (posterior superior temporal gyrus), and acerebellar hemisphere. The effect of acupuncture transmitted through the cortical–thalamic–cortical pathway thereby positively modulates the corresponding areas of memory, the language center, the motor center, or balance. As for the retention time of the needle, a curative effect is positively associated with the length of the retention time. Retaining needles for 2 h is optimal for intellectual and gross motor development in children with cerebral palsy based on our previous research ( ).
According to the basic theory of traditional Chinese medicine, the location of the disease in VS is the brain. The brain is made up of “marrow.” It has been recognized that the functions of the sense organs and body motion are linked with the brain in Errors on Medicine Corrected ( Yilin Gai Cuo ). The governing vessel has a close relationship with the brain, spinal marrow, and kidneys. Tongdu Xingshen acupuncture mainly stimulates the governing vessel to nourish the brain via ascending Yang Qi and enriching the marrow beneficial for the recovery of consciousness and nerve repair.
This case report has some limitations. First, the latest version of the diagnostic criteria and clinical efficacy scales for PVS were not adopted, but we propose that this did not influence diagnostic accuracy. Furthermore, intervention adherence and tolerability were not accessed. Nevertheless, the patient and his parents strictly followed the treatment routine and were hospitalized on time, suggesting good intervention adherence and tolerability.
In this case presentation, a 3.5-year-old boy with PVS after HSVE recovered from VS, and his cognitive and motor function generally improved after receiving a combination of Tongdu Xingshen acupuncture and modern medicine. This showed that acupuncture as an adjunctive therapy is effective in the recovery stage of PVS. It is recommended that patients with PVS receive acupuncture therapy as soon as possible when vital signs become stable. Acupoints located on the head are frequently used to stimulate the scalp projection area of each brain functional area. Earlier application of acupuncture is associated with a higher probability of awakening and fewer neurological sequelae.
### Patient's perspective
We sought medical help from Chinese medicine after receiving Western medicine therapy. Considering that the conditions were complex, we attempted to receive acupuncture in combination with routine treatment. After a week-long treatment, our son was able to swallow thick porridge or rice cereal. This favorable turnaround gave us confidence in the current treatment plan. The medical staff were patient and attentive. Throughout the therapy session, we were delighted to see his progress.
## Data availability statement
The original contributions presented in the study are included in the article/ , further inquiries can be directed to the corresponding author/s.
## Ethics statement
Written informed consent was obtained from the minor(s)' legal guardian/next of kin for the publication of any potentially identifiable images or data included in this article.
## Author contributions
BJ contributed to data collection, the design of the case report design, data analysis, and interpretation. YT contributed to the design of the case report, writing the initial and subsequent drafts after they were revised by all involved authors, and submitting the final case report. YW contributed to data analysis and interpretation. ZL contributed to data analysis and interpretation, as well as critically revising this paper. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
|
## Introduction
Parkinson's disease (PD) patients frequently engage in rehabilitation to ameliorate symptoms. During the Coronavirus disease 2019 (COVID-19) pandemic, access to rehabilitation programs has been markedly limited, consequently, telerehabilitation gained popularity. In this prospective, open-label, and pilot study, we aimed to investigate feasibility, safety, and efficacy of telerehabilitation in mild-to-moderate PD patients.
## Materials and Methods
Twenty-three PD patients, with Hoehn and Yahr stage <3, without gait disturbances or dementia and capable of using the televisit platform, were recruited for a 5-week telerehabilitation program, consisting of 1 remote visit with a therapist and a minimum of two sessions of >30-min of self-conducted exercises per week. Patients received video tutorials of exercises and were asked to keep a diary of sessions. At baseline (T0), at the end of the intervention (T1), and 1 month after the end of treatment (T2), patients were remotely assessed with MDS-UPDRS part I-III, PDQ-39, Functional Independence Measure (FIM), and Frontal Assessment Battery scales, respectively. Acceptable compliance to the program was defined as >60% matching of frequency and duration of sessions, whereas optimal compliance was set at >80% matching.
## Results
The dropout rate was 0%. Over 85% of patients reached acceptable adherence cut-off and around 70% reached optimal one. No adverse events were reported during sessions. The repeated measure analysis of variance (rANOVA) showed a significant effect of factor “time” for MDS-UPDRS-III ( p < 0.0001) with a mean reduction of 4.217 points between T0 and T1 and return to baseline at T2. No significant effect was found for other outcome measures.
## Conclusion
Our findings demonstrate that telerehabilitation is safe, feasible, and effective on motor symptoms in mild-to-moderate PD patients.
## Introduction
Parkinson's disease (PD) is the second most common neurodegenerative disease in terms of prevalence and burden of disability ( ). The primary symptoms of PD include bradykinesia, rigidity, and resting tremor. Additional and more disabling motor symptoms, such as postural instability and gait disturbances, frequently occur with disease progression and carry heavy impact on independence and quality of life (QoL) ( , ). Moreover, PD patients may experience a variety of non-motor symptoms (NMS), such as sensory alterations, dysautonomia, sleep disturbances, mood disorders, and cognitive impairment, which may precede the motor onset or arise along disease course, and further deteriorate the QoL of patients ( ). The management of PD relies mostly on symptomatic pharmacological therapy with L-Dopa or other dopaminergic agents ( ). Several drugs are available for treating NMS as well ( ). However, even with optimal pharmacological management, most PD patients engage in rehabilitation to reduce disability in daily activities. Physiotherapy is the most widely used rehabilitation approach and has the most solid result evidence, in particular on motor symptoms of PD ( – ). In this respect, the European Physiotherapy Guidelines for PD offer a useful tool for clinicians to evaluate patients and refer them to physiotherapists. Moreover, these guidelines represent the evidence-based supports to physiotherapists for identifying treatment goals and intervention strategies tailored to the management of disease staging and severity ( ).
The recent Coronavirus disease 2019 (COVID-19) pandemics widely disrupted most of our daily life aspects and forced administrations to lockdown and strict social distancing measures. This had a heavy impact on the healthcare systems as well, with chronic disease patients being the most affected. Indeed, reports of worsening of some NMS, in particular anxiety, in PD patients have accumulated in the last 2 years ( – ). This was associated mostly with difficulties in accessing clinical services and medications ( ), reduction of physical activity, and inability to access rehabilitation clinics ( ), with up to 88% of patients reporting the interruption of physiotherapy during lockdown ( ). To overcome these limitations, a transition from in-person to remote visits has been supported by several PD centers for implementing telemedicine and telehealth management of PD patients ( – ).
Telemedicine represents an interface in a virtual patient–physician relationship to provide primary and secondary care for a variety of neurological disorders ( ). With respect to PD, telemedicine has been applied to assist remote management of devices for advanced therapies, teleconsultation, telerehabilitation, and monitoring of motor and non-motor parameters in an ecologically valid environment ( ). In the field of rehabilitation, the call for implementing telemedicine instruments to ensure continuity in the management of neurological patients was strong ( – ). In Italy, the Italian Society for the Neurological Rehabilitation published a guideline containing urgent measures to face limitations imposed by severe acute respiratory syndrome coronavirus 2 (SARS-CoV2)-pandemics, including the use of remote assessments and management solutions ( ). The remote administration of physiotherapy in PD patients is rather challenging, and the feasibility of treatment is hampered by the fear of adverse events (AEs), particularly falls without the possibility of prompt intervention by the operator. Despite these concerns, there is growing evidence in favor of the efficacy of telerehabilitation to sustain physical activity, mobility, and emotional wellbeing ( , , – ). Most reports dated before the COVID-19 pandemics were focused on cognitive training, speech therapy, and dance therapy in small cohorts of patients affected by different neurological disorders. In the present study, we sought to investigate the feasibility, safety, and efficacy of telerehabilitation in mild-to-moderate PD patients. The program was originally designed and carried out during the lockdown due to the COVID-19 pandemics in Italy, then maintained after the reopening of rehabilitation facilities.
## Materials and Methods
This was a prospective, open-label pilot study, aimed to investigate the feasibility, safety, and efficacy of telerehabilitation in mild-to-moderate PD patients. The study was performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments. Approval was granted by the Local Ethical Committee of the Sapienza University of Rome. Data collection and processing followed the current European regulation for data protection. Patients with PD, referring to our Movement Disorder Outpatient Service in the period between January 2020 and August 2021 were screened for enrollment with a 1:10 ratio according to the visit schedule. The inclusion criteria were: (i) diagnosis of idiopathic PD according to the MDS criteria ( ); (ii) disease stage <3 according to the modified Hoehn and Yahr (H&Y) scale ( ); (iii) stable antiparkinsonian treatment in the previous 3 months; (iv) availability of technical instruments for remote video-call (tablet, laptop, or computer/webcam) and ability to use them by patients and/or caregiver; (v) availability and motivation of patients to participate to a 5-weeks telerehabilitation program; and (vi) attendance of a caregiver during remote and self-conducted sessions for patients with H&Y score >1. The exclusion criteria were: (i) contraindications to rehabilitation treatment; (ii) patients already undergoing rehabilitation treatment; (iii) co-morbidity with non-stabilized major medical illnesses; (iv) cognitive impairment as defined by a Mini-Mental State Examination (MMSE) score <24; and (v) presence of freezing of gait (FOG).
Enrolled patients matching inclusion and exclusion criteria underwent a 5-week telerehabilitation program consisting of a remote session with a physiotherapist once weekly and at least two self-conducted sessions per week. In the 1st week of the treatment, an additional assisted remote session was scheduled for further training and exercise feedback. Moreover, patients had free access to video tutorials, showing the exercises performed with physiotherapists and were instructed to exercise at least twice weekly with a minimum of 30 min for each session. Areas of intervention included general mobility, static, and dynamic balance, coordination, dexterity, postural transitions, and facial mobility. Mobility and postural transition exercises focused mainly on sit-to-stand and lying mobility to address in-bed turning difficulties. A number of exercises ranging from 8 to 12, for duration of 40–60 min were included in each session depending on the patients' condition, functional demands, and reported difficulties. Examples of video tutorials are available in the .
To evaluate compliance, patients were instructed to keep a diary of self-conducted sessions. Patients were evaluated before treatment (T0), at the end of the 5-week treatment program (T1) and 1 month after the end of treatment (T2). All evaluations were performed remotely on a digital platform for telemedicine freely available by Regione Lazio, named “Salute Digitale” ( ). The platform consists of an easy-to-access audio/video remote conference call interface based on the open-source set Jitsi Meet. A unique room for teleconsultation is generated by the healthcare provider and the private link for participation is communicated to the patient. The teleconsultation room is canceled automatically at the end of the call. The platform is compliant with GDPR and current regulations for web and software privacy and security.
The primary outcome measures of the present study were feasibility and safety of telerehabilitation. To assess them, we investigated three variables: dropout rate, adherence to the program, and occurrence of AEs. Dropout rate was defined as the rate of patients who did not complete the study from enrollment to post-training evaluation. The a priori criterion for adherence was set at a 20% dropout rate. Patient adherence to the telerehabilitation program was defined as the rate of training sessions matching frequency (≥3 sessions per week) and duration (≥30 min). This was considered acceptable for at least 60% and optimal for at least 80% rate, respectively. Falls during the telerehabilitation program were considered the primary AEs. The a priori criterion was set at 0 falls. Moreover, any other possible AE occurring during the training program was recorded. Six secondary outcome measures were collected to evaluate the patients' status.
The MDS-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) parts I-III were used to assess the motor symptoms severity and the impact of motor and non-motor symptoms on daily life ( ). The Parkinson's Disease Questionnaire-39 (PDQ-39) was used to evaluate patients QoL ( ). The Functional Independence Measure (FIM) was used to assess functional independence in daily life activities ( ). The Frontal Assessment Battery (FAB) was employed to evaluate the frontal cognitive abilities of enrolled patients ( ). At the end of the telerehabilitation program, patients were administered a questionnaire composed of five questions constructed as a 7-items Likert scale, investigating the satisfaction for the telerehabilitation program (Q1), the usefulness of the program for PD patients (Q2), the satisfaction for the remote visit modality (Q3) and the willingness to participate again in the same telerehabilitation protocol or other telemedicine programs (Q4 and Q5; ).
Study design.
Due to the exploratory nature of the study, a rigorous sample size calculation was not carried out. However, we predicted high compliance for telerehabilitation programs with a low dropout rate. Therefore, we fixed the number of enrolled patients at 25, considering a dropout rate of 20%. All statistical analyses were carried out using the SPSS version 23 software for Windows. The normality of distribution of the variables was assessed using the Shapiro–Wilk test. To assess the effect of the telerehabilitation program across the different time-points on the evaluated variables, repeated measure analysis of variance (rANOVA) was performed. Greenhouse–Geisser correction for non-sphericity and Bonferroni's correction for multiple tests were applied when needed. To evaluate the effect size of our intervention partial η (η ) was reported and a post-hoc analysis to compute achieved power was performed using G Power software 3.1.9.7 for Windows. The level of significance was set at p < 0.05. All data are reported as Mean ± SD or Median (Q1–Q3; Min–Max).
## Results
Forty-seven patients were screened for eligibility for the study and 23 (48.9%) were enrolled based on inclusion and exclusion criteria ( ). Demographic and clinical features of enrolled patients are shown in . All patients completed the study, resulting in a dropout rate of 0%. A total of 452 training sessions were completed, 380 of which (83.9%) reached the duration cut-off of 30 min. In 94 out of 115 training weeks (81.7%), the a priori criteria of at least 3 sessions/week for minimum 30 min each were reached. When considering single patients, 20/23 (87%) patients reached the cut-off for acceptable adherence of at least 60% of matching frequency and duration, and 16/23 patients (69.6%) reached the optimal cut-off of 80%. No falls or other AEs were reported and no interventions by caregivers were necessary during supervised or self-conducted sessions. Repeated measure ANOVA showed a significant effect of the factor “time” for the MDS-UPDRS-III score across the different time points ( F = 10.539; p < 0.0001). The post-hoc analysis showed a motor severity score significantly reduced right after the treatment with a mean decrease of 4.217 (95% CI, 1.637–6.798; p = 0.001), with a return to baseline values at 1-month evaluation (T1 vs. T2 p = 0.036; T0 vs. T2 p = 0.147; ). No significant effect of factor “time” was found for the other secondary outcome measures, which remained stable from the beginning to the end of the study. Variables values across time points, the values of η and achieved power are shown in . Over 90% of patients were “extremely satisfied” or “very satisfied” for the telerehabilitation and remote visit modality and considered the intervention “extremely useful” or “very useful” for PD patient. Furtherly, all except a single patient were highly interested in undergoing again the telerehabilitation program or other telemedicine projects ( ).
Demographic and clinical characteristics of enrolled patients.
DA, dopamine agonist; iMAO-B, MAO-B inhibitors; LEDD, levodopa equivalent daily dose .
Variables are shown as Mean ± SD or Median (Q1–Q3; Min–Max) for numerical variables and N (%) for categorical variables .
Histogram showing the MDS-UPDRS-III score across time points. Standard error of the mean is shown by vertical bars. Statistically significant differences are marked with an asterisk. The post-hoc analysis showed a reduction between T0 and T1 and a return to baseline at T2.
Secondary outcome measures scores at T0–T2.
For repeated measures ANOVA, F-statistics, effect sizes, and power are reported. Statistically significant results are marked in bold with an asterisk. , partial eta squared. Variables are shown as Mean ± SD or Median (Q1–Q3; Min–Max) .
## Discussion
In this open-label pilot study, we investigated the feasibility, safety, and efficacy of telerehabilitation in mild-to-moderate PD patients. Telemedicine has been applied recently under specific circumstances, for specific indications and eligible patients. Despite the potential relevance of telemedicine for diagnosis, consultation, monitoring and treatment management, availability, and diffusion of telemedicine is still limited by the clinical and sociodemographic features ( , ). The issue of telerehabilitation in PD has been promoted during the lockdown for COVID-19 pandemics; however, it appears promising for the management of early stages of PD under normal conditions as well. Safety is a major concern to remote physiotherapy, in particular because of the limited possibility of direct intervention by the operator if the case of AEs. Based on the previous reports that 35–90% of PD patients experience at least 1 fall/year, and 2/3 of cases are recurrent fallers ( ), the occurrence of falls was the main safety measure in our study. The a priori criterion of no falls was matched in our cohort, indicating the high safety of our telerehabilitation program in mild-to-moderate PD patients. Moreover, there was no report of any other AE, in line with the results of previous studies underlying the safety of remote rehabilitation in PD patients ( ). Dropout rate and adherence to the program were considered as measures of feasibility. All participants completed the program and the post-training evaluation (dropout rate 0%), confirming that duration and complexity of exercises were accessible to all participants. Despite the potential bias due to lockdown, we would like to point out that participation in our program remained absolute after the reopening of rehabilitation structures as well. The present findings are, therefore, much more promising compared to those of previous studies showing a 20% dropout rate in elderly subjects engaging in a rehabilitation program ( ), and confirm the awareness and willingness of PD patients toward rehabilitation. This concept is further supported by the high adherence to the protocol, as almost 85% of patients reached the acceptable cut-off and 70% reached the optimal cut-off for participation. Thus, the present results indicate that telerehabilitation is a feasible, accessible, and likely rewarding intervention in mild-to-moderate PD patients. However, among screened patients, less than half-matched inclusion and exclusion criteria. This at least partially reflects the strict enrollment criteria used in the present studies and must be taken into account when considering the general applicability of remote physiotherapy intervention in PD. Finally, the high rate of satisfaction and willingness to engage in similar programs among our patients demonstrates that PD subjects are interested in the rehabilitation program and can ensure notable compliance and adherence to treatment.
As to motor outcome measures, we found a significant reduction of MDS-UPDRS-III after telerehabilitation. Despite being a secondary outcome measure, post-hoc power analysis demonstrated a statistical power >98% with high effect size, confirming the reliability of the finding. Moreover, the previous studies showed a minimum clinical impact for MDS-UPDRS-III between 2.4 and 3.25 ( ), thus the score reduction of 4.22 in our study had a clinically significant impact on the patient's motor symptoms severity. In the literature, the efficacy of physiotherapy on motor symptoms is widely demonstrated ( ). Moreover, preliminary studies showed efficacy of non-conventional remote administered rehabilitation strategies, including dance or virtual reality training, on motor and non-motor outcomes in PD patients ( ). Our study confirms this extended knowledge to the efficacy of remote administered physiotherapy program on motor symptoms of PD, as measured by the MDS-UPDRS-III score. No significant variation was, however, found regarding functional independence, QoL, NMS, and executive cognitive functions in mild-to-moderate PD patients. This lack of significance may depend on several reasons. First, we enrolled PD patients with a modified Hoehn and Yahr score <3. In particular, patients using ambulation aids, with postural instability or reporting FOG were excluded, primarily for safety reasons. Balance and gait disturbances are among the most disabling impairments in PD patients, strongly limiting functional independence and having a strong impact on QoL ( , – ). Secondly, the enrolled patients were mostly cognitively stable and patients with significant cognitive impairment were excluded. The previous studies demonstrated an effect of physical exercise on cognitive function and some effect on NMS ( , – ), but the relatively good cognitive and NMS status of our patients could have masked the improvement with a roof effect on our secondary outcome measures.
Beyond these considerations, we acknowledge that this exploratory study suffers from limitations due to the open-label and non-controlled design, the small cohort, the relatively good status of our patients, and the remote motor evaluation. Regarding the number of subjects, this was a pilot study, thus a precise sample size calculation was not carried out. However, the post-hoc power analysis confirms the reliability of the reported results. Again, the characteristics of enrolled patients could limit the generalizability of our data due to the relatively good functional and cognitive status and a roof effect in outcome measures. Further studies, including intermediate-to-advanced patients with balance and gait disturbances, cognitive impairment and using ambulation aids could help addressing this issue. Finally, the remote motor evaluation could somehow limit the reliability of our data. MDS-UPDRS-III items 3 and 12 (rigidity and postural instability) cannot be performed during remote visits and some evidence showed the reduced validity of tremor assessment when performed through video ( ). However, recent studies demonstrated the feasibility and reliability of MDS-UPDRS-III remote administration ( , ). Thus, we decided remote evaluation of our patients, also to address the difficulties to access medical services during lockdowns and COVID-19 related restrictions. Future studies, implementing remote evaluation instruments, such as wearable devices, could help overcome this limitation.
## Conclusion
Our findings demonstrate that telerehabilitation is safe, feasible, and effective on motor symptoms in mild-to-moderate PD patients. Thus, remote physiotherapy programs could be viable and useful tools to overcome situations with limited access to healthcare services. Further controlled studies with greater sample size, including patients with higher disease severity, cognitive impairment, and implementing remote assessment instruments could help further expand our results.
## Data Availability Statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics Statement
The studies involving human participants were reviewed and approved by Ethical Committe Sapienza. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.
## Author Contributions
EB, CO, and FP designed the study and wrote the first draft of the manuscript. CO and CM performed the physiotherapy treatment. EB and MA evaluated patients and collected data. EB performed data analyses. DR, PA, AM, and MS reviewed the manuscript draft. All authors contributed to the article and approved the submitted version.
## Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's Note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
|
## Introduction
Coronavirus disease 2019 (COVID-19) is prevalent among young people, and neurological involvement has been reported. We investigated neurological symptoms, cognitive test results, and biomarkers of brain injury, as well as associations between these variables in non-hospitalized adolescents and young adults with COVID-19.
## Methods
This study reports baseline findings from an ongoing observational cohort study of COVID-19 cases and non-COVID controls aged 12–25 years (Clinical Trials ID: NCT04686734). Symptoms were charted using a standardized questionnaire. Cognitive performance was evaluated by applying tests of working memory, verbal learning, delayed recall, and recognition. The brain injury biomarkers, neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAp), were assayed in serum samples using ultrasensitive immunoassays.
## Results
A total of 405 COVID-19 cases and 111 non-COVID cases were prospectively included. Serum Nfl and GFAp concentrations were significantly elevated in COVID-19 cases as compared with non-COVID controls ( p = 0.050 and p = 0.014, respectively). The COVID-19 cases reported more fatigue ( p < 0.001) and post-exertional malaise (PEM) ( p = 0.001) compared to non-COVID-19 controls. Cognitive test performance and clinical neurological examination did not differ across the two groups. Within the COVID-19 group, there were no associations between symptoms, cognitive test results, and NfL or GFAp levels. However, fatigue and PEM were strongly associated with older age and female sex.
## Conclusions
Non-hospitalized adolescents and young adults with COVID-19 reported more fatigue and PEM and had slightly elevated levels of brain injury markers, but showed normal cognitive performance. No associations were found between symptoms, brain injury markers, and cognitive test results, but fatigue and PEM were strongly related to female sex and older age.
## Introduction
The pandemic of Coronavirus Disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an unprecedented threat to health and welfare globally. In the early stages of the pandemic, several case studies provided evidence that infected individuals could suffer neurological complications ( – ). There are reports of neurological symptoms being associated with high SARS-CoV-2 antibody levels in cerebrospinal fluid (CSF) ( ), and with demyelinating lesions and other abnormal brain MRI findings ( , ). In addition, neurological and neuropsychological symptoms such as fatigue, memory loss, and “brain fog” have emerged as prevalent and debilitating symptoms in the acute and subacute stages of COVID-19 ( , ). However, it is not clear to which extent the neurological manifestations described in severe COVID-19 infections are caused by the virus per se , or if they more likely should be attributed to more general consequences of severe disease courses ( , ). Further, it is yet to be established whether mild COVID-19 is associated with neurological involvement and whether the subjective experience of “brain fog,” fatigue, and other neuropsychological symptoms correspond with objectively measurable cognitive deficits.
With the progression of the COVID-19 pandemic, there is growing concern that symptoms can persist after the initial illness, a condition often referred to as “post-COVID syndrome” ( ). A wide range of persisting symptoms are reported, including neurological and neuropsychological complaints such as fatigue, post-exertional malaise (PEM), memory and concentration problems, headache, and muscular pain ( , ). There are theories that post-COVID syndrome is caused by neuroinflammation ( ), induced or exacerbated by a combination of mast cell activation, cytokine storm, and activation of the hypothalamic–pituitary–adrenal (HPA) axis linked to the initial COVID-19 infection ( , ). Thus, a detailed study of neurological aberrations in the subacute stage of the infection may provide theories of post-COVID syndrome development.
Neurofilament light chain (NfL) is a neuronal protein and is considered a specific biomarker for axonal damage regardless of the cause ( ), and is released into CSF upon neuronal injury ( , ). Though details of kinetics and distribution remain unknown, several studies have shown a tight correlation between levels in CSF and blood (serum and plasma) samples ( , ), making it widely usable as a biomarker for neuroinflammation and degeneration in neurological conditions ( – ), and has also caught interest as a predictor for neurological outcome in intensive care medicine ( , ). Another established blood biomarker for brain injury is the glial fibrillary acidic protein (GFAp) ( – ), which is known to increase rapidly in both CSF and serum as a response to acute cerebral injury ( – ), signaling astrocytic activation ( ). Thus, GFAp is directly linked to the brain's intrinsic inflammatory system.
The aims of the current study were two-fold: (a) to compare neurological/neuropsychological symptoms, cognitive test results, and serum markers of brain injury (NfL/GFAp) across non-hospitalized adolescents and young adults with COVID-19 (COVID-19 cases) and healthy controls (non-COVID-19 controls); (b) to investigate associations between these variables among the COVID-19 cases.
## Methods
### Study Design
The long-term effects of COVID-19 in Adolescents (LoTECA) project is a longitudinal observational cohort study of SARS-CoV-2 positive and negative non-hospitalized adolescents and young adults, with a total follow-up time of 12 months (Clinical Trials ID: NCT04686734). Details of the design are reported elsewhere ( ). In this study, results from the baseline visit are reported. The project has been approved by the Norwegian National Committee for Ethics in Medical research. Informed consent was obtained from all participants.
### Participants
From late December 2020 through May 2021, adolescents and young adults were recruited to the LoTECA study. Inclusion criteria for the COVID-19 cases were: (1) age between 12 and 25 years; (2) positive PCR test for SARS-CoV-2. Exclusion criteria were: (1) more than 28 days since the first day of symptoms (for asymptomatic patients, day one of the disease episode was considered the date of the positive PCR test); (2) hospitalization due to COVID-19; (3) pregnancy. Inclusion criteria for the non-COVID-19 controls were: (1) age between 12 and 25 years; (2) negative PCR test for SARS-CoV-2, no older than 28 days. Exclusion criteria were: (1) history of COVID-19 prior to inclusion; (2) pregnancy.
Individuals eligible for inclusion in either of the two groups were identified through lists of individuals tested for SARS-CoV-2 by PCR received from two accredited microbiological laboratories (Fürst Medical Laboratories; Dept. of Microbiology and Infection Control, Akershus University Hospital), serving the counties of Oslo and Viken, Norway. For those who consented to participate, an appointment at the study center at Akershus University Hospital, Norway, was scheduled as soon as possible after the end of their 10-day quarantine period.
### Investigational Program
The investigational program included clinical examination, blood sampling, spirometry, 3-lead ECG monitoring for 5 min at rest, cognitive testing, and questionnaire charting ( ). Approximately halfway through the inclusion period, a neurological examination was included in the clinical examination. Only selected variables relevant to the specific aims of the present study are reported here.
#### Laboratory Assays
Blood samples were obtained from antecubital venipuncture and assayed for routine clinical markers. All samples were tested with Elecsys Anti-SARS-CoV-2 immunoassay (Roche Diagnostics, Cobra e801, Mannheim, Germany) to detect IgG/IgM against SARS-CoV-2 nucleocapsid antigen. Serum samples from some study participants were retested with the Liaison SARS-CoV-2 S1/S2 IgG immunoassay (DiaSorin, Saluggia, Italy) to quantify antibodies (IgG) against the spike (S)1 and S2 protein of SARS-CoV-2.
Blood for GFAp and NfL measurements in the serum was collected in 3.5 mL Vacuette R (Greiner bio-one GmbH) with gel, allowed to clot for at least 30 min, processed within 2 h by centrifugation (2200 g, 10 min), and aliquots stored immediately at −80°C until analysis. Serum GFAp and NfL measurements were performed at the Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Sweden, by board-certified laboratory technicians blinded to clinical data using commercially available Single molecule array (Simoa) assays on an HD-X Analyzer (Human Neuro 2-Plex B assay), as described by the manufacturer (Quanterix, Billerica, MA). Calibrators were run in duplicates, while samples were diluted four-fold and run in singlicates. Two quality control (QC) samples with different levels were run in duplicates at the beginning and the end of each run. Repeatability and intermediate precision were both 8.7% for the QC sample with an NfL concentration of 8.4 pg/mL and 5.9% for the 79.6 pg/mL sample. For GFAP, repeatability was 6.5% and intermediate precision 7.3% for the QC sample at 102 pg/mL, and repeatability was 5.8% and intermediate precision 6.7% for the QC sample at 388 pg/mL.
#### Cognitive Testing
All participants underwent cognitive testing in the form of digit-span test from the Wechsler Intelligence Scale for Children, 4th edition (WISC) ( ) and the Hopkins Verbal Learning Test-Revised (HVLT-R) ( ). The digit span test is used for verbal and auditory working memory assessment. A string of random digits is read aloud by the examiner. The first string consists of two random numbers, and for every other string, one more number is added. The digit span forward mode requires the test subject to repeat the digits in the same order as they are presented; in the digit span backward mode, digits are repeated in reverse order. Each correctly repeated string is scored one point. The test is discontinued when two strings of equal length are answered incorrectly. Sum scores for digit span forward and backward, as well as total sum score are reported.
In the HVLT-R test of verbal learning, delayed recall, and recognition, the examiner reads aloud a list of 12 words and the participant is asked to repeat as many words as possible in three consecutive trials. Verbal learning memory is the sum score of remembered words (0–36) in the three trials. Delayed verbal memory is measured as the number of words the test subject recalls after 20 min. Finally, 24 words are read aloud, of which 12 are identical to the previous list of words; the number of correctly recognized and falsely recognized words is recorded separately.
#### Questionnaires
The questionnaire contained questions on demographic background information and symptoms during the disease episode. In general, the frequency of specific symptoms was scored on five-point Likert scales (1–5) ranging from never to each day/always. Information on sex and ethnicity was self-reported. In addition, results from the following validated instruments are reported in the current paper:
Chalder Fatigue questionnaire (CFQ) addresses symptoms of mental and physical fatigue. The 11-item version used in this study has been validated as an assessment tool of chronic fatigue syndrome ( ). Each item was scored on a four-point Likert scale (0–3), and CFQ was reported with a total range of 0–33.
Five items from DePaul Symptom Questionnaire ( ) were used to address post-exertional malaise (PEM). The frequency of symptoms was rated on a five-point Likert scale, each item scored 0–4, ranging from never to each day/always. Scoring across all items was averaged and then multiplied with 25 to obtain a 0–100 scoring range.
Sleep-related problems were assessed using 12 items from the Karolinska Sleep Questionnaire (KSQ) ( ), each item scored on a six-point Likert scale. Results were reported as the average score of all items ranging from 1 to 6 (lower scores correspond to more symptoms), as well as sub-scores for insomnia, awakening problems, and sleepiness.
Brief pain inventory (BPI) ( ) is a four-item tool scoring pain from no pain to worst pain ever on a ten-point Likert scale. Results are reported as a summary score (ranging from 4–40) as well as the scores on each item.
### Statistical Analysis
For the cross-sectional comparisons across the COVID-19 cases and non-COVID-19 controls, chi-square test, t -test, and Wilcoxon rank-sum test were applied as appropriate, depending on distribution. Associations between variables were first explored by the non-parametric statistics Spearman's rho; thereafter, associations between fatigue score and markers of neuronal injury (NfL/GFAp) were assessed by applying linear regression modeling while adjusting for possible demographic confounders (age, sex, and chronic disease).
Statistical analyses were performed using Stata Statistical Software: Release 16 (StataCorp LLC, College Station, TX). A p < 0.05 was considered statistically significant (two-sided test); p- values were not adjusted for test multiplicity.
## Results
In the period from 24/12/2020 through 18/05/2021, patients were recruited among individuals between 12 and 25 years of age who had a SARS-CoV-2 PCR test performed at the two collaborating microbiological laboratories. A flowchart of the recruitment proses is presented in .
Flowchart of patient availability, identification, and recruitment process.
Of all individuals in the background population with a positive SARS-CoV-2 test, 49% were women. Of all SARS-CoV-2 positive cases enrolled, 60% were female. Of individuals younger than 18 years of age, the proportion of recruited participants did not differ between the sexes. For individuals older than 18 years of age, significantly more of the invited women accepted study participation compared to men. The median time from onset of symptoms to enrolment was 18 days.
Sensitivity analysis was performed between models including and excluding six SARS-CoV-2 negative controls who turned out to have IgG/IgM against SARS-CoV-2 nucleocapsid antigen and/or IgG against the spike protein. Their exclusion did not affect the results in the final model.
### Cross-Sectional Comparison of COVID-19 Cases and Non-COVID-19 Controls
Background characteristics of cases and controls are reported in . There was no difference in demographic variables between the COVID-19 cases and non-COVID-19 controls, except for ethnicity, where Caucasians were overrepresented among controls.
Baseline characteristics of included children and adolescents by SARS-CoV-2 positivity.
Comparison of self-reported symptoms showed no difference between the two groups in terms of headache, disorientation, concentration or memory difficulties, sleep, and pain, but the COVID-19 cases scored significantly higher on both fatigue ( p < 0.001) and PEM ( p = 0.001). Non-COVID-19 controls reported more difficulties making decisions ( ).
Symptoms, clinical and laboratory findings, and cognitive test results among COVID-19 cases and non-COVID controls.
The markers NfL and GFAp were significantly elevated in COVID-19 cases as compared to non-COVID-19 controls ( p = 0.05 and p = 0.01, respectively; and ). Cognitive test results did not differ between the COVID-19 cases and non-COVID-19 controls. As for neurological examination, findings were generally sparse, and no difference was observed between the two groups. Differences in ethnicity among cases and controls did not significantly confound other between-group differences in adjusted analyses.
Boxplot of brain injury biomarkers according to SARS-CoV-2 status.
### Associations to Fatigue Within the SARS-CoV-2 Positive Cohort
Among COVID-19 cases, serum GFAp was negatively correlated with fatigue score, HVLT-R delayed recall, and HVLT-R false recognition, though none of these findings were significant after Bonferroni correction ( ). Neither NfL nor GFAp was correlated with any other symptom score, cognitive symptoms, or cognitive test results. Female sex and older age were correlated with all symptom scores for fatigue, sleep, and pain, as well as several cognitive symptoms. Age was associated with cognitive test results. There was no correlation between cognitive test results and reported cognitive symptoms ( ).
Correlation (Spearman's rho) between background variables, symptoms, brain injury markers, and cognitive test results within the COVID-19 group .
A total of 90 statistical tests are displayed in this table; a Bonferroni-correction for test multiplicity suggests a level of significance at 0.05/90 = 0.0006 .
Correlation (Spearman's rho) between cognitive symptoms and cognitive test results within the COVID-19 group .
In an adjusted linear regression model, there was no association between chalder fatigue score and NfL/GFAp, but fatigue was associated with older age, female sex, and chronic disease ( ).
Association between chalder fatigue score and neuro-injury markers at baseline.
Multiple linear regression focusing on NfL and GFAp respectively .
Neurofilament light chain ;
Glial fibrillary acidic protein ;
## Discussion
The present study of a large group of young, non-hospitalized COVID-19 cases in the late acute stage of the infection show that (a) serum biomarkers of brain injury are slightly elevated, whereas cognitive function tests are normal; (b) fatigue and post-exertional malaise are persistent symptoms, but overall the symptom load was relatively mild; (c) symptoms were not associated with brain injury markers or cognitive tests but correlated with female sex and older age.
The slightly, but significantly increased levels of NfL and GFAp among COVID-19 cases corroborate results from other studies reporting elevation of biomarkers for brain involvement after COVID-19 ( ). For instance, Ameres et al. ( ) found NfL to be significantly increased in a population of adult health care workers who recently recovered from mild to moderate COVID-19. Also, a small observational study found elevated NfL among severe COVID-19 cases and elevated of GFAp in both moderate and severe cases ( ). In a follow-up study prior to the COVID-19 pandemic, elevated levels of NfL in cognitively healthy adults showed an association with the development of mild cognitive impairment ( ). In the current study, we found no association between NfL and cognitive test results. The interpretation of NfL results is complicated due to its dependency on age, but since this seems to be a non-linear pattern, and levels are contemplated as quite stable in younger adults ( ), we do not think this phenomenon influences our findings.
The absence of between-group differences regarding cognitive test results was surprising, given the frequent report of subjective experiences of cognitive impairment in COVID-19 sufferers. Another study ( ) compared cognitive test results in adults recovering from COVID-19 with non-COVID-19 cases and found significantly reduced cognitive performance in the COVID-19 group. However, in this latter study, data were collected from the general population where people were encouraged to answer a questionnaire and/or perform cognitive online, a recruitment procedure vulnerable to selection bias. The psychological stress caused by quarantine, fear, and loneliness will activate stress responses that in turn may influence cognitive capabilities ( , – ). It is therefore crucial to compare COVID-19 cases to a matching control population who experienced the same level of social restrictions and other stressors during the pandemic. Even though the proportion of invitees who accepted participation in our study is low, especially among non-COVID-19 controls, we still believe our recruitment procedure is less vulnerable to selection bias compared with the studies reporting contrary results ( , ).
A previous study of self-reported cognitive symptoms ( ) found COVID-19 cases to report significantly more memory problems compared with controls. In contrast, we found no increase in specific cognitive symptoms in the COVID-19 group; in fact, “difficulty making decisions” was significantly (and probably coincidentally) more common among non-COVID-19 controls. However, the COVID-19 group had higher scores for fatigue and post-exertional malaise. Interestingly, these symptoms are a hallmark of post-COVID syndrome as well as persistent symptoms following other infectious diseases ( ). The results from the present study may suggest that there is a general tendency for these symptoms to resolve more slowly than other neuropsychological complaints.
We observed a correlation between ethnicity and cognitive test results, and this is suggested to be explained by a strong correlation between parental educational level (proxy of socioeconomic level) and ethnicity.
Interestingly, symptoms were neither associated with serum NfL or GFAp nor with cognitive test results, but did correlate strongly with female sex and older age. This finding endorses previous results from post-COVID syndrome research, where the female sex is consistently reported to be a risk factor, but with limited findings of biological abnormalities ( , ). The apparent disconnection between clinical symptoms and biological aberrations is an intriguing observation suggesting a biopsychosocial rather than a strict biomedical derivation for the development of persisting symptoms following COVID-19, a perspective deserving further investigations ( ).
### Strengths and Limitations
Strengths of the present study include a well-defined group of young individuals undergoing a mild course of COVID-19, recruitment soon after infection, and a comparable control group. A weakness of the study is the higher degree of enrolment among female cases invited to participate in the study, ensuing is a skewness of COVID-19 cases toward more women compared with the background population.
There could be a bias of classification between COVID-19 cases and controls. The COVID-19 controls were recruited among individuals who had a negative PCR test for SARS-CoV-2. These individuals most likely had the PCR test performed either because they had symptoms consistent with COVID-19 or because they had a history of exposure. COVID-19 controls were excluded from the analysis if they had antibodies against SARS-CoV-2. There could still be participants who had a false negative SARS-CoV-2 PCR test and had not undergone seroconversion.
The study is further limited by the delayed implementation of neurological examination in the investigational program. This was not implemented until approximately halfway through the inclusion period, and consequently, this clinical information is not available to all participants. It would have been beneficial if the baseline examination had been completed even earlier in the course of the infection to give a better picture of the acute findings following mild disease. However, the delay in sampling serum for NfL analysis might give a more trustworthy picture, as the rise in serum values following neuronal damage will peak weeks to months after the event ( ).
## Conclusion
Non-hospitalized adolescents and young adults in the early convalescent stage of COVID-19 showed no difference in cognitive test results compared with healthy controls, even though blood biomarkers for astrocytic activation and neuronal injury were slightly elevated. Fatigue and post-exertional malaise were more prevalent among the COVID-19 cases but did not correlate with the brain injury markers serum NfL or GFAp nor cognitive test results; however, both symptoms correlated with female sex and older age.
## Data Availability Statement
The datasets presented in this article are not all readily available because of data protection laws. Requests to access the datasets should be directed to corresponding author.
## Ethics Statement
The studies involving human participants were reviewed and approved by Regional Comittee for Medical Research Ethics South East Norway. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.
## Author Contributions
VW drafted the study protocol and coordinated the study. LH, LB, JS, TS-S, TL, TT, KB, and HZ contributed directly to the acquisition of data. LH conducted the statistical analysis. LH drafted the manuscript with VW. All authors contributed to the interpretation of the results, revision of the manuscript, and approved the final version for submission.
## Funding
This work was supported by the Norwegian Research Council (grant #302079). HZ is a Wallenberg Scholar supported by grants from the Swedish Research Council (#2018-02532), the European Research Council (#681712), Swedish State Support for Clinical Research (#ALFGBG-720931), the Alzheimer's Drug Discovery Foundation (ADDF), USA (#201809-2016862), the AD Strategic Fund and the Alzheimer's Association (#ADSF-21-831376-C, #ADSF-21-831381-C, and #ADSF-21-831377-C), the Olav Thon Foundation, the Erling-Persson Family Foundation, Stiftelsen för Gamla Tjänarinnor, Hjärnfonden, Sweden (#FO2019-0228), the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 860197 (MIRIADE), European Union Joint Program for Neurodegenerative Disorders (JPND2021-00694), and the UK Dementia Research Institute at UCL. KB is supported by the Swedish Research Council (#2017-00915), the Alzheimer's Drug Discovery Foundation (ADDF), USA (#RDAPB-201809-2016615), the Swedish Alzheimer's Foundation (#AF-930351, #AF-939721, and #AF-968270), Hjärnfonden, Sweden (#FO2017-0243 and #ALZ2022-0006), the Swedish state under the agreement between the Swedish government and the County Councils, the ALF-agreement (#ALFGBG-715986 and #ALFGBG-965240), the European Union Joint Program for Neurodegenerative Disorders (JPND2019-466-236), the National Institute of Health (NIH), USA, (grant #1R01AG068398-01), and the Alzheimer's Association 2021 Zenith Award (ZEN-21-848495).
## Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's Note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
|
## Objectives
Cognitive impairment may affect one-third of stroke survivors. Cardiovascular risk factors and stroke severity were known to be associated with cognitive function after stroke. However, it is unclear whether cardiovascular risk factors directly affect cognition after stroke, indirectly affect cognition by changing stroke severity, or both. Moreover, the effect of a combination of hypertension and diabetes mellitus was conflicting. We aimed to investigate the multiple direct and indirect associations and inspire potential intervention strategies.
## Materials and methods
From February 2020 to January 2021, 350 individuals received cognitive tests within 7 days after incident stroke. Cognitive tests were performed using the Chinese version of the Mini-Mental State Examination (MMSE). A moderated mediation model was constructed to test the indirect associations between cardiovascular and demographic risk factors and cognition mediated through stroke severity, the direct associations between risk factors and cognition, and the moderating effects of hypertension and diabetes.
## Results
Age (estimate, −0.112), atrial fibrillation (estimate, −4.092), and stroke severity (estimate, −1.994) were directly associated with lower cognitive function after stroke. Vascular disease (estimate, 1.951) and male sex (estimate, 2.502) were directly associated with better cognition after stroke. Higher education level was associated with better cognition directly (estimate, 1.341) and indirectly (estimate, 0.227) through stroke severity. The combination of hypertension decreased the magnitude of the negative association between atrial fibrillation and cognition (estimate, from −4.092 to −3.580).
## Conclusion
This is the first Chinese study exploring the moderated and mediating associations between cardiovascular risk factors, stroke severity, and cognitive function after stroke. Age, female sex, and atrial fibrillation were directly associated with lower cognition after stroke. The combination of hypertension might have a positive effect on cognition.
## Introduction
Currently, the incidence rate of ischemic stroke is growing ( ). It is well known that stroke is associated with acute cognitive decline and an increased risk of dementia ( , ). Among those who suffer from a mild stroke, even though their motor functions might recover through rehabilitation training, their cognitive decline is hard to reverse and has been frequently overlooked. Lower cognition is associated with poor health and quality of life, impairments in functional abilities, and increased medical costs ( ). For most, losing one's cognitive abilities is feared more than physical ability. Decreased cognition inflicts a great burden on caregivers and global health ( , ). Therefore, learning modifiable risk factors and exploring novel complex associations are important steps for developing strategies aimed at maintaining healthy cognitive aging among stroke survivors.
One question is why do some stroke survivors have worse cognitive trends than others? The acute cognitive decline after stroke is obviously associated with acute brain damage (i.e., lesions) and subsequent response (i.e., the inflammatory factors) ( ). Meanwhile, recent studies reported that compared with participants without stroke, those who experienced incident stroke suffer from steeper cognitive decline even before stroke onset ( , ). That is, for some stroke patients, their cognition had already declined before the stroke. An explanation was the long-term exposure to cardiovascular risk factors before stroke ( , ). A more severe stroke is more harmful to post-stroke cognition. Meanwhile, cardiovascular risk factors are reported to be associated with stroke and cognitive function ( ). It is unclear whether the risk factors harm cognition during the long term before stroke or indirectly affect cognition by stroke severity (risk factors affect stroke severity, which in turn affects post-stroke cognition), or both.
Cardiovascular disease and stroke severity have been of great interest in studies investigating post-stroke cognitive function for the following reasons. Patients with stroke have more cardiovascular comorbidities ( ). They are well-known risk factors for dementia ( , ). Among healthy populations, hypertension and diabetes increase the risk of stroke and are associated with cross-sectionally lower cognition, cognitive decline, and incident dementia. Moreover, the heart and brain communicate intensively regarding cognition. However, among stroke survivors, the previous results are inconclusive or conflicting ( , ). For example, previous studies found that the combination of hypertension or diabetes may be detrimental or even beneficial to cognitive function among stroke patients ( – ). The possible mechanisms include collateral circulation ( ). The combined effect is still worth investigating. Furthermore, most studies analyzed the associations between risk factors and post-stroke cognition through multiple regression, but the interaction relationship and multiple routes are seldom investigated.
Here, we introduced a moderated mediation model to explore the association. The moderated mediation model is an appropriate statistical method for understanding the complex relationships between variables by calculating the mediated effect and moderated effect ( ). In the mediating effect model, the independent variable is related to the mediating variable, while the mediating variable, in turn, is related to the dependent variable. In other words, it learns whether intervention in the mediating variable could change the effect of the independent variable on the dependent variable. In this study, the model considered the direct effects of risk factors (independent variable) on post-stroke cognition (dependent variable) and the indirect effects of risk factors on post-stroke cognition through changing stroke severity (mediating variable), which also affects post-stroke cognition. The moderating model hypothesizes that the moderating variable influences the direction or extent of the association between the independent variable and dependent variable. The moderating effect is assumed to occur while the direction or extent of the association between the independent and dependent variables changes according to the existence or level of the moderating variable. For example, the moderating effect of hypertension or diabetes was considered to exist while the combination of them changed the extent of the relationship between risk factors and post-stroke cognition.
Till now, only a few studies have learned the moderated and mediated effects in the domain of the post-stroke cognitive function ( – ). Drozdowska et al. ( ) studied the moderated and mediated effects of cardiovascular disease on post-stroke cognitive impairment (PSCI) and found that some risk factors were indirectly associated with cognition after stroke ( ). To the best of our knowledge, these associations have not been explored in China. The Chinese elders had a lower education level relative to those in Europe and the United States, resulting in less “cognitive reserve.” We hypothesized that less cognitive reserve might lead to different patterns of association ( , ). The aim of our study was to answer the following questions: (1) Are cardiovascular risk factors directly associated with post-stroke cognitive function or (2) indirectly associated with post-stroke cognitive function through the mediating effect of stroke severity? (3) Do hypertension and diabetes moderate the relationships between cardiovascular risk factors and post-stroke cognitive function?
## Materials and methods
### Study design and participants
This was a retrospective study from a stroke neuropsychological database. The database included patients with ischemic stroke who were admitted to the stroke unit of the First Affiliated Hospital of Soochow University. Every patient in the stroke unit was provided with standardized treatment and high-dependency clinical and nursing care ( ). During June 2020 and May 2021, three senior vascular neurologists (YZ, XT, and SD) collected demographic and clinical data and conducted standardized cognitive assessments for all study patients during admission. The inclusion criteria were (1) diagnosis of ischemic stroke was confirmed after admission by CT or MRI ( ) and (2) patients were admitted within 7 days of illness. The exclusion criteria were ( , , ) (1) patients were unable to complete the cognitive test due to existing impairment, such as aphasia. (2) Patients had disturbance of consciousness caused by a severe stroke. (3) A history of or current major depression, as determined by clinical reports or PHQ-9 of >9.
### Standard protocol approvals
All protocols followed those outlined in the Declaration of Helsinki and were approved by the Institutional Review Board of the First Affiliated Hospital of Soochow University Hospital (IRB No. 2021-172). In reporting our study, we followed Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines ( ).
### Data availability statement
Data were entered into the stroke registration system of the First Affiliated Hospital of Soochow University (SR-FHSU). Researchers could obtain data after the approval of the corresponding author and the ethics committee of the hospital.
### Predictors
Predictors included demographic factors and cardiovascular risk factors. Demographic factors included age, sex, education, and current smoking ( ). Age was treated as a continuous variable. Education level included the following categories: primary school or less, middle school, high school, and bachelor's degree or higher. As almost all patients lived in the city where the hospital is based, we did not register living areas. Cardiovascular risk factors were associated with post-stroke dementia, hypertension, diabetes mellitus, previous stroke, vascular disease (peripheral and coronary), and atrial fibrillation (AF). The risk factors were collected by self-reported medical history and would be reevaluated during admission. After admission, due to the lack of previous medical records, we did not differentiate a previous transient ischemic attack (TIA) and a previous stroke.
### Mediators
Every patient was evaluated by the National Institute of Health Stroke Scale (NHISS) immediately upon arriving at the stroke unit. To achieve a more parsimonious model, we categorized the NHISS into four groups, namely, no stroke signs (0), minor stroke (1–4), moderate stroke (5–15), and severe stroke (16–42).
### Cognitive outcome
Cognitive function was evaluated within 7 days of ischemic stroke or TIA. It was measured by the Chinese version of the Mini-Mental State Examination (MMSE) ( ). We treated MMSE scores as continuous variables.
The MMSE is a widely used tool for the assessment of cognitive function in older participants. It reflects five aspects of cognitive function, namely, orientation, registration, attention and calculation, recall, and language. The total score of MMSE ranges from 0 to 30.
### Statistical analysis
As mediation analyses assume an actual temporal order, we constructed the variables in the following orders. Stroke severity regressed on the nine predictors (including demographic and cardiovascular risk factors). Cognitive function regressed on stroke severity and regressed on the nine predictors. This reflected the direct effects of predictors and stroke severity on cognitive function and the indirect effect of predictors on cognitive function mediated by stroke severity. To avoid overfitting, we retained all predictors, regardless of whether the path was significant ( ).
We developed a second-stage dual moderated mediation model. First, we assumed that hypertension and diabetes might moderate the following paths: (1) the direct path between predictor and outcome and (2) the mediator path between predictor and mediator. That is, to what extent do hypertension and diabetes (1) change the direct effect of predictors on cognitive function and (2) change the indirect effect of predictors on cognitive function through stroke severity? We conducted the second-stage dual moderated mediation model for AF, vascular disease, and the previous stroke separately ( ). For example, while exploring the moderated effect on AF, the model fitted four situations (presence or absence of hypertension × presence or absence of diabetes) ( ). We then explored the moderating effect on the other two predictors separately. Second, we removed interaction terms with a p -value over 0.2 ( ). In the final model ( ), we kept two interaction terms (arrow pointed from hypertension to the arrow between AF and cognitive function and the arrow between vascular disease and cognitive function).
The theoretical framework model gram. We examined the moderating effect on atrial fibrillation, vascular disease, and previous stroke separately.
The final diagram of the moderated and mediation model.
The dependent variables were all continuous; therefore, we used the maximum likelihood (ML) estimation method. It is not constricted to a normal distribution and offers better precision for calculating confidence intervals (CIs). A 1,000-replication bootstrapping process was used to estimate the CI ( , ). Structural equation modeling was performed with Mplus 8.3 (Muthén & Muthén) ( , ).
## Results
A total of 350 patients with ischemic stroke were included in our study. The baseline characteristics are shown in . The mean ± SD age of all participants was 63.9 ± 11.3 years; 65.1% of them were men. Half of the participants had not finished primary school. The median (interquartile range) NHISS score was 3 (1, 5). The mean±SD MMSE score of all participants was 22.6 ± 6.1.
Demographic, clinical, and cognitive data of all participants ( n = 350).
### Final model contracture
First, three individual two-stage moderated mediation models were fitted to evaluate the moderation effect. Second, we opted to retain two interaction terms in the final model. We considered hypertension as a moderator for the effects of AFs and vascular disease on cognitive function. The final model is shown in . The overall model showed excellent fitting, with SMSR = 0.002.
### Mediation effects
For direct associations, there was a statistically significant association between the mediator and cognitive function. More severe stroke was directly associated with a lower MMSE score (β = −1.994; 95% CI −2.492, −1.496; p < 0.001). Age, sex, education, hypertension, and AF were directly related to cognitive function ( ). Also, 1 year of aging was associated with a 0.122-point decrease in MMSE score (β = −0.122; 95% CI −0.149, −0.095; p < 0.001). Men had high MMSE scores (β = 2.502; 95% CI 1.935, 3.069; p < 0.001). Higher education level was associated with a high score (β = 1.341; 95% CI 1.041, 1.641; p < 0.001). Patients with AF were also correlated with a lower score (β = −4.092; 95% CI −5.595, −2.229; p = 0.028). Vascular disease was associated with higher cognitive function (β = 1.951; 95% CI 0.995, 2.907; p = 0.041). Smoking, hypertension, diabetes mellitus, or previous stroke was not significantly associated with cognitive function.
Direct associations between predictors and cognitive function.
For indirect associations, higher education level (β = 0.227; 95% CI 0.135, 0.319; p = 0.014) and hypertension (β = 0.580; 95% CI 0.351, 0.809; p = 0.011) were associated with higher cognitive function through stroke severity ( ).
Indirect associations between predictors and cognitive function.
### Moderation effects
The direct effects of AF and vascular disease could be moderated by hypertension. A combination of hypertension could decrease the magnitude of the negative association between AF and cognitive function (β changed from −4.092 to −3.580). There was also a trend that hypertension increased the magnitude of the positive association between cognitive function and vascular disease (β = 2.464; 95% CI 0.911, 4.017; p = 0.113). However, this association did not reach statistical significance.
## Discussion
Using a real-world sample of stroke unit patients, this article investigated the moderated and mediated associations between cardiovascular risk factors, stroke severity, and cognition. Age, female sex, AF, and stroke severity were directly associated with lower cognitive function after stroke. Vascular disease was directly associated with better cognition after stroke. For patients with AF or vascular disease, there was a trend that a combination of hypertension was associated with better cognitive function.
We achieved results different from the previous study ( ). Drozdowska reported that age and AF indirectly correlated with lower cognitive function through stroke severity. In our data set, age and AF were directly associated with cognition. We further found that hypertension decreased the magnitude of the negative association between AF and post-stroke cognitive function. Generally, AF was associated with more severe stroke and thus indirectly affects cognition ( , ). AF is strongly correlated with cardioembolic stroke. Patients who experienced cardioembolic stroke tended to be aphasia or unconsciousness. This also explains why the incidence rate of AF in our study was lower than that in other stroke data sets (~ 17%). Therefore, our results could only reflect that AF was directly associated with cognition among relatively healthier stroke survivors. Moreover, it was reasonable that other predictors were not correlated with stroke severity ( , ).
The direct association between stroke severity and cognitive function during the acute phase after stroke was more relevant to the acute brain damage (i.e., lesions) and the subsequent immune response (i.e., the inflammatory factors) ( ). A Rotterdam study included 1,443 participants with stroke (mean age at a stroke: 80.3 years), whose cognitive function had been assessed 10 years before stroke onset and several years after stroke. Compared with the control group without stroke, those with stroke had shown a faster decline 10 years before the stroke. In other words, even before the stroke, the patients with stroke had lower cognitive scores. In the acute phase after stroke, the participants with stroke exhibited cognitive decline, not surprisingly. We hypothesized that the long-term cognitive decline before stroke could explain the direct association between risk factors before the stroke. Those who were older, women, or with lower education might have lower cognitive scores before the stroke, regardless of the severity of the stroke. A similar study from the UK (participants with stroke: 694; mean follow-up, 8.2 years) thought that older patients with stroke showed more cognitive decline in the acute phase after stroke ( ). Older patients are more likely to have the neurodegenerative disease (i.e., Alzheimer's disease-related pathology) and comorbidities, which may amplify the stroke injury ( , ). Inversely, stroke could exacerbate age-related neurodegenerative pathology ( ). In this article, education was directly and indirectly associated with lower cognition via stroke severity during the acute phase after stroke. A meta-analysis in 2009 included 79 studies learning risk factors for PSCI, 11 of which reported that lower education was a risk factor (pooled OR of the 11 studies, 2.5), while 24 of which reported that female sex was a risk factor (pooled OR of the 24 studies, 1.3) ( ). A lower educational level means less cognitive reserve. Patients with less cognitive reserve already showed lower cognitive scores before the stroke. In the acute phase after stroke, education also seemed to modify the effect of stroke on cognitive decline ( ). Patients who received better education suffered less from cardiovascular disease due to various reasons, including a healthy lifestyle, safe working conditions, and better access to healthcare before stroke ( ). The negative associations between sex and post-stroke cognitive function were in accordance with results from other studies in China. A national representative epidemiological study, using data from the China Health and Retirement Longitudinal Study, attributed the cognitive difference to schooling, family, and community levels of economic resources ( ). Therefore, female patients had lower cognition than male patients before the stroke, which could explain the direct associations. After stroke onset, female stroke survivors were reported to experience faster cognitive decline after stroke than male stroke survivors. The mechanism underlying the gender difference after stroke remains to be elucidated. Sex differences in the expression of brain-derived neurotrophic factors, sex hormones, and stroke subtype were involved in the gender difference in cognition after stroke ( ).
Atrial fibrillation was independently associated with lower cognitive function among participants with or without stroke ( – ). It results in a series of mechanisms that would cause lower cognition, such as cerebral hypoperfusion, inflammatory responses, silent ischemia, reduced brain volumes, and cerebral microbleeds. The effect of blood pressure on clinical outcomes was contradictory. Higher blood pressure might increase infarct volume, brain edema, and hemorrhagic transformation ( ). A study including 306 patients with stroke learned the association between baseline blood pressure and outcomes. Among a subgroup, higher blood pressure was associated with improved collateral flow, decreased infarct growth, and better functional outcomes ( ). This was a potential explanation of our findings. For patients with AF or vascular disease, a combination of hypertension might bring better collateral flow and higher cognitive scores. Future studies are needed to explore objective evidence. The association between vascular disease and cognitive function is still controversial ( , ).
To the best of our knowledge, this was the first study to identify the moderated and mediated association between cardiovascular risk factors, stroke severity, and cognitive function in Chinese patients with stroke. We highlighted the importance of comorbidities while learning cognitive function after stroke. However, we also had several disadvantages. First, we only performed cognitive tests within 7 days after stroke. Future studies could examine the effects of risk factors on cognitive function 3–6 months after stroke. Second, our data set included a few patients with severe stroke. However, patients suffering from severe stroke often have problems with consciousness or aphasia. The disability prevented us from performing cognitive tests. Hence, our results could only reflect situations in relatively healthy stroke survivors. Third, we could not distinguish previous TIA from the previous stroke. We were not able to obtain the previous medical records of our patients who were not admitted to our hospital before. Furthermore, in our district, patients had a relatively lower education level and thus had a lower awareness rate of the previous history. Fourth, our study participants were from China. The generalizability of the results to other countries might be a concern. Fifth, due to a lack of medical records, we could not identify those with mild cognitive impairment or dementia before the stroke. Future studies could obtain information on pre-stroke cognition through questionnaires like the Informant Questionnaire of Cognitive Decline in the Elderly.
## Conclusion
Our research explored the complex relationships between cardiovascular risk factors, stroke severity, and cognitive function. Age and AF were directly associated with lower cognition after stroke. Hypertension could decrease the magnitude of the negative association between AF and post-stroke cognition. Future studies are needed to learn whether the associations are causal.
## Data availability statement
Data were entered into the stroke registration system of the First Affiliated Hospital of Soochow University (SR-FHSU). Researchers could obtain data after the approval of the corresponding author and the ethics committee of the hospital.
## Ethics statement
All protocols followed those outlined in the Declaration of Helsinki and were approved by the Institutional Review Board of The First Affiliated Hospital of Soochow University Hospital (IRB No. 2021-172). In reporting our study, we followed Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.
## Author contributions
QF and JH contributed to the conception and design of the study. SD, YZ, and XT performed cognitive tests and collected medical data. JH performed the statistical analysis. JH and LC wrote the first draft of the manuscript. QF and XT reviewed the manuscript. All authors approved the final version of the paper.
## Funding
This study was supported by grants from the National Science Foundation of China (82071300), Health Expert Training Program of Suzhou-Gusu District (GSWS2020002), and Medical Team Introduction Program of Soochow (SZYJTD201802). It was supported by the National Natural Science Foundation of China (no. 82001125, XT) and Natural Science Foundation of Jiangsu Province (no. BK20180201 XT).
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
|
A novel system to cultivate and record from organotypic brain slices directly on high-density microelectrode arrays (HD-MEA) was developed. This system allows for continuous recording of electrical activity of specific individual neurons at high spatial resolution while monitoring at the same time, neuronal network activity. For the first time, the electrical activity patterns of single neurons and the corresponding neuronal network in an organotypic hippocampal slice culture were studied during several consecutive weeks at daily intervals. An unsupervised iterative spike-sorting algorithm, based on PCA and k-means clustering, was developed to assign the activities to the single units. Spike-triggered average extracellular waveforms of an action potential recorded across neighboring electrodes, termed "footprints" of single-units were generated and tracked over weeks. The developed system offers the potential to study chronic impacts of drugs or genetic modifications on individual neurons in slice preparations over extended times. |
Heart failure (HF) is a complex syndrome representing the clinical endpoint of many cardiovascular diseases of different etiology. Given its prevalence, incidence and social impact, a better understanding of HF pathophysiology is paramount to implement more effective anti-HF therapies. Based on left ventricle (LV) performance, HF is currently classified as follows: (1) with reduced ejection fraction (HFrEF); (2) with mid-range EF (HFmrEF); and (3) with preserved EF (HFpEF). A central tenet of HFrEF pathophysiology is adrenergic hyperactivity, featuring increased sympathetic nerve discharge and a progressive loss of rhythmical sympathetic oscillations. The role of reflex mechanisms in sustaining adrenergic abnormalities during HFrEF is increasingly well appreciated and delineated. However, the same cannot be said for patients affected by HFpEF or HFmrEF, whom also present with autonomic dysfunction. Neural mechanisms of cardiovascular regulation act as "controller units," detecting and adjusting for changes in arterial blood pressure, blood volume, and arterial concentrations of oxygen, carbon dioxide and pH, as well as for humoral factors eventually released after myocardial (or other tissue) ischemia. They do so on a beat-to-beat basis. The central dynamic integration of all these afferent signals ensures homeostasis, at rest and during states of physiological or pathophysiological stress. Thus, the net result of information gathered by each controller unit is transmitted by the autonomic branch using two different codes: <i>intensity</i> and <i>rhythm</i> of sympathetic discharges. The main scope of the present article is to (i) review the key neural mechanisms involved in cardiovascular regulation; (ii) discuss how their dysfunction accounts for the hyperadrenergic state present in certain forms of HF; and (iii) summarize how sympathetic efferent traffic reveal central integration among autonomic mechanisms under physiological and pathological conditions, with a special emphasis on pathophysiological characteristics of HF. |
The provision of continuous passive, and intent-based assisted movements for neuromuscular training can be incorporated into a robotic elbow sleeve. The objective of this study is to propose the design and test the functionality of a soft robotic elbow sleeve in assisting flexion and extension of the elbow, both passively and using intent-based motion reinforcement. First, the elbow sleeve was developed, using elastomeric and fabric-based pneumatic actuators, which are soft and lightweight, in order to address issues of non-portability and poor alignment with joints that conventional robotic rehabilitation devices are faced with. Second, the control system was developed to allow for: (i) continuous passive actuation, in which the actuators will be activated in cycles, alternating between flexion and extension; and (ii) an intent-based actuation, in which user intent is detected by surface electromyography (sEMG) sensors attached to the biceps and triceps, and passed through a logic sequence to allow for flexion or extension of the elbow. Using this setup, the elbow sleeve was tested on six healthy subjects to assess the functionality of the device, in terms of the range of motion afforded by the device while in the continuous passive actuation. The results showed that the elbow sleeve is capable of achieving approximately 50% of the full range of motion of the elbow joint among all subjects. Next, further experiments were conducted to test the efficacy of the intent-based actuation on these healthy subjects. The results showed that all subjects were capable of achieving electromyography (EMG) control of the elbow sleeve. These preliminary results show that the elbow sleeve is capable of carrying out continuous passive and intent-based assisted movements. Further investigation of the clinical implementation of the elbow sleeve for the neuromuscular training of neurologically-impaired persons, such as stroke survivors, is needed. |
Spiking neural networks (SNNs) are inspired by information processing in biology, where sparse and asynchronous binary signals are communicated and processed in a massively parallel fashion. SNNs on neuromorphic hardware exhibit favorable properties such as low power consumption, fast inference, and event-driven information processing. This makes them interesting candidates for the efficient implementation of deep neural networks, the method of choice for many machine learning tasks. In this review, we address the opportunities that deep spiking networks offer and investigate in detail the challenges associated with training SNNs in a way that makes them competitive with conventional deep learning, but simultaneously allows for efficient mapping to hardware. A wide range of training methods for SNNs is presented, ranging from the conversion of conventional deep networks into SNNs, constrained training before conversion, spiking variants of backpropagation, and biologically motivated variants of STDP. The goal of our review is to define a categorization of SNN training methods, and summarize their advantages and drawbacks. We further discuss relationships between SNNs and binary networks, which are becoming popular for efficient digital hardware implementation. Neuromorphic hardware platforms have great potential to enable deep spiking networks in real-world applications. We compare the suitability of various neuromorphic systems that have been developed over the past years, and investigate potential use cases. Neuromorphic approaches and conventional machine learning should not be considered simply two solutions to the same classes of problems, instead it is possible to identify and exploit their task-specific advantages. Deep SNNs offer great opportunities to work with new types of event-based sensors, exploit temporal codes and local on-chip learning, and we have so far just scratched the surface of realizing these advantages in practical applications. |
The classification of sleep stages is the first and an important step in the quantitative analysis of polysomnographic recordings. Sleep stage scoring relies heavily on visual pattern recognition by a human expert and is time consuming and subjective. Thus, there is a need for automatic classification. In this work we developed machine learning algorithms for sleep classification: random forest (RF) classification based on features and artificial neural networks (ANNs) working both with features and raw data. We tested our methods in healthy subjects and in patients. Most algorithms yielded good results comparable to human interrater agreement. Our study revealed that deep neural networks (DNNs) working with raw data performed better than feature-based methods. We also demonstrated that taking the local temporal structure of sleep into account a priori is important. Our results demonstrate the utility of neural network architectures for the classification of sleep. |
Rett syndrome (RTT) is a neurodevelopmental disease in children that is mainly caused by mutations in the <i>MeCP2</i> gene, which codes for a transcriptional regulator. The expression of insulin-like growth factor-1 (IGF-1) is reduced in RTT patients and animal models, and IGF-1 treatment is a promising therapeutic strategy for RTT. However, the mechanism underlying the effects of IGF-1 remains to be further explored. FXYD1 is an auxiliary subunit of Na, K-ATPase. Overexpression of FXYD1 is involved in the pathogenesis of RTT. However, whether IGF-1 exerts its effect through normalizing FXYD1 is completely unknown. To this end, we evaluated the effect of IGF-1 on FXYD1 expression and posttranslational modification in a mouse model of RTT (MeCP2<sup>308</sup>) using both <i>in vitro</i> and <i>in vivo</i> experiments. The results show that FXYD1 mRNA and phosphorylated protein (p-FXYD1) were significantly elevated in the frontal cortex in RTT mice, compared to wild type. In RTT mice, IGF-1 treatment significantly reduced levels of FXYD1 mRNA and p-FXYD1, in parallel with improvements in behavior, motor coordination, and cognitive function. For mechanistic insight into the effect of IGF-1 on p-FXYD1, we found the decreased phosphorylated forms of PI3K-AKT-mTOR signaling pathway components in the frontal cortex of RTT mice and the normalizing effect of IGF-1 on the phosphorylated forms of these components. Interestingly, blocking the PI3K/AKT pathway by PI3K inhibitor could abolish the effect of IGF-1 on p-FXYD1 level, in addition to the effect of IGF-1 on the phosphorylation of other components in the PI3K/AKT pathway. Thus, our study has provided new insights into the mechanism of IGF-1 treatment for RTT, which appears to involve FXYD1. |
Many post-lingually deafened cochlear implant (CI) users report that they no longer enjoy listening to music, which could possibly contribute to a perceived reduction in quality of life. One aspect of music perception, vocal timbre perception, may be difficult for CI users because they may not be able to use the same timbral cues available to normal hearing listeners. Vocal tract resonance frequencies have been shown to provide perceptual cues to voice categories such as baritone, tenor, mezzo-soprano, and soprano, while changes in glottal source spectral slope are believed to be related to perception of vocal quality dimensions such as <i>fluty</i> vs. <i>brassy.</i> As a first step toward understanding vocal timbre perception in CI users, we employed an 8-channel noise-band vocoder to test how vocoding can alter the timbral perception of female synthetic sung vowels across pitches. Non-vocoded and vocoded stimuli were synthesized with vibrato using 3 excitation source spectral slopes and 3 vocal tract transfer functions (mezzo-soprano, intermediate, soprano) at the pitches C4, B4, and F5. Six multi-dimensional scaling experiments were conducted: C4 not vocoded, C4 vocoded, B4 not vocoded, B4 vocoded, F5 not vocoded, and F5 vocoded. At the pitch C4, for both non-vocoded and vocoded conditions, dimension 1 grouped stimuli according to voice category and was most strongly predicted by spectral centroid from 0 to 2 kHz. While dimension 2 grouped stimuli according to excitation source spectral slope, it was organized slightly differently and predicted by different acoustic parameters in the non-vocoded and vocoded conditions. For pitches B4 and F5 spectral centroid from 0 to 2 kHz most strongly predicted dimension 1. However, while dimension 1 separated all 3 voice categories in the vocoded condition, dimension 1 only separated the soprano stimuli from the intermediate and mezzo-soprano stimuli in the non-vocoded condition. While it is unclear how these results predict timbre perception in CI listeners, in general, these results suggest that perhaps some aspects of vocal timbre may remain. |
Cyclooxygenases (COX) are enzymes catalyzing arachidonic acid into prostanoids. COX exists in three isoforms: COX-1, 2, and 3. COX-1 and COX-2 have been widely studied in order to explore and understand their involvement in Alzheimer's disease (AD), a progressive neuroinflammatory dementia. COX-2 was traditionally viewed to be expressed only under pathological conditions and to have detrimental effects in AD pathophysiology and neurodegeneration. However, an increasing number of reports point to much more complex roles of COX-2 in AD. Mammalian/mechanistic target of rapamycin (mTOR) has been considered as a hub which integrates multiple signaling cascades, some of which are also involved in AD progression. COX-2 and mTOR are both involved in environmental sensing, growth, and metabolic processes of the cell. They are also known to act in cooperation in many different cancers and thus, their role together in normal cellular functions as well as AD has been explored in this review. Some of the therapeutic approaches targeting COX-2 and mTOR in AD and cancer are also discussed. |
Alzheimer's disease (AD) is characterised by synaptic dysfunction accompanied by the microscopically visible accumulation of pathological protein deposits and cellular dystrophy involving both neurons and glia. Late-stage AD shows pronounced loss of synapses and neurons across several differentially affected brain regions. Recent studies of advanced AD using post-mortem brain samples have demonstrated the direct involvement of microglia in synaptic changes. Variants of the Apolipoprotein E and Triggering Receptors Expressed on Myeloid Cells gene represent important determinants of microglial activity but also of lipid metabolism in cells of the central nervous system. Here we review evidence that may help to explain how abnormal lipid metabolism, microglial activation, and synaptic pathophysiology are inter-related in AD. |
The dynamic vascular responses during cortical spreading depolarization (CSD) are causally related to pathophysiological consequences in numerous neurovascular conditions, including ischemia, traumatic brain injury, cerebral hemorrhage, and migraine. Monitoring of the hemodynamic responses of cerebral penetrating vessels during CSD is motivated to understand the mechanism of CSD and related neurological disorders. Six SD rats were used, and craniotomy surgery was performed before imaging. CSDs were induced by topical KCl application. Ultrasound dynamic ultrafast Doppler was used to access hemodynamic changes, including cerebral blood volume (CBV) and flow velocity during CSD, and further analyzed those in a single penetrating arteriole or venule. The CSD-induced hemodynamic changes with typical duration and propagation speed were detected by ultrafast Doppler in the cerebral cortex ipsilateral to the induction site. The hemodynamics typically showed triphasic changes, including initial hypoperfusion and prominent hyperperfusion peak, followed by a long-period depression in CBV. Moreover, different hemodynamics between individual penetrating arterioles and venules were proposed by quantification of CBV and flow velocity. The negative correlation between the basal CBV and CSD-induced change was also reported in penetrating vessels. These results indicate specific vascular dynamics of cerebral penetrating vessels and possibly different contributions of penetrating arterioles and venules to the CSD-related pathological vascular consequences. We proposed using ultrasound dynamic ultrafast Doppler imaging to investigate CSD-induced cerebral vascular responses. With this imaging platform, it has the potential to monitor the hemodynamics of cortical penetrating vessels during brain injuries to understand the mechanism of CSD in advance. |
In this review article we have consolidated the imaging literature of patients with schizophrenia across the full spectrum of modalities in radiology including computed tomography (CT), morphologic magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), magnetic resonance spectroscopy (MRS), positron emission tomography (PET), and magnetoencephalography (MEG). We look at the impact of various subtypes of schizophrenia on imaging findings and the changes that occur with medical and transcranial magnetic stimulation (TMS) therapy. Our goal was a comprehensive multimodality summary of the findings of state-of-the-art imaging in untreated and treated patients with schizophrenia. Clinical imaging in schizophrenia is used to exclude structural lesions which may produce symptoms that may mimic those of patients with schizophrenia. Nonetheless one finds global volume loss in the brains of patients with schizophrenia with associated increased cerebrospinal fluid (CSF) volume and decreased gray matter volume. These features may be influenced by the duration of disease and or medication use. For functional studies, be they fluorodeoxyglucose positron emission tomography (FDG PET), rs-fMRI, task-based fMRI, diffusion tensor imaging (DTI) or MEG there generally is hypoactivation and disconnection between brain regions. However, these findings may vary depending upon the negative or positive symptomatology manifested in the patients. MR spectroscopy generally shows low <i>N</i>-acetylaspartate from neuronal loss and low glutamine (a neuroexcitatory marker) but glutathione may be elevated, particularly in non-treatment responders. The literature in schizophrenia is difficult to evaluate because age, gender, symptomatology, comorbidities, therapy use, disease duration, substance abuse, and coexisting other psychiatric disorders have not been adequately controlled for, even in large studies and meta-analyses. |
Cognitive flexibility is the ability to rapidly adapt to a constantly changing environment. It is impaired by aging as well as in various neurological diseases, including dementia and mild cognitive impairment. In rodents, although many behavioral test protocols have been reported to assess learning and memory dysfunction, few protocols address cognitive flexibility. In this study, we developed a novel cognitive flexibility test protocol using touch screen operant system. This test comprises a behavioral sequencing task, in which mice are required to discriminate between the "rewarded" and "never-rewarded" spots and shuttle between the two distantly positioned rewarded spots, and serial reversals, in which the diagonal spatial patterns of rewarded and never-rewarded spots were reversely changed repetitively. Using this test protocol, we demonstrated that dysbiosis treated using streptomycin induces a decline in cognitive flexibility, including perseveration and persistence. The relative abundances of Firmicutes and Bacteroides were lower and higher, respectively, in the streptomycin-treated mice with less cognitive flexibility than in the control mice. This is the first report to directly show that intestinal microbiota affects cognitive flexibility. |
Mild traumatic brain injury (mTBI) accounts for more than 80% of the total number of TBI cases. The mechanism of injury for patients with mTBI has a variety of neuropathological processes. However, the underlying neurophysiological mechanism of the mTBI is unclear, which affects the early diagnosis, treatment decision-making, and prognosis evaluation. More and more multimodal magnetic resonance imaging (MRI) techniques have been applied for the diagnosis of mTBI, such as functional magnetic resonance imaging (fMRI), arterial spin labeling (ASL) perfusion imaging, susceptibility-weighted imaging (SWI), and diffusion MRI (dMRI). Various imaging techniques require to be used in combination with neuroimaging examinations for patients with mTBI. The understanding of the neuropathological mechanism of mTBI has been improved based on different angles. In this review, we have summarized the application of these aforementioned multimodal MRI techniques in mTBI and evaluated its benefits and drawbacks. |
Parkinson's disease (PD) is characterized by motor and non-motor signs, which are accompanied by progressive degeneration of dopaminergic neurons in the substantia nigra. Although the exact causes are unknown, evidence links this neuronal loss with neuroinflammation and oxidative stress. Repeated treatment with a low dose of reserpine-inhibitor of VMAT2-has been proposed as a progressive pharmacological model of PD. The aim of this study was to investigate whether this model replicates the neuroinflammation characteristic of this disease. Six-month-old Wistar rats received repeated subcutaneous injections of reserpine (0.1 mg/kg) or vehicle on alternate days. Animals were euthanized after 5, 10, or 15 injections, or 20 days after the 15th injection. Catalepsy tests (motor assessment) were conducted across treatment. Brains were collected at the end of each treatment period for immunohistochemical and RT-PCR analyzes. Reserpine induced a significant progressive increase in catalepsy duration. We also found decreased immunostaining for tyrosine hydroxylase (TH) in the substantia nigra <i>pars compacta</i> (SNpc) and increased GFAP + cells in the SNpc and dorsal striatum after 10 and 15 reserpine injections. Phenotyping microglial M1 and M2 markers showed increased number of CD11b + cells and percentage of CD11b + /iNOS + cells in reserpine-treated animals after 15 injections, which is compatible with tissue damage and production of cytotoxic factors. In addition, increased CD11b + /ArgI + cells were found 20 days after the last reserpine injection, together with an increment in IL-10 gene expression in the dorsal striatum, which is indicative of tissue repair or regeneration. Reserpine also induced increases in striatal interleukin TNF-alpha mRNA levels in early stages. In view of these results, we conclude that reserpine-induced progressive parkinsonism model leads to neuroinflammation in regions involved in the pathophysiology of PD, which is reversed 20 days after the last injection. These findings reveal that withdrawal period, together with the shift of microglial phenotypes from the pro-inflammatory to the anti-inflammatory stage, may be important for the study of the mechanisms involved in reversing this condition, with potential clinical applicability. |
A neuromorphic sound localization system is presented. It employs two microphones and a pair of silicon cochleae with address event interface for front-end processing. The system is based the extraction of interaural time difference from a far-field source. At each frequency channel, a soft-winner-takes-all network is used to preserve timing information before it is processed by a simple neural network to estimate auditory activity at all bearing positions. The estimates are then combined across channels to produce the final estimate. The proposed algorithm is adaptive and supports online learning, enabling the system to compensate for circuit mismatch and environmental changes. Its localization capability was tested with white noise and pure tone stimuli, with an average error of around 3° in the −45° to 45° range.
## Introduction
Sound localization is the ability to identify the direction of a sound and is a key to survival in the animal world. In robotics, however, sound localization has received much less focus compared to vision. Nevertheless, sound localization is expected to become more important as robots are required to operate in the real world and must handle both visual and auditory stimuli.
Unlike the retina, which creates a two-dimensional map of electromagnetic activity in the visible spectrum, the cochlea decomposes sound into its frequency components, i.e., a tonotopic representation. Spatial information, i.e., the position of the sound sources, must therefore be extracted from the tonotopic information. Several cues are available for the brain to perform this task. The first cue is the interaural time difference (ITD). It arises because of the difference in time of arrival of the sound to the two ears – the ear nearer to the source will receive the sound before the far ear (Figure ). At low frequencies, this appears in the form of interaural phase difference (IPD), whereas at high frequency, it takes the form of interaural envelope delay (IED). This is a result of the half-wave rectification and first order low pass filtering introduced by the inner hair cells (IHCs) that sense the vibration of the basilar membrane in the cochlea.
ITD arises from the difference in time of arrival of sound to the two ears, while IID is a result of sound attenuation by the head .
The second and equally important cue is the interaural intensity difference (IID), also known as interaural level difference (ILD), and is the result of the head being an obstacle that shadows the sound's path to the eardrum. Since the head is a dense medium, a sound must diffract around the head to reach the far ear and its amplitude drops as a result. This effect is more perceptible when the wavelength of the sound is less than or comparable to the size of the head. As a result, the IID is more pronounced at high frequencies and provides complimentary information to the ITD. Other localization cues include the spectral cues and motion cues, which are useful in removing ambiguities associated with elevation discrimination when localization of both azimuth and elevation is required.
One of the earliest ITD processing models was proposed by Jeffress over 60 years ago (Jeffress, ), where he modeled the computation of ITD by having signals from the two ears propagating along delay lines in opposite directions and arriving at an array of coincidence detectors, each responding best to a particular ITD (Figure ). This neural arrangement is analogous to a mathematical cross-correlation operation, with the delay between the signals given by the position of maximum correlation. Such neural computational circuits were later found in the owl's brainstem (Konishi, ).
An illustration of Jeffress′ model . Each coincidence detector responses best to a particular ITD corresponding to sound arriving from a specific direction.
Localization systems are dominated by those based on ITD because time delay can be determined accurately, is relatively frequency independent (compared to IID), and its relationship with the source position can be most easily determined among all localization cues. Further, in many localization systems, the microphones are mounted on a plane or in free space so that no spectral cues or IIDs are available. Traditional ITD-based localization systems, whether using two microphones or a microphone array, often perform cross-correlation in software to determine the time delays between microphones (Huang et al., , ; Julian et al., , ). In systems with more than two microphones, these delays are combined using statistical methods, such as maximum likelihood or minimizing mean square error, to estimate the location of the source (Rabinkin et al., ; Svaizer et al., ). Alternatively, spatial temporal processing can be performed on the microphone array signals to compute the gradient of the sound field to obtain source direction (Clapp and Etienne-Cummings, , ; Stanacevic and Cauwenberghs, ; Gore et al., ). While software implementations are more flexible, hardware implementations offer lower power consumption and guarantee real-time operation, which is especially important for sensor and robotic applications.
Some researchers have taken the bio-mimetic approach in an effort to build more human-like artificial systems. Subsequently, many of these systems employ biologically realistic strategies to perform sound localization. For instance, they use only two microphones mounted on a spherical head or manikin. A filter bank or Fourier Transform is often used to mimic the function of the biological cochlea, and the ITD and the IID in each band are then extracted. Some examples include the Cog project (Irie, ), the humanoid robot SIG (Nakadai et al., ; Okuno and Nakadai, ; Okuno et al., ), the robots used by Andersson and colleagues (Handzel et al., ; Andersson et al., ), and an audio-visual object localization system being developed by Schauer and Gross ( ).
Others have taken the challenge one step further by implementing some of these processing in analog VLSI (a-VLSI). The most notable was Lazzaro's silicon model of sound localization (Lazzaro and Mead, ) based on Konishi's owl model. In this implementation, the silicon cochleae decompose the incoming signals from the left and right ears into different frequency bands and convert the signals into spike trains. Cross-correlations are then performed on the spike trains using the silicon axons as delay lines and logic AND gates as coincident detectors, similar to the computation performed at the nucleus laminaris in the owls. The cross-correlation results are summed across frequency, and finally, a non-linear inhibition circuit is used to model the competition among inferior colliculus neurons, producing a neural map of ITD. A similar architecture was adopted by Bhadkamkar, who also implemented sound localization systems on chip but with limited success (Bhadkamkar and Fowler, ; Bhadkamkar, ). Both Lazzaro and Bhadkamkar's work suffered from mismatch, particularly at the delay lines. Methods of extracting ITD without delay lines have been proposed by Shamma et al. ( ), van Schaik and Shamma ( ), and Grech et al. ( , ). However, all these ITD extraction methods deviated somewhat from biology.
Once the ITDs from multiple bands are extracted they have to be processed to estimate the source location and several techniques can be used. In the first and simplest technique, it assumes the relationship between source location and ITD is known, e.g., if ITD = sin(θ), where θ is the azimuth angle of the source, then source location can be directly computed using the inverse function or a look-up table. Examples include (Huang et al., ; Julian et al., ).
The second method is a search strategy similar to the Nearest Neighbor Search, where the system searches through an entire range of discrete positions and the position resulting in the best match becomes the estimate. This is used by the ITD algorithm in Huang et al. ( ) and the IPD/IID algorithm in Handzel et al. ( ), Andersson et al. ( ). A more elaborated version is used by Grech et al. ( ) to localize sound in both azimuth and elevation. This method is more computationally intensive but offers greater flexibility and accuracy.
In the last method, the localization system is trained to learn the relationship between the sound features and source position, and the learning can be either supervised or unsupervised. In supervised learning, training data with known source positions are presented to the system, while in unsupervised learning, the source positions are not given explicitly but have to be determined by the system itself. This is usually achieved via the interaction of motion (head-turning) and sensing (both audition and vision). Examples of system which learns sound localization can be found in Irie (1995), Nakashima et al. ( ), Nakashima and Mukai ( ), Hornstein et al. ( ).
In this paper, we propose an ITD-based sound localization system that can be implemented in a-VLSI. The proposed system is biologically realistic as it uses only two sensors and it employs an a-VLSI cochlea model. Unlike some previous a-VLSI implementations, our solution requires no prior model of ITD and can be trained to localize sound in any environment. In addition, the training allows it to adapt to compensate for ITD variation across frequency and mismatch in circuit components.
This paper is organized as follows: the experimental setup is described in Sections “Experimental Setup” and “Materials and Methods,” we will introduce our approach to the localization problem, cumulating to a neuromorphic architecture supporting learning and adaptation; experimental results are presented next in Section “Results”; this is followed by a discussion in Sections “Discussion” and “Conclusion” will conclude the paper.
### Experimental setup
The experimental setup is shown in Figure . Two electret microphone capsules are mounted on opposite sides of a sphere 15 cm in diameter, made of foam. The microphone capsules measure 10 mm in diameter and are omnidirectional with a frequency range from 50 Hz to 12.5 kHz. The sphere itself is then fixed atop a robot, 15 cm from the ground. This sphere simulates the effect of head shadowing and diffraction introduced by the head, hence the recording from one microphone is not simply a time-delayed version of the other. Furthermore, because the head is mounted near the front of the robot, there are front-back asymmetries, which become evident in later sections. The microphone signals are amplified before being fed into the silicon cochlea chip, the AER EAR (Chan et al., ), and can be recorded and played back via a computer sound card.
Localization setup . Two microphones are installed on opposite side of a 15 cm foam ball mounted on a 6-wheel robot. Azimuth = 0° at the front, 90° on the left, −90° on the right, and ±180° at the back.
We recorded the “head”-related impulse responses (IRs) of the microphones in response to a loudspeaker (Tannoy System 600A) at different azimuth positions in an “almost anechoic” environment. Although our “head” is a simple sphere, it is a good approximation in this case, since our system is ITD-based and pinna related spectral cues are minimal at the frequencies where ITD is thought to operate in humans (<3 kHz). The audio environment consists of a room in which the walls are fitted with sound absorbing material to minimize reflection with the only major reflection coming from the floor, which is covered with thick carpet. The Tannoy loudspeaker features concentric bass driver and tweeter unit to provide a single point source for all audio frequencies, and has a flat spectrum from 44 Hz to 20 kHz. It was placed at the same height as the sphere, 2.6 m from the center of the sphere, and the IRs were recorded at 10 steps. These IRs allow us to present any stimulus to the AER EAR to simulate a far-field source in an open environment for both learning and testing, from different directions, by simply convolving the source signal with the appropriate left and right IRs. This method also allows simulated automatic gain control to be applied to the signals before they enter the cochleae, which is required due to a limited dynamic range in our silicon cochlea.
Each of the two silicon cochleae in the AER EAR contain 32 sections and is tuned to cover the frequency range from 200 Hz to 10 kHz, logarithmically spaced. In the human cochlea the cut-off frequency of the low pass filter created by the inner hair cell (IHC) is around 1 kHz and significant phase locking cannot be expected for frequencies above 3 kHz. In biology, around 10 auditory nerves innervate a single IHC and many IHCs would cover a frequency range equivalent to the bandwidth of our silicon cochlea channels. To simulate many fibers innervating a single cochlear region with our AER cochlea, which has only one output address for this region, we have turned off the low-pass filtering in the IHC and used a high spike rate. At the same time any cochlear section with a best frequency above 3 kHz will not be used by the system, leaving us with 19 pairs of left and right cochlea channels for the current bias settings of the cochlea.
Each channel generates, on average, 6000 spikes per second when a 35 mV sine wave is presented at the channel's best frequency (BF). The leakage current at the integrate-and-fire neuron is adjusted to strike a balance between sensitivity and spontaneous spike rate. For demonstrative purposes, all processing after the cochlea has been performed in MATLAB.
## Materials and Methods
### Traditional implementation
The block diagram of a commonly used bio-inspired algorithm for ITD-based sound localization (Lazzaro and Mead, ; Bhadkamkar and Fowler, ; Bhadkamkar, ; Lotz et al., ; Schauer and Paschke, ; Schauer and Gross, ) is shown in Figure . It is based on Jeffress’ model, where a pair of cochleae analyze the incoming sound and separate it into different frequency bands. Cross-correlation, typically implemented by delay lines and coincidence detectors, is then performed on the left and right outputs of each section, Y and Y
A commonly employed sound localization algorithm . A block arrow signifies a signal in multiple frequency bands. The cross-correlation results are summed across frequency without any adjustment for the frequency dependency of ITD.
before being summed across frequency. The delay position with maximum correlation is selected using a winner-takes-all (WTA) circuit (Lazzaro et al., ; Indiveri et al., ) and becomes the estimate of the ITD.
If ITD is independent of frequency and determined by
where θ is the direction of the source (Figure ), then direction can be computed from by applying the inverse function,
However, if the microphones are mounted on a head, the introduced diffraction will cause f (θ) to be frequency dependent, as shown in Figure . According to Kuhn ( ), at frequency less than 500 Hz, it can be approximated by a sine function, but becomes proportional to sin(θ) + θ as frequency increases above 1.5 kHz. Thus, different estimates will be given as the frequency of the source changes. The task is further complicated when implemented in a-VLSI as there will be mismatch in the delay lines at the cross-correlator, phase mismatch between the left and right cochleae, as well as mismatch in delay introduced by the signal conditioning circuits at the inputs of the cochleae.
A plot of ITD vs . azimuth for two microphones mounted on opposite sides of the foam ball, for four different octave bands . The delay is larger at low frequencies, which is consistent with Kuhn's model (Kuhn, ). Note that there are small front-back asymmetries (e.g., at 60º and 120º) at some frequencies due to the sphere being mounted near the front of the robot. Sound arriving from the back will experience more interference introduced by the robot's body.
### Mapping and soft-WTA
The first and most intuitive solution to the ITD variation problem is to extract the delay in each band and individually map these delays to azimuth angles. They can then be averaged to obtain a global estimate. Since mapping is performed before the results are combined, the frequency dependency is corrected. A block diagram of the algorithm is shown in Figure .
The first method to correct for ITD variation . For each band, the position of a single maximum is selected from the cross-correlation result using a WTA and mapped to azimuth position individually. These azimuth positions are then averaged to obtain the global estimate. The block arrows represent signals in multiple frequency bands.
While this algorithm works fine for noise inputs, it is less suitable for stimuli consisting of pure tones because the cross-correlation would result in more than one peak in some bands. If the wrong peak is picked, then in the best scenario, it is discarded (because it is physically impossible or is an outlier compared to the results from the other bands), resulting in some loss of information. In the worst case, however, it would generate a completely wrong estimate. This algorithm is also sensitive to noise and error introduced by mismatch at the WTA since only the delay corresponding to maximum correlation is extracted. Lastly, the implementation of a circuit capable of discarding outliers is not trivial. Therefore, we will investigate an alternative that is more robust and simpler to implement neuromorphically.
Instead of extracting only the global maximum in each band, it is more beneficial to retrieve all local maxima of significant magnitudes. In this way, even if the stimulus is a pure tone and the correlation result at the true time delay is not the global maximum, the true delay would still be passed on to subsequent stages rather than being discarded. This is accomplished by tuning the WTA. A typical WTA network is shown in Figure and by adjusting the strength of the inhibition relative to that of the excitation, one can vary the selectivity. A weak to moderate global inhibition allows it to be used to implement the soft-max function, which selects not only the strongest but also those similar in strength (Indiveri and Delbruck, ). Figure shows this system and Figure shows the result of the application of a soft-WTA (with the strength of inhibition equal to that of excitation) to a cross-correlation resulting from a pure tone stimulus. Both peaks are well-preserved.
A winner-take-all network consists of neurons with excitatory and inhibitory synaptic connections . The global inhibitory neuron (in black) provides the negative feedback necessary for competition to occur. By adjusting the strength of the inhibition ( W ) relative to that of the excitation ( W ), one can vary the selectivity – more and more neurons go to zero as inhibition increases. (Adapted from (Indiveri and Delbruck, )).
An improved algorithm using a Soft-WTA .
Application of soft-WTA to the result of cross-correlation . The stimulus is a 650 Hz pure tone with an ITD of approximately −0.6 ms. This ITD information would have been lost if a normal WTA is used, since there is a larger maximum at +1.0 ms.
Referring to Figure , for each frequency band, given the soft-WTA output S (τ), we can create a new function by mapping time (τ) to azimuth angle (θ) with the measured ITD function τ = f (θ),
This new function can be thought of as a measure of auditory activity at different bearing positions. Assuming there is only one source, the G (θ) in each band should produce a peak at the position of the source (even though there may be more than one peak if the signal is a mixture of pure tones). When the results are summed together, there will be one global maximum which gives us the correct estimate of the source direction:
### Mapping as matrix multiplication
For the algorithm presented in Section “Mapping and Soft-WTA,” in each frequency band, the mapping essentially connects neurons representing the soft-WTA output at different delays, τ, with neurons representing auditory activity at different azimuth, θ. Since both time delay and angle are discrete, we can rewrite the WTA output as a vector S ∈ R and the activities at different azimuth as a vector G ∈ R . The mapping can then be expressed as:
where W ∈ R × R is a weight matrix. In each row, there will be only one “1” with all other entries being “0,” and the positions of the 1’s are given by the relationship between azimuth and ITD at that band. Thus, each neuron in G will receive spikes from exactly one neuron in S .
In biology, connections between neurons are never one-to-one. A typical neuron has dendritic trees that collect inputs from hundreds to thousands of other neurons, weighted differently depending on the synaptic strength. Such a rich network of interconnecting neurons allows computations involving hundreds of variables to be performed in parallel. Furthermore, it allows learning to take place gradually by making small incremental changes to synaptic strength, in contrast to the abrupt changes of updating a lookup table. With this mind, we generalize equation and allow each element of W to take any value.
Now the question becomes: how do we determine W such that given the WTA output S , it can be transformed into G to represent activity in the auditory space? The solution can be found based on the gradient descent method.
In gradient descent, the goal is to find the point P such that f ( P ) is minimized. This is implemented by computing the gradient of f at the current position and move in the opposite direction, which gives the steepest rate of descent (Anderson, ). Mathematically, this can be expressed as:
where P is the current position, P is the new position, and ε controls the rate of descent. In our case, given the input S and target T ∈ R , we define the error to be
and our aim is to find W which will minimize the square error function:
where t and g are the i -th elements of T and G , s is the j -th element of S , and W is the element at the i -th row and j -th column of W . To determine the gradient, we take the partial derivative with respect to each element of W ,
since this is the only term in the sum containing W
So,
and our weight update rule is
With a sufficiently small learning rate, ε, the error function will always converge to a local minimum. One of the elegant features of such an update rule is that learning can be performed online, i.e., the system can gradually adapt while in operation, as long as feedback is provided about the target position. This will allow us to implement a sound localization system that is continually trained by visual feedback, which will be the subject of a companion paper.
Figure shows the complete block diagram of the final system. The mapping is replaced by a multiplication with a weight matrix. Such an operation is essential in artificial neural networks and has been implemented in VLSI with examples include (Morie, ; Serrano-Gotarredona and Linares-Barranco, ; Wang and Liu, ). Most implementations consist of an array of programmable synapses, with the weights stored in either digital or analog memory.
Block diagram of the final system . Mapping is replaced by matrix multiplication. S in each band is multiplied by a weight matrix to generate the activity map G . These frequency specific maps are then summed to produce the final estimate.
The complete system is simulated in MATLAB, except for the AER EAR, which was implemented in hardware. We use 101 delay positions (−1 to 1 ms with 20 μs resolution) and 61 azimuth angles (−90° to 90° with 3° step), resulting in weight matrices that are 61 × 101.
The weights are trained with band-limited noise stimuli under supervised learning. For each training example, we set the target T to be a Gaussian function centered at the expected position of the source, with σ = 25°. One of the advantages of choosing a Gaussian function instead of an impulse function is that it updates not only the weights going into the neuron representing the position of the source, but also those surrounding it. As a result, there is no need to provide training data at every source position and the system will be able to interpolate upon successful training. For simplicity, a fixed learning rate of 0.02 is used. While more complicated learning rate schedules can be used to speed up learning, we consider them outside the scope of this paper.
## Results
After the weights have been trained, we are able to transform the soft-WTA outputs to a spatial map representing auditory activity. Figure demonstrates this at one frequency channel. The outputs are essentially in a straight line, showing good correspondence between the actual and the perceived sound sources, with small imperfections at the larger azimuth positions. We repeat this process at a higher frequency channel with a different weight matrix, and again good results are shown in Figure .
Before and after transformation at one channel with a best frequency (BF) of 340 Hz . (A) The soft-WTA output S , is transformed into (B) G , a representation of activity in auditory space for this frequency channel.
Before and after transformation at one channel with a BF of 2 kHz . (A) The soft-WTA output S , is transformed into (B) G , a representation of activity in auditory space for this frequency channel. The stimulus is band-limited noise centred at the BF. Unlike mapping, the transformation process is able to extract a unique source position from the positions of the three peaks in the cross-correlation result and there is no ambiguity.
Figure shows how the error function f ( W ), in equation , reduces over time as the weights slowly adapt to the training data. The weights in the 2 kHz channel converge much slower than those in the 340 Hz channel. This is probably due to the gradual loss in phase-locking at the cochlea as frequency increases, resulting in more variation in the cross-correlation results, degrading the quality of the training data.
The mean square error as a function of epoch at two frequency channels with BF's of 2 kHz and 340 Hz . It shows the weights adapt to slowly reduce the error. An epoch is a term used to describe the training of artificial neural network in which every training sample has been applied once.
Localization tests were performed after the weights had been trained. We tested the system with white noise (3 kHz bandwidth), a 400 Hz pure tone, and a 650 Hz pure tone, and the results are presented in Figures – . 10 trials are performed at each source position and the average as well as the error of estimates is recorded. The front-back asymmetries that we saw in Figure , caused by the interference of the robot, are evident when the 650 Hz pure tone is played. Since the weights are trained with the source in front of the robot, the errors are large when the source comes from behind. The average RMS errors in the different ranges are presented in Table . The overall RMS error within the entire range is under 6º.
Localization result for a white noise stimulus, showing both the average estimate and RMS error at each position over 10 trials .
Localization result for a 400 Hz pure tone stimulus, showing both the average estimate and RMS error at each position over 10 trials .
Localization result for a 650 Hz pure tone stimulus, showing both the average estimate and RMS error at each position over 10 trials .
RMS error for the three types of stimuli .
## Discussion
In Table , we compare the performance of our system with other localization systems in which localization results are available or can be computed from published data. RMS errors are calculated manually from the average error and the standard deviation at each position, before they are combined across the two ranges, [0°, 45°] and [45°, 90°].
Comparison with other sound localization systems .
The accuracy of our system is comparable with all the other 2-microphone hardware implementations (Julian et al., ; van Schaik and Shamma, ; Grech et al., ) and some software systems (Nakashima et al., ; Okuno and Nakadai, ). It can be seen that software systems generally offer better accuracy, with errors as low as 1° in the [0°, 45°] range, as computation can be performed in higher precision, at the expense of higher power consumption. For accurate 3-D source localization (i.e., azimuth, elevation and distance) in a reverberant and noisy environment, a microphone array has to be used.
Although our system only offers average performance in terms of accuracy, it is one of the most biologically realistic and the only one employing a pair of spiking cochleae. It is capable of localizing both white noise and pure tone sounds. This is in contrast to some existing systems which are tested with only one type of sound. Furthermore, our system is designed to adapt and learn during operation as long as feedback is provided. As a result, it is no longer necessary to accurately calibrate it to ensure good localization – instead the system will adapt and compensate for mismatch at the sensors and the processing circuitries. This is an important feature for both biological and robotic systems. However, for the system to adapt, feedback is needed with regards to the correct target position when the system encounters a new environment. In a companion paper, we will present a system that uses a silicon retina to provide visual feedback about the target positions in the visual field, which will be used to train the sound localization system.
### Conclusion
An ITD-based neuromorphic sound localization system has been proposed. It uses the AER EAR as a front-end and unlike earlier attempts to implement neuromorphic sound localization systems in a-VLSI (Lazzaro and Mead, ; Bhadkamkar and Fowler, ; Bhadkamkar, ), by using a modular approach and processing each frequency channel individually, circuit mismatch and frequency dependent variations are overcome. The final system demonstrates the ability to reliably determine the azimuth position of the source for both pure tone and white noise sounds. In addition to the modest localization performance, this new architecture supports online learning, allowing the system to learn while in operation.
|
The paper by Henckens and colleagues is a very welcome addition to the literature on emotion-cognition interactions, in general, and on the impact of stress, in particular. By combining functional magnetic resonance imaging (fMRI) with carefully controlled cortisol administration, this study explored the neural substrates of time-dependent effects of stress hormones on attentional processing and emotion interference. Findings suggest a temporally fine-tuned cortisol's action, with an initial surge in vigilance that impairs selective attention (reflected in increased emotional interference), followed by a facilitation of sustained attention, seemingly contributing to the restoration of brain function following stress. These findings have important implications for understanding the stress hormones' effects on affective and cognitive processing in healthy functioning, and provide insights into possible mechanisms for stress-related disorders, such as post-traumatic stress disorder (PTSD).
Previous research provided evidence that stress hormones impact cognition and behavior (McEwen et al., ; de Kloet et al., ; Arnsten, ), with corticosteroids, in particular, having profound influences on both affective and cognitive functions (de Kloet et al., ; Erickson et al., ; Roozendaal et al., ), as they can easily cross the blood–brain barrier and readily bind to receptors located in emotion (amygdala—AMY) and cognitive (hippocampus—HC and prefrontal cortex—PFC) processing brain regions (Lupien et al., ; Roozendaal et al., ). Recent evidence from research in rodents suggests that corticosteroids can induce rapid, non-genomic effects followed by slower, genomic effects that can impact cognitive functions in opposite and complementary ways (Karst et al., ; Wiegert et al., ). Traditionally, animal research has focused on the effects of corticosteroids on HC, where corticosteroids' genomic effects have been known for decades to suppress neuronal excitability (Joëls and de Kloet, ; Kerr et al., ) and long-term potentiation (LTP) (Pavlides et al., ; Wiegert et al., ), the alleged neurobiological substrate of memory formation (Martin and Morris, ). However, recent findings indicated that corticosteroids increase hippocampal neuronal excitability (Karst et al., ) and LTP (Korz and Frey, ; Wiegert et al., ) in a rapid, non-genomic fashion, but only when present around the time when LTP is induced. Similar excitatory rapid effects have been also observed in AMY (Karst et al., ).
Despite evidence of time-dependent effects of corticosteroids in rodents, temporal dynamic effects of cortisol on affective and cognitive functions have only recently started to be investigated in humans (Henckens et al., , , ; Hermans et al., ). Henckens et al. ( ) investigated the time-dependent impact of cortisol on the neural correlates of attentional processing by using a randomized, double-blind, placebo-controlled approach, involving the following 3 groups: (1) placebo (receiving placebo 270 and 60 min before the task), (2) rapid cortisol (receiving placebo and hydrocortisone, 270 and 60 min before task, respectively), and (3) slow cortisol (receiving hydrocortisone and placebo 270 and 60 min before the task, respectively).
Functional MRI data were recorded while participants performed an emotional distraction task, which allowed examination of both selective and sustained attention. Selective attention was measured as the difference in interference produced by emotional compared to neutral distraction, whereas sustained attention was reflected in the overall performance in trials with both emotional and neutral distraction. Thus, compared to previous studies, the approach used by Henckens and colleagues has the clear advantage of allowing examination of corticosteroid effects in a time-dependent manner on different types of attentional processing and on emotion processing. First, results indicated that the rapid effects of corticosteroids were associated with increased bottom-up/stimulus-driven attentional processing, which caused impaired selective attention (as reflected in increased emotional interference), associated with increased activity in the AMY and increased AMY-PFC connectivity while processing aversive relative to neutral distraction. These findings from the fast cortisol group suggest that the rapid corticosteroid effects cause stimulus-driven behavior, and can contribute, together with those of catecholamines, to a state of hypervigilance (Roozendaal et al., ; Joëls and Baram, ). Second, the slow effects of corticosteroids modulated the neural correlates of sustained attention, by reducing bottom-up processing. Specifically, the slow cortisol group showed reduced activation in visual brain regions linked to sustained attentional processing, as well as reduced negative connectivity between activity in the AMY and insula. These findings suggest that the slow corticosteroid effects might counteract the rapid effects by reducing automatic visual/stimulus-driven processing and engaging more controlled processing to restore brain functions following stress.
Overall, these findings indicate that corticosteroids influence brain function in a time-dependent manner, affecting activity and connectivity of visual, emotional, and cognitive processing brain regions in an opposite manner, in order to serve adaptation to changing environmental demands. Thereby, this study proposes a more adaptive view on the impact of cortisol on attention and emotion according to the temporal profile of action, with an initial effect optimizing detection of potential threat at the cost of impaired cognitive processing, and a delayed effect normalizing cognitive brain functions following stress (see also Joëls et al., ; Hermans et al., ). Of note, while these effects might allow for optimal responding to stressful situations and subsequent recovery in healthy individuals, they are likely impaired in PTSD, which is characterized by a continuous state of hyper vigilance (Dolcos, ). These findings highlight the importance of timing in the effects of stress hormones, as a critical factor to take into account in future studies, and point to a more adaptive view on the effects of emotion (or stress) on cognition, depending on the circumstances. The importance of considering opposing effects of emotion on cognition is also reflected in the success of the Special Research Topic that this report is part of Dolcos et al. ( ), which attracted numerous outstanding contributions regarding the mechanisms of emotion-cognition interactions. I anticipate that this is only the beginning of what is yet to come in the field, and the paper by Henckens and colleagues is riding right at the top of this exciting emerging research wave!
## Conflict of interest statement
The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
|
Like many neurodegenerative diseases, the clinical symptoms of Parkinsons disease (PD) do not manifest until significant progression of the disease has already taken place, motivating the need for sensitive biomarkers of the disease. While structural imaging is a potentially attractive method due to its widespread availability and non-invasive nature, global morphometric measures (e.g., volume) have proven insensitive to subtle disease change. Here we use individual surface displacements from deformations of an average surface model to capture disease related changes in shape of the subcortical structures in PD. Data were obtained from both the University of British Columbia ( UBC ) [ n = 54 healthy controls (HC) and n = 55 Parkinsons disease (PD) patients] and the publicly available Parkinsons Progression Markers Initiative ( PPMI ) [ n = 137 (HC) and n = 189 (PD)] database. A high dimensional non-rigid registration algorithm was used to register target segmentation labels (caudate, putamen, pallidum, and thalamus) to a set of segmentation labels defined on the average-template. The vertex-wise surface displacements were significantly different between PD and HC in thalamic and caudate structures. However, overall displacements did not correlate with disease severity, as assessed by the Unified Parkinson's Disease Rating Scale (UPDRS). The results from this study suggest disease-relevant shape abnormalities can be robustly detected in subcortical structures in PD. Future studies will be required to determine if shape changes in subcortical structures are seen in the prodromal phases of the disease.
## 1. Introduction
Parkinson's disease (PD) is the second most common age related neurodegenerative disorder after Alzheimers disease (de Lau and Breteler, ). Routine clinical MRI is rarely used in diagnosis, and is often used only to rule out other conditions that may mimic PD (e.g., vascular Parkinsonism). Although there are no gross structural abnormalities seen in PD, the use of structural MRI is still potentially attractive as a biomarker because of the ubiquitous nature of the technology. Several studies have suggested subtle morphological alterations such as atrophy in the putamen and/or caudate (Schulz et al., ; Ghaemi et al., ; Krabbe et al., ; Pitcher et al., ). Most of these studies have looked at overall volume as a measure of atrophy as it easy to measure, invariant to position of the subject in the scanner, and, if appropriately normalized, directly comparable across subjects. However, in some structures such as the thalamus, volume may actually increase as a compensatory mechanism when cortical regions are damaged (Pol and van der Flier, ), complicating overall volume as a marker of disease progression. This may be why the thalami undergo significant shape change with PD, even when no significant difference in volume can be detected (McKeown et al., ), presumably on the basis of specific nuclei being affected and/or compensatory hypertrophy of other regions.
Commonly-used brain morphometric analysis methods to assess progression of neurodegenerative disease can be categorized into three general types: voxel-based, surface-based, and deformation-based methods. By far, the majority of morphometry studies to date have been based on voxel-based morphometry (VBM), because of its conceptual simplicity and widespread availability of suitable software. In VBM, subjects brain images are registered to a common template image, and statistical analyses are performed on a voxel-by-voxel basis on the registered subject brain images. This technique has demonstrated cortical loss in some brain areas in PD (Burton et al., ; Nagano-Saito et al., ) and in other PD-related diseases (e.g., Multi system atrophy) (Paviour et al., ; Brenneis et al., ; Tzarouchi et al., ). Nevertheless, there is widespread recognition of the limitations of VBM: it tends to find focal changes as opposed to more spatially distributed changes (Davatzikos, ), and may be insensitive to subtle morphological alterations (Bergouignan et al., ).
An alternative approach is to use surface-based morphometry (SBM) for measuring shape changes. In SBM, a surface representation of a structural boundary is investigated rather than at the level of the individual voxels. Using such a method, PD related shape and volume changes in the hippocampus, caudate and ventricles can be detected (Apostolova et al., ). In a similar approach, deformation-based morphometry (DBM), the deformation fields obtained from one-to-one non-rigid registrations are analyzed in place of the final registered images (Duchesne et al., ). In our work, we use a SBM method where we model the change in shape as the deformation of a template surface to the individual target surfaces for the disease and control groups. The surface displacement metric obtained as a signed normal component of the displacement vector (from template to target surface) at each vertex on the template surface captures the deformation information. This metric directly models deformation on the surface of the template.
Here we present a SBM method that incorporates anatomically-defined regions of interest (ROIs). Accurate segmentation labels for subcortical ROIs were obtained via an automated registration based segmentation process (Khan et al., ). These labels were then registered to a prototype via a non-linear registration algorithm (Beg et al., ). The template for this study was generated via an alternating registration and averaging process (Khan and Beg, ), that encapsulates information from the entire cohort and thus with minimal bias to data from a specific subject. Given a mean template, the surface displacement obtained from taking the difference in coordinates between the reference (template) and the deformed surfaces on a vertex-by vertex basis represent a feature for subsequent classification. Based on prior work (McKeown et al., ; Apostolova et al., ) we study the change in shape of caudate, thalamus, putamen, and pallidum structures due to PD. We processed the imaging data from two cohorts with PD patients and healthy control groups, and analyzed the surface displacement feature for the group level difference in the features.
The rest of the paper is organized as follows: in Section 2 the data and processing methods are introduced, followed by the results of experiments in Section 3. The results are further discussed and concluded in Section 4.
## 2. Materials and methods
In this section we discuss the methods for extraction of shape features from the MRI data for the individual subcortical ROIs. The feature extraction process consists of a number of steps applied sequentially to perform the tasks of data pre-processing, construction of a prototype, diffeomorphic registration followed by final feature extraction. These features are then tested for differences between the PD and HC groups. The following sub-sections provide a description of these steps in detail.
### 2.1. Subjects and scans
#### 2.1.1. University of British Columbia ( UBC ) dataset
Data were taken from 55 non-demented PD subjects and 54 healthy subjects seen at the UBC Pacific Parkinson's Research Center . Table gives the clinical and demographic details of the PD and control groups. Written informed consent was obtained from all subjects prior to participation in the study. This study was approved by the Clinical Research Ethics Board of the University of British Columbia and conforms to the Declaration of Helsinki.
Demographics for the data in the UBC dataset .
Patients were examined after overnight medication withdrawal with >12 h for L-dopa and >18 h for dopamine agonists. Exclusion criteria included atypical parkinson's (cerebellar ataxia, prominent dementia, early postural instability), symmetrical onset of symptoms, evidence of severe memory impairment or signs of dementia [Montreal Cognitive Assessment (MoCA) scores < 24] or a history of cerebrovascular disease or other neurological disorders. The Unified Parkinson's Disease Rating Scale (UPDRS) motor scale data were recorded and used in further analysis.
Subjects were scanned using a Philips Achieva 3.0 T scanner (Philips, Best, The Netherlands), with a 8-channel head coil. A memory foam pillow was used to minimize head motion. The acquisition parameters for the 3D T1-TFE sequence were as follows: 170 axial slices, repetition time = 7.7 ms, echo time = 3.6 ms, flip angle 8°, field of view 256 × 200 mm, acquired matrix size 256 × 200, and voxel size 1 × 1 × 1 mm .
#### 2.1.2. Parkinson's progression marker initiative ( PPMI ) cohort
The data available under the PPMI project was obtained from the LONI Image data archive ( ). The data acquisition details are available on the website for the PPMI project and can be obtained from the url ( ). The data from the baseline visit was selected for the analysis. We randomly chose a subset of subjects so that they would be age-matched with other subject groups. The demographic details for this subset are presented in the Table .
Demographics for the data in the PPMI dataset .
The PPMI cohort is a multicenter study where multiple scanners and imaging protocols are used. An example protocol for a MPRAGE sequence as used in the study is as follows: Siemens Magnetom TrioTim sungo MR B17 3T scanner (Siemens, Germany). T1 weighted images were acquired with the MPRAGE sequence with the following parameters: 176 axial slices, repetition time = 2300 ms, echo time = 2.98 ms, flip angle = 9°, field of view = 256 mm, acquired matrix size = 256 × 200, and voxel size 1 × 1 × 1 mm . Some other scanners used in the study include GE Signa 3.0T, GE Discovery 3.0T (G.E., USA), Philips Acheiva 1.5T (Philips, Netherlands), Siemens TrioTim 3.0T, Siemens Symphony 1.5T, Siemens Verio 3.0T, Siemens Espree 1.5T (Siemens, Germany). Extensive pre-processing and data normalization was performed by the members of the PPMI-core group prior to release of the data for public use. For detailed image acquisition protocols and scanner specific preprocessing the reader is referred to the website of the PPMI cohort at
### 2.2. Data analysis process
In this section, we present the details of the image analysis steps that was used to obtain the surface displacement data for individual subcortical structures from the raw MRI data. We briefly describe the registration algorithm, followed by the methods for segmentation of anatomical structures, prototype creation and surface displacement computation. The process flow diagram (Figure ) illustrates the stages of the process.
A process flow diagram for the computation of the surface displacement feature from the raw MRI data .
#### 2.2.1. Large-deformation diffeomorphic metric mapping (LDDMM)
Diffeomorphic registration methods are desirable in processing of medical imaging data because of their inherent smoothness and ability to model large and small displacements. Here we briefly describe the LDDMM (Beg et al., ) process, which generates a diffeomorphic transformation by minimizing the difference between the source and transformed target images.
Let us define Ω ⊂ ℝ as the coordinate space of the source image, and G :Ω ↔ Ω as the set of diffeomorphic transformations on Ω. The LDDMM algorithm seeks a geodesic ϕ : [0, 1] → G where each point ϕ ∈ G, t ∈ [0, 1] is a diffeomorphism on the domain Ω. Then the source image I evolves along the path to the target image I according to ϕ I = I ◦ ϕ . At the endpoint t = 1, the source I is connected to the target image via I = ϕ I = I ◦ ϕ . The associated velocity field v , taken from the space of smooth velocity fields V on the domain Ω ⊂ ℝ , is a solution to the differential equation = v (ϕ ), t ∈ [0, 1] satisfying
By integrating the optimizer of this cost function we get the optimal change of coordinates . The superscript v in ϕ is used to explicitly denote the dependence of ϕ on the associated velocity field v . The mapping is guaranteed to be a diffeomorphism by enforcing sufficient smoothness on the elements of V . We do this by defining a norm on V through a 3 × 3 differential operator L of the type L = (αΔ + γ) I where α > 1.5 in 3D space such that ‖ f ‖ = ‖ Lf ‖ , and ‖ · ‖ is the standard L norm for the square integrable functions defined on Ω. The gradient of the cost function (Equation 1) is given by the following Freche derivative in V :
Where J = I ◦ ϕ and J = I ◦ ϕ , | Dg | is the determinant of the Jacobian matrix and K is a compact self-adjoint operator K : L (Ω, ℝ ) → V uniquely defined by 〈 a, b 〉 = 〈 K a, b 〉 such that for any smooth vector field f ∈ V, k ( L L ) f = f holds. Also L is the adjoint of L and the notation ϕ = ϕ · ϕ is employed. Finally the parameter provides weighted optimization between the regularization and data matching components, and is chosen to be the same for all matchings.
In order to compute v , this variational gradient is used in the standard gradient descent procedure, yielding the update v = v − ε ∇ E where n denotes the simulation number.
The optimal mapping ϕ is used in further steps to transform the images from the source space to the target image space.
#### 2.2.2. FS + LDDMM segmentation
The FS + LDDMM segmentation steps (Khan et al., ) combine the probabilistic labels obtained from the FreeSurfer (FS) program along with the LDDMM registration with ground truth template data, to create accurate labels for the subcortical ROIs in the target MRI data.
Freesurfer (v4.5.0) (Fischl et al., ) was utilized to obtain initial segmentation of subcortical structures for each MRI image volume. With this process, non-brain tissue was removed from the images, followed by automated Talairach transformation and segmentation of caudate, pallidum, putamen, thalamus (Fischl et al., ; Fischl and Kouwe, ). Subsequently, an ROI was defined for each structure on the target and template MR images using FS labels and manual labels, respectively. These ROIs from target images were then aligned via an intensity-based affine transformation to those in the template images. A bounding box, predefined in the template space using the extent of the template FS labels plus a 12-voxel padding, was used to generate sub-volumes. These pre-processed MRI sub-volumes were then registered via the LDDMM method as described above (Section 2.2.1) to obtain the final segmentation labels.
Within the FS + LDDMM process, the LDDMM registration was performed in a multi-stage fashion, each stage using different image pairs and with each subsequent stage initialized with the velocity vector fields, of the previous stage. In the first stage, the FS labels of the hippocampus, amygdala, and lateral ventricles were used, in the second stage, Gaussian smoothed (σ = 5) MRI images were used, and in the final stage non-smoothed MRI images were used. The velocity vector fields were discretized into 5 timesteps. Finally, the atlas segmentations for each hemisphere were propagated to the target by applying the LDDMM and affine transformation, using linear interpolation to maintain precision when resampling the segmentations. The propagated segmentations from each template were fused with equal weights for each template. This resulted in a binary segmentation volume with the same dimensions and orientation as the original MRI in the native image space with each subcortical region of interest represented by a voxel intensity of 1 (intensity threshold = 127.5) and background intensity as 0.
The templates for segmentation of the MRI images in this study were obtained by manual segmentation of 6 healthy control subjects from the UBC scanner but not included in this study. The protocols for definition of structural boundaries for all the structures of interest were obtained from previously published work (Hammers et al., ).
#### 2.2.3. Segmentation quality check
The final labeling for each structure obtained from the segmentation process was checked for segmentation accuracy through an extensive quality control process. A surface model was fit for each binary segmentation volume via the marching cubes algorithm to provide a set of vertices and triangles representing the segmentation boundaries. These surfaces were overlayed on the original MRI images in the three orthogonal views to check for accuracy of segmentation labels (e.g., Figure ). A visual verification of each such visualization was performed by an expert in neuro-anatomy. In conventional quality control approaches, the subjects with inaccurate segmentation for any structure are removed from the subsequent analysis. In our work, all subjects were found to have accurate segmentation labels and were included in the subsequent analyses. The demographics reported in Tables , present the set of subjects with accurate and acceptable segmentation labels. The detailed visualizations for all the subjects in the analysis are available at the website ( ).
Saggital upper left Coronal upper right Axial lower left views of the segmentation outlines overlayed on the corresponding MRI slices . The 3D surface renderings show the smooth surfaces lower right .
#### 2.2.4. Unbiased average template (prototype) generation
The choice of template for registration influences the accuracy of the surface displacement data. To this end we created a “prototype” for the cohort (an average template) from the binary segmentation labels obtained for individual structures (Khan and Beg, ). Pre-processed data were obtained by affine alignment of each binary subcortical image to an initial template, followed by extraction of a sub-volume ROI in the prototype space. The process alternated between (1) registration (LDDMM) of individual ROIs to a template ROI and (2) computation of an average from the registered ROIs in the template space. The average computed in the previous step formed the template for registration in the next step. A healthy control subject was chosen as an initial template and the pipeline was run for three iterations to obtain the final, average unbiased template. A subset of normal subjects ( n = 10) from the UBC dataset were selected for creation of the prototype.
#### 2.2.5. Surface displacement
The prototype generated above (Section 2.2.4) was used for computation of the surface displacement data. The target binary labels were pre-registered to the prototype prior to non-rigid registration. Two different approaches were considered for the pre-registration step: (1) using a 6 degrees-of-freedom (DOF) rigid transformation (2) using a 9 DOF affine transformation. The rigid pre-registration approach corrects only for the translational and rotational discrepancies between the target structures, whereas the affine approach corrects the scale discrepancy as well. Both pre-registered sets (rigid and affine) were further used in the pipeline to extract two sets of the surface displacement features.
High dimensional non-rigid registration (LDDMM) from the prototype segmentation image, M , to each pre-registered segmentation image M was performed to obtain the mapping ϕ = LDDMM ( M , M ). The injected-surface was then computed as , where each has the same set of corresponding nodes, obtained from the template mesh, S , and thus all segmentations from different subjects can be compared at a vertex-wise level. One benefit of this surface injection technique is that it can deal with many types of topological defects that can be present in the automated segmentation by enforcing a smoothness in the deformation that ignores holes and handles, as has been previously suggested (Khan et al., ).
The vertex-wise correspondence in the meshes for each subcortical ROI across subjects enables the quantification of the disease related atrophy or hypertrophy on the surface of the ROI. The deformed surfaces may lie inside (atrophy) or outside (hypertrophy) of the prototype surface. To achieve this we used a signed closest point distance metric computed at each vertex on the prototype surface to that on the target surface. At each node, a ∈ , m ∈ S , we find the dot-product of the displacement from prototype to the deformed injected surface, and the surface normal on the average, . The normal distance, d ( m, a ) = · is negative or positive when the deformed surface is respectively inward or outward relative to the average, effectively an indication of the deformation.
### 2.3. Statistical analysis
In this section we describe the methods for the statistical analysis of the volume data and surface displacement features with the aim to find the group level differences in the data. Volume difference would suggest a global morphological alteration whereas surface displacement difference would suggest a localized shape change as a disease effect. The statistical tests for the surface displacement data were performed using both the rigidly and affinely pre-registered surfaces.
#### 2.3.1. Volume: group difference
The volume measurements from the surfaces generated from segmentation labels were tested for the statistical differences between the PD and HC groups. A two-tailed t -test was performed between the data from the two groups. The significance level was maintained at p < 0.05 for all tests. The test was repeated for both UBC and PPMI cohorts.
#### 2.3.2. Surface displacement data: group difference
The surface displacement data as described in the Section 2.2.5 provided a signed distance value at each vertex on the prototype surface for individual structures. The surfaces for each target subject were in vertex-to-vertex correspondence enabling a direct comparison of the displacement data. A vertex-wise comparison of group difference across the cohort between the patient and healthy control groups provided insight into the spatially-localized, disease-related alteration in shape of the subcortical structures.
A linear model was fit to regress out the effect of variation in age and gender effects from the SD data. The SD data were kept as the outcome variable and the age and gender were used as predictors in the model. The residuals from the linear model fit were used in the statistical analysis to test for the effect of disease on the data. Vertex wise group difference analysis was conducted using SurfStat software (Worsley et al., ), which employs Random Field Theory to correct for multiple comparisons (Worsley et al., ). The vertex wise comparison and cluster-forming thresholds were set at p < 0.05. The contrast between the two groups was evaluated as HC — PD , where a positive t -value suggested atrophy in patients in comparison to the healthy controls and vice versa .
#### 2.3.3. Relationship with clinical score
The surface displacement data were then tested for their potential to predict the UPDRS scores in both the studies.
The surface displacement data were rearranged into a 1-dimensional vector for each structure. These displacement vectors for all structures were then concatenated into a long column vector for each subject. In order to avoid the curse of dimensionality (very large dimensional data in a small sample size), dimensionality reduction was performed using Principal Component Analysis (PCA) decomposition. Sufficient number of PCs were retained to account for 95% of the variability in the data. The PC loadings were tested for the potential to predict the clinical scores in a linear model via linear regression. A leave-one-out procedure was conducted where each patients' score was predicted based on the model fit to the data from the remainder of the patient group. The PC loadings were the predictors and the clinical score were the response variables. A 2-D scatter plot (e.g., Figure 6 ) between the predicted vs. the actual score was obtained and a least squares line was fit to check for statistically significant relationship between the two. The coefficients of the linear model and their statistics are reported.
The UPDRS score in the UBC and PPMI cohorts was tested for association to the surface displacement data. We tested the UPDRS scores to be normally distributed via a lilleofors test (Lilliefors, ). The data rejected the null hypothesis of “not normally distributed.”
## 3. Results
The processed MRI data provided segmentation of subcortical structures for the caudate, putamen, thalamus, and pallidum in the left and right hemispheres (Figure ). The segmentation labels for the anatomical structures were thoroughly checked for accuracy via visualization of surface outlines overlayed on the MRI slices (Figure ). In our data, all subjects in the two cohorts were found to have acceptable (surface outline following structure boundary) and accurate segmentation labels, and were retained for subsequent analysis. Similarly, the surface displacement computed for each structure was visualized to check for presence of inaccuracies (extreme displacement values) due to registration errors. As an example, visualizations for three subjects in the patient and control groups are presented for the caudate and pallidum (right), and putamen and thalamus (left) structures (Figure ).
Visualization for the quality control of the surface displacement data . Data for three representative subjects from the healthy control and patients groups, respectively have been presented from the UBC dataset for left thalamus and caudate and right pallidum and putamen structures. Color (legend) represents surface displacement from the prototype surface.
The volumes of structures were significantly different ( p < 0.05) for the right thalamus ( p = 0.034, t = 2.15), right putamen ( p = 0.041, t = 2.07), and left thalamus ( p = 0.035, t = 2.14) in the UBC dataset. In contrast, volumes in the PPMI dataset did not present a statistically significant difference for all structures (Table ). In the rigid pre-registration case, the vertex-wise group analysis did not show any statistically significant difference. However, in the affine pre-registration case, the analysis of the surface displacement data found vertex clusters with significant difference in the displacements between patient and control groups (Figures , ). As the rigid pre-registration failed to highlight the significant differences between the CN and PD groups, we present the results from only the affine case in the rest of this paper. Disease-related shape changes in subcortical structures were widespread. Table presents the top 2 clusters ordered by the residuals of the model fit with their number of vertices, average t -value, and p -value for each structure.
Results for the analysis of group level difference in the volume of the subcortical structures between the participants in the Parkinson's disease and healthy control groups .
Statistical significance at p < 0.05 marked by asterisk ( ) .
Results for the vertex-wise group difference analysis of the surface displacement data for the left and right, caudate and thalamus structures in the UBC and PPMI datasets . The colored patches represent the t -values in the areas with statistically significant ( p < 0.05) difference between the patient and healthy control group. Gray colored area had no statistically significant difference between the groups. Positive t -value represents lower value in the patient group.
Results for the vertex-wise group difference analysis of the surface displacement data for left and right, pallidum and putamen structures in the UBC and PPMI datasets . The colored patches represent the t -values in the areas with statistically significant ( p < 0.05) difference between the patient and healthy control group. Gray colored area had no statistically significant difference between the groups. Positive t -value represents lower value in the patient group.
Results for the vertex-wise group difference in the surface displacement (affine pre-registered) between Parkinson's disease patients [ n = 55 ( UBC ), n = 189 ( PPMI )] and healthy controls [ n = 54 ( UBC ), n = 137 ( PPMI )] for 8 subcortical structures .
Data presented for top 2 vertex clusters and their corresponding mean value for the t-statistic, p-value and the mean ± standard-deviation of the surface displacement in the HC and PD group. Structures and clusters with statistically significant (p < 0.05) difference marked with an asterisk ( ) .
Spatial clusters with statistically significant difference in the surface displacement feature in the two groups were found on all the structures. Two key observations appear from the statistical comparisons: (1) Structures with net-inward deformation for the PD group (+ve t -value, right putamen, right, and left pallidum) and (2) structures with both net-inward and outward deformation for the PD group (−ve and +ve t -value, left thalamus, right, and left caudate). Additionally, similar spatial locations with difference of same (+ve or −ve t -value) nature were observed in the two datasets (e.g., right and left pallidum). The clinical score prediction experiment with the data from all structures used simultaneously as a single column vector was not able to predict the UPDRS scores with the statistical significance ( p > 0.5).
## 4. Discussion
Analysis of local shape change in comparison to global measures of morphology highlights the importance of examining spatially-localized alterations as a disease-related effect. In an extrinsic approach, the features extracted from the deformation fields rely on the choice of template to which individual images/surfaces are registered. These features can then be compared between groups for effect of a disease or clinical intervention. We computed the surface displacement metric as the signed closest point distance at each vertex of the surface model to quantify the net surface deformation. Surface displacement data (affine pre-registered) showed very strong differences between the two groups for all 4 structures (Table , Figures , ). Such differences were observed in UBC and the PPMI datasets, where patches of vertex-clusters were present throughout the surface (Figures , ). Both atrophy (positive t -value) and hypertrophy (negative t -value) were present in the patient group, suggesting compensatory alteration in shape within the same structure.
It is interesting to note that the volume of the subcortical structures did not show statistically significant difference for any of the structures in the PPMI dataset (Table ). In contrast, many clusters with statistically significant ( p < 0.05) difference in the surface displacement metric were observed in the vertex-wise group difference analysis (Table , Figures , ). Surface displacement being a local, sensitive measure of shape change is able to present group level differences. In contrast, the gross measurement of the structural volume is shown to not yield sensitivity to disease changes. This finding is consistent with previous observations (McKeown et al., ) where volume did not show any difference between the disease and control group, but a shape feature was able to show statistically significant results.
### 4.1. Technical strengths of the proposed analysis approach
We applied an automated registration based segmentation approach (FS + LDDMM) which has been validated to provide accurate segmentation labels (Section 2.2.2) (Khan et al., ). The application of the automated method with multiple templates is expected to yield accurate segmentation of the subcortical ROI, unaffected from potential variability due to a manual labeling procedure. This segmentation method has already been shown to provide high quality segmentation for segmentation of caudate, thalamus, putamen, and hippocampus (average dice coefficient = 0.85). Another validation study in the context of segmentation of pediatric MRI images found the dice coefficients of 0.89 for thalamus, 0.89 for caudate and 0.87 for putamen, thereby emphasizing accurate segmentation results (Garg et al., ).
The choice of templates for segmentation is known to impact segmentation quality (Garg et al., ). In our study, we used templates ( n = 6) from the UBC cohort which were manually segmented by a neuro-anatomy expert. The templates do differ from the data in the PPMI cohort, which, in itself, being a collection of data acquired at different sites, has inherent inhomogeneities due to scanner characteristics and acquisition protocols. In order to ensure good quality segmentation of the data in the two cohorts we performed thorough segmentation quality check as explained in Section 2.2.3. All the subjects in the two cohorts were found to have accurate segmentation labels, and hence were retained for subsequent analysis. This prevented the errors in segmentation from propagating into subsequent processing and analysis steps. Additionally, the use of an unbiased prototype (Sections 2.2.4, 2.2.5) to obtain the surface displacement metric ensures that the deformation data is free from bias toward a subset of the group. Therefore, the statistical outcomes obtained in our study represent the characteristic differences in the anatomy captured by the MRI data.
### 4.2. Strength of the shape feature
Structural MRI is a potential marker for alterations observed in PD as it can assess brain systems associated with motor and non-motor deficits. It has been shown that less sensitive measures of change in morphology such as volume presents contradictory results for putamen (e.g., Krabbe et al., ; Pitcher et al., ) vs. (Schulz et al., ), Ghaemi et al. ( ) and caudate atrophy (e.g., Pitcher et al., vs. Schulz et al., ; Ghaemi et al., ). Additionally, thalami in PD showed significant shape change despite no significant difference in volume (McKeown et al., ). Results from our study are in alignment with the previous observations where volumes of anatomical structures did not show a statistically significant change, whereas widespread shape change was observed as an effect of Parkinson's disease. Our shape analysis approach and its derived features contain both global and local shape information and show ability to capture sensitive disease related shape change in two large and independent datasets.
Previous work (McKeown et al., ) exploring differences in thalamic shape utilized a SPHARM representation. A recent morphometric analysis (Apostolova et al., ) also detected changes in basal ganglia structures between PD subjects with and without dementia. Apostolova's method focused on volume, where the radial distance—an intuitive measure of ROI thickness—was used as morphometric feature. Similarly, other work (Sterling et al., ) also modeled shape with a SPHARM-PDM representation and found areas with significant differences in the caudate and putamen structures. In contrast, our work quantified the shape change with a surface displacement measure at every vertex in the average surface model for each sucortical region. The high dimensional non-rigid registration algorithm (LDDMM, Beg et al., ) used in this study has been shown to account for the non-linearity of the anatomical shape space. The metric thus derived is able to accurately capture the shape alteration due to disease. Using this metric, we were able to spatially localize the areas with significant disease related alteration.
We found both similarities as well as differences in the anatomical location of shape changes between the two cohorts. The observed differences can be attributed to the difference in scanners, image acquisition protocols, inhomogeneities due to multi-center data collection in the PPMI cohort and differences in clinical features. Nonetheless, there are very encouraging overlapping changes in the PD groups in the two cohorts, mainly in sensorimotor and limbic areas and we will preliminarily discuss the functional implications of these most robust changes: The globus pallidus shows the most widespread changes with several areas of atrophy bilaterally in both cohorts; in the two datasets, areas of atrophy overlap along the medial aspect of the globus pallidus internus, most likely in areas implied in motor function (Obeso et al., ). In the caudate, atrophy is present along the dorso(medial) tail of the caudate on the right, affecting areas belonging to sensorimotor, frontostriatal circuits (Redgrave et al., ). On the left, fewer changes are observed, there is some overlap for atrophy along the ventral tail of the caudate, again likely implying sensorimotor circuits. In the left putamen, we found mostly hypertrophy in the UBC cohort but atrophy in the PPMI cohort without clear areas of overlap between the groups. On the right, areas of atrophy overlap in the dorsal area of the posterior putamen, which is associated with sensorimotor function (Redgrave et al., ). Both areas of hypertrophy and atrophy were observed in the thalamus, atrophy with partial overlap between the two groups is found in mediodorsal aspects of the thalami bilaterally. The mediodorsal thalamus is part of the limbic circuit and has been implied in depression in PD (Cardoso et al., ; Li et al., ). The shape feature computed in our study presents the potential to detect the differences between PD and control groups. The discriminant function that can be developed from this work could then be applied in longitudinal studies to see if it is able to detect alterations in subjects in the prodromal phase of PD.
### 4.3. Shape differences and clinical association
The surface displacement shape feature was not able to predict the UPDRS motor score (Figure ). The UPDRS motor score is a fairly crude clinical measurement as it combines different features of PD such as tremor and postural instability which have differing anatomical bases and this might partially explain why we did not find a correlation of the UPDRS motor score with shape changes. In order to identify correlations of shape changes with clinical indices, future studies should examine motor subscores for rigidity, bradykinesia, tremor and axial stability separately, assess mood, cognitive and reward function, control for more affected side and handedness and match groups closely for age and gender.
Scatter plots for the predicted and actual clinical scores from the leave one out prediction experiment using the PC loadings from the PCA decomposition of surface displacements where data from all structures was combined . Experimental data from the (A) UBC dataset and (B) PPMI dataset. Blue line is the least squares fit between the predicted and actual clinical score values.
### 4.4. Limitation and conclusions
In our study, we considered the pre-registration of the binary segmentations using rigid and affine transformations, respectively (see Section 2.2.5). The surface displacement feature computed from the rigid registered data did not show a statistically significant difference between the groups (HC vs. PD), whereas, the affine registered data showed spatial clusters with significant difference (Table , Figures , ). This suggests that the scale variability among the target binary segmentations has a confounding effect on the CN vs. PD group differences, and the diseases related changes are likely smaller than the variability in the scale of the structures seen across the population. The use of an affine pre-registration step between the binary segmentations removed the scale-related variability, however, in the process, it may also have removed some of the scale related changes seen due to PD. Hence, using the structures which are changing due to the disease, to further remove effects due to overall scale change across the population is likely sub-optimal. Further, the cross-talk between the normally occurring scale variability of structures in the population vs. the changes in scale due to disease (such as atrophy, or hypertrophy) likely affects all shape analysis methods that rely on surface deformation based morphometry. This observation highlights the need for shape assessment methods to deal with scale-related variability in the population better. As a suggestion, perhaps accounting for scale variability using a feature that does not change with disease such as the cranial vault instead of the binary segmentations can be investigated.
We applied volumetric registration method to obtain the mappings between the source and target binary images. As the binary images carry shape information at the boundary pixels and the pixels in the interior contain minimal or no information regarding shape of the structures, such methods are expected to provide registration accuracy and shape information, at par with surface registration methods. However, a direct comparison of the two sets of methods is beyond the focus of the current work and forms a scope for future investigation. Additionally, in order to be used as a biomarker in the clinical setting, the method needs to be tested further on much larger and independent data cohorts for its ability to predict clinical features and the ability to detect changes in the prodromal stages of the disease.
In conclusion, we present a first study to investigate the change in shape in Parkinson's disease tested on a large publicly available dataset ( PPMI ) and validated on an independently acquired dataset at UBC . Our results suggest that systemic changes in the shape of subcortical structures (caudate, pallidum, putamen and thalamus) can be non-invasively assessed in PD in vivo . The surface displacement feature encodes the spatially localized shape information. In this study, we have been able to highlight regions on the surface of subcortical structures that show changes in PD. The automated method presented in the study provides a new avenue to assess the progress of the neuro-degenerative processes. Further validation using data from larger cohorts is needed to assess the predictive capability of this method.
### Conflict of interest statement
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
|
Pre-clinical deep-brain stimulation (DBS) research has observed a growing interest in the use of portable stimulation devices that can be carried by animals. Not only can such devices overcome many issues inherent with a cable tether, such as twisting or snagging, they can also be utilized in a greater variety of arenas, including enclosed or large mazes. However, these devices are not inherently designed for water-maze environments, and their use has been restricted to individually-housed rats in order to avoid damage from various social activities such as grooming, playing, or fighting. By taking advantage of 3D-printing techniques, this study demonstrates an ultra-small portable stimulator with an environmentally-protective device housing, that is suitable for both social-housing and water-maze environments. The miniature device offers 2 channels of charge-balanced biphasic pulses with a high compliance voltage (12 V), a magnetic switch, and a diverse range of programmable stimulus parameters and pulse modes. The device's capabilities have been verified in both chronic pair-housing and water-maze experiments that asses the effects of nucleus reuniens DBS. Theta-burst stimulation delivered during a reference-memory water-maze task (but not before) had induced performance deficits during both the acquisition and probe trials of a reference memory task. The results highlight a successful application of 3D-printing for expanding on the range of measurement modalities capable in DBS research.
## Introduction
In animal behavioral studies, deep-brain stimulation (DBS) research has traditionally relied on the use of a cable tether, for connecting an awake animal to the stimulating hardware. Not only does this require a purpose-built arena for each animal for accommodating such a tether, but such a method can reduce animal mobility and increase stress (Tang et al., ). Also, the risk of cable breakages, snagging or entanglement is present, and further exacerbated over long periods of time. To circumvent these issues, numerous portable stimulators have been developed for animal use, including head-mount systems (Arfin et al., ; Forni et al., ; Hentall, ; Kouzani et al., , ), back-mount systems use using a Velcro jacket (Song et al., ; Feng et al., ; Ewing et al., ), and implantable systems (Millard and Shepherd, ; de Haas et al., ). While these devices are successful in granting the operator increased flexibility with regards to the experimental design and arena selection, there are behavioral paradigms with which these devices cannot be utilized.
Until now, such experiments involving portable stimulators have been limited mainly to individually-housed rat use, due to the risk of damage to the device, implant, or wound, through various social behaviors such as grooming, playing and fighting. The benefits of group-housing rats include a normalization in many behavioral and physiological effects that would otherwise occur in healthy rats, including weight gain (Levitsky, ; Fiala et al., ; Pérez et al., ; Lopak and Eikelboom, ; Pinnell et al., ), stress-induced FOS activity (Westenbroek et al., ), as well as heart-rate and blood pressure changes (Sharp et al., ). This becomes important when chronic stimulation paradigms are utilized, which may involve weeks of social isolation (e.g., Forni et al., ). Being able to co-house animals during prolonged periods of stimulation, may offer a way to normalize stress-induced behavioral and physiological deficits that may otherwise interfere with the parameters under study.
Another test condition that proves problematic for portable stimulators is water-maze use. In most studies that have assessed the effects of DBS using a water-maze, stimulation has been carried out either before (Hamani et al., ; Zhang et al., ; Hescham et al., ) or after (Ruiz-Medina et al., ; Schumacher et al., ; Jeong et al., ) the maze task. Several attempts at providing waterproof EEG recording or stimulation have been made using a cable tether (Hollup et al., , ; Fyhn et al., ; McNaughton et al., ; Korshunov and Averkin, ; Sweet et al., ). While mobility deficits cannot be ruled out with such a method, the use of an overhead cable has the potential to cause artifacts on overhead video-tracking systems. Alternatively, animal implantable stimulators can offer the inherent ability of being waterproof, but they cannot undergo battery changes, their parameters are fixed, and they may cause discomfort to the animal during tasks that require locomotion or swimming.
To address these issues, a portable DBS device was developed for head-mount use in rats, by combining a 3D-printed device housing with a miniature PCB assembly. The device, its battery and housing weighs 2.7 g, and offers protection from both the environment and from other rats. Furthermore, the device can be utilized inside a water maze, using a magnetic switch to activate/deactivate the device as needed.
Generally speaking, portable stimulators do not match the performance or functionality of their tethered counterparts, owing to the limited size of the system and its battery, and the necessity to employ space-saving and low-power techniques. While there exist numerous portable stimulators that can function for well over 10 days (e.g., Millard and Shepherd, ; Harnack et al., ; Forni et al., ; Hentall, ), the majority of portable stimulators may lack either a high compliance voltage (>10 V), voltage regulation, charge-balancing, biphasic pulses, adjustable parameters, or a combination thereof (Millard and Shepherd, ; Harnack et al., ; Arfin et al., ; de Haas et al., ; Hentall, ; Kouzani et al., , ). A high compliance voltage (>10 V) can ensure a stable constant current that is maintained through a range of tissue types and electrode impedances, and regulation ensures that it is fixed throughout the duration of the system's battery life. Such voltages are more likely to be present on larger devices weighing over 5 g (Harnack et al., ; Zhou et al., ; Ewing et al., ), due to the additional space for accommodating voltage amplification stages and a larger battery. Charge-balanced biphasic pulses can offer both the ability to normalize net charge inside the brain following a stimulus pulse, as well as causing less tissue damage when compared to monophasic pulses (Merrill et al., ). Furthermore, the ability to fully configure the device's parameters (pulse-width, frequency, current intensity, and pulse mode) can enhance the system's versatility between experiments, while paving the way toward repeated-measures experimental designs involving multiple stimulus paradigms. The current system aims to provide some of the expanded functionality of tethered systems, by offering 2 charge-balanced biphasic channels with a high compliance voltage (>12 V) and a flat pulse profile, a magnetic on/off switch, a programmable LED, and a wide range of programmable stimulus parameters and pulse modes.
The device was verified in two rat behavioral experiments that investigated the effects of nucleus reuniens DBS. Such a structure was investigated due to its dense, bi-directional connections with the prefrontal cortex and hippocampus, and its possible role in learning and memory (see Cassel et al., , for a review Cassel et al., ). In the first experiment, animals ( n = 44) were trained in a place-learning variant of the Morris Water Maze (MWM) task, during which they had received either theta-burst-stimulation (TBS; before or during acquisition) or sham-stimulation. TBS involves a high-frequency burst of pulses (e.g., 500 Hz) delivered at a theta frequency (e.g., 7 Hz), to provide a means of mimicking the naturally-occurring theta rhythm. These parameters were chosen to facilitate or disrupt the naturally occurring theta signal in the nucleus reuniens, an area suspected for providing modulation of prefronto-hippocampal interactions (Griffin, ). Animals stimulated inside the maze (but not before) were seen to display mild performance deficits both during the acquisition and probe sessions, as observed by measures of the time spent in the target quadrant, mean distance to target, and the swim efficiency. The second experiment verified the ability of the devices to be used with pair-housed rats ( n = 14), in a chronic high-frequency stimulation paradigm. No malfunctions, leakages, or problems were reported in any of the devices throughout the entire test period; highlighting its capability as a robust and versatile device for expanding the range of behavioral paradigms for pre-clinical DBS research.
## Materials and methods
### Device design
A circuit diagram is shown, alongside its corresponding PCB layout that depicts the top, bottom and internal copper layers (Figures ). During operation, a microcontroller (MSP430F2013; Texas Instruments) is used to generate pre-programmed voltage pulses, which in turn gates the flow of constant current through a transistor switch. The current is generated by arranging a PNP transistor pair alongside an LED, such that the LED maintains a fixed voltage reference across a variable resistor. The system supports a current range of 20 μA−2 mA, if the compliance voltage limit (12.29 V) is not exceeded. As such, two LED's are active during stimulation, which provides visual feedback to the operator. The constant current pulses are interfaced to a quad single-pole double-throw (SPDT) digital switch (ADG1634; Analog Devices), which can switch the direction of the current across the channel pair, thereby producing biphasic pulses. The switch is also configured to connect stimulus electrodes to ground immediately following a stimulus pulse, which can enable charge balancing for monophasic pulses, while improving it somewhat for biphasic pulses. The compliance voltage for the constant-current circuitry is generated amplifying a fixed/rectified 2.048 V voltage by 6, using 2 voltage doublers connected in series (MAX1682; Maxim Integrated Products); the second of which is configured to triple the voltage by inclusion of a Schottky-capacitor rectifier. The voltage reference (REF3320; Texas Instruments) ensures that the compliance voltage is fixed at 12.29 V throughout the duration of the battery life.
A circuit diagram for the portable stimulator is shown (A) , alongside a flowchart for the firmware (B) . The layout of copper tracks, pads, vias, and components are shown for the top, bottom, and internal layers of the PCB (C) .
A miniature Reed switch is provided (RI-80 SMD; Comus, USA), for allowing the system to be magnetically activated/deactivated. During activation, a timer-controlled interrupt is used for generating timed stimulus pulses (Figure ). During deactivation, the microcontroller enters its lowest power state (LPM4), and the voltage reference, DC amplification, and constant-current generators are deactivated via a digital power switch (TPS22945; Texas Instruments, USA). The system was programmed to accept only magnets held in place from 0.8 to 1.2 s, to prevent unwanted activation/deactivation by accidental means.
All the device components and integrated circuits were distributed onto 2 sides of a 1 mm × 12.5 mm diameter, circular 4-layer PCB (PCB-Pool; Beta Layout GmbH, Germany). The smallest available packages were chosen for every component (while meeting electronic requirements e.g., capacitor voltage ratings), including 0201 passives and quad-flat-pack (QFN) integrated circuits; and the design was repeatedly optimized to provide a maximum reduction in PCB space. Circuit track widths are 125 μm, and the vias are 0.2 mm. Miniature custom connectors were used for both the programming connectors, and the DBS terminals (Fischer's Elektronic, Germany), the latter of which are connected to the PCB by a 2 cm pair of twisted insulated wire strands. All the device components were covered with a layer of UV-curing adhesive (Loctite, USA).
Prior to use, the system is programmed using an MSP-FET programmer/debugger tool (Texas Instruments, USA), with the chosen stimulus parameters. The current intensity is set by placing a 20 KΩ fixed-value resistor across the DBS output terminals, and inferring the current from the voltage drop across the resistor, using a digital oscilloscope.
### 3D-printed head-cap
A 3D-printed head cap (12.4 × Ø19.2 mm; 1.1 g) was designed in Solid Edge ST6 (Siemens PLM Software) and printed with clear ABS plastic using a 3D printer (Ultimaker 2; Ultimaker). The cap was designed to enclose the portable stimulator and its battery, and to attach to a 3D-printed skull socket (Pinnell et al., ) using two electronic self-tapping screws (M1.4 × 4 mm; Phillips). The interior of the cap was shaped as appropriate, to fit to the contours of the device and its battery.
### Electrodes
LFP electrodes consisted of a single strand of 150 μm diameter (125 μm bare) polyimide-coated stainless-steel wire (005SW/2.0 S; Plastic's One, USA), whereas the bipolar stimulating electrodes consisted of two strands twisted together using a dental drill. For both electrode types, 200 μm of Polyimide was scraped from the electrode tip using a scalpel, for providing a suitable contact area for stimulation or recording. After fabrication, both electrode types were immersed into saline, and the stimulator was used to send a 90% duty cycle, 1 mA current through them. The resulting hydrogen bubbles that formed (via hydrolysis) could expose any breaches in the material resulting from assembly, as well as any connectivity problems such as short-circuits. Immediately prior to surgery, bipolar stimulating electrodes (measured at < 10 KΩ impedance) were further tested by sending 200 μA pulses through them, and observing the voltage drop across them. Any electrode that fell outside a 2–4 V median range were discarded.
### Surgery
All experiments were conducted in adherence to the regulations and guidelines, as specified by the international (NIH publication no 86–23, revised 1985) laws and policies, and the European Committee Council Directive of November 24th, 1986 (86/609/EEC). All protocols were approved by the Animal Care Committee of the University of Freiburg (permit 35-9185.81/G-13/97), and the French Department of Agriculture, where appropriate.
Male Long Evans rats (280–300 g; n = 44) were anesthetized with ketamine/xylene (0.23 ml.kg i.p.; 23% Xylazine; 38% Ketamine; 38% Saline), and were then secured into a stereotaxic frame (David Kopf Instruments, USA). Rats were implanted with a single bipolar stimulating electrode into the midline thalamus (AP-2.3; ML-1.6; DV-7.4 mm, at 13° inclination), as measured relative to the skull surface at Bregma. The electrode connectors were encapsulated inside a 3D-printed implant, which was attached to the skull using two stainless steel mounting screws (0–80 × 1/8; Plastics One; USA). The rear mounting screw functioned also as a reference electrode, for EEG recordings. An additional 3 mounting screws were applied around the skull perimeter to provide additional support. The enclosure was then filled with dental cement (Palapress; Heraeus Holding GmbH; Germany). In the chronic group, female Sprague Dawley rats (280–300 g; n = 14) underwent the same procedure, but with an additional LFP electrode implanted into the medial prefrontal cortex (AP+3.0; ML-0.7; DV-3.5 mm), and dCA1 region of the hippocampus (AP-3.6; ML-2.5; DV-2.6 mm).
Animal breathing and reflexes were checked throughout the surgery period, and animals were examined daily for signs of distress or discomfort. Sprague Dawley rats received an analgesic during immediately before surgery, and for the next 4 days afterwards (Carprieve, 1 ml kg s.c.; Norbrook, UK). Long-Evans rats were alternatively provided with a general anesthesia (Duphamox, 300 μl i.m.; Zoetis, USA) and local anesthesia (Lidocaine, 200 μl s.c.; Ceva Santé, France) before the surgery.
### Water-maze DBS
A MWM (1.6 m diameter) was situated in a diffusely-lit room with high-contrast extra-maze cues surrounding the walls. The water was rendered opaque using skimmed milk powder, and a thermometer was used to ensure a water temperature of 21°C. Rats were trained on a reference memory paradigm, consisting of an initial day of habituation, followed by 8 days of acquisition. During habituation, rats underwent 4 trials in which to locate a visible platform (11 cm, painted black, 1 cm above the water surface in the SE quadrant), whereby the starting position was randomized around the edge of the pool. A curtain was provided around the pool during this session, to obscure external cues. During acquisition, all rats underwent 4 trials/day in order to locate a hidden platform using external cues. A transparent platform was placed in the NW quadrant of the maze, and was submerged 2 cm below the surface of the water. The starting location was varied daily between the N, S, E, and W locations. For both session types, rats were given 1 min to swim to the platform location, after which they were left there for 10 s. Rats that did not reach the platform within 60 s were guided there by the experimenter and left there for 10 s. Rats were always placed into the maze facing the wall, and their test order was randomized for each day. Probe trials were given on days 3 and 6, which took place immediately prior to the day's acquisition training. In this session, the platform was removed, and animals were released from the SE quadrant, and left to swim for 60 s.
Long-Evans male rats were divided into the following groups: Sham stimulation (SHAM; n = 21), TBS before (BEF; n = 11), or TBS during (DUR; n = 12). Rats in all groups were affixed with a portable stimulator, 30 min prior to starting the task. Prior to attachment, a small amount of petroleum jelly (Vaseline) was applied to the inside of the device housing, to provide additional waterproofing. Stimulation was activated during the 30-min period prior to the task (BEF group), or during the MWM task (DUR group). Stimulation was not provided in the DUR group during any of the probe sessions. Animals in the SHAM group did not undergo stimulation at any point in the test, but they carried the devices in all test sessions. The stimulus pulses were delivered in 7 Hz bursts, each consisting of 16 × 200 μA biphasic pulses delivered at 500 Hz, and 100 μS pulse-width. The 7 Hz burst frequency was chosen to match a pre-recorded theta-frequency inside the nucleus reuniens during mobility (exactly 7 Hz).
Following each session, rats were gently dried with a towel, and were returned to their home cages whereby the portable devices were removed. Each recovered device was checked using an oscilloscope to verify that it could still deliver stimulus pulses at the correct settings. Numerous parameters were recorded during the test sessions using a video tracking system (Smart; Panlab), including the rat's position, latency, path length, quadrant time, average distance to target, and Whishaw's Index (a percentage measure of swim path traveled between a straight line connecting the start and goal locations, representing swim efficiency).
### Chronic DBS
Rats were pair-housed for 8 days, during which they had received DBS on days 3–7. Stimulation was activated in the STIM rats ( n = 7; 130 Hz, 90 μS/phase pulse-width, 50 μS inter-pulse spacing, 200 μA biphasic) for 1 h, at 12 p.m. each day. Rats underwent recordings of EEG and mobility before and after this period, using a wireless recording system (W32; Multichannel Systems) and a video-tracking system (Cinelab; Plexon). During stimulation sessions, the status of the animals was monitored in another room using a camera mounted above the cages (Hero 3; GoPro).
### Statistics and representation
All data was imported into Matlab (Mathworks), for representation and statistical comparisons. Statistics in the MWM task utilized 2-way ANOVA's, looking at effects of session number (1–8) and group type (SHAM; BEF; DUR). Probe-trial differences used a 1-way ANOVA (looking at all groups). Student's T -tests were utilized for post-hoc comparisons between sham and stimulus groups.
For MWM swim position representation, the paths of each group were combined for a particular session, and converted to a normalized, 2D histogram. Each tracking point was converted to a 10 cm diameter circle prior to this, for better highlighting the group position preference.
## Results
### Device capabilities
The portable stimulator (Figure ) features two bipolar, charge-balanced channel pairs for DBS, with a 12 V compliance (see Table for a full list of parameters). Numerous parameters can be programmed for use through a 4-pin micro connector, including the pulse mode (monophasic or biphasic), frequency (0.1-5,000 Hz) or the pulse-width (10 μS−100% duty cycle). Pulse trains can be selected as a fixed frequency (e.g., 130 Hz) or can employ a dual-frequency bursting pattern (e.g., 7/500 Hz theta-burst stimulation). The constant current is adjusted by manually turning potentiometers on the device (12 V compliance, delivering 20 μA−2 mA as tested in saline; see Figure ).
Photographs of the portable stimulator. The top side is shown, complete with a battery, its programming port (highlighted), potentiometers, reed switch and DBS LED's (A) . The system was utilized with a 3D-printed waterproof cap, which mounts onto a skull-implanted electrode socket (B) . The underside of the system is shown, alongside a coin for scale; highlighting the high component density (C) .
A comparison between the attributes of the proposed, and existing recent stimulators.
A question mark is placed where information is unavailable. Note that the compliance voltage is shown instead of constant-current intensity, since the latter depends on the compliance voltage, and the brain and electrode impedances .
The current and voltage of the stimulus pulses was observed for a wide range of currents, from 20 to 2,000 μA, for a bipolar stimulating electrode immersed in saline (A) . A comparison between charge-balanced (black) and charge un-balanced (gray) monophasic/biphasic voltage waveforms is shown using 200 μA pulses (B) . Note that passive charge balancing is more noticeable with monophasic pulses, due to the increased charge build-up that would otherwise occur.
The device can be powered down to an ultra-low power stage during inactivation, and subsequently reactivated by placing a magnet near the device. During a low-power stage, the device consumes approximately 35 μA, and can theoretically remain in this stage for many months. The magnetic activation/deactivation parameters are programmed, such that the device activates/deactivates when a magnet is held in place for 1 ± 0.2 s. This “time window” of activation reduces the likelihood of the device being accidentally activated/deactivated by a magnetic object. In addition to two LED's that are active during DBS, a separate status LED provides the user with a feedback regarding the magnetic activation/deactivation, and can be programmed with a variable brightness and a flash sequence during normal use. Finally, pulses can be programmed to be continuous, finite-duration (e.g., 30 s), and/or to begin after a fixed time duration following magnetic activation.
The device is housed inside a 3D-printed protective cap (Figure ) during use, and can be removed and reattached to a surgically-affixed skull-socket. This provides for a strong and stable device attachment, which can withstand various rat social behaviors including grooming, playing and fighting. The device utilizes a single removable CR1225 battery (0.9 g), which is situated directly underneath the device inside the protective cap. This battery provides approximately 30 h of constant DBS, when tested in saline at the following parameters: 2 channels, 130 Hz, biphasic, 90 μS/phase, 200 μA current, 50 μS inter-pulse interval.
### Flat constant-current pulses
The characteristics of the constant current pulses were verified by delivering stimulus pulses into 0.9% NaCl solution, through a twisted-pair bipolar electrode. Flat constant-current pulses could be produced from 20 μA to 2 mA (Figure ), with a rise time of 2.8 μS (0–90%; tested at 1 mA). Although active charge-balancing is provided with biphasic stimulation, both monophasic and biphasic pulses are also passively charge-balanced, by grounding the stimulating electrode immediately following a pulse phase (Figure ). This feature is programmable, and when used during monophasic stimulation, it leads to a brief reversal of the current direction following a pulse phase, for achieving zero net charge at the electrode-electrolyte interface.
### Stimulation inside the water maze
The devices had shown to function correctly in every rat and in every trial (>1,400 acquisition trials; 88 probe sessions), and remained operational when rats swam underwater (see Figure for photographs of the device inside the water maze). During acquisition, all rats had demonstrated a robust pattern of learning (Figure ), as shown by significant effect of test session on both the latency to platform [ F = 51.89; p < 0.0001] and path length [ F = 64.62; p < 0.0001]. Significant group-effects were observed for platform latency [ F = 3.42; p = 0.034], path length [ F = 4.26; p < 0.015], and average distance to target [ F = 11.96; p < 0.0001], Whishaw's Index [a measure of swim efficiency; F = 6.49; p < 0.0017] and the percentage time in the target quadrant [ F = 11.7; p < 0.0001]. Many of these changes are indicative of performance deficits in the DUR group, as opposed to the BEF group which had shown a performance closer to that of the SHAM group. No significant group difference was observed for thigmotaxis [ F = 1.99; p = 0.14].
Photographs show the device operating inside the water-maze for Sprague Dawley (A) and Long Evans (B) rats. Devices were activated inside either the home-cages or the water-maze room, using a magnet (C) .
Acquisition data is shown for both stimulated and sham-stimulated rats. Statistical significance is shown for differences between groups for a given session; p < 0.05; p < 0.01; p < 0.001 when comparing DUR vs. SHAM rats, # p < 0.05; ##p < 0.01; ###p < 0.001 when comparing BEF vs. SHAM rats. Performance deficits are observed mainly in the rats receiving stimulation during the task (DUR), including an increased average distance to target and a reduced time spent in the target quadrant, compared to SHAM controls. This highlights a transient effect of DBS on task performance that is less pronounced in the BEF stimulation group.
By the second probe session, all groups had demonstrated a robust memory performance, as highlighted by an increased time in the target quadrant, relative to chance level (Figure ). Significant group effects were only observed during the second probe session, including the average distance to target [ F = 6.26; p = 0.0042], Whishaw's Index [ F = 3.48; p = 0.04], and the time spent in the target quadrant [ F = 6.99; p = 0.0024]. Post-hoc t -tests had shown that DUR rats had demonstrated slight reductions in Whishaw's Index [ t = 2.59; p = 0.014], time in the target quadrant [ t = 2.55; p = 0.016], and a slight increase in the average distance to the target [ t = 2.37; p = 0.024], as compared to SHAM controls. While no significant differences were observed between the BEF and SHAM groups, the BEF group had performed better during than the DUR group during the second probe trial, with regards to the average distance to target [ t = 3.53; p = 0.002] and the time spent in the target quadrant [ t = 3.26; p = 0.0038].
Rat swim paths during the probe sessions are represented as group-normalized 2D-histograms (A) . The platform position (during acquisition) is indicated as a small circle in the goal quadrant (NW). All rats had demonstrated an ability to learn the task, as shown by an increased activity in the NW quadrant during the second probe session, as compared to chance-level (25%). The mean distance to the target ( B -left), the % time in the target quadrant ( B -center), and the Whishaw's Index ( B -right) are shown for both probe sessions. P < 0.05; p < 0.01.
### Group-housing performance
In this preliminary study, female Sprague Dawley rats were implanted with electrodes in the ReRh, and were pair-housed for 8 days, following the recovery period (Figures ). For 5 of these days, rats had received either high-frequency stimulation (130 Hz; 90 μS/phase monophasic or biphasic; 200 μA), or sham-stimulation for 1 h daily, with recordings of prefronto-hippocampal EEG and mobility taken before and after this period. During this period, no obvious malfunctions were observed resulting from the environment or social activities. The use of charge-balanced biphasic pulses is demonstrated during simultaneous EEG recording (Figure ), using a commercial wireless system (W32; Multichannel Systems).
Photographs of pair-housed rats during a stimulation session (A,B) . EEG from dCA1 is shown with and without high-frequency stimulus pulses (C) . Having an electrically isolated stimulator ensured that only volume conduction artifacts were present in the EEG, due to stimulus pulses propagating through the brain.
## Discussion
A portable stimulator was developed by combining an ultra-small PCB assembly with 3D printing techniques, in order to expand on the range of currently available stimulus paradigms. For its small size (0.8/2.8 g with battery and head-cap), the device offers a high compliance voltage, and the ability to generate charge-balanced biphasic pulses from 2 separate channels (see Table for a comparison with existing devices). This system can also be cheaply produced (<€30 per device), which can allow many rats to be stimulated simultaneously inside their home cages, without having to consider tethered solutions, or alternative housing. This is the first portable stimulator to be utilized in both chronic group-housing and water-maze environments.
### 3D-printed device housing
The 3D-printed head-socket has previously been utilized for the pair-housing of rats, following stereotaxic surgery (Pinnell et al., ). While this previous study had utilized a metal thimble as the protective cap, a smaller 3D-printed thimble was designed for the current study that housed the device and its battery. Transparent ABS was chosen for this as it offered the strength to withstand the environment for prolonged periods of time, as well as allowing the device's LED's to be visible during experiments. The 3D-printed device housing had functioned adequately during the pair-housing experiments, and had not sustained any damage resulting from normal rat activities such as grooming or playing. During supervision, rats were not observed to bite or chew the implant of their cage-mates, and no signs of such damage was observed. In addition to the practical and ethical benefits of keeping animals pair-housed inside their home cages, this device can help to enrich DBS studies by potentially ameliorating numerous physiological and behavioral deficits that otherwise pertain to social isolation. Furthermore, this method can pave the way toward novel stimulation paradigms, such as studies that assess the social effects of DBS.
During the water-maze experiments, no signs of leakages or malfunctions were observed in any of the devices, throughout the test period (>1,500 trials). When combined with petroleum jelly, the circular design of the cap and socket was found to be optimal for keeping water away from the cap interior during vigorous pre-experiment waterproof testing. Notably, the devices remained operational when rats swam underwater, which was common during the early stages of training. By allowing stimulation to take place inside a water maze, experimenters have the opportunity to directly observe the acute effects of stimulation on the behavior they are trying to assess. Given the widespread popularity of the MWM as a tool for assessing various aspects of learning and memory, this device can pave the way toward integration of DBS with more complex behaviors. In addition to these benefits, the portable stimulators were found to simplify the execution of the experiment, as compared to previous in-house experiments utilizing a cable tether. Animals could be transferred between the holding and test rooms without changing connectors or manipulating the implant, and DBS could be seamlessly activated at any part of the experiment without touching the rat. Such measures allowed stimulation to be activated/deactivated immediately prior to placing the rats inside the water maze, without any delay periods.
### Stimulator design
From an early design stage, strict size restrictions were placed on the overall size of the device cap/housing, to ensure that it can fit onto a pre-existing head-socket. As such, this had necessitated a 12.5 mm diameter PCB using ultra-small electronic components and high-density circuit design. Some design concessions were made through this process, including the use of variable resistors for setting the constant current intensity, instead of e.g., a digital potentiometer. Since current intensity adjustments were carried out using an oscilloscope, accuracy penalties within the range of ± 3–5 μA were expected. By comparison, existing systems may offer comparatively higher accuracy through e.g., a digital potentiometer (Ewing et al., ), or a lower accuracy, due to a constant-current that is dependent on the system's orientation (Millard and Shepherd, ) or dependent partly on the electrode and brain impedances (de Haas et al., ; Forni et al., ).
Many of the design choices with this system reflect functionality over battery life, making this system more suited for acute experiments, or for stimulation sessions lasting up to 30 h. The constant-current generator for instance, had utilized LED's for maintaining a fixed reference voltage, as opposed to standard diodes. This had allowed for a visible feedback of DBS that would vary based on the stimulus parameters. For example, increasing the duty cycle results in an increased LED brightness, and using low-frequency or bursting stimulation causes the LED's to flash. An additional bright LED was included, which could be programmed to flash at any part of the experiment, such as when the system had finished a 30-min stimulation period. Furthermore, this additional LED could be utilized in video-tracking software that supports head-mounted LEDs. Further battery life reductions are a result of the switched-capacitor charge pumps that are used to generate the high compliance voltages. Such voltage amplification is normally omitted from ultra-small devices weighing <5 g (Millard and Shepherd, ; Arfin et al., ; de Haas et al., ; Kouzani et al., , ), which instead source the compliance directly from the system's battery. While this method can extend the battery life and reduce the device size, it places a limitation on the maximum stimulation current, and carries the risk of the compliance voltage becoming too low toward the end of the battery's lifetime. An exception to this is where silver oxide batteries are used, which can maintain a relatively stable voltage throughout its lifetime (de Haas et al., ). Inadequately designed electrodes, or those that are mishandled during surgery, may become of higher impedance than normal, with the effect of increasing the compliance voltage requirements further. As observed in the present experiments and bench tests, the required voltage will largely depend on the impedance of the electrode that is used; and this can typically vary by up to a few volts, from electrode to electrode. It is of note that although the present device is capable of up to 18 V compliance, this was fixed at the lowest limit of 12 V using a voltage regulator, for ensuring that the compliance voltage level is guaranteed throughout the lifetime of the battery. The regulated 12 V compliance can thus drive up to 2 mA in saline, using the same twisted-pair bipolar stimulating electrodes as those used during the behavioral test session.
Chronic experiments are possible with a single battery, provided that rats undergo fixed daily stimulation sessions, as demonstrated in the current study. Otherwise, the system's battery can be quickly replaced as required. For chronic continuous stimulation, there is the possibility of using a larger battery with a higher capacity. For example, a 3 V CR1/3N battery can theoretically provide up to 5 days of continuous DBS (200 μA, 130 Hz), based on its rated capacity (170 mAh) and increased efficiency; and it can be adapted for use with a slightly larger head-cap enclosure. Such a device would weigh an estimated 4.6 g (including additional ABS for the head cap), and could thus easily be carried on the head of the animal.
### Behavioral effects of thalamic stimulation
The reuniens and rhomboid thalamic nuclei (ReRh) were chosen as part of an ongoing investigation into their role in learning and memory (Cassel et al., ; Griffin, ). In the present experiment, TBS of the ReRh had shown primarily acquisition deficits, which were mainly observed during the second-half of the acquisition period for rats stimulated during the maze task; yet all rats were nonetheless capable of learning the platform location and displaying a robust memory performance during the probe trial. Notably, stimulated rats were more likely to take an indirect path to the target, which may explain the slight increases in path length, platform latency, and the average distance to the target. Previously, inactivation (Cholvin et al., ) or lesion (Dolleman-van der Weel et al., ) of this structure was not found to impair acquisition performance on the MWM task, when compared to sham controls. However, strategy changes were highlighted, as either a modification of the search strategy during probe trials (Dolleman-van der Weel et al., ), or as an impaired ability for rats to switch from a procedural to a place strategy inside the double-H maze (Cholvin et al., ). Theta-burst stimulation parameters have previously been used as an alternative to high or low-frequency parameters, as it has been proposed to better mimic the functional activity of limbic networks. Previously, fornix-TBS has previously been shown to improve memory in rats with either medial-septal muscimol inactivation (Shirvalkar et al., ), or traumatic brain injury (Sweet et al., ). In the present experiment, TBS of the ReRh could be interfering with the natural theta rhythm in a disruptive way, and affecting the functional cooperation of both the ReRh and the hippocampus; the latter of which is known to be sensitive to the performance of reference memory tasks (Morris, ). This could have acute implications, as rats that were stimulated before the maze task (but not during) had displayed an acquisition and probe performance that was more in line with the sham-group. Furthermore, it is known that not only does CA1 receive strong afferent fibers from the ReRh (Wouterlood et al., ), but strong excitatory responses are also observed in this region, following ReRh stimulation (Dolleman-Van der Weel et al., ). Future experiments that include EEG recordings alongside stimulation may help to build a clearer picture of the functional implications of ReRh stimulation during the behavioral task.
## Data availability
The design files and detailed assembly instructions for the portable devices can be found in the Figshare repository at . The datasets generated can also be found at . Technical data: LINK, ; doi: . Experimental data: LINK, ; doi: .
## Author contributions
RP designed, developed, and tested the devices, electrodes and implants. RP designed, executed and analyzed both behavioral experiments. RP wrote the manuscript. JC and AP provided valuable input into the manuscript and the MWM experiment design, and AP assisted with animal upkeep and euthanasia. JC and UH are the project leaders.
### Conflict of interest statement
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
|
In this technical note, we address an unresolved challenge in neuroimaging statistics: how to determine which of several datasets is the best for inferring neuronal responses. Comparisons of this kind are important for experimenters when choosing an imaging protocol—and for developers of new acquisition methods. However, the hypothesis that one dataset is better than another cannot be tested using conventional statistics (based on likelihood ratios), as these require the data to be the same under each hypothesis. Here we present Bayesian data comparison (BDC), a principled framework for evaluating the quality of functional imaging data, in terms of the precision with which neuronal connectivity parameters can be estimated and competing models can be disambiguated. For each of several candidate datasets, neuronal responses are modeled using Bayesian (probabilistic) forward models, such as General Linear Models (GLMs) or Dynamic Casual Models (DCMs). Next, the parameters from subject-specific models are summarized at the group level using a Bayesian GLM. A series of measures, which we introduce here, are then used to evaluate each dataset in terms of the precision of (group-level) parameter estimates and the ability of the data to distinguish similar models. To exemplify the approach, we compared four datasets that were acquired in a study evaluating multiband fMRI acquisition schemes, and we used simulations to establish the face validity of the comparison measures. To enable people to reproduce these analyses using their own data and experimental paradigms, we provide general-purpose Matlab code via the SPM software.
## Introduction
Hypothesis testing involves comparing the evidence for different models or hypotheses, given some measured data. The key quantity of interest is the likelihood ratio—the probability of observing the data under one model relative to another—written p ( y | m )/ p ( y | m ) for models m and m and dataset y . Likelihood ratios are ubiquitous in statistics, forming the basis of the F -test and the Bayes factor in classical and Bayesian statistics, respectively. They are the most powerful test for any given level of significance by the Neyman-Pearson lemma (Neyman and Pearson, ). However, the likelihood ratio test assumes that there is only one dataset y –and so cannot be used to compare different datasets. Therefore, an unresolved problem, especially pertinent to neuroimaging, is how to test the hypothesis that one dataset is better than another for making inferences.
Neuronal activity and circuitry cannot generally be observed directly, but rather are inferred from measured timeseries. In the case of fMRI, the data are mediated by neuro-vascular coupling, the BOLD response and noise. To estimate the underlying neuronal responses, models are specified which formalize the experimenter's understanding of how the data were generated. Hypotheses are then tested by making inferences about model parameters, or by comparing the evidence under different models. For example, an F -test can be performed on the General Linear Model (GLM) using classical statistics, or Bayesian Model Comparison can be used to select between probabilistic models. From the experimenter's perspective, the best dataset provides the most precise estimates of neuronal responses (enabling efficient inference about parameters) and provides the greatest discrimination among competing models (enabling efficient inference about models).
Here, we introduce Bayesian data comparison (BDC)—a set of information measures for evaluating a dataset's ability to support inferences about both parameters and models. While they are generic and can be used with any sort of probabilistic models, we illustrate their application using Dynamic Causal Modeling (DCM) for fMRI (Friston et al., ) because it offers several advantages. Compared to the GLM, DCM provides a richer characterization of neuroimaging data, through the use of biophysical models, based on differential equations that separate neuronal and hemodynamic parameters. This means one can evaluate which dataset is best for estimating neuronal parameters specifically. These models also include connections among regions, making DCM the usual approach for inferring effective (directed) connectivity from fMRI data.
By using the same methodology to select among datasets as experimenters use to select between connectivity models, feature selection and hypothesis testing can be brought into alignment for connectivity studies. Moreover, a particularly useful feature of DCM for comparing datasets is that it employs Bayesian statistics. The posterior probability over neuronal parameters forms a multivariate normal distribution, providing expected values for each parameter, as well as their covariance. The precision (inverse covariance) quantifies the confidence we place in the parameter estimates, given the data. After establishing that an experimental effect exists, for example by conducting an initial GLM analysis or by reference to previous studies, the precision of the parameters can be used to compare datasets. DCM also enables experimenters to distinguish among models, in terms of which model maximizes the log model evidence ln p ( y | m ). We cannot use this quantity to compare different datasets, but we can ask which of several datasets provides the most efficient discrimination among models. For these reasons, we used Bayesian methods as the basis for comparing datasets, both to provide estimates of neuronal responses and to distinguish among competing models.
This paper presents a methodology and associated software for evaluating which of several imaging acquisition protocols or data features affords the most sensitive inferences about neural architectures. We illustrate the framework by assessing the quality of fMRI time series acquired from 10 participants, who were each scanned four times with a different multiband acceleration factor. Multiband is an approach for rapid acquisition of fMRI data, in which multiple slices are acquired simultaneously and subsequently unfolded using coil sensitivity information (Larkman et al., ; Xu et al., ). The rapid sampling enables sources of physiological noise to be separated from sources of experimental variance more efficiently, however, a penalty for this increased temporal resolution is a reduction of the signal-to-noise ratio (SNR) of each image. A detailed analysis of these data is published separately (Todd et al., ). We do not seek to draw any novel conclusions about multiband acceleration from this specific dataset, but rather we use it to illustrate a generic approach for comparing datasets. Additionally, we conducted simulations to establish the face validity of the outcome measures, using estimated effect sizes and SNR levels from the empirical multiband data.
The methodology we introduce here offers several novel contributions. First, it provides a sensitive comparison of data by evaluating their ability to reduce uncertainty about neuronal parameters, and to discriminate among competing models. Second, our procedure identifies the best dataset for hypothesis testing at the group level, reflecting the objectives of most cognitive neuroscience studies. Unlike a classical GLM analysis—where only the maximum likelihood estimates from each subject are taken to the group level—the (parametric empirical) Bayesian methods used here take into account the uncertainty of the parameters (the full covariance matrix), when modeling at the group level. Additionally, this methodology provides the necessary tools to evaluate which imaging protocol is optimal for effective connectivity analyses, although we anticipate many questions about data quality will not necessarily relate to connectivity. We provide a single Matlab function for conducting all the analyses described in this paper, which is available in the SPM ( ) software package (spm_dcm_bdc.m). This function can be used to evaluate any type of imaging protocol, in terms of the precision with which model parameters are estimated and the complexity of the generative models that can be disambiguated.
## Methods
We begin by briefly reprising the theory behind DCM and introducing the set of outcome measures used to evaluate data quality. We then illustrate the analysis pipeline in the context of an exemplar fMRI dataset and evaluate the measures using simulations.
### Dynamic Causal Modeling
DCM is a framework for evaluating generative models of time series data. At the most generic level, neural activity in region i of the brain at time t may be modeled by a lumped or neural mass quantity . Generally, the experimenter is interested in the neuronal activity of a set of interconnected brain regions, the activity of which can be written as a vector z . The evolution of neural activity over time can then be written as:
Where ż is the derivative of the neural activity with respect to time, u are the time series of experimental or exogenous inputs and θ are the neural parameters controlling connectivity within and between regions. Neural activity cannot generally be directly observed. Therefore, the neural model is combined with an observation model g , with haemodynamic/observation parameters θ , specifying how neural activity is transformed into a timeseries, y:
Where ϵ is zero-mean (I.I.D.) additive Gaussian noise, with log-precision specified by hyperparameters λ = λ …λ for each region r . The I.I.D. assumption is licensed by automatic pre-whitening of the timeseries in SPM, prior to the DCM analysis. In practice, it is necessary to augment the vector of neuronal activity with hemodynamic parameters that enter the observation model above. The specific approximations of functions f and g depend on the imaging modality being used, and the procedures described in this paper are not contingent on any specific models. However, to briefly reprise the basic model for fMRI—which we use here for illustrative purposes— f is a Taylor approximation to any nonlinear neuronal dynamics:
There are three sets of neural parameters θ = ( A, B, C ) and j experimental inputs. Matrix A represents the strength of connections within (i.e., intrinsic) and between (i.e., extrinsic) regions—their effective connectivity. Matrix B represents the effect of time-varying experimental inputs on each connection (these are referred to as modulatory or condition-specific effects) and the corresponding vector u ( t ) is a timeseries encoding the timing of experimental condition j at time t . Matrix C specifies the influence of each experimental input on each region, which effectively drives the dynamics of the system, given u ( t ) which is the vector of all experimental inputs at time t .
The hemodynamics (the observation model g above) are modeled with an extended Balloon model (Buxton et al., ; Stephan et al., ), which comprises a series of differential equations describing the process of neurovascular coupling by which activity ultimately manifests as a BOLD signal change. The majority of the parameters of this hemodynamic model are based on previous empirical measurements; however, three parameters are estimated on a region-specific basis: the transit time τ, the rate of signal decay κ, and the ratio of intra- to extra-vascular signal ϵ .
The observation parameters θ are concatenated with the neural parameters θ and the hyperparameters λ and a prior multivariate normal density is defined (see Table ). The parameters are then estimated using a standard variational Bayes scheme called variational Laplace (Friston et al., ; Friston, ). This provides a posterior probability density for the parameters, as well as an approximation of the log model evidence (i.e., the negative variational free energy), which scores the quality of the model in terms of its accuracy minus its complexity.
Priors on DCM parameters.
### Group Analyses With PEB
Having fitted a model of neuronal responses to each subject individually (a first level analysis), the parameters can be summarized at the group level (a second level analysis). We used a Bayesian GLM, implemented using the Parametric Empirical Bayes (PEB) framework for DCM (Friston et al., ). With N subjects and M connectivity parameters for each subject's DCM, the group-level GLM has the form:
The dimensions of this GLM are illustrated in Figure . Vector θ ∈ ℝ are the neuronal parameters from all the subjects' DCMs, consisting of all parameters from subject 1, then all parameters from subject 2, etc. The design matrix X ∈ ℝ was specified as:
Where 1 is a column vector of 1 s of dimension N and I is the identity matrix of dimension M . The Kronecker product ⊗ replicates the identity matrix vertically for each subject. The use of a Kronecker product at the between subject level reflects the fact that between subject effects can be expressed at each and every connection. In this instance, we are just interested in the group mean and therefore there is only one between-subject effect. The resulting matrix X has one column (also called a covariate or regressor) for each connectivity parameter (Figure ). The regressors are scaled by parameters β ∈ ℝ , which are estimated from the data and represent the group average strength of each connection. Finally, the errors ϵ ∈ ℝ are modeled as zero-mean additive noise:
Where precision matrix Π ∈ ℝ is estimated from the data. This captures the between-subject variability in the connection strengths, parameterised using a single parameter γ:
This is a multi-component covariance model. is the lower bound on precision and ensures it is a positive number. Matrix is the prior precision. When the parameter γ is zero, the precision is equal to Q + Q . More negative values of γ equate to higher precision than the prior and vice versa. The Kronecker product ⊗ replicates the precision matrix for each subject, giving rise to the matrix Π of dimension NM × NM where the leading diagonal is the precision of each DCM parameter θ.
Form of the General Linear Model (GLM). The M parameters from all N subjects' DCMs are arranged in a vector θ. This is modeled using a design matrix X that encodes which DCM parameter is associated with which element of θ. After estimation of the GLM, parameters β are the group average of each DCM connection. Between-subjects variability ϵ is specified according to Equation 6. In this figure, white = 1 and black = 0. Shading in parameters θ, β, ϵ is for illustrative purposes only.
Prior (multivariate normal) densities are specified on the group-level parameters representing the average connection strengths across subjects β and the parameter controlling between-subject variability γ:
Where , , , . The prior on β is set to be identical to the prior on the corresponding DCM parameter at the first (individual subject) level. In the example analysis presented here, parameters governing condition-specific neural effects (B) with prior p ( B ) = N (0, 1) for each parameter i were taken to the group level. The prior on γ, the log precision of the between-subject random effects, was set to p (γ) = N (0, 1/16). This expresses the prior belief that the between-subject variance is expected to be much smaller (16 times smaller) than the within-subject effect size.
To summarize, within and between-subject parameters are estimated using a hierarchical scheme, referred to as Parametric Empirical Bayes (PEB). Model estimation provides the approximate log model evidence (free energy) of the group-level Bayesian GLM—a statistic that enables different models to be compared (see Appendix ). We take advantage of the free energy below to compare models of group-level data. For full details on the priors, model specification, and estimation procedure in the PEB scheme, see Friston et al. ( ). Readers familiar with Random Effects Bayesian Model Selection (RFX BMS) (Stephan et al., ) will note the distinction with the PEB approach used here. Whereas, RFX BMS considers random effects over models, the PEB approach considers random effects at the group level to be expressed at the level of parameters; namely, parametric random effects. This means that uncertainty about parameters at the subject level is conveyed to the group level; licensing the measures described in the next section.
### Outcome Measures
#### Parameter Certainty
To measure the information gain (or reduction in uncertainty) about parameters due to the data, we take advantage of the Laplace approximation used in the DCM framework, which means that the posterior and prior densities over parameters are Gaussian. In this case, the confidence of the parameter estimates can be quantified using the negative entropy of the posterior multivariate density over interesting parameters:
Equation 9 uses the definition of the negative entropy for the multivariate normal distribution, applied to the neuronal parameter covariance matrix Σ . This has units of nats (i.e., natural units) and provides a summary of the precision associated with group level parameters—such as group means—having properly accounted for measurement noise and random effects at the between-subject level.
Datasets can be compared by treating the entropies as log Bayes factors (detailed in Appendix : Bayesian data comparison). In brief, this follows because the log Bayes factor can always be decomposed into two terms—a difference in accuracy minus a difference in complexity. The complexity is the KL-divergence between the posteriors Σ and the priors Σ , and it scores the reduction in uncertainty afforded by the data, in units of nats. Under flat or uninformative priors, the KL-divergence reduces to the negative entropy in Equation 9. A difference in entropy between 1.1 nats and 3 nats is referred to as “positive evidence” that one dataset is better than another, and corresponds to a difference in information gain between e ≈3 fold and e ≈20 fold (Kass and Raftery, ). Similarly, a difference in entropy between 3 and 5 nats is referred to as “strong evidence,” and differences in entropy beyond this are referred to as “very strong evidence.” These same labels apply for the measures below.
#### Information Gain (Parameters)
The data quality afforded by a particular acquisition scheme can be scored in terms of the relative entropy or KL-divergence between posterior and prior distributions over parameters. This measure of salience is also known as Bayesian surprise, epistemic value or information gain and can be interpreted as the quantitative reduction of uncertainty after observing the data. In other words, it reflects the complexity of the model (the number of independent parameters) that can be supported by the data. This takes into account both the posterior expectation and precision of the parameters relative to the priors, whereas the measure in part (a) considered only the posterior precision (relative to uninformative priors).
The KL-divergence for the multivariate normal distribution, between the posterior N and the prior N , with mean μ and μ and covariance Σ and Σ , respectively, is given by:
Where k = rank (Σ ). This statistic increases when the posterior mean has moved away from the prior mean or when the precision of the parameters has increased relative to the precision of the priors. Note that this same quantity also plays an important role in the definition of the free energy approximation to log model evidence, which can be decomposed into accuracy minus complexity, the latter being the KL-divergence between posteriors and priors.
The measures described so far are based on posterior estimates of model parameters. We now turn to the equivalent measure of posterior beliefs about the models per se .
#### Information Gain (Models)
The quality of the data from a given acquisition scheme can also be assessed in terms of their ability to reduce uncertainty about models. This involves specifying a set of equally plausible, difficult to disambiguate models that vary in the presence or absence of experimental effects or parameters, and evaluating which dataset best enables these models to be distinguished.
Bayesian model comparison starts with defining a prior probability distribution over the models P . Here, we assume that all models are equally likely, therefore P = 1/ p for each of p models. This prior is combined with the model evidence, to provide a posterior distribution over the models, P . To quantify the extent to which the competing models have been distinguished from one another, we measure the information gain from the prior P to the posterior P . This is given by the KL-divergence used above for the parameters. After describing how we specified these models, we provide an example of this KL-divergence in practice.
Typically with Bayesian inference (e.g., DCM), the experimenter embodies each hypothesis as a model and compares the evidence for different models. In the example dataset presented here, we did not have strong hypotheses about the experimental effects, and so we adopted the following procedure. We first estimated a “full” group-level Bayesian GLM with all relevant free parameters from the subjects' DCMs. Next, we identified a set of reduced GLMs that only differed slightly in log evidence (i.e., they were difficult to discriminate). To do this we eliminated one connection or parameter (by fixing its prior variance to zero) and retained the model if the change in log evidence was >-3. This corresponds to a log odds ratio of approximately one in e ≈ 20, meaning that the model was retained if it was no more than 20 times less probable than the full model. We repeated this procedure by eliminating another parameter (with replacement), ultimately obtaining the final model space. This procedure was performed rapidly by using Bayesian Model Reduction (BMR), which analytically computes the log evidence of reduced models from a full model (Friston et al., ).
Having identified a set of plausible but difficult to disambiguate models (GLMs) for a given dataset, we then calculated the posterior probability of each model. Under flat priors, this is simply the softmax function of the log model evidence, as approximated by the free energy (see Appendix ). We then computed the KL-divergence between the posterior and prior model probabilities, which is defined for discrete probability distributions as:
The behavior of the KL-divergence is illustrated in Figure , when comparing k = 10 simulated models. When one model has a posterior probability approaching one, and all other models have probability approaching zero, the KL-divergence is maximized and has the value D = ln k = 2.30 (Figure ). As the probability density is shared between more models, so the KL-divergence is reduced (Figures ). It reaches its minimum value of zero when all models are equally likely, meaning that no information has been gained by performing the model comparison (Figure ).
Illustration of the KL-divergence in four simulated model comparisons. The bars show the posterior probabilities of 10 models and the titles give the computed KL-divergence from the priors. (A) Model 1 has posterior probability close to 1. The KL-divergence is at its maximum of ln 10 = 2.3. (B) The probability density is shared between models 1 and 2, reducing the KL-divergence. (C) The probability density shared between 3 models. (D) The KL-divergence is minimized when all models are equally likely, meaning no information has been gained relative to the prior.
### Summary of Measures and Analysis Pipeline
A key contribution of the measures introduced in this paper is the characterization of information gain in terms of both the parameters, and the models that entail those parameters. Together they provide a principled means by which to characterize the optimality of a given scheme for acquiring data. These are intended for use where the presence or absence of experimental effects in the data is already known—for example, based on previous studies and/or the results or an initial analysis collapsed across datasets (e.g., a mass-univariate GLM analysis).
We now suggest a pipeline for applying these measures to neuroimaging data. Step 1 provides estimates of neuronal parameters from each dataset. Steps 2 and 3 use the estimated parameters from all datasets to automatically identify a suitable model architecture (and could be skipped if the experimenter has strong priors as to what the model architecture should be). Steps 4 and 5 provide estimates of the group level parameters for each dataset and compare them using the measures described above. For convenience, steps 2–5 of this procedure can be run with a single Matlab function implemented in SPM (spm_dcm_bdc.m):
1) Model each subject's data using a Bayesian model (e.g., DCM). The objective is to obtain posterior estimates of neuronal parameters from each dataset. These estimates take the form of a multivariate probability density for each subject and dataset.
2) Identify the optimal group-level model structure. This step identifies a parsimonious model architecture, which can be used to model all datasets (in the absence of strong hypotheses about the presence and absence of experimental effects). A Bayesian GLM is specified and fitted to the neuronal parameters from all subjects and datasets. To avoid bias, the GLM is not informed that the data derive from multiple datasets. The estimated GLM parameters represent the average connectivity across all datasets. This GLM is pruned to remove any redundant parameters (e.g., relating to the responses of specific brain regions) that do not contribute to the model evidence, using Bayesian Model Reduction (Friston et al., ). This gives the optimal reduced model structure at the group level, agnostic to the dataset.
3) Re-estimate each subject's individual DCM having switched off any parameters that were pruned in step 2. This step equips each subject with a parsimonious model to provide estimates of neuronal responses. This is known as “empirical Bayes,” as the priors for the individual subjects have been updated based on the group level data. Again, this is performed analytically using Bayesian Model Reduction.
4) Fit separate Bayesian GLMs to the neuronal parameters of each dataset. This summarizes the estimated neuronal responses for each dataset, taking into account both the expected values and uncertainty of each subject's parameters.
5) Apply the measures outlined above to compare the quality or efficiency of inferences from each dataset's Bayesian GLM—in terms of parameters or models.
Collectively, the outcome measures that result from this procedure constitute an assessment of the goodness of different datasets in terms of inferences about connection parameters and models. Next, we provide an illustrative example using empirical data from an experiment comparing different fMRI multiband acceleration factors.
### Multiband Example
For this example, we use fMRI data from a previously published study that evaluated the effect of multiband acceleration on fMRI data (Todd et al., ). We will briefly reprise the objectives of that study. For a given effect size of interest, the statistical power of an fMRI experiment can be improved by acquiring a greater number of sample points (i.e., increasing the efficiency of the design) or by reducing measurement noise. This has the potential to enable more precise parameter estimates and provide support for more complex models of how the data were generated. Acquiring data with high temporal resolution both increases the number of samples per unit time and allows physiologically-driven fluctuations in the time series to be more fully sampled and subsequently removed or separated from the task-related BOLD signal (Todd et al., ). One approach to achieving rapid acquisitions is the use of the multiband or simultaneous multi-slice acquisition technique (Setsompop et al., ; Xu et al., ); in which multiple slices are acquired simultaneously and subsequently unfolded using coil sensitivity information (Setsompop et al., ; Cauley et al., ). The penalty for the increased temporal resolution is a reduction of the signal-to-noise ratio (SNR) of each image. This is caused by increased g-factor penalties, dependent on the coil sensitivity profiles, and reduced steady-state magnetization arising from the shorter repetition time (TR) and concomitant reduction in excitation flip angle. In addition, a shorter TR can be expected to increase the degree of temporal auto-correlation in the time series (Corbin et al., ). This raises the question of which MB acceleration factor offers the best trade-off between acquisition speed and image quality.
We do not seek to resolve the question of which multiband factor is optimal in general. Furthermore, there are many potential mechanisms by which multiband acquisitions could improve or limit data quality, including better sampling of physiological noise, and increasing the number of samples in the data. Rather than trying to address these questions here, we instead use these data to exemplify comparing datasets. In these data, physiologically-driven fluctuations—that are better sampled with higher multiband acceleration factor due to the higher Nyquist sampling frequency—were removed from the data by filtering. Subsequently, the data were down-sampled so as to have equivalent numbers of samples across multiband factors, as described in Todd et al. ( ). The framework presented here could be used to test the datasets under many different acquisition and pre-processing procedures.
### Data Acquisition
Ten healthy volunteers were scanned with local ethics committee approval on a Siemens 3T Tim Trio scanner. For each volunteer, fMRI task data with 3 mm isotropic resolution were acquired four times with a MB factor of either 1, 2, 4, or 8 using the gradient echo EPI sequence from the Center for Magnetic Resonance Research (R012 for VB17A, ). The TR was 2,800, 1,400, 700, and 350 ms for MB factor 1, 2, 4, and 8, respectively, resulting in 155, 310, 620, and 1,240 volumes, respectively, leading to a seven and a half min acquisition time per run. The data were acquired with the blipped-CAIPI scheme (Setsompop et al., ), without in-plane acceleration, and the leak-block kernel option for image reconstruction was enabled (Cauley et al., ).
### fMRI Task
The fMRI task consisted of passive viewing of images, with image stimuli presented in 8 s blocks. Each block consisted of four images of naturalistic scenes or four images of single isolated objects, displayed successively for 2 s each. There were two experimental factors: stimulus type (images of scenes or objects) and novelty (2, 3, or 4 novel images per block, with the remainder repeated). This paradigm has previously been shown to induce activation in a well-established network of brain regions that respond to perceiving, imagining or recalling scenes (Spreng et al., ; Zeidman et al., ).
### Preprocessing
All data were processed in SPM (Ashburner and Friston, ), version 12. This comprised the usual image realignment, co-registration to a T1-weighted anatomical image and spatial normalization to the Montreal Neurological Institute (MNI) template space using the unified segmentation algorithm, and smoothing with a 6 × 6 × 6 mm full width at half maximum (FWHM) Gaussian kernel.
As described in Todd et al. ( ), all data were filtered using a 6th-order low pass Butterworth filter with a frequency cut-off of 0.18 Hz (corresponding to the Nyquist frequency of the MB = 1 data). This removed all frequency components between the cut-off frequency and the corresponding Nyquist frequency of the particular MB factor. In order to ensure equal numbers of samples per data set—regardless of MB factor used—the time series were decimated by down-sampling all datasets to the TR of the MB1 (TR = 2.8 s) data.
After initial processing and filtering, all data sets were modeled with a general linear model (GLM), with a high pass filter (cut-off period = 128 s) and regressors for motion, and physiological effects. In addition to these confounding effects, the stimulation blocks were modeled with boxcar functions convolved with the canonical hemodynamic response function. Temporal autocorrelations were accounted for with an autoregressive AR(1) model plus white noise. This was deemed sufficient given that after filtering and decimation each time series had an effective TR of 2.8 s (Corbin et al., ). The contrast of scenes>objects was computed and used to select brain regions for the DCM analysis.
### DCM Specification
We selected seven brain regions (Figure ) from the SPM analysis which are part of a “core network” that responds more to viewing images of scenes rather than images of isolated objects (Zeidman et al., ). These regions were: Early Occipital cortex (OCC), left Lateral Occipital cortex (lLOC), left Parahippocampal cortex (lPHC), left Retrosplenial cortex (lRSC), right Lateral Occipital cortex (rLOC), right Parahippocampal cortex (rPHC), and right Retrosplenial cortex (rRSC).
DCM specification. (A) Locations of the seven brain regions included in the DCM projected onto a canonical brain mesh. (B) Structure of the DCM model estimated for each subject. The circles are brain regions, which were fully connected to one another (gray lines). The self-connection parameters (black arrows), which control each region's sensitivity to input from other regions, were modulated by each of the three experimental manipulations (colored arrows). (C) The optimal group-level (GLM) model after pruning away any parameters that did not contribute to the free energy. The numbered parameters correspond to the bar charts in Figure . Key: a, early visual cortex; b, left lateral occipital cortex; c, right lateral occipital cortex; d, left parahippocampal cortex; e, right parahippocampal cortex; f, left retrosplenial cortex; g, right retrosplenial cortex.
We extracted timeseries from each of these regions as follows. The group-level activation peak (collapsed across multiband factor to prevent bias) was identified from the contrast of scenes>objects (thresholded at p < 0.05 FWE-corrected) using a one-way ANOVA as implemented in SPM. Subsequently, a spherical region of interest (ROI) with 8 mm FWHM was centered on the peaks at the individual level that were closest to the group-level peaks. This size of the ROI sphere was arbitrary and provided a suitable trade-off between including a reasonable number of voxels and not crossing into neighboring anatomical areas. Voxels within each sphere surviving at least p < 0.001 uncorrected at the single-subject level were summarized by their first principal eigenvariate, which formed the data feature used for subsequent DCM analysis.
The neuronal model for each subject's DCM was specified as a fully connected network (Figure ). Dynamics within the network were driven by all trials, modeled as boxcar functions, driving occipital cortex (the circle labeled a in Figure ). The experimental manipulations (scene stimuli, object stimuli, and stimulus novelty) were modeled as modulating each region's self-inhibition (colored arrows in Figure ). These parameters control the sensitivity of each region to inputs from the rest of the network, in each experimental condition. Neurobiologically, they serve as simple proxies for context-specific changes in the excitatory-inhibitory balance of pyramidal cells and inhibitory interneurons within each region (Bastos et al., ). These parameters, which form the B-matrix in the DCM neuronal model (Equation 3), are usually the most interesting from the experimenters' perspective—and we focused on these parameters for our analyses.
### Simulation
We also conducted simulations to confirm the face validity of the Bayesian data comparison approach presented here. Specifically, we wanted to ensure that subject-level differences in the precision of parameters across datasets were properly reflected in the group-level PEB parameters and the ensuing outcome measures. Note that these simulations were not intended to recapitulate the properties or behavior of multiband fMRI data. Rather, our intention was to conduct a simple and reasonably generic assessment of the outcome measures under varying levels of SNR.
We generated simulated neuroimaging data for 100 virtual experiments, each consisting of 100 datasets with differing SNR levels, and applied the Bayesian data comparison procedure to each. These data were generated and modeled using General Linear Models (GLMs), which enabled precise control over the parameters and their covariance, as well as facilitating the inversion of large numbers of models in reasonable time (minutes on a desktop PC). For each simulation i = 1…100, subject j = 1…16, and level of observation noise k = 1…100 we specified a GLM:
There were three regressors in the design matrix X matching the empirical multiband fMRI experiment reported in the previous section (corresponding to the scenes, objects, and novelty experimental conditions). For each virtual subject, the three corresponding parameters in vector β were sampled from a multivariate normal distribution:
Where I is the identity matrix of dimension three. Vector μ were the “ground truth” parameters, chosen based on the empirical analysis (the first two parameters were set to 0.89, the mean of scene and object effects on occipital cortex, and the third parameter was set to half this value, to provide a smaller but still detectable effect). The between-subject variance was set to 0.18, computed from the empirical PEB analyses and averaged over multiband datasets. Finally, we added I.I.D observation noise ϵ with a different level of variance in each dataset, chosen to achieve SNRs ranging between 0.003 (most noisy) and 0.5 (least noisy) in steps of 0.005. Here, SNR was defined as the ratio of the variance of the modeled signal to the variance of the noise (residuals); the median SNR from the empirical data was 0.5 across subjects and datasets.
Having generated the simulated data, we then fitted GLMs using a variational Bayesian scheme (Friston et al., ) with priors on the parameters set to:
Where the within-subject prior variance was set to , to match the DCM parameters of interest in the empirical analysis above. To compute the information gain over models (the third outcome measure), we defined a model space with seven permutations of the parameters switched on or off (i.e., we specified a model for every possible permutation of the parameters, excluding the model with all three parameters switched off).
## Results
We followed the analysis pipeline described above (see Summary of measures and analysis) to compare data acquired under four levels of multiband acceleration. The group-level results below and the associated figures were generated using the Matlab function spm_dcm_bdc.m.
### MB Leakage/Aliasing Investigation
While not the focus of this paper, we conducted an analysis to ensure that our DCM results were not influenced by a potential image acquisition confound. As with any accelerated imaging technique, the multiband acquisition scheme is vulnerable to potential aliased signals being unfolded incorrectly. This is important since activation aliasing between DCM regions of interest could potentially lead to artificial correlations between regions (Todd et al., ). This analysis, detailed in the , confirmed that the aliased location of any given region of interest used in the DCM analysis did not overlap with any other region of interest.
### Identifying the Optimal Group-Level Model
We obtained estimates of each subject's neuronal responses by fitting a DCM to their data, separately for each multiband factor (the structure of this DCM is illustrated in Figure ). Then we estimated a single group-level Bayesian GLM of the neuronal parameters and pruned any parameters that did not contribute to the model evidence (by fixing them at their prior expectations with zero mean and zero variance). This gave the optimal group-level model, the parameters of which are illustrated in Figure . The main conditions of interest were scene and object stimuli. Redundant modulatory effects of object stimuli were pruned from bilateral PHC, while the effect of scene stimuli was pruned from right PHC only. Redundant effects of stimulus novelty were pruned from all regions. This was not surprising, as the experimental design was not optimized for this contrast—and the regions of interest were not selected on the basis of tests for novelty effects.
### Modeling Each Dataset
Having identified a single group-level model architecture across all datasets (Figure ), we next updated each subject's DCMs to use this reduced architecture (by setting their priors to match the group level posteriors and obtaining updated estimates of the DCM parameters). We then estimated a group-level GLM for each dataset. The parameters of these four group-level GLMs are illustrated in Figures – . The numbered parameters, which correspond to those in Figure , describe the change of sensitivity of each region to their inputs. More positive values signify more inhibition due to the task and more negative values signify dis-inhibition (excitation) due to the task. The results were largely consistent across multiband factors, with scene and object stimuli exciting most regions relative to baseline. Interestingly, modulation of early visual cortex by scenes and objects (parameters 1 and 7) were the largest effect sizes, so contributed the most to explaining the network-wide difference in scene and object stimuli.
Parameters of the group-level General Linear Model fitted to each dataset. (A–D) Posterior estimates of each parameter from each dataset. The bars correspond to the parameters labeled in Figure , and for clarity these are divided into regional effects of scene stimuli (solid red) and of object stimuli (chequered blue). These parameters scale the prior self-connection of each region, and have no units. Positive values indicate greater inhibition due to the experimental condition and negative values indicate disinhibition (excitation). Pink error bars indicate 90% confidence intervals. MBx, multiband acceleration factor x. (E) The precision of each parameter—i.e., the inverse of the variance which was used to form the pink error bars in (A–D) . Parameters 1–6 relate to the effects of scene stimuli (S) and parameters 7–11 relate to the effects of object stimuli (O). Each group of four bars denote the four datasets in the order MB = 1, 2, 4, and 8 from left to right.
Figure shows the precision (inverse variance) of each parameter from Figures – . Each group of bars relates to a neuronal parameter, and each of the four bars relate to the four datasets (i.e., each of the four multiband factors). It is immediately apparent that all parameters (with the exception of parameter 7) achieved the highest precision with dataset MB4 (i.e., multiband acceleration factor 4). However, examining each parameter separately in this way is limited, because we cannot see the covariance between the parameters. The covariance is important in determining the confidence with which we can make inferences about parameters or models. Next, we apply our novel series of measures to these data, which provide a simple summary of the qualities of each dataset while taking into account the full parameter covariance.
### Comparing Datasets
In agreement with the analysis above, the dataset with multiband acceleration factor 4 (MB4) gave neuronal parameter estimates with the greatest precision or certainty (Figure ), followed by MB1 and MB2, and MB8 had the least precision. The difference between the best (MB4) and worst (MB8) performing datasets was 1.64 nats, equivalent to 84% probability of a difference (calculated by applying the softmax function to the plotted values). This may be classed as “positive evidence” for MB4 over MB8 (Kass and Raftery, ), however the evidence was not strong enough to confidently claim that MB4 was better than MB1 or MB2.
Proposed measures for comparing datasets applied to empirical data. (A) The negative entropy of the neuronal parameters of each dataset, relative to the worst dataset (MB8) which is set to zero. (B) The information gain (KL-divergence) of the estimated neuronal parameters and the priors, relative to the worst dataset (MB8). (C) The information gain (KL-divergence) from the prior belief that all models were equally likely to the posterior probability over models. In each plot, the bars relate to four datasets which differed in their multiband (MB) acceleration factor: MB1, MB2, MB4, and MB8.
The information gain over parameters (see Outcome measures) is the extent to which the parameters were informed by the data. It reflects the number of independent parameters in the model (its complexity) that the data can accommodate. The best dataset was MB4 (Figure ), followed closely by MB2 and then MB1 and MB8. The difference between the best (MB4) and worst (MB8) datasets was 1.82 nats, or a 86.06% probability of a difference (positive evidence). There was also positive evidence that MB4 was better than MB1 (1.31 nats = 78.75%), but MB4 could not be distinguished from MB2 (0.33 nats = 58.18%). Thus, not only were the parameters most precise in dataset MB4 (Figure ), but they also gained the most information from the data, relative to the information available in the priors. This effect was most pronounced in comparison to MB8, and to a lesser extent, in comparison to MB1.
Next we computed the information gain over models (see Outcome measures, part c), which quantified the ability of the datasets to discriminate between similar models. Whereas, the previous measures were relative to the worst performing dataset, this measure was relative to the prior that all models were equally likely (zero nats). The automated procedure described in the Methods section identified eight similar candidate models that differed only in their priors (i.e., which parameters were switched on or off). Because there were eight models, the maximum possible information gain over models was ln8 = 2.08 nats. We found that MB4 afforded the best discrimination between models (Figure ), with an information gain of 1.34 nats relative to the prior that all models were equally likely (positive evidence, 79% probability). The other three datasets provided poorer discriminability: MB8 with 0.67 nats, MB2 with 0.62 nats, and MB1 with 0.34 nats.
To summarize, all three of the measures favored the dataset with multiband acceleration 4 (MB4). However, the magnitudes of these differences were generally not substantial, with “positive evidence” rather than “strong evidence” under all measures. MB4 consistently fared better than MB8—with positive evidence that it provided more confident parameter estimates (Figure ) and greater information gain (Figure ). There was also positive evidence that MB4 offered greater information gain than MB1 (Figure ). Finally, MB4 supported greater information gain over models than any other dataset (Figure ). Given these results, if we were to conduct this same experiment with a larger sample, we would select multiband acceleration factor MB4 as our preferred acquisition protocol.
### Simulation Results
To validate the software implementing the outcome measures, we simulated 100 experiments, each of which compared 100 datasets with varying levels of SNR. As expected, increasing SNR was accompanied by an increase in the certainty (precision) of the group-level parameters (Figure ). This showed an initial rise and then plateaued, reaching 5.62 nats (very strong evidence) for the dataset with the highest SNR (dataset 100) compared to the dataset with the lowest SNR ( Dataset 1 ). The information gain over parameters, which quantified the KL-divergence from the priors to the posteriors relative to the first dataset, was very similar (Figure ). Finally, we compared the datasets in terms of their ability to distinguish seven models, one of which matched the model that generated the data. This measure could range from zero nats (the prior that all seven models were equally likely) to ln(7) = 1.95 nats (a single model having a posterior probability of 100%). We found that the information gain increased with increasing SNR, to a ceiling of 1.91 nats (Figure ). Inspecting the results revealed that in the datasets with the lowest SNR, the probability mass was shared between model one (the “full” model which generated the data) and model three, in which the parameter quantifying the small novelty effect was disabled (fixed at zero). As SNR increased, model one was correctly selected with increasing confidence.
Bayesian data comparison of 100 simulated datasets. The datasets are ordered into increasing levels of SNR at the individual subject level (see Methods). (A) The negative entropy of the neuronal parameters of each dataset, relative to the worst dataset. (B) The information gain (KL-divergence) of the estimated parameters and the priors, relative to the worst dataset. (C) The information gain (KL-divergence) between the estimated probability of each model and the prior belief that all models were equally likely. In each plot, the line and dots indicate the mean across 100 simulations, and the shaded error indicates the 90% confidence interval across simulations.
To summarize, these simulations provided a basic check that the within-subject parameters were conveyed to the group level and were captured by the outcome measures proposed in this work. All three outcome measures showed a monotonic increase with SNR, which consisted of a large initial increase followed by diminishing returns as SNR further increased. Accompanying this paper, we provide a Matlab script for performing these simulations, which can be used for testing the impact of differing effect sizes or levels of between-subject variability, under any choice of (Bayesian) forward model.
## Discussion
This paper introduced Bayesian data comparison, a systematic approach for identifying the optimal data features for inferring neuronal architectures. We proposed a set of measures based on established Bayesian models of neuroimaging timeseries, in order to compare datasets for two types of analyses—inference about parameters and inference about models. We exemplified these measures using data from a published experiment, which investigated the performance of multiband fMRI in a cohort of 10 healthy volunteers. This principled scheme, which can be applied by experimenters using a software function implemented in SPM (spm_dcm_bdc.m), can easily be applied to any experimental paradigm, any group of subjects (healthy or patient cohorts), and any acquisition scheme.
Comparing models based on their evidence is the most efficient procedure for testing hypotheses (Neyman and Pearson, ) and is employed in both classical and Bayesian statistics. The model evidence is the probability of the data y given the model m i.e., p ( y | m ). Model comparison involves taking the ratio of the evidences for competing models. However, this ratio (known as the Bayes factor) assumes that each model has been fitted to the same data y . This means that when deciding which data to use (e.g., arbitrating between different multiband acceleration factors) it is not possible to fit models to each dataset and compare them based on their evidence. To address this, the measures we introduced here can be used in place of the model evidence to decide which of several datasets provides the best estimates of model parameters and best distinguishes among competing models.
The first step in our proposed analysis scheme is to quantify neuronal responses for each data acquisition. This necessarily requires the use of a model to partition the variance into neuronal, hemodynamic, and noise components. Any form of model and estimation scheme can be used, the only requirement being that it is probabilistic or Bayesian. In other words, it should furnish a probability density over the parameters. Here, we modeled each subject's neuronal activity using DCM for fMRI, in which the parameters form a multivariate normal distribution defined by the expected value of each parameter and their covariance (i.e., properly accounting for conditional dependencies among the parameters). Given that the main application of DCM is for investigating effective (causal) connectivity, the method offered in this paper is especially pertinent for asking which acquisition scheme will offer the most efficient estimates of connectivity parameters. Alternatively, the same analysis approach could be applied to the observation parameters rather than the neuronal parameters, to ask which dataset provides the best estimates of regional neurovascular coupling and the BOLD response. More broadly, any probabilistic model could have been used to obtain parameters relating to brain activity, one alternative being a Bayesian GLM at the single subject level (Penny et al., ).
Hypotheses in cognitive neuroscience are usually about effects that are conserved at the group level. However, the benefits of advanced acquisition schemes seen at the single subject level may not be preserved at the group level due to inter-subject variability (Kirilina et al., ). We were therefore motivated to develop a protocol to ask which acquisition scheme offers the best inferences at the group level, while appropriately modeling inter-subject variability. To facilitate this, the second step in our analysis procedure is to take the estimated neuronal parameters from every subject and summarize them using a group level model. Here we use a Bayesian GLM, estimated using a hierarchical (PEB) scheme. This provides the average (expected value) of the connectivity parameters across subjects, as well as the uncertainty (covariance) of these parameters. It additionally provides the free energy approximation of the log model evidence of the GLM, which quantifies the relative goodness of the GLM in terms of accuracy minus complexity. The key advantage of this Bayesian approach, unlike the summary statistic approach used with the classical GLM in neuroimaging, is that it takes the full distribution over the parameters (both the expected values and covariance) from the single subject level to the group level. This is important in assessing the quality of datasets, where the subject-level uncertainty over the parameters is key to assessing their utility for parameter-level inference. Together, by fitting DCMs at the single subject level and then a Bayesian GLM at the group level, one can appropriately quantify neuronal responses at the group level.
Having obtained parameters and log model evidences of each dataset's group-level GLM, the final stage of our analysis procedure is to apply a set of measures to each dataset. These measures are derived from information theory and quantify the ability of the data to support two complementary types of inference. Firstly, inference about parameters involves testing hypotheses about the parameters of a model; e.g., assessing whether a particular neuronal response is positive or negative. A good dataset will support precise estimates of the parameters (where precision is the inverse variance) and will support the parameters being distinguished from one another (i.e., minimize conditional dependencies). We evaluated these features in each dataset by using the negative entropy of the parameters and the information gain. These provide a straightforward summary of the utility of each dataset for inference over parameters. A complementary form of inference involves embodying each hypothesis as a model and comparing these models based on their log evidence ln p ( y | m ). This forms the basis of most DCM studies, where models differ in terms of which connections are switched on and off, or which connections receive experimental inputs (specified by setting the priors of each model). We assessed each dataset in terms of its ability to distinguish similar, plausible and difficult-to-discriminate models from one another. This involved an automated procedure for defining a set of similar models, and the use of an information theoretic quantity—the information gain—to determine how well the models could be distinguished from one another in each dataset. This measure can be interpreted as the amount we have learnt about the models by performing the model comparison, relative to our prior belief that all models were equally likely.
To exemplify the approach, we compared four fMRI datasets that differed in their multiband acceleration factor. The higher the acceleration factor, the faster the image acquisition. This affords the potential to better separate physiological noise from task-related variance—or to increase functional sensitivity by providing more samples per unit time. However, this comes with various costs, including reduced SNR and increased temporal auto-correlations. The datasets used were acquired in the context of an established fMRI paradigm, which elicited known effects in pre-defined regions of interest. The conclusion of the original study (Todd et al., ), which examined the datasets under the same pre-processing procedures used here, was that a multiband acceleration factor between 4 (conservative) and 8 (aggressive) should be used. In the present analysis, the dataset acquired with multiband acceleration factor 4 (MB4) afforded the most precise estimates of neuronal parameters, and the largest information gain in terms of both parameters and models (Figure ), although the differences between MB4 and MB2 were small. Our analysis of residual leakage artifact ( ) showed this result was not confounded by aliasing, a potential issue with multiband acquisitions (Todd et al., ). Given that these data were decimated so as to have equivalent numbers of samples, regardless of MB factor, our results suggest that the improved sampling of physiological effects provided by multiband acceleration counterbalanced the loss of SNR. Speculatively, MB4 may have been optimal in terms of benefiting from physiological filtering (a sufficiently high Nyquist frequency to resolve breathing effects), despite any reduction in SNR. MB2 may have performed slightly less well because it suffered from the penalty of reduced SNR, without sufficient benefit from the filtering of physiological effects. Any advantage of MB8 in terms of physiological filtering may have been outweighed by the greater reduction in SNR.
One should exercise caution in generalizing this multiband result, which may not hold for different paradigms or image setups (e.g., RF coil types, field strength, resolution, etc.) or if using variable numbers of data points. Though congruent with a previous study (Todd et al., ), without these further investigations, the conclusions presented here should not be generalized. Going forward, the effect of each of these manipulations could be framed as a hypothesis, and tested using the procedures described here. One interesting future direction would be to investigate the contribution of the two pre-processing steps: filtering and decimation. Our data were filtered to provide improved sampling of physiological noise and were subsequently decimated in order to maintain a fixed number of data points for all multiband factors under investigation. This ensured a fair comparison of the datasets with equivalent handling of temporal auto-correlations. The protocol described here could be used to evaluate different filtering and decimation options. One might anticipate that the increased effective number of degrees of freedom within the data would be tempered by increased temporal auto-correlations arising from more rapid sampling.
A further specific consideration for the application of multiband fMRI to connectivity analyses is whether differences in slice timing across different acquisition speeds could influence estimates of effective connectivity in DCM. An approach for resolving this is slice timing correction—adjusting the model to account for acquisition time of each slice. DCM has an inbuilt slice timing model to facilitate this (Kiebel et al., ). Whether this is helpful for all application domains is uncertain. Following spatial realignment, coregistration, and normalization, the precise acquisition time of each slice is lost, so the modeled acquisition times can deviate from the actual acquisition times. On the other hand, if the modeled acquisition times are reasonably accurate, there may be some benefit—particularly for fast event-related designs. This uncertainty can be resolved using Bayesian model comparison—DCMs can be specified with different slice timing options and their evidence compared. In the example dataset presented here, we had a slow block design which is unlikely to benefit significantly from slice timing correction, so for simplicity we used the default setting in DCM—aligning the onsets to the middle of each volume, thereby minimizing the error on average.
The procedure introduced here involves evaluating each dataset in terms of its ability to provide estimates of group-level experimental effects. An alternative approach would be to compare datasets at the individual subject level—for instance, by comparing the variance of each model's residuals, parameterized in DCM on a region-by-region basis (hyperparameters λ which control the log precision of the noise ϵ , see Equation 2). However, this would only characterize the fit of each model as a whole, and would not evaluate the quality of inferences about neural parameters specifically, which are typically the quantities of interest in neuroimaging studies. Furthermore, neuroimaging studies typically evaluate hypotheses about groups of subjects rather than individuals, and thus assessing the quality of inferences is ideally performed using group-level models or parameters. For the example analysis presented here, we therefore chose to compare datasets in terms of the specific parameters of interest for the particular experiment (DCM B-matrix), summarized by the group level PEB model.
An important consideration—when introducing any novel modeling approach or procedure—is validation. Here, for our example analysis using empirical data, we used two extant models from the neuroimaging community—DCM for fMRI and the Bayesian GLM implemented in the PEB framework. The face validity of DCM for fMRI has been tested using simulations (Friston et al., ; Chumbley et al., ), its construct validity has been tested using extant modeling approaches (Penny et al., ; Lee et al., ), and its predictive validity has been tested using intracranial recordings (David et al., , Reyt et al., ). The PEB model and the associated Bayesian Model Reduction scheme is more recent and so far has been validated in terms of its face validity using simulated data (Friston et al., ), its reproducibility with empirical data (Litvak et al., ) and its predictive validity in the context of individual differences in electrophysiological responses (Pinotsis et al., ).
The next validation consideration regards the novel contribution of this paper—the application of a set of outcome measures to the probabilistic models discussed above. These measures are simply descriptions or summary statistics of the Bayesian or probabilistic models to which they are applied. The measures themselves depend on two statistics from information theory—the negative entropy and the KL-divergence, which do not require validation in and of themselves, just as the t -statistic does not need validation when used to compare experimental conditions using the GLM. Rather, the implementation of the statistical pipeline needs validation, and we have assessed this using simulations. These confirmed that the measures behaved as expected, increasing monotonically with increasing SNR until they reached a saturation point, when further increases in SNR offered no additional benefit. It should be emphasized that although these simulations were based on the effect sizes from the empirical multiband fMRI data, they were not intended to capture the detailed properties of multiband fMRI per se . For example, the simulated datasets differed in their level of additive Gaussian white noise, which cannot fully capture the complex noise properties specific to multiband fMRI. These simulations are therefore an illustration of any generic aspect of the imaging protocol that influences SNR. Additionally, we used general linear models (GLMs) to generate and model the 160,000 simulated datasets (100 repetitions × 100 datasets per repetition × 16 subjects), which would not have been tractable in reasonable time using DCMs. The use of GLMs did not recreate the nonlinearities and parameter covariance present in a more complex models (e.g., DCM), which may be expected to reduce parameter identifiablilty. Nevertheless, the use of GLMs was sufficient for establishing the face validity of the measures, while emphasizing that the outcome measures are not specific to the choice of forward model.
A complementary approach to comparing data would be to assess their predictive validity—i.e., whether effect sizes are detectable with sufficient confidence to predict left-out data (Strother et al., ). We haven't pursued this here because our objective is to select the data that maximizes the confidence of hypothesis testing (the precision of inferences over parameters or models). However, in contexts where the objective is to select the dataset which has the best predictive accuracy—such as when identifying biomarkers—this could be performed in the PEB software framework using tools provided for leave-one-out cross-validation (Friston et al., ).
Practically, we envisage that a comparison of datasets using the methods described here could be performed on small pilot groups of subjects, the results of which would inform decisions about which imaging protocol to use in a subsequent full-scale study. Regions of interest would be selected for inclusion in the model which are known to show experimental effects for the selected task—based on an initial analysis (e.g., SPM analysis) and/or based on previous studies. The pilot analysis would ideally have the same design—e.g., model structure—as intended for the full-scale study. This is because the quality measures depend on the neuronal parameters of the specific model(s) which will be used by the experimenter to test hypotheses. Following this, we do not expect there exists a “best” acquisition protocol in general for any imaging modality. Rather, the best dataset for a particular experiment will depend on the specific hypotheses (i.e., models) being tested, and the ideal dataset will maximize the precision of the parameters and maximize the difference in evidence between models. We anticipate that the protocol introduced here, implemented in the software accompanying this paper, will prove useful for experimenters when choosing their acquisition protocols.
## Ethics Statement
This study was carried out in accordance with the recommendations of the Ethics Committee of University College London with written informed consent from all subjects. All subjects gave written informed consent in accordance with the Declaration of Helsinki. The protocol was approved by the Ethics Committee of University College London.
## Author Contributions
PZ and SK authored the manuscript, designed, and ran the analyses. NT, NW, and KF provided technical guidance and edited the manuscript. MC supervised the study and edited the manuscript.
### Conflict of Interest Statement
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
|
Methyl-CpG binding protein 2 (MECP2) is a gene associated with DNA methylation and has been found to be important for maintaining brain function. In humans, overexpression of MECP2 can cause a severe developmental disorder known as MECP2 duplication syndrome. However, it is still unclear whether MECP2 overexpression also causes auditory abnormalities, which are common in people with autism. MECP2-TG is a mouse model of MECP2 duplication syndrome and has been widely used for research on social difficulty and other autism-like disorders. In this study, we used a combination of multiple electrophysiological techniques to document the response properties of the auditory cortex of awake MECP2-TG mice. Our results showed that while the auditory brainstem responses are similar, cortical activity patterns including local field potentials (LFPs), multiunit activity (MUA), and single-neuron responses differ between MECP2-TG and wild-type (WT) mice. At the single-neuron level, the spike waveform of fast-spiking (FS) neurons from MECP2-TG mice is different from that of WT mice, as reflected by reduced peak/trough ratios in the transgenic mice. Both regular-spiking (RS) and FS neurons exhibited atypical response properties in MECP2-TG mice compared with WT mice, such as prolonged latency and an elevated intensity threshold; furthermore, regarding the response strength to different stimuli, MECP2-TG mice exhibited stronger responses to noise than to pure tone, while this pattern was not observed in WT mice. Our findings suggest that MECP2 overexpression can cause the auditory cortex to have atypical response properties, an implication that could be helpful for further understanding the nature of auditory deficits in autism.
## Introduction
Autism spectrum disorder (ASD) comprises a variety of neurological disorders characterized by delayed language learning and impairments in social communication ( ; ; ). Methyl-CpG-binding protein 2 (MECP2), encoded by the X-linked transcriptional repressor gene Mecp2, is critical for normal brain development ( ). Previous studies have demonstrated that either loss or duplication of MECP2 results in neurodevelopmental disorders characterized by multiple autistic behaviors ( ; ). Loss-of-function mutations in MECP2 can cause Rett syndrome (RTT) ( ), while gain-of-function mutations result in MECP2 duplication syndrome ( ; ). The auditory sense receives messages from the outside environment and plays a critical role in language and social communication. Previous studies indicate that patients with ASD exhibit different degrees of aberrant reactivity to auditory stimuli, especially noise stimuli ( ; ), suggesting dysfunction in auditory perception. Experimental evidence has shown that the auditory brainstem responses (ABRs) of patients with ASD to auditory stimuli are largely normal ( ; ), suggesting that higher regions in the auditory pathway are an underlying cause of abnormal auditory perception in ASD. In addition, previous studies have also revealed that patients with Rett syndrome exhibit degraded auditory cortex responses, including delayed onset and peak latencies, reduced local field potential (LFP) amplitudes and weakened multiunit responses ( ; ). However, it remains largely unclear how MECP2 overexpression affects the auditory cortex in mouse models of MECP2 duplication syndrome.
To explore the potential change in fundamental properties of the auditory cortex caused by MECP2 overexpression, we investigated a transgenic mouse line called MECP2-TG, which overexpresses MECP2 due to insertion of the human MECP2 gene. MECP2-TG mice exhibit many symptoms similar to those of MECP2 duplication syndrome in humans, including repetitive stereotypes and abnormal communication and social behavior ( ). We first measured the ABRs in both MECP2-TG mice and wild-type (WT) littermates to examine changes in basic hearing. Multiunit activity (MUA) recordings and single-cell recordings were performed in vivo to investigate the fundamental properties of the auditory cortex, such as tonotopy, threshold map and spike activities. Single-cell recordings were then clustered into regular-spiking (RS) and fast-spiking (FS) cells based on the bimodal distribution of spike waveforms. Tonal receptive fields (TRFs) were compared in detail between different cells in the two animal groups. A stronger response to white noise stimulation than to pure tone stimulation was found in MECP2-TG mice but not WT controls. Our findings suggest that MECP2 overexpression can cause the auditory cortex to have atypical response properties, an implication that could be helpful for further understanding the nature of auditory deficits in autism.
## Materials and Methods
### Animals
Adult male mice (12–16 weeks old, weighing 27–34 g) were used in this study. The mice were maintained in standard housing on a 12 h light/12 h dark cycle. All the male MECP2-TG mice and WT littermate mice were on an FVB/N background. The experimenters were blinded to the genotypes of the animals. All experimental procedures used in this study were approved by the Animal Care and Use Committee of Army/Third Military Medical University (SYXK-PLA-20120031).
### In vivo ABR Recording
The mice were anesthetized with ketamine (45 mg/kg) and xylazine (6.4 mg/kg) by intraperitoneal injection. The experiments were performed in a double-shielded sound-attenuating booth (Shenyang Sound-attenuating Booth Factory, China). Three metal electrodes were inserted subcutaneously into the left mastoid process, calvarium and right hindlimb. A free-field magnetic speaker (MF1, TDT Inc., United States) was positioned 10 cm away from the left ear. The speaker was driven by a stereo power amplifier (RZ6, TDT Inc., United States). Click sounds (0.1 ms duration per click, 10 ms interval) of various intensities [0–30 dB sound pressure level (SPL) at 5 dB intervals and 30–70 dB SPL at 10 dB intervals] were delivered to the left ear for a total of 1024 trials. The location of peaks in ABR data were determined online by SigGenRZ (TDT Inc., United States) and stored offline for statistical analysis.
### Animal Preparation and Tone Stimulation
Mice were anesthetized with ketamine (45 mg/kg) and xylazine (6.4 mg/kg) by intraperitoneal injection. The experiments were performed in a double-shielded sound-attenuating booth. After the skin was removed and the calvarium was cleaned, a customized apparatus was used to fix the head in place. Intramuscular injection of lidocaine was used to further relieve the pain. A craniotomy was performed to expose the right auditory cortex. The ear canal on the same side was plugged with a cotton ball. A free-field magnetic speaker was positioned 5 cm from the contralateral ear (left ear). The speaker was driven by a stereo power amplifier (SA1, TDT Inc., United States) and calibrated using a 1/4-pressure prepolarized condenser microphone setup (377A01 microphone, 426B03 preamplifier, 480E09 signal conditioner, Piezotronics Inc., United States). The captured signals were sampled at 1 MHz/s with a high-speed data acquisition (DAQ) board (PCI-6251, National Instruments, United States). Customized LabVIEW programs were used for calibration and sound generation with a distortion of ± 0.6 dB SPL at 70 dB SPL (0.5–64 kHz). We mapped the tonotopy of the cortex through extracellular recordings with parylene-coated tungsten electrodes (0.1 MΩ, WPI Inc., United States) 400–600 μm below the pia. Pure tones (35 ms duration, 5 ms ramp) of various frequencies (0.5–64 kHz, 0.2-octave intervals) and intensities (0–70 dB SPL at 10 dB intervals) were generated by custom software (LabVIEW, National Instruments), and 288 testing stimuli were presented in a pseudorandom sequence (150 ms intervals). The primary auditory cortex was identified by its anatomical location in the mouse brain (1.5–3.5 mm from the bregma) and by its patterned tonotopy as previously described ( ). The cortical surface was covered with warm (37°C) artificial cerebrospinal fluid [ACSF, containing (in mM) 124 NaCl, 1.2 NaH PO , 2.5 KCl, 25 NaHCO , 20 glucose, 2 CaCl , and 1 MgCl ]. After mapping, the opening was temporarily sealed with silicone rubber (Body Double, Smooth on Inc., United States), and the mouse was returned to a clean cage to recover for 1 day.
### In vivo LFP Recording and Extracellular Multiunit Recording
After mapping, LFP recording and extracellular multiunit recording were performed with parylene-coated tungsten electrodes (0.1 MΩ, WPI Inc., United States) in awake mice. The electrode was inserted into the cortex vertically with a micromanipulator (MC1000e and 7600, Siskiyou, United States) to a depth of 400–600 μm below the pia. Pure tones (7000 Hz, 70 dB SPL, 35 ms duration, 5 ms ramp) or white noise (70 dB SPL, 35 ms duration, 5 ms ramp) were delivered to the left ear at least 30 times. Neural signals were amplified and collected by a TDT System 3 (LFP signals – gain: 5000, sampling rate: 50 kHz; extracellular multiunit signals – gain: 20000, sampling rate: 50 kHz; TDT Inc., United States). For LFP recording, the high-pass filter was set at 1 Hz, and the low-pass filter was set at 300 Hz for neural activity. The LFP amplitude was calculated as the difference between the peak amplitude and the averaged amplitude in a 10 ms window before the onset of stimulation. The onset latency was measured based on a threshold set at three times the standard deviation above the baseline. For the recording of extracellular multiunit signals, the high-pass filter was set at 300 Hz, and the low-pass filter was set at 3000 Hz for spike activity. The threshold for spike detection was set at three times the standard deviation above the baseline. The data were analyzed online and stored for offline analysis with BrainWare (TDT Inc., United States).
### Single-Unit Activity (SUA) Recordings
In vivo patch-clamp recording and extracellular microwire recording were used to obtain SUA from awake mice. In vivo loose-patch-clamp recording was performed as previously described ( ). For extracellular microwire recording, customized 4-channel microwire electrodes with an impedance of approximately 0.5 MΩ were used. The electrode was inserted into the cortex vertically with a micromanipulator (MC1000e and 7600, Siskiyou, United States) in a similar manner to the tungsten electrode used for mapping. Pure tones (0.5–64 kHz at 0.2-octave intervals; 0–70 dB in 10 dB steps) (35 ms duration, 5 ms ramp) were delivered to the left ear at least 3 times (at an interstimulus interval of 150 ms) to obtain the TRF. Data were recorded using a Plexon Omniplex system (Plexon Inc., United States) and further analyzed offline (Offline Sorter 4.0, Plexon, United States). The peak latency was the lag between the stimulus onset and the peak of the firing rate. The half-peak duration of the peri-stimulus spike time histogram (PSTH) was calculated as the lag between two points where the PSTH crossed a threshold set at half its maximum amplitude (“peak”). The intensity selectivity index was calculated as previously described ( ). The average activity was calculated over a 50 ms time window after the onset of sound stimulation.
### Statistical Analysis
All data analysis was performed using customized codes in MATLAB (Mathworks Inc., United States) that blinds the researcher to the experimental groups. Experimental data are presented as the mean ± SEM unless specified. Statistical analyses were conducted using SPSS 16.0 software (SPSS, United States). The significance of differences between groups was calculated by Student’s t -test. Two-sided p -values were calculated, and the graphs are marked with one star “*” to indicate p < 0.05, two stars “**” to indicate p < 0.01, or three stars “***” to indicate p < 0.001, all of which were considered statistically significant.
## Results
### ABRs in MECP2-TG Mice Are Normal
To examine whether MECP2 overexpression causes fundamental hearing difficulties, we recorded ABRs from MECP2-TG and WT mice (see section Materials and Methods for details). showed two representative ABR results (10–70 dB) and showed more raw traces with marked peaks at 70 dB. No significant difference was found between the thresholds for MECP2-TG mice and WT mice ( ). The temporal properties of ABRs, such as the I–III inter-peak latency interval, III–V inter-peak latency interval, and I–V inter-peak latency interval, also showed no significant difference between the two animal groups ( ). These results suggest that MECP2-TG mice have no major hearing abnormalities in early hearing pathway.
(A,B) Examples of auditory brainstem responses (ABRs) in MECP2-TG mice and wild-type (WT) mice, respectively. (C) The threshold of ABRs in MECP2-TG mice and WT mice, respectively. t -test, mean ± standard error of the mean (SEM). No significant difference, p = 0.97, t -test. (D) The I–III inter-peak latency interval of ABR in MECP2-TG mice and WT mice, respectively. t -test, mean ± SEM. No significant difference, p = 0.77, t -test. (E) The III–V inter-peak latency interval of ABR in MECP2-TG mice and WT mice, respectively. t -test, mean ± SEM. No significant difference, p = 0.40, t -test. (F) The I–V inter-peak latency interval of ABR in MECP2-TG mice and WT mice, respectively. t -test, mean ± SEM. No significant difference, p = 0.58, t -test.
### An Increased Threshold Was Found in MECP2-TG Mice
Characteristic frequency (CF) distribution and threshold reflect fundamental properties of hearing function and related structure in the auditory cortex. We used extracellular recordings to investigate the tonotopy of the cortex and generated cortical threshold in awake MECP2-TG mice and WT mice. shows representative cases of tonotopy. The CF distribution and frequency gradient are clear and largely similar in both animal groups. On the other hand, the cortical threshold map that show the distribution of thresholds are different between MECP2-TG mice and WT mice ( ). Statistical results showed a significantly higher threshold in MECP2-TG mice than in WT mice in low-frequency, middle-frequency, and high-frequency areas ( ). This difference suggests that the transgenic mice could have hearing abnormalities related to the auditory cortex, gradually arising in the ascending auditory pathway.
(A) A representative case of the characteristic frequency (CF) distribution in the auditory cortex in MECP2-TG mice and wild-type (WT) mice, respectively. Black dots indicate that there was no response at the recording site. Scale bar, 0.5 mm. (B) Distribution of characteristic frequency of 168 recording sites from MECP2-TG mice ( n = 5) and 174 recording sites from WT mice ( n = 5). (C) Example cortical threshold map from the auditory cortex of MECP2-TG mice and WT mice. Black dots indicate that there was no response at the recording site. Scale bar, 0.5 mm. (D) Average intensity threshold at CF of low-frequency regions, middle-frequency regions and high-frequency regions in MECP2-TG mice ( n = 5) and WT mice ( n = 5). * p < 0.05, t -test, mean ± standard error of the mean (SEM).
### MECP2 Overexpression Alters the Spike Waveforms of FS but Not RS Cells
The waveform of spikes can be used as a standard to group single-cell recordings into different subtypes ( ).
shows representative traces recorded from MECP2-TG and WT mice. The peak-trough interval and peak/trough ratio were measured to quantify the shape of spike waveforms ( ). A bimodal distribution of peak-trough intervals was found, and 0.4 ms was chosen as a cut off to separate RS neurons (putatively excitatory pyramidal cells) and FS neurons (putatively inhibitory basket and chandelier cells). In addition, the peak/trough ratio of RS neurons from two animals showed no significant difference, but FS neurons of MECP2-TG mice had a lower peak/trough ratio than WT neurons had ( ). This finding shows that overexpression of MECP2 had a larger influence on FS neurons than on RS neurons in MECP2-TG mice. The abnormal waveform could be a result of altered intracellular dynamics (such as ion channels) related to the overexpression of the MECP2 gene. Normal RS and abnormal FS cells could further cause an imbalance between excitation and inhibition in the processing of auditory information.
(A) Representative spike waveforms. Dashed lines indicate the trough and peak of the spike waveform. (B) Upper panel, scatter plot of peak/trough ratios and trough-peak intervals of spike waveforms from all recorded neurons. Bottom panel, histogram of the peak-trough intervals recorded from all recorded neurons. (C) Scatter plot of the peak/trough ratio and trough-peak interval of spike waveforms from regular-spiking neurons and fast-spiking neurons, respectively. Peak/trough ratio: ** p < 0.01; t -test, mean ± standard deviation.
### MECP2 Duplication Shapes the TRFs of Both RS and FS Neurons
A tonal receptive field is a basic property of auditory neurons. Different intensities (0–70 dB in 10 dB steps) and frequencies (0.5–64 kHz in 0.2-octave steps) of pure tone stimulation were used to determine the TRF properties of RS neurons from MECP2-TG and WT mice. For better demonstration, showed the results of CF around 16 KHz and other data were provided in as a function of CF. To compare the response temporal profiles, we generated spike TRFs and PSTHs from spike responses ( ). We found that the peak latency in response to tone stimulation was significantly delayed in MECP2-TG mice compared to WT mice ( ). Then, we measured the half-peak durations of the PSTHs to examine the temporal response properties. The half-peak durations of RS neurons from MECP2-TG mice were broader than those of RS neurons from WT mice ( ). To quantify the frequency selectivity of RS neurons, we measured the bandwidth of spike TRF at intensities 10 and 30 dB above the threshold. RS neurons from MECP2-TG mice exhibited broader bandwidth at BW10 and BW30 than those from WT ( ), suggesting that the former neurons have lower frequency selectivity than the latter at high intensity levels. The intensity threshold of RS neurons from MECP2-TG mice was significantly higher than that of RS neurons from WT mice ( ). To quantify the intensity tuning, we calculated an intensity selectivity index (ISI) for the CF-tone-evoked responses of each recorded RS neuron. The majority of recorded RS neurons had monotonically increasing response-versus-intensity functions for both animal groups ( ). The firing rate in response to 70 dB pure tones [best frequency (BF) ± 0.2 octave] showed no significant difference between the two mouse strains ( ).
(A,B) Left, representative tonal receptive field (TRF) of a regular-spiking (RS) neuron. Right, representative peri-stimulus spike time histogram (PSTH) of an RS neuron. (C) Averaged peak latency of RS neurons recorded from MECP2-TG mice and wild-type (WT) mice, respectively. * p < 0.05, t -test, mean ± standard error of the mean (SEM). (D) Averaged duration at the half-maximum level in the PSTHs of RS neurons recorded from MECP2-TG mice and WT mice, respectively. * p < 0.05, t -test, mean ± SEM. (E,F) Average tuning bandwidth of TRFs at 10 and 30 dB above the intensity threshold of RS neurons recorded from MECP2-TG mice and WT mice. * p < 0.05, t -test, mean ± SEM. (G) Average intensity threshold at characteristic frequency (CF, 16 kHz) in RS neurons recorded from MECP2-TG mice and WT mice, respectively. * p < 0.05, t -test, mean ± SEM. (H) Average intensity threshold for each frequency tested all along the tuning curve in RS neurons recorded from MECP2-TG mice and WT mice, respectively. (I) Distribution of intensity selectivity indices of RS neurons recorded from MECP2-TG mice and WT mice, respectively. Bin size = 0.1. (J) Averaged activity of RS neurons to best frequency (BF) ± 0.2 octave at 70 dB. t -test, mean ± SEM.
Similar to , showed the results of FS neurons with CF around 16 KHz and other data were provided in as a function of CF. The FS neurons from MECP2-TG and WT mice both exhibited V-shaped TRFs ( ). The peak of the PSTH for the FS neurons from MECP2-TG mice appeared later than that of the PSTH for the FS neurons from WT mice ( ). The half-peak durations of FS neurons from MECP2-TG mice were narrower than those of FS neurons from WT mice ( ). To quantify the frequency selectivity of FS neurons from MECP2-TG and WT mice, we measured BW10 and BW30 for FS neurons. The FS neurons from MECP2-TG mice exhibited a narrower bandwidth than WT mice at both BW10 and BW30 ( ). The intensity threshold of FS neurons from MECP2-TG mice was significantly higher than that of FS neurons from WT mice ( ). The majority of recorded FS neurons in each animal group had monotonically increasing response-versus-intensity functions ( ). The firing rate of FS neurons to 70 dB SPL pure tones (BF ± 0.2 octave) was lower in MECP2-TG mice than in WT mice ( ). Overall, these results are different from the results obtained for RS neurons, suggesting that the overexpression of MECP2 plays a distinct role in shaping the TRF of cortical neurons in MECP2-TG mice.
(A,B) Left, representative tonal receptive fields (TRFs) of fast-spiking (FS) neurons. Right, representative peri-stimulus spike time histograms (PSTHs) of FS neurons. (C) Averaged peak latency of FS neurons recorded from MECP2-TG mice and wild-type (WT) mice. * p < 0.05, t -test, mean ± standard error of the mean (SEM). (D) Averaged duration at the half-maximum level on the PSTHs of FS neurons recorded from MECP2-TG mice and WT mice. * p < 0.05, t -test, mean ± SEM. (E,F) Average tuning bandwidth of TRFs at 10 dB and 30 dB above the intensity threshold in FS neurons recorded from MECP2-TG mice and WT mice. * p < 0.05, t -test, mean ± SEM. (G) Average intensity threshold at the characteristic frequency (CF, 16 kHz) in FS neurons recorded from MECP2-TG mice and WT mice. * p < 0.05, t -test, mean ± SEM. (H) Average intensity threshold for each frequency tested all along the tuning curve in FS neurons recorded from MECP2-TG mice and WT mice. (I) Distribution of intensity selectivity indices of FS neurons recorded from MECP2-TG mice and WT mice. Bin size = 0.1. (J) Averaged activity of FS neurons in response to the best frequency (BF) ± 0.2 octave at 70 dB. * p < 0.05, t -test, mean ± SEM.
### MECP2 Duplication Increases Cortical Responses to Noise
Overreaction or hypersensitivity to noise is commonly observed in people with ASD ( ; ; ). We compared the properties of responses to pure tones (7000 Hz) and white noise in both animal groups. At 70 dB SPL, white-noise stimulation evoked a stronger LFP than a pure tone (7000 Hz) in MECP2-TG mice but not WT mice ( ). The temporal profiles of LFPs, such as onset latency and peak latency, were similar in response to pure tones and white noise in both animal groups ( ). However, a significant delay in response was found in MECP2-TG mice compared with WT mice ( ). In addition to LFPs, we also investigated the MUA in the auditory cortex of both animal groups ( ). As with LFP, white noise evoked stronger responses than a pure tone at 70 dB in MECP2-TG mice ( ). For temporal profiles, the half-peak duration and peak latency of the PSTH were measured. The prolonged half-peak duration is consistent with the strengthened response to noise stimulation ( ). The peak latency in response to noise stimulation was significantly increased ( ).
(A) Representative traces of local field potentials (LFPs) recorded from MECP2-TG and wild-type (WT) mice. The gray bar indicates the occurrence of stimuli (red line: tone stimulus; blue line: noise stimulus). (B) Averaged amplitude of LFPs recorded from MECP2-TG mice and WT mice. * p < 0.05, paired t -test, mean ± standard error of the mean (SEM; red: tone stimulus; blue: noise stimulus). (C) Averaged onset latency of LFPs recorded from MECP2-TG mice and WT mice. *** p < 0.001, t -test, mean ± SEM (red: tone stimulus; blue: noise stimulus). (D) Averaged peak latency of LFPs recorded from MECP2-TG mice and WT mice. *** p < 0.001, t -test, mean ± SEM (red: tone stimulus; blue: noise stimulus). (E) Representative peri-stimulus spike timing histograms (PSTHs) in response to different stimulation. The gray bar indicates the occurrence of stimuli. (F) Averaged multiunit activity in response to different stimulation. ** p < 0.01, t -test, mean ± SEM (red: tone stimulus; blue: noise stimulus). (G) Averaged half-peak durations on PSTHs recorded from MECP2-TG mice and WT mice. * p < 0.05, t -test, *** p < 0.001, paired t -test, mean ± SEM (red: tone stimulus; blue: noise stimulus). (H) Averaged peak latency of PSTHs recorded from MECP2-TG mice and WT mice. * p < 0.05, t -test, mean ± SEM (red: tone stimulus; blue: noise stimulus).
We then recorded 88 RS and 34 FS neurons from MECP2-TG mice as well as 60 RS neurons and 22 FS neurons from WT mice using single-cell recording. shows the intensity-dependent tuning of normalized responses to a pure tone (7000 Hz) and white noise. For RS neurons, MECP2-TG mice exhibited a stronger response to white noise than to the pure tone. For FS neurons, WT mice showed stronger responses to white noise than to the pure tone ( ). These comparisons suggest a potential lack of cortical inhibition when MECP2-TG mice were exposed to a noisy environment. Meanwhile, the thresholds of both RS and FS neurons were higher in MECP2-TG mice than in WT mice ( ), which is consistent with the findings from the cortical threshold map ( ).
(A) Average receptive fields of regular-spiking (RS) and fast-spiking (FS) neurons recorded from MECP2-TG and wild-type (WT) mice in response to a pure tone [7000 Hz, 70 dB sound pressure level (SPL)] and white noise (70 dB SPL). (B) Averaged activity of RS and FS neurons in response to a pure tone (7000 Hz, 70 dB SPL) and white noise (70 dB SPL) at 70 dB. * p < 0.05, *** p < 0.001; paired t -test; mean ± SEM. (C) Average intensity threshold of RS and FS neurons recorded from MECP2-TG mice and WT mice to a pure tone and white noise. ** p < 0.01, *** p < 0.001, paired t -test, mean ± SEM (red: tone stimulus; blue: noise stimulus).
## Discussion
Previous studies have reported that individuals with RTT (loss of MECP2) have normal ABRs but delayed auditory cortex responses ( ; ; ; ). In this study, we found that MECP2 overexpression did not strongly affect the major properties of ABRs, such as threshold and peak latencies. These results suggested that there was no major difference between MECP2-overexpressing animal and their WT littermates at the early stage of the auditory system. However, a significant increase in threshold was found in the cortical audiogram, suggesting that the processing of auditory information at the cortical level could be different. Thus, a hearing abnormality could gradually arise from MECP2 overexpression in the ascending auditory pathway. We then focused on the properties of cortical neurons in both genotypes. Analysis and comparison of spikes revealed similar waveforms from RS cells but not FS cells. The altered spike waveform of FS cells indicates that aberrant intracellular dynamics were induced by MECP2 overexpression (e.g., composition of ion channels). These results suggest that MECP2 overexpression might not completely silence the activity of a neuron but could gradually change the transmission of information in a multiple-step circuit such as those in the visual, auditory and somatosensory ascending pathways.
Previous studies have found that individuals with ASD often have difficulty understanding speech in noisy environments ( ; ). In this study, we found that the responses evoked by noise were stronger than those evoked by tone stimulation in MECP2-TG mice. In addition, we found that MECP2-TG mice have a higher intensity threshold than WT mice. These findings suggest that MECP2-TG mice might have difficulty hearing clearly in a noisy environment. In other words, the perception of noise could overwhelm the ability of the animal to perceive the frequency features of sound. This phenotype is potentially related to the noise hypersensitivity commonly found in people with ASD. However, this hypothesis needs to be tested with behavioral evidence and neuron manipulation approaches such as optogenetics.
In addition to noise sensitivity, another interesting finding of this study is that many atypical responses in MECP2-TG mice are directly related to FS neurons, which largely overlap with parvalbumin inhibitory neurons ( ). The excitation/inhibition (E/I) balance plays an important role in the physiology of the nervous system. Increased E/I in key neural systems has long been considered a critical cause of autism-related abnormalities ( ), and inhibitory circuits are critical for adjusting the E/I balance. A disrupted E/I balance in a mouse model of ASD can cause sensory abnormalities such as tactile hypersensitivity ( ). Goffin and his colleagues ( ) demonstrated that the appropriate level of MECP2 in GABAergic neurons was crucial for auditory information processing, and the preservation of MECP2 function in GABAergic neurons can restore auditory processing in MECP2-null mice. Thus, inhibitory neurons might also cause auditory abnormalities and play a critical role in the atypical responses observed in MECP2-TG mice. The narrowed tuning bandwidth of FS neurons might result in a lack of inhibition, which could be related to the unusual noise sensitivity found in MECP2-TG mice. In summary, our study revealed that many fundamental response properties of the auditory cortex in MECP2-TG mice are atypical compared with those of WT littermates, including latency, duration, spike waveform, firing rate, and firing threshold; this finding could be helpful for further understanding auditory deficits in autism.
## Ethics Statement
This study was carried out in accordance with the recommendations of Animal Care and Use Committee of Army Medical University. The protocol was approved by the Animal Care and Use Committee of Army Medical University.
## Author Contributions
YZ and YX designed and supervised the experiments. CZ and SY performed most of the experiments. SQ, ZW, and ZS assisted the work. CZ and YZ wrote the manuscript.
## Conflict of Interest Statement
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
|
As the capacity to isolate distinct neuronal cell types has advanced over the past several decades, new two- and three-dimensional in vitro models of the interactions between different brain regions have expanded our understanding of human neurobiology and the origins of disease. These cultures develop distinctive patterns of activity, but the extent that these patterns are determined by the molecular identity of individual cell types versus the specific pattern of network connectivity is unclear. To address the question of how individual cell types interact in vitro , we developed a simplified culture using two excitatory neuronal subtypes known to participate in the in vivo reticulospinal circuit: HB9 spinal motor neurons and Chx10 hindbrain V2a neurons. Here, we report the emergence of cell type-specific patterns of activity in culture; on their own, Chx10 neurons developed regular, synchronized bursts of activity that recruited neurons across the entire culture, whereas HB9 neuron activity consisted of an irregular pattern. When these two subtypes were cocultured, HB9 neurons developed synchronized network bursts that were precisely correlated with Chx10 neuron activity, thereby recreating an aspect of Chx10 neurons’ role in driving motor activity. These bursts were dependent on AMPA receptors. Our results demonstrate that the molecular classification of the neurons comprising in vitro networks is a crucial determinant of their activity. It is therefore possible to improve both the reproducibility and the applicability of in vitro neurobiological and disease models by carefully controlling the constituent mixtures of neuronal subtypes.
## Introduction
The nervous system is composed of thousands of types of neurons with distinct molecular and anatomical properties ( ). These neurons are organized into circuits with specific patterns of activity that enable an organism to perform adaptive behaviors. The early life experiences of an organism are essential for reinforcing adaptive patterns of neuronal activity ( ; ; ). But in order to ensure rapid and effective development, the behavior of these circuits and the molecular properties of their constituent neurons must be at least partially genetically encoded ( ). This genetic encoding is exploited by models of neuronal disease grown in cell culture dishes, which are not functionally constrained by the rest of the nervous system.
To derive clinically relevant findings, most in vitro models of neurological disease seek to incorporate as realistic a mixture of cells from the modeled region as possible. Such models hold therapeutic promise, as some have already been used to identify small molecule candidates for drug development ( ; ; ). Models of CNS regions have also been shown to develop complex patterns of activity similar to their in vivo counterparts ( ). Primary tissue taken from different areas of the brain and cultured on multi-electrode arrays (MEAs) develops regular bursts of spiking activity with tissue-specific differences in the shape of spike waveforms and the timing and structure of bursts ( ; ; ). When the different tissue types were co-cultured on a single array they developed correlated activity, suggesting that they could communicate with each other.
However, it is unclear whether these observations of bursts and changes in activity are an emergent property of the mixture of cell types in these cultures or governed by the presence of specific neuronal cell types ( ; ; ). To appreciate how network activity emerges in such cultures and how it is determined by the properties of individual neuronal cell types, it is important to study each cell type in isolation prior to combining them into a more complex system.
To address this question, we created a simple system from two neuronal subtypes known to participate at different levels of the locomotor control system, motor neurons and reticulospinal hindbrain neurons. Motor neurons relay patterned input from spinal cord central pattern generator circuits to skeletal muscles to initiate behavior ( ). Reticulospinal neurons are components of a prominent behavioral circuit that relays rhythmic locomotor drive from the brainstem to the spinal cord ( ; , ; ; ).
Of the subtypes of reticulospinal neurons, we specifically focused on V2a excitatory interneurons, identified by expression of the transcription factor Ceh-10 Homeodomain-Containing Homolog (Chx10, also known as Visual System Homeobox 2, or Vsx2), which are found in the spinal cord and medullary reticular formation. Chx10 V2a neurons within the hindbrain reticular formation play a role in regulating hindlimb locomotion ( ; ; ) and respiratory rhythm ( , ) and have functional connectivity to the mesencephalic locomotor region and pre-Bötzinger complex.
In rodents, reticulospinal neurons are critical for setting the timing and gait of locomotion without altering the left-right and flexor-extensor alternation important for the correct expression of gait ( ; ). Glutamatergic reticulospinal neurons are known to form polysynaptic contacts with motor neurons by way of commissural interneurons that participate in the rhythm generating component of the spinal central pattern generator ( ; ; ; ). The specific class of interneuron that hindbrain Chx10 neurons contact has not yet been identified, but these neurons have been shown to be involved in transmitting locomotor stop and turn signals that are relayed via premotor networks within the spinal cord, rather than by direct synapses with motor neurons ( ; ).
By contrast, in the zebrafish and Xenopus tadpole, hindbrain Chx10 neurons directly contact spinal motor neurons and provide patterned excitatory input that is critical for sensory-evoked swimming ( ; ; ). A similar patterning role can be seen among V2a neurons of the rodent spinal cord, which form an important component of the pattern generating circuitry responsible for left-right alternation ( ; ; ; ). When cultured as a purified population in vitro , these spinal V2a neurons developed spontaneous coordinated bursting activity consistent with their role in rhythm generation ( ).
In our simplified reticulospinal cultures containing hindbrain Chx10 and HB9 spinal motor neurons, we hypothesized that each cell type would develop a distinct pattern of network activity, which would be consistent with their distinct behavioral roles. Because motor neurons are controlled by reticulospinal neurons in vivo , we further hypothesized that the reticulospinal neurons’ activity would come to dominate in a combined coculture. This would support the idea that the electrical and biochemical properties of one neuronal subtype can drive the activity of the entire network in vitro .
Here, we report that Chx10 hindbrain neurons develop synchronized network bursts that differ from the uncorrelated and irregular activity of HB9 motor neurons, and that in coculture motor neurons are recruited into Chx10 neuron bursts. We then further identify some synaptic mechanisms that drive these circuit dynamics.
## Materials and Methods
### Cell Culture
All cells were cultured at 37°C in 5% carbon dioxide and 95–100% humidity in Revco Ultima II CO incubators (Thermo Electron). Primary cortical glia were dissected and dissociated from Swiss Webster mice at P1-4 using the protocol described in . Mouse pups were anesthetized with 5% isoflurane for 5 min, then decapitated. The forebrain was separated from the cerebellum and midbrain. The corpus callosum was severed, then the meningeal covering was peeled away. Forebrain tissue was dissociated in 10% trypsin (0.25% EDTA, Gibco, 25200-056) and passed through a 35 μm filter (Corning, 352235). Cells were cultured on 100 mm cell culture dishes treated with 0.1% gelatin (ATCC, PCS-999-027) at a density of ∼5 × 10 cells/cm and grown until confluent, usually within 8 days. Glial culture media contained high glucose DMEM (Sigma-Aldrich, 51441C), 10% heat inactivated fetal bovine serum (ATCC, SCRR-30-2020), and 1% penicillin/streptomycin/antimycotic (Sigma-Aldrich, A5955). Once the glia reached confluence, they were dissociated with trypsin and cultured on sterile 5mm n.1 glass coverslips (Warner, 640700) treated with 1 mg/ml Poly- -Lysine (Millipore, A-003-E) and 1 mg/ml laminin (Corning, 354232) in 24-well plates at a density of 5 × 10 cells/well. Neurons were seeded on this feeder layer of glia once it reached confluence, after about 8 days.
ES-cell derived motor neurons were generated using the protocol described in from the HBG3 ES cell line, in which enhanced green fluorescent protein (eGFP) is expressed under the control of the HB9 promoter (courtesy of Wichterle lab). ES cells were grown in ADFNK media that consisted of 1:1 DMEM/F12 (Millipore, DF-041-B): Neurobasal (Gibco, 21103049), 10% knock out serum replacement (Gibco, 10828010), 1% penicillin/streptomycin/antimycotic, and 1% GlutaMax supplement (Gibco, 35050061) for 2 days until they formed embryoid bodies. Media were supplemented on days 2 and 5 with 1 μM retinoic acid (Sigma-Aldrich, R2625) and 1 μM smoothened agonist (Calbiochem, 566661). On day 6, embryoid bodies were dissociated with papain according to manufacturer’s instructions (Worthington, LK003150).
Unsorted HB9 motor neurons were plated on 5 mm glass coverslips in a 24-well plate on top of a feeder layer of glia at a density of 1 × 10 cells/well. HB9 neurons that underwent FACS sorting were plated on Poly- -lysine and laminin coated 5 mm glass coverslips at a density of 5 × 10 cells/well. For glial coculture, sorted HB9 neurons were seeded on glass coverslips with a feeder layer of astroctyes at a density of 5 × 10 cells/well. For multi-electrode recordings, standard 60-elecrode MEAs (MultiChannel Systems, 890276) were sterilized and then coated with poly- -lysine and laminin and seeded with 1 × 10 sorted HB9 neurons. For glial coculture on MEAs, poly- -lysine and laminin treated arrays were seeded with 5 × 10 glial cells that were grown to confluence prior to seeding with 1 × 10 sorted HB9 neurons. Media consisted of the BrainPhys neuronal medium (StemCell, 5792) supplemented with 2% NeuroCult SM1 neuronal supplement (StemCell, 5711), 1% N2-supplement (Gibco, 17502048), 1% GlutaMax supplement, 1% pen/strep/antimycotic, 1 μM Adenosine 3′,5′-cyclic monophosphate, N , O 2′-dibutyryl-sodium salt (dbCaMP, Calbiochem, 28745), 10 ng/ml Brain derived neurotrophic factor (BDNF, MACS, 130-093-811), 10 ng/ml Glial derived neurotrophic factor (GDNF, GoldBio, 1170-14-10), and 1 μM ascorbic acid (Sigma-Aldrich, A4403). To produce HB9:GFP negative control for FACS sorting, ES-cell derived HB9 motor neurons were generated in parallel from the E14 ES cell line (courtesy of Hatten lab).
Reticulospinal Chx10 neurons were dissected from E12.5 mouse embryonic hindbrains using the protocol described in from mice in which cyan fluorescent protein (CFP) is expressed under the control of the Chx10 promoter ( ). To produce the cells, a male mouse homozygous for Chx10:CFP (courtesy of Sharma lab) was mated with a Swiss Webster female mouse (Taconic). On day E12.5 of the pregnancy, the pregnant female was anesthetized in 5% isofluorane and oxygen and euthanized via cervical dislocation.
For the hindbrain dissection, each embryo was decapitated just rostral to the forelimb and the neural tube was isolated from the rest of the tissue. The developing rhombencephalon (hindbrain) segment corresponding to the position of the reticular formation in adults was excised and trimmed at the rostral and caudal ends. Dissections were performed in ice cold HBSS buffer (Gibco, 14175-095) supplemented with 1% pen/step/antimycotic, 20 mM -glucose (Sigma-Aldrich, G8769), and 1 μM ascorbic acid. Hindbrains were dissociated with papain and sorted using flow cytometry to isolate the Chx10 subpopulation. To produce Chx10:CFP negative control for FACS sorting, E12.5 hindbrains were derived from Swiss Webster mouse embryos. Sorted Chx10 hindbrain neurons were seeded on either 5 mm glass coverslips in a 24-well plate or MEAs, both prepared with a confluent layer of glia, at a density of 1 × 10 neurons/well of coverslips or 4 × 10 neurons/array. All Chx10 hindbrain neurons were cultured in Neurobasal medium supplemented with 2% SB-27 (Gibco, 17504044), 1% GlutaMax, 1% pen/strep/antimycotic, 1 μM dbCaMP, 10 ng/ml BDNF, 10 ng/ml GDNF, and 1 μM ascorbic acid.
For reticulospinal cocultures, sorted HB9:GFP motor neurons and Chx10:CFP hindbrain neurons were seeded together on a confluent layer of glia on either 5 mm coverslips or MEAs. On coverslips in a 24-well plate, HB9 neurons were seeded at a density of 2.5 × 10 cells/well and Chx10 neurons were seeded at a density of 1 × 10 cells/well. On MEAs, HB9 neurons were seeded at a density of 1 × 10 cells/array and Chx10 neurons were seeded at a density of 4 × 10 cells/array. Cocultures were grown in the same supplemented BrainPhys medium used for HB9 cultures.
### Animals
Mice were group housed in a 12-h light/dark schedule, with food and water provided ad libitum . For timed matings, two females were introduced into the home cage of a single male, where they remained for the duration of the mating. Females were checked for vaginal plugs every 24 h and removed to separate cages after plug was detected, and singly housed for the duration of the timed pregnancy. All animal procedures and protocols were approved by the Rockefeller Institutional Animal Care and Use Committee.
### Flow Cytometry
All samples were sorted on the basis of fluorescent marker expression on the BD FACSAriaII benchtop flow cytometer with a 100 μm nozzle and 20 psi sheath pressure. Flow cytometry was performed at the Flow Cytometry Resource Center at Rockefeller University.
To isolate HB9:GFP motor neurons, embryoid bodies derived from HBG3 ES cells were dissociated on day 6 using papain and resuspended in FACS buffer for embryoid bodies that contains phenol-free HBSS supplemented with 2% heat-inhibited horse serum (Gibco, 26050088) and 5 U/mL DNAse (Worthington, LK003172). For the GFP negative control, embryoid bodies were derived from E14 ES cells and prepared under parallel conditions. Between 10 and 20 nM DAPI (Invitrogen, D1306) was added to each sample as a dead-cell exclusion dye. Each sample was excited by a violet 405 nm laser and dead cells were excluded on the basis of emission in the DAPI wavelength 461 nm using the 405D filter. Single cells were distinguished from doublets on the basis of forward and side scatter of the sample comparing the scatter area versus width. GFP fluorescence was detected using illumination from a 488 nm blue laser equipped with a 535/30 nm filter and the gate for GFP cell isolation was set based on a comparison of the GFP fluorescence of the HBG3-derived sample and the E14-derived sample. Typically, 50–60% of input cells from HBG3-derived embryoid bodies expressed GFP.
For Chx10:CFP hindbrain neurons, hindbrains from heterozygous Chx10::CFP mice were dissociated at E12.5 using papain and resuspended in FACS buffer for hindbrains that contained high glucose phenol-free DMEM supplemented with 10% heat-inactivated fetal bovine serum, 1% pen/strep/antimycotic, and 5 U/ml DNAse. For the CFP-negative control, hindbrains from Swiss Webster mice were prepared under parallel conditions. Approximately 20 nM ToPro3 (Invitrogen, T3605) was added to each sample as a dead cell exclusion dye. Each sample was excited by a red 640 nm laser, dead cells were excluded on the basis of emission in the ToPro3 wavelength using the 640C 670/30 nm filter. As with the HB9:GFP motor neurons, single cells were distinguished from doublets on the basis of forward and side scatter area versus width. CFP fluorescence was detected using illumination from a 445 nm blue violet laser equipped with a 490/30 nm filter and the gate for CFP cell isolation was set based on a comparison of the CFP fluorescence of the Chx10:CFP -derived sample and the Swiss Webster-derived sample. Typically, 2.5–3% of input cells from Chx10:CFP mouse hindbrains expressed CFP.
### Electrophysiology
Coverslips containing neurons cultured on a feeder layer of astrocytes as described above (see Cell Culture methods) for 5–10 days were perfused with 1x HEPES-ACSF in the recording chamber (HEPES-ACSF: 135 mM NaCl, 10 mM HEPES, 10 mM glucose, 5 mM KCl, 1 mM CaCl -2H O, 1 mM MgCl ) under constant flow (∼5 ml/min). All cells were patched using pulled glass pipettes with an R of 5 to 12 MΩ filled with a standard internal pipette solution ( -gluconate: 14 mM, HEPES-K: 10 mM, NaHCO : 60 μM, Mg-ATP: 4 mM, Na -ATP: 2 mM, Na-GTP: 30 μM, sucrose: 8 mM, CaCl : 1 mM, EGTA: 5 μM). Data were acquired on the MultiClamp 700B (Axon instruments) using ClampEx software. HB9 spinal motor neurons were identified by GFP signal imaged using an Olympus BXS1W1 upright fluorescence microscope equipped with a FITC/EGFP filter (480/535nm ex/em, Chroma). Chx10 hindbrain neurons were identified by CFP signal from an ECFP filter (436/480 nm ex/em, Chroma).
Once a giga-seal was achieved, the membrane voltage of the neuron was recorded for 1 min at 1 kHz sampling frequency without injecting additional current to measure the spontaneous activity of the neuron. For current-clamp experiments, current was injected to bring V to −70 mV and current steps were applied in 10 pA increments from −10 to 130 pA for 1 s duration, returning to −70 mV holding potential between steps. For voltage clamp experiments, the cell was held at −80 pA for 100 ms before stepping voltage injection from −100 to 150 mV in 10 mV increments for 100 ms, returning to the −80 pA holding potential between each step.
Data analysis and plotting of patch clamp data were performed using ClampFit and Matlab (see for specific scripts used). To generate IV plots of voltage-gated sodium current from voltage-clamp data, the local minimum evoked current within 30 ms of voltage step onset was subtracted from the mean current during the last 30 ms of the voltage step and plotted against the magnitude of the injected voltage.
### Multi-Electrode Recordings
Multi-electrode arrays were cultured with HB9 motor neurons or Chx10 hindbrain neurons as described above (see Cell Culture methods). For the duration of the lifetime of the culture (D3 to D30 days after plating for Chx10 and D7 to D30 days after plating for HB9 neurons), spontaneous extracellular activity was recorded using the MEA2100-Lite system (MultiChannel Systems). The array was placed in the recording apparatus and allowed to equilibrate at room temperature for 30 min prior to recording for 4 min. Data acquisition was performed on MCRack with an input voltage range of −19.5 to +19.5 mV and a sampling frequency of 20 kHz. Raw electrode data for 60 electrodes were processed through a Bessel 4th order high pass filter with a cutoff at 400 Hz. The spike detection threshold was 5 standard deviations below the mean of the filtered recordings. Raw and filtered data, along with spike timestamps were converted to.txt files using MC_DataTool and the resulting files were analyzed in Matlab.
For wash-in experiments of the synaptic blockers 6-cyano-7-nitroquinoxaline-2,3-dione disodium salt (CNQX), -(-)-2-amino-5-phosphonopentanoic acid (AP5), and bicuculline on the MEAs, warmed 1x HEPES-ACSF was perfused through the MEA after the 30-min equilibration period at ∼5 ml/min for 10 min. The baseline activity of the MEA was recorded for 2 min under the previously mentioned parameters. Then, 100 μL of a 10x solution of the drug was slowly perfused in at 50 μL/min for 2 min while recording. After 2 min the pump was stopped and the steady-state activity of the array in the presence of the drug was recorded for 4 min. The MEA was washed with 1x HEPES-ACSF at 5 ml/min for 10 min in between drug applications. Filtered electrode data were converted using MC_DataTool and analyzed in Matlab. The final concentrations of the drugs used were 20 μM CNQX (Tocris, 479347-85-8), 50 μM AP5 (Tocris, 79055-68-8), and 60 μM bicuculline (Tocris, UN1544).
Data analysis for perfusion experiments was performed in Matlab (see for specific scripts used). For initial spike data extraction, the high-pass filtered recordings from each electrode generated by MC_Rack (multichannel systems, Reutlingen, Germany) were converted into .txt format using MC_DataTool (multichannel systems). Spikes were detected in each channel using a manual threshold adjusted to pick up deviations that were approximately five standard deviations below the baseline of the recording and analysis of spike waveforms was used to determine whether one or more neurons was contributing to the observed signal. Spike sorting was performed on these data by plotting the aggregate collection of waveforms from recorded spikes. If this collection of waveforms fell within multiple visually distinguishable distributions, manual thresholds for each distribution were set by drawing a line through the waveforms visually classified as similar and then categorizing all recorded spikes according to whether they cross this threshold line or not. Then, spike rate was calculated for each waveform type by counting the number of spikes that fall within bins of 100ms width and multiplying by 10 to covert to units of Hz.
To facilitate comparison of different spike rates across all recordings, spike rates were smoothed using a spline function, binned according to the average spike rate in non-overlapping 10 s intervals, then normalized to set the average spike rate from the first 10 bins (corresponding to the first 100 s of recording) to 1.
To determine whether synaptic blocker wash-in had a dose-dependent effect on the activity of each culture type, normalized spike rate data from each electrode on the MEA that recorded spontaneous neuronal activity were pooled across all drug wash-in trials for a given culture type. The data corresponding to the period when the drug was washed in (2–4 min into the recording) were fit to a linear mixed effects model using the function fitlme() in Matlab with the normalized spike rate as the predictor variable and electrode as the random effect:
### Calcium Imaging
HB9 , Chx10 , or combined cocultures grown on 5 mm coverslips with a feeder layer of glia were loaded with Rhod-3 AM dye according to the manufacturer’s instructions (Molecular Probes, R10145), then washed with 1x HEPES-ACSF. Calcium imaging was subsequently performed in 1x HEPES-ACSF.
For Chx10 cultures, calcium reporter dye fluorescence during spontaneous activity was imaged using an inverted spinning disk confocal microscope (Zeiss Axiovert 200) equipped with an EMCCD camera (Andor iXon). Solid state lasers were used for excitation at 443 and 561 nm (Spectral Applied) paired with a polychroic filter with 440, 491, 561, and 640 nm filters. Imaging acquisition was performed using MetaMorph software. Chx10 neurons were identified by CFP signal (440/480 nm ex/em) and rhodamine3 signal was identified on the Texas Red channel (561/620-60 nm ex/em). Calcium imaging data were acquired via time-lapse, with a 150 ms interval and 100 ms exposure time for 2 min.
For HB9 cultures, HB9/Chx10 cocultures, and astrocyte cultures, spontaneous calcium activity was imaged at room temperature and ambient CO using an Olympus BXS1W1 upright fluorescence microscope equipped with an Evolution QEi digital CCD camera (MediaCybernetics). A 120W mercury vapor short arc bulb was used as the fluorescence light source (X-Cite series 120Q). Imaging acquisition was done using NIS-Elements BR software. Hb9 spinal motor neurons were identified by GFP signal using a FITC/EGFP filter (480/535 nm ex/em, Chroma) and imaged with an exposure time of 100 ms.
Chx10 hindbrain neurons were identified by CFP signal using an ECFP filter (436/480 nm ex/em, Chroma) and imaged with an exposure time of 100ms. Rhodamine3 signal was imaged using a CY3/TRITC filter (545/605 nm ex/em, Chroma) with an exposure time of 60 ms per frame for 40–80 s.
For experiments involving application of the AMPA blocker CNQX, the spontaneous calcium activity of HB9/Chx10 cocultures in HEPES-ACSF solution was imaged to determine a baseline level of activity. Then, 200 μl of a 100x solution of CNQX was injected into the bath for a final drug concentration of 40 μM. The culture was allowed to equilibrate for 5 min before imaging of spontaneous calcium activity in the presence of the drug. The drug was washed out by replacing 50% of media with fresh HEPES-ACSF in 5 repeated washes, then the culture was allowed to equilibrate for 5 min before measuring recovery of spontaneous activity.
Calcium imaging data for all experiments were analyzed in Matlab (see for specific scripts used). Due to overlap between CFP and GFP emissions spectra, CFP neurons appear on the GFP fluorescence channel and were distinguished from HB9:GFP neurons on the basis of their fluorescence on the CFP channel. ROIs were manually drawn around the cell bodies of identified CFP and GFP neurons and the mean Rhodamine3 fluorescence within the ROI was calculated at each frame of the recording in the Rhod3 channel.
Editing of rhodamine3 fluorescence time-course videos was performed in Fiji. For each video, brightness and contrast was adjusted uniformly across the image stack using the “auto” adjust function. Then, the minimum intensity for each pixel across the image stack (calculated using the “Z-project, minimum” function) was subtracted from each image in the stack to remove noise. Then, brightness and contrast were adjusted again across the image stack. Video playback is 20fps.
### Statistical Methods
All statistical analyses were performed in Matlab. Results are presented as mean ± SEM. The statistical significance level for all of these analyses was set to p < 0.05. For patch clamp experiments, age matched HB9:GFP neurons from cultures that were either immediately plated after dissociation from embryoid bodies or underwent flow cytometry prior to plating were subjected to the same battery of current clamp, voltage clamp, and spontaneous activity recordings. The specific codes used to analyze and plot data from the patch clamp experiments can be found at . Student’s t -test was used for between-group comparisons of voltage-gated I and spike threshold.
For MEA recordings, raw data was passed through a Bessel 4th order high pass filter with a cutoff of 400 Hz to filter out noise, and a threshold of 5 standard deviations below the mean was used to detect spikes from the filtered recordings. Cross-correlation, using the Matlab function xcorr(), was used to determine the degree of coordination of spikes across electrodes that detected spontaneous activity from each recording. For synaptic blocker experiments, all electrodes with spontaneous activity from MEA recordings were pooled according to culture type and normalized. To determine if there was a dose-dependent effect of synaptic blocker on spike rate, this data was fit to a linear mixed effects model. The specific codes used to process and analyze data from the perfusion experiments can be found at .
For calcium imaging experiments, ROIs were manually drawn around neurons identified on the basis of CFP and GFP fluorescence to be Chx10 or HB9 and the mean rhodamine calcium indicator fluorescence was calculated within each ROI over the time course of the recording. The specific codes used to process and analyze calcium imaging data can be found at . To facilitate plotting of calcium imaging data in – , these calcium activity traces were further normalized to a level baseline that takes into account gradual photobleaching over the course of the experiment. The baseline of each calcium trace, calculated by passing the raw data through a smoothing spline function using the Matlab fit() function with a smoothing parameter of 0.0001, was subtracted from the raw data. Then, each trace was normalized along the 0-to-1 scale to take into account arbitrary differences between the overall fluorescence of different cells.
### Code Accessibility
All custom written code used for this study is available on github. The code used to analyze and visualize patch clamp data is available at . Code for quantifying calcium imaging data is available at . The code used for extracting and analyzing data from synaptic blocker perfusion experiments on MEAs is available at .
## Results
### Developing Reticulospinal Cultures
Numerous studies of mixed populations of neurons from various brain regions including cortex, amygdala, and spinal cord have demonstrated a strong tendency to develop network bursts when cultured on MEAs. These bursts occur when many neurons across the cultured network fire at once at regular intervals ( ; ; ; ). The generation of such bursts is the product of a precise balance between different ionic conductances within individual neurons ( ; ) and interactions between classes of excitatory and inhibitory interneurons that work in concert to balance network activity between a state of excitation and complete quiescence ( ; ; ).
We sought to test whether networks of excitatory neurons could generate coordinated bursts in the absence of inhibitory interneurons by purifying neuronal subpopulations, which allowed us to culture identified neurons at defined stages of development. We focused specifically on reticulospinal cultures containing homogeneous populations of HB9 spinal motor neurons and hindbrain Chx10 neurons. Hindbrain Chx10 neurons are known to play a role in regulating locomotor gait and breathing rhythm and they have descending projections to the spinal cord ( ; ; ). Spinal motor neurons provide direct limb muscle innervation. Thus, the in vivo function of both neuronal subtypes predisposes them to rhythmic bursts.
To isolate pure populations of HB9 spinal motor neurons and hindbrain Chx10 neurons, we employed fluorescence activated cell sorting (FACS). We cultured these cell types as single populations and also as a mixed reticulospinal culture ( ). We differentiated HB9 spinal motor neurons from HBG3 embryonic stem cells using protocol to induce the spinal motor neuron identity ( ). We note that a second population of HB9 interneurons with rhythm generating function distinct from motor neurons exists in the intact spinal cord, but previous studies have demonstrated that these ChAT interneurons constitute < 5% of HB9 neurons generated by the program of ventralization and caudalization used here ( ; ; ; ).
Isolation and culture of HB9 motor neurons and Chx10 hindbrain neurons. (A) Timeline schematic of isolating and setting up HB9:GFP , Chx10:CFP , and combined co-cultures. (B–E) Sample FACS plots and thresholds for isolation of HB9:GFP and Chx10:CFP neurons. (B) GFP neurons from HB9:GFP stem-cell derived embryoid bodies after 6 days in culture (DIC) (C) embryoid bodies derived from non-transgenic ES cells (negative control) (D) CFP neurons from E12.5 hindbrains of Chx10:CFP mice and (E) Swiss Webster mice (negative control). (F–H) Fluorescent photomicrographs of neurons cultured after sorting. Yellow arrowheads indicate HB9:GFP neurons and white arrowheads indicate Chx10:CFP neurons. (F) Sorted HB9:GFP neurons, 16 DIC. (G) sorted Chx10:CFP hindbrain neurons, 10 DIC (scale bar 20 μm) (H) combined culture of both subtypes, 16 DIC (scale bar 20 μm).
Following the motor neuron differentiation, embryoid bodies were dissociated 6 days after formation and sorted on the basis of HB9:GFP expression. E14 stem cells lacking GFP were used as a negative control for FACS ( ). Approximately 50–60% of unsorted cells in the embryoid body derived from HBG3 ES cells expressed GFP. FACS sorting for GFP expression enriched this population to >96% purity. HB9:GFP motor neurons were subsequently cultured on a layer of cortical astrocytes to improve axonal outgrowth and network development ( ).
To test whether sorting affected the electrophysiological activity of HB9 neurons, we performed whole cell patch clamp on HB9:GFP neurons from sorted and unsorted cultures grown in parallel under identical conditions. After 7 days in culture, HB9 neurons in both treatments responded to brief current pulses with spike trains, having a spike threshold around 20 pA ( ). They developed voltage gated sodium current (I ) with maximum current evoked at −2 ± 12 mV ( ) that was not significantly different between sorted and unsorted populations (Student’s two-tailed T -test p = 0.879). After 13 days in culture, both sorted and unsorted HB9 motor neurons also developed spontaneous spike trains ( ).
Effects of FACS sorting on neuron electrophysiology. (A,B) Comparison of the response of (A) , unsorted and (B) , sorted HB9:GFP neurons (bottom panels) to injections of 20, 30, and 40 pA current (top panels). (C,D) Response of (C) , unsorted and (D) , sorted HB9:GFP neurons (bottom panels) to voltage step injection of –90 to 30 mV (top panels, result for –10 to 30 mV injections shown) (7 DIC). Sodium current (I ) was calculated at each injected voltage step by subtracting the steady state current response (solid circle) from the initial current minimum (empty circle) (formula shown in insert Ei ). (E) I-V plot of voltage-gated Na currents for sorted and unsorted HB9:GFP neurons calculated from the voltage clamp experiment results shown in (C,D) . (F) Spontaneous activity of unsorted (top panel) and sorted (bottom panel) HB9:GFP cells at 13 DIC. (G) Spontaneous activity of sorted Chx10:CFP neurons at 5 DIC (top), 6 DIC (middle) and 10 DIC (bottom).
We then isolated and cultured primary hindbrain neurons expressing the transcription factor Chx10, also using the FACS approach. We first assessed the Chx10 neurons’ behavior in vitro as a homogeneous population, and then in combination with HB9 neurons to determine if they could form a reticulospinal circuit in vitro . For these experiments, we dissected neurons from embryonic Chx10:CFP mice at E12.5, prepared a single cell suspension and used FACS to isolate the CFP population. As a negative control for CFP expression, we used hindbrains taken from wildtype (WT) Swiss Webster E12.5 mouse embryos that do not express CFP ( ).
The hindbrains contained 2–3% Chx10:CFP neurons, and sorting enriched this population to >95% purity. These CFP neurons were then cultured on a layer of cortical astrocytes, which is known to improve the development and long-term viability of neuronal cultures ( ) ( ; ; ).
It is possible that, when removed from the intact reticular formation with its descending inputs and diversity of other cell types, Chx10 hindbrain neurons would not develop any intrinsic activity that could pattern a reticulospinal circuit. To assess the electrophysiological development of sorted Chx10 neurons, we used whole-cell patch clamp to record the spontaneous activity of single cells in cultures at different ages ranging from 1 to 30 days in culture. For Chx10 hindbrain neurons, the measured membrane capacitance was 22.75 ± 2pF, membrane resistance was 787.27 ± 105 MΩ, access resistance was 29.01 ± 3 MΩ, and membrane voltage was −22.6 ± 4 mV. We found that Chx10 hindbrain neurons developed spontaneous electrophysiological activity after 5 days in culture. This activity started off as random trains of spikes, but gradually became organized into robust, regular bursts by 10 days in culture and this pattern of activity continued throughout the remaining lifetime of the cultures ( ).
### Motor and Chx10 Neuron Cultures Develop Distinct Patterns of Network Activity
Having established that HB9 motor neurons and Chx10 hindbrain neurons develop spontaneous electrophysiological activity at the single cell level, we sought to determine whether cultures of either cell type, which are composed almost exclusively of excitatory neurons and astrocytes, could generate spontaneous patterns of network activity, whether these patterns would organize into network bursts, and whether there were any cell-type specific differences in such activity.
To record the activity of multiple neurons at different time points, we cultured sorted HB9 motor neurons on multielectrode arrays (MEAs) containing a grid of 64 extracellular recording electrodes ( ). We recorded their spontaneous activity daily over 30 days, starting from the day after plating.
We found that on their own, without astrocytes, sorted HB9 motor neurons did not develop any spontaneous activity on the MEA ( n = 6). However, when these neurons were cultured on a confluent layer of astrocytes, they gradually developed robust network activity that remained stable over a month of recording ( n = 14). We note that astrocytes cultured on their own did not develop spontaneous activity when recorded on MEAs ( n = 3), although we did observe spontaneous calcium flux in astrocyte cultures visualized with the calcium-sensitive dye Rhodamine3 ( ). The activity of HB9 motor neuron/astrocyte cultures was not well coordinated, even among neighboring recording electrodes ( ). To assess whether the overall activity of the culture had a hidden underlying temporal structure, we calculated the mean spike rate across all active channels of the HB9 motor neuron cultures and found that it remained constant throughout the recording session ( ).
HB9 motor neurons and Chx10 hindbrain neurons develop different patterns of activity in vitro . (A–C) Example of a multielectrode array (MEA) recording of sorted HB9:GFP neurons (18 DIC), both as (A) , high pass filtered MEA data ( y -axis scale bar on bottom right) and (B) , raster plot. Locations of the electrodes shown are indicated in red in (C) . (D) Mean spike rate of entire HB9:GFP neuron culture from (A,B) . (E,F) Quantification of calcium-sensitive Rhodamine3 dye fluorescence in the cell bodies of HB9:GFP neurons (E) , 19 DIC, see for rhodamine fluorescence time course, (F) , 32 DIC, see . Corresponding right panels are photomicrographs of neurons quantified for calcium activity, indicated by yellow arrowheads. (G–I) Example of a multielectrode array (MEA) recording of sorted Chx10:CFP neurons (5 DIC) as (G) , high-pass filtered MEA data ( y -axis scale bar on bottom right) and (H) , raster plot. locations of electrodes on array in red in (I) . (J) Mean spike rate of entire Chx10:CFP neuron culture from (G,H) . (K) Calcium imaging of Chx10:CFP neurons (10 DIC), also shown in . Right panel is a photomicrograph of identified neurons quantified for calcium activity, indicated by white arrowheads.
The patterns that we observed for neurons cultured on MEAs were consistent with widespread activation that recruits many neurons across the network. To determine what fraction of the neurons in each culture were contributing to overall activity, we used calcium imaging with the calcium-sensitive dye Rhodamine3 to assess HB9 motor neuron activity with single-cell resolution. We observed randomly distributed calcium spikes that were asynchronous between neighboring neurons ( and ), though more mature cultures did develop some synchrony ( and ). The mean correlation coefficient between the spike rates of multiple neurons within the same HB9 neuron culture was 0.15 ± 0.17 ( p = 0.15).
When we cultured Chx10 hindbrain neurons on MEAs with a confluent layer of astrocytes, we observed the emergence of spontaneous activity with these neurons as well. Unlike HB9 neurons, Chx10 neurons developed robust and coordinated network bursts ( ). Practically no spikes occurred outside of these sharply delineated bursting periods. The time between bursts (inter-burst interval) varied between 2 and 10 s throughout the lifetime of the cultures, with no apparent long-term trend. We observed the same sort of robust network bursts in Chx10 hindbrain neuron cultures that recruited all cells visualized with calcium imaging ( and ).
### HB9 and Chx10 Neurons Develop Correlated Activity in Coculture
Despite their common excitatory identity, we observed that HB9 and Chx10 hindbrain neurons develop distinct patterns of spontaneous network activity. If these two cell types fail to form functional connections to one another in vitro , these patterns of activity should remain unchanged in coculture, but if a unidirectional functional connection forms between Chx10 and HB9 neurons, we might expect to see one activity pattern dominate in coculture. To test these possibilities, we cultured the two cell types together as a mixed population on MEAs and recorded their spontaneous activity daily over 30 days. Since the survival of Chx10 and HB9 neurons after FACS sorting was highly dependent on their initial plating concentration, we cocultured these neurons at concentrations that were empirically determined to optimize the survival of each cell type, a ratio of 5:2 HB9 to Chx10 neurons.
Such cocultures develop spontaneous bursts of comparable time scale and duration to pure Chx10 cultures, though some neurons continue to have spiking activity that resembles HB9 motor neurons in between network bursts ( ). When the overall network activity was measured by averaging spike rates across all active electrodes, the Chx10-like network bursts predominated ( ).
In reticulospinal coculture, Chx10 hindbrain neurons drive patterned HB9 neuron activity. (A,B) Example of an MEA recording of HB9:GFP /Chx10:CFP neuron coculture (8 DIC), both as (A) , high pass filtered MEA data ( y -axis scale bar on bottom right) and (B) , raster plot. Locations of the electrodes shown are indicated in red in (C) . (D) Mean spike rate of entire coculture from (A,B) over the course of 120 s. (E,F) Calcium imaging of neurons in co-culture. (E) Normalized calcium-sensitive fluorescence intensity over time in cocultured HB9:GFP and Chx10:CFP neurons participating in coordinated bursts, shown also in . (F) Normalized calcium-sensitive fluorescence intensity of two HB9:GFP neurons from coculture (Chx10:CFP neurons not pictured) participating in network bursts, shown also in . Corresponding right panels indicate identified neurons quantified for calcium activity, white arrowheads for Chx10:CFP and yellow arrowheads for HB9:GFP .
It is possible that the bursts we observed in the reticulospinal culture were generated only by the Chx10 neurons in the dish and that the HB9 motor neurons were quiescent and did not contribute to network activity. In order to determine which cell type participates in the cultures’ network bursts, we used calcium imaging to obtain single cell resolution recordings of the coculture. We found that neighboring HB9 and Chx10 neurons both participate in network burst events ( and ). Some HB9 motor neurons in coculture also have brief, non-coordinated calcium spiking events that occur between the larger bursts ( and ).
The percentages of Chx10 and HB9 neurons from calcium-imaging experiments that were spiking, bursting, both spiking and bursting, or inactive in each of the culture conditions are summarized in .
Overview of activity patterns of Chx10 and HB9 neurons from calcium imaging experiments.
### HB9 and Chx10 Network Activity Is an AMPA Receptor-Dependent Process
The spontaneous coordinated activity we observed in Chx10 and HB9 neuron cultures could be the product of intrinsic pacemaker properties of these neurons or an emergent property of the network that is dependent on synaptic transmission. To distinguish between these alternatives, we applied a panel of synaptic blockers targeting α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors, N -methyl -aspartate (NMDA) receptors, and γ-aminobutyric acid, type A (GABA ) receptors, while recording from the cultures on MEAs to observe changes in spontaneous activity. The blockers used included the AMPA receptor antagonist 6-cyano-7-nitroquinoxaline-2,3-dione disodium salt (CNQX), the NMDA receptor antagonist -(-)-2-amino-5-phosphonopentanoic acid (AP5), and the GABA receptor antagonist bicuculline. Washing in the AMPA antagonist CNQX on cultures of spiking HB9 neurons caused a gradual decrease in activity to about 40% of initial levels ( ). There was a significant relationship between drug dose and spike rates (linear mixed effects model: β = −0.04, p = 2.65e-63). Similarly, CNQX application resulted in a significant decrease in the activity of Chx10 neurons to about 40% of the initial rate ( ) (β = −0.021, p = 8.61e-15). The application of CNQX to cocultures caused the majority of cells to abruptly stop bursting ( ). Other neurons gradually became decoupled from the network bursts and fired tonically for a brief period before also being silenced during CNQX application ( ). The average response of cocultured neurons to CNQX application reflects this transient increase in activity followed by eventual inhibition ( ) (β = −0.012, p = 0.0015).
Spontaneous activity in reticulospinal cultures is an AMPA -dependent process. (A–D) Examples of high pass filtered MEA recordings of spiking neurons during wash-in of a 200 μM solution of the AMPA blocker CNQX at 50 μL/min (final CNQX concentration 20 μM), orange bars show time course of blocker wash-in. (A) Neuron from HB9:GFP culture, (B) neuron from Chx10:CFP culture, (C,D) examples of two different kinds of responses to CNQX of neurons from HB9:GFP /Chx10:CFP coculture. (E–G) Normalized mean responses of all neurons recorded from electrodes with activity to CNQX wash-in, (E) HB9:GFP cultures ( n = 3), (F) Chx10:CFP cultures ( n = 3), (G) HB9:GFP /Chx10:CFP cocultures ( n = 4). (H–J) Calcium imaging of coculture (H) , bursting prior to CNQX application (shown also in ), (I) inhibition of bursting, but not HB9:GFP spiking, by application of 40 μM CNQX (shown also in ), and (J) bursting recovers after washout of CNQX (shown also in ). Corresponding right panels are photomicrographs of neurons quantified for calcium activity, indicated by white arrowheads for Chx10:CFP and yellow arrowheads for HB9:GFP .
We repeated the CNQX drug application on cocultures and used calcium imaging with Rhodamine3 to visualize the activity of the culture prior to and after application of 40 μM CNQX. Despite a loss of network bursting activity, we observed that some HB9 neurons in the coculture continued to have spontaneous spiking activity in the presence of a blocking concentration of CNQX ( and – ).
We also tested the effects of the NMDA receptor antagonist AP5 on all three cultures ( ) and found that there was no significant relationship between blocker dose and spike rates during AP5 wash-in (linear mixed effects model for: HB9 neurons, β = 0.0005 p = 0.23, Chx10 neurons, β = 0.004, p = 0.25, coculture, β = 0.006, p = 0.24). The GABA receptor blocker bicuculline also had no detectable effect on Chx10 hindbrain neurons, HB9 motor neurons, or cocultures ( ) (linear mixed effects model for: HB9 neurons, β = 0.0003, p = 0.54, Chx10 neurons, β = 0.0026, p = 0.34, coculture, β = 0.0057, p = 0.13).
Responses of reticulospinal cultures to NMDA and GABA R blockers. (A–C) Examples of high pass filtered MEA recordings of spiking neurons during wash-in of a 500 μM solution of NMDA blocker AP5 at 50 μL/min (final AP5 concentration 50 μM), green bars show approximate time course of AP5 wash-in. (A) Neuron from HB9:GFP culture, (B) neuron from Chx10:CFP culture, (C) neuron from HB9:GFP /Chx10:CFP coculture. (D–F) Normalized mean responses of all recorded neurons to AP5 wash-in, (D) HB9:GFP cultures ( n = 3), (E) Chx10:CFP cultures ( n = 3), (F) HB9:GFP /Chx10:CFP cocultures ( n = 4). (G–I) Examples of high pass filtered MEA recordings of spiking neurons during wash-in of a 600 μM solution of GABA R blocker bicuculline at 50uL/min (final bicuculline concentration 60 μM). Magenta bars show time course of bicuculline wash-in. (G) Neuron from HB9:GFP culture, (H) neuron from Chx10:CFP culture, (I) neuron from HB9:GFP /Chx10:CFP coculture. (J–L) Normalized mean responses of all recorded neurons to bicuculline wash-in, (J) HB9:GFP cultures ( n = 3), (K) Chx10:CFP cultures ( n = 3), (L) HB9:GFP /Chx10:CFP cocultures ( n = 4).
## Discussion
In this study, we used flow cytometry to isolate HB9 motor neurons and Chx10 hindbrain neurons and cultured these cell types separately and together to form a reticulospinal circuit. We found that the sorting process did not significantly impact the development of HB9 and Chx10 neuron electrophysiology. When isolated, these two cell types developed distinct patterns of network activity. HB9 neurons tended toward uncoordinated spike trains, while Chx10 hindbrain neurons were characterized by regular, network-wide bursts of activity. Cocultures of these two cell types developed the network bursts characteristic of Chx10 neurons that recruited neighboring HB9 neurons. We further note that the activity of all these cultures was insensitive to NMDA and GABA receptor blockers but could be inhibited by the AMPA receptor blocker CNQX.
### Effect of Cell Sorting on Electrophysiology of Isolated Cells
Although HB9 plays an important role in consolidating motor neuron identity ( ; ), we note that there exists a second population of HB9 interneurons with a rhythm-generating role in the spinal cord ( ; , ; ). These interneurons are distinguished from cholinergic motor neurons by their lack of ChAT immunoreactivity. Other studies have found that over 95% of the HB9 neurons derived from HB9:GFP stem cells using the protocol developed by are ChAT , indicating that HB9 ChAT interneurons, although prominent in the spinal cord, form a minor segment of the total HB9 population of stem cell derived neurons ( ; ; ; ). Therefore, we consider our FACS-sorted stem cell-derived HB9 neurons to primarily have a motor neuron identity.
FACS-sorted stem-cell derived HB9 motor neurons develop complex morphology and electrical excitability in vitro ( ; ; ; ), but previous studies had not established whether the nature of their electrical responses had been altered. Our results show that sorted Hb9 motor neurons develop spontaneous spiking activity, fast inactivating sodium currents, and repetitive trains of action potentials in response to current injection. These results are consistent with the reported electrophysiology of unsorted stem cell-derived motor neurons ( ). Thus, our single-cell electrophysiology indicates that the presence of other neuronal subtypes and progenitors does not alter the electrical properties of HB9 motor neurons, indicating that they are determined by cell type identity.
Hindbrain Chx10 neurons comprise a very small subset of the total hindbrain ( ), which confounds attempts to study their network activity in unsorted cultures. We found that FACS-sorted Chx10 hindbrain neurons developed spontaneous rhythmic bursting activity in vitro . This behavior is consistent with the observation that a closely related population of spinal V2a neurons develops spontaneous rhythmic activity following FACS isolation and reaggregation into three-dimensional cultures ( ). The resting membrane potential of the hindbrain Chx10 neurons started off with a resting potential close to 0 mV, but became increasingly negative as they matured and developed spontaneous activity. Immature neurons tend to have a depolarized resting membrane potential that becomes increasingly hyperpolarized with age ( ). This well documented observation is thought to arise from a combination of real developmental changes in ionic conductances ( ; ; ) and leak current artifacts introduced by the patch clamp pipette that are particularly strong in immature neurons, which have a high input resistance ( ).
In this study, we selectively isolated and cultured Chx10 neurons of the E12.5 embryonic hindbrain, which specifically excludes spinal Chx10 V2a neurons. However, we note that even this spatially and molecularly defined population of neurons is likely to contain a considerable degree of diversity, as demonstrated by the different roles that hindbrain Chx10 neurons play in regulating respiratory rhythms ( ) and hindlimb locomotion ( ; ).
Taken together, these experiments demonstrate that FACS is a viable option for the isolation and subsequent long-term culture of molecularly defined neuronal subtypes.
### Cell Type Specific Patterns of Activity in Cultures of Sorted Neurons
Several prior studies have arranged neurons on MEAs in very specific patterns ( ; ), but not defined subtypes. The random patterning of molecularly defined cells on our arrays allowed us to explore whether there is a consistent influence of cell type on network behavior, regardless of network architecture.
Our observation that HB9 motor neurons fail to develop spontaneous activity in the absence of glia is consistent with other studies that have demonstrated the essential support that astrocytes provide for cultured neurons ( ; ), including motor neurons ( ). When we cultured sorted HB9 neurons with astrocytes they developed unsynchronized spike trains. FACS appears to be critical for this behavior, as previous studies of stem cell-derived HB9 motor neurons cultured without FACS isolation reported coordinated network bursts ( ). In unsorted cultures, a cell type other than HB9 neurons must have contributed to the generation of this activity pattern. We note that the spinal motor neuron differentiation protocol generates a small but prominent subpopulation of spinal V2a neurons, a cell type that is closely related to our rhythmogenic hindbrain Chx10 neurons ( ).
We found that Chx10 hindbrain neurons isolated by FACS and cultured on MEAs developed robust and highly coordinated network bursts. Calcium imaging ( ) indicates that virtually all Chx10 neurons participate in these bursts, with no discernible time delay. Thus, simultaneous spiking is an intrinsic feature of Chx10 neurons in culture and does not appear to require the presence of other neuron types.
### Chx10-Like Pattern of Activity Is Dominant in Coculture
Recordings from the coculture indicate that HB9 neurons develop rhythmic bursting activity that is correlated with that of neighboring Chx10 neurons ( ). The HB9 neurons’ activity under these conditions noticeably differed from their behavior in monoculture, where they failed to develop coordinated network activity. In contrast, Chx10 hindbrain neurons were able to generate their own patterns of activity in monoculture without the need for exogenous cell types besides astrocytes. Taken together, these results suggest that in coculture, Chx10 neurons are driving patterned HB9 neuron activity, although it is as yet unclear whether this interaction is the result of direct innervation of HB9 neurons by Chx10 neurons or some indirect effect that Chx10 neurons exert via diffusible factors or changes to HB9 neurons’ intrinsic excitability.
Our results indicate that electrically excitable cell types develop different spontaneous patterns of activity that are driven by the intrinsic properties of that cell type. However, the genetic identity of the neurons being cultured is not the sole determinant of their network behavior. Astrocytes play an important role in modulating neuronal activity, as we were unable to detect any spontaneous activity in cultures lacking astrocytes, consistent with previous results with neuronal culture on MEAs ( ; ). It is likely that one way that astrocytes facilitate neuronal activity is by removing excess glutamate to prevent excitotoxicity ( ; ). Consistent with previous reports ( ), the astrocytes in our culture were active, as indicated by slow waves of calcium activity which we were able to observe in calcium imaging ( ), but which did not produce electrical excitation on MEAs.
Our observation that Chx10 neurons are able to impose temporally patterned activity on HB9 neurons is consistent with their in vivo function of driving rhythmic behaviors such as hindlimb locomotion and respiration. Prior studies suggest that activation of these neurons is associated with bouts of locomotion, and may drive locomotor stop and turn signals ( ; ; ). Additionally, Chx10 neurons project to the pre-Bötzinger complex, and their ablation disrupts respiratory rhythms in newborn mice, with normal respiratory rhythms gradually reasserting themselves as the mice grow older ( , ).
### Emergent Properties of Neuronal Cultures as Revealed by Synaptic Inhibition
Our results from applying a panel of synaptic blockers targeting AMPA, NMDA, and GABA receptors to spontaneously active HB9 and Chx10 neuron cultures ( , ) show that the AMPA blocker CNQX effectively blocked all bursts in Chx10 cultures and significantly decreased the activity in HB9 neuron cultures. This is consistent with the observation that spinal motor neurons cultured in vitro form glutamatergic synapses that are entirely blocked by CNQX ( ). CNQX application similarly eradicates spontaneous network bursting in cultures of spinal Chx10 neurons that are otherwise insensitive to glycine and GABA antagonists ( ). Our finding that bursts of hindbrain Chx10 neurons could be effectively eradicated by blocking glutamatergic transmission suggests that the robust rhythmicity of these neurons is an emergent property of the network, as opposed to pacemaker activity generated by individual cells. This contrasts with true pacemaker neurons, such as those of the pre-Bötzinger complex, where bursts are intrinsic to individual cells, and therefore insensitive to the same cocktail of synaptic blockers ( ). Thus, we observe that AMPA receptor activation can drive very different outcomes that depend on cell type.
When we applied CNQX to the coculture, some neurons switched from rhythmic bursting to a transient period of tonic spiking before becoming quiescent. This emergent property may be driven by HB9 neurons that revert to their native spiking phenotype in the absence of the patterning influence of network bursts. This is consistent with our calcium imaging data in which we identified HB9 neurons in coculture that continued to have calcium spikes even in the presence of a dose of CNQX that effectively disrupted network bursts ( ). CNQX is also known to act as a partial AMPA receptor agonist under certain conditions, which could explain why some neurons in the coculture condition increased their firing rate following CNQX application ( ).
### Implications of Our Results for Modeling Reticulospinal Circuits
The results of our study can be applied to modeling of reticulospinal circuits, different aspects of which are currently being examined by multiple groups ( ; ; ). However, in the rodent reticulospinal circuit, hindbrain Chx10 neurons primarily contact premotor networks within the spinal cord, as opposed to synapsing directly onto motor neurons the way that we have modeled in our cultures ( ; ). Thus, further elaboration of our Chx10-HB9 coculture model is required to fully recapitulate the mammalian reticulospinal circuit. In the mammalian motor system, reticulospinal projections provide descending input to spinal central pattern generators that is then translated into the patterned output onto motor neurons essential for the appropriate expression of gait ( ). This reticulospinal signal, although important for initiating and halting locomotion, is broadly unpatterned ( ; ; ). So, incorporating additional spinal interneuron cell types that participate in the premotor central pattern generator would provide an essential layer of complexity to our reticulospinal culture that could potentially model how descending signals from glutamatergic hindbrain neurons like the Chx10 population are transformed into rhythmic locomotor-like activity in an experimentally tractable system.
Despite such caveats, it can be argued that the circuit created by our in vitro cocultures is similar to the basic circuitry found in fish and amphibians. In the zebrafish hindbrain Chx10 neurons directly contact spinal motor neurons and generate swimming when selectively stimulated ( ). Likewise, in Xenopus tadpoles the Chx10 dorsoventral hindbrain provides patterned excitatory input directly to motor neurons, driving sensory-evoked swimming before other motor control systems have developed ( ; ). Thus, even in our highly simplified system, we have been able to recreate some biologically relevant behaviors.
Ultimately, the most generalizable aspect our findings is the observation that the aggregate activity of neuronal networks is influenced by the specific molecular identity of their constituent neurons, beyond specific pacemaker cells or broad categories of excitatory-inhibitory cells. The method that we have developed for isolating specific cell types and culturing them in vitro allows us to distinguish the extent to which the properties of a given neuronal class are explained by unique features of its cell type rather than its connectivity within a larger circuit. These aspects would be difficult to parse in an in vivo system. Our results demonstrate how certain electrical properties of neurons are intrinsic to their specific subtype, which is an important consideration for modeling the effects of mutations and disease on network function. The cell type compositions of circuit models can have profound effects on patterns of activity and therefore need to be considered and interpreted carefully.
## Data Availability Statement
Calcium imaging datasets generated for this study are included in the manuscript/ . Other raw data, supporting the conclusions of this manuscript will be made available by the authors, without undue reservation, to any qualified researcher.
## Ethics Statement
The animal study was reviewed and approved by The Rockefeller University Animal Care and Use Committee.
## Author Contributions
AB, IT, LK, and DP designed the research. AB and HK performed the experiments. AB and IT wrote the manuscript.
## Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
|
A prior cue or stimulus allows prediction of the future occurrence of an event and therefore reduces the associated neural activity in several cortical areas. This phenomenon is labeled expectation suppression (ES) and has recently been shown to be independent of the generally observed effects of stimulus repetitions (repetition suppression, RS: reduced neuronal response after the repetition of a given stimulus). While it has been shown that attentional cueing is strongly affected by the length of the cue-target delay, we have no information on the temporal dynamics of expectation effects, as in most prior studies of ES the delay between the predictive cue and the target (i.e., the inter-stimulus interval, ISI) was in the range of a few hundred milliseconds. Hence, we presented participants with pairs of faces where the first face could be used to build expectations regarding the second one, in the sense that one gender indicated repetition of the same face while the other gender predicted the occurrence of novel faces. In addition, we presented the stimulus pairs with two different ISIs (0.5 s for Immediate and 1.75 or 3.75 s for Delayed ISIs). We found significant RS as well as a reduced response for correctly predicted when compared to surprising trials in the fusiform face area. Importantly, the effects of repetition and expectation were both independent of the length of the ISI period. This implies that Immediate and Delayed cue-target stimulus arrangements lead to similar expectation effects in the face sensitive-visual cortex.
## Introduction
Repetition related phenomena have been widely studied using both electrophysiological and neuroimaging techniques. Typically these studies report suppression of the neural signal for repeated when compared to alternating stimuli (repetition suppression, RS; ; for review see ). RS has been explained in many ways (i.e., synaptic depression, network dynamics, and facilitation of the neural response) and has become one of the most intensively studied phenomena in cognitive neurosciences. Further, it is broadly applied as a tool to investigate the selective properties of neuronal populations in neuroimaging experiments (fMRI adaptation; ).
Recently, the neural mechanisms of RS have been connected to predictive coding theories of sensory perception (PC, see ; ). According to models of PC, the brain constantly generates predictions about sensory inputs and then computes the difference between these predictions and the actual sensory input. Therefore, surprising/incorrectly predicted events cause higher neural activity than expected/correctly predicted events ( , ; ). In other words, the occurrence of an expected event can also lead to reduced neuronal activity when compared to incorrect predictions, i.e., to surprising events. This phenomenon was recently labeled expectation suppression (ES, ).
In an influential study, presented participants with pairs of faces that could either repeat or alternate. These faces were grouped into blocks with either high (75%, RB) or low (25%, AB) repetition probabilities (P(rep)]. The results revealed larger RS in the fusiform face area (FFA; ) in blocks with more repetitions (RB), and hence more expected when compared to blocks with fewer repetitions, and thus surprising repetitions (AB). Therefore, the authors suggested that higher-order contextual expectations modulated repetition-related processes. Other studies confirmed the existence of such P(rep) modulations of RS for faces ( , ; ; ) and Roman letters ( ). While no such modulations were found for chairs ( ) or unfamiliar characters ( ), but for a different conclusion see . All of these studies used a factorial design in which repetition and repetition probability varied orthogonally. However, they did not allow the independent testing of expectation and repetition effects due to the use of high and low repetition blocks to manipulate expectations.
Other studies have induced explicit perceptual expectations on a trial-by-trial basis by associating a given stimulus with a preceding schematic cue or image ( ; ). Current MEG and neuroimaging studies have combined such paradigms with stimulus repetitions, in the sense that the first stimulus of a pair signals the likelihood of repetitions or alternations, and found both ES and RS to be present in the target-related signal ( ; ; ). Importantly, both the MEG and the neuroimaging studies have found that the effects of expectation and repetition are independent and additive processes in the human brain. Moreover, a recent EEG study ( ) also investigated whether repetition effects are influenced by perceptual expectations and found distinct spatiotemporal patterns of repetition and expectation effects, supporting the idea of separable mechanisms underlying these phenomena.
Earlier studies have explored the influence of the inter-stimulus interval (ISI) length on RS and showed similarities between short and long-lagged repetition effects ( ; ), but it has also been suggested that different neuronal mechanisms explain RS for long and short ISIs ( ; ; ; ). Additionally, both electrophysiological ( ) and behavioral ( ) studies of RS and repetition priming, describing behavioral response improvements for repeatedly presented stimuli, have reported distinct effects of stimulus duration and ISI variability.
Moreover, it is also known that ISI length affects attentional cueing ( ; ). Briefly, attentional cueing experiments rely on the flexible allocation of attention to specific aspects of the sensory stimulation, such as certain features of the stimuli, as well as their temporal or spatial properties. In general, attention can be driven both by top-down (i.e., cognitive expectations, called “endogenous” attention) or bottom-up (i.e., sensory events, called “exogenous” attention) processes ( ). The nature of the cue determines the type of attentional process (see ). Interestingly, the ISI length seems to interfere with exogenous and endogenous attention in a different manner. At short durations (at around 2 s), endogenous attention enhances perceptual sensitivity (through an improvement in the accuracy of the responses). However, at longer durations (typically larger than 4 s) endogenous attention can actually impair stimulus sensitivity ( ). In the case of exogenous attentional processes, the responses are faster and more accurate when valid cues are presented with short intervals between the cue and the target. If, however, the ISI length is large the participants’ reactions for valid cues will be slower (i.e., larger than 300 ms; see ) and less accurate ( ) than for invalid cues. Also, found facilitation of the behavioral response (in terms of shorter RTs) with short cue-target ISIs, only when both location and feature cues were valid. Longer ISIs induced the opposite effect, as the RTs were longer when the targets appeared at the cued location.
In terms of the PC theory expectations are probability-based top-down information that are tested against sensory input. Endogenous attention can be connected to the term perceptual expectation as both can rely on cues on a trial-by-trial basis ( ). In spite of the demonstrated effects of ISI on RS and on attentional cueing, previous studies which have investigated ES have invariably used short (in the range of few hundred milliseconds) delay-intervals between the predictive cue and the target ( ; ; ).
Since we have no information on the temporal dynamics of cue-based expectation effects ( ), the current study aimed to investigate whether additive effects of RS and ES are consistent across changes of the presentation delay. To this end, we used the methods, task, and paradigm of with different ISI lengths. To anticipate our results, we observed significant RS and ES in the FFA, but we did not find any interaction between ES and RS for either ISI conditions, suggesting that the length of ISI does not influence the neural mechanisms of ES and RS.
## Materials and Methods
### Participants
Twenty-six healthy Caucasian volunteers participated in the experiment. The number of participants was chosen based on our prior published works. In our previous papers testing RS ( : n = 26; : n = 17; : n = 25; : n = 22; : n = 29) we invariably tested similar number of participants and observed always strong and reliable RS as well as probability-based modulations of RS. Therefore, we did not use any specific way to estimate the sample sizes here. No participant reported any neurological or psychiatric illnesses and all subjects had normal or corrected to normal visual acuity and gave written informed consent in accordance with the protocols approved by the Ethical Committee of the Friedrich-Schiller-University Jena by following the Declaration of Helsinki. Overall, three participants were excluded from the final analysis. One was excluded due to excessive head-movements (i.e., translation/rotation of > 5 mm/degrees) during the recording, while another participant failed to perform the experimental task properly (the performance was below 50% in one experimental run) and one participant interrupted the recording session. Therefore, the current report is based on the data of 23 participants (17 females; 20 right-handed, mean age (±SD): 21.6 (0.7) years).
### Stimulation and Procedure
Stimuli were 300 gray-scale, digital photos of full-frontal Caucasian faces (2.75° visual angle), identical to those of . Briefly, stimuli were fit behind a circular mask, placed in the center of the screen on a uniform black background. Stimulus pairs were presented, with 250 ms presentation time for each stimulus. We only used Caucasian faces as it is known that the own-race bias results in differences regarding the perceptual expertise with own when compared to other-race faces (for review see: ). Two ISI conditions were used. In the Immediate condition, the ISI was 500 ms, and hence identical to that of previous publications ( ; ). In the Delayed condition, the ISI was varied randomly between 1.75 and 3.75 s (this temporal jitter was introduced to help the separation of the BOLD response, related to S1 and S2 as these two are not presented within one TR anymore). The two ISI trial types ( Immediate and Delayed ) were presented in two separate runs in an order randomized across participants. The inter-trial intervals were randomized between 1 and 3 s or between 3.75 and 5.75 s for the Immediate and Delayed conditions, respectively (see ). This relatively short time-range for the Delayed ISI condition was chosen because the further elongation of the ISI (to the order of minutes) would have led to an experiment-duration up to 2 h. Two runs were recorded from each participant (one for each ISI condition) and no stimulus occurred in more than one trial during a given run (i.e., the same stimulus could occur in two different runs). The runs contained 180 trials and lasted for about 11 and 25 min for the Immediate and Delayed conditions, respectively. Stimuli were back-projected via an LCD video projector (NEC GT 1150, NEC Deutschland GmbH, Ismaning, Germany, with modified lens for short focal point) onto a translucent circular screen, placed inside the scanner bore [stimulus presentation was controlled by Matlab R2013a (The MathWorks, Natick, MA, United States), using Psychtoolbox (Version 3.0.9)].
Overview of the stimulation parameters and arrangements. At the beginning of each trial, a yellow fixation cross was presented for 1 or 3 s in the Immediate ISI condition and for 3.75 or 5.75 s in the Delayed ISI condition. The cross was followed by the predictive cue, S1, which was shown for 250 ms. During the ISI a small white circle appeared on the screen. The ISI conditions correspond to Immediate and Delayed lengths of fix 500 ms and varying 1.75/3.75 s, respectively. Finally, the target, S2, was presented for 250 ms. Note that Immediate and Delayed trials were given in separate runs.
Trial structure and design were identical to those of and . We used a paired stimulus presentation where the predictive cue, the first stimulus (S1), could either be different [Alternation Trial (Alt)] or identical [Repetition Trial (Rep)] to the second, target stimulus (S2). To reduce local feature adaptation the size of either S1 or S2 (chosen randomly) was reduced by 18%. Both stimuli of a pair were either female or male and participants were presented with 50% female/male trials randomly. The gender of S1 cued stimulus repetition or alternation to the participants probabilistically, meaning high (75%) or low (25%) probabilities of repetition/alternation of the target stimulus (S2). For example, for half of the subjects, female faces signaled high repetition probability (75%), while male faces signaled high alternation probability (75%). This way, participants could form expectations regarding the likelihood of repetitions and alternations. Correctly predicted trials correspond to a congruence between the given cue (S1) and the repetition/alternation occurrence during S2 (75% of the trials), whereas the incorrectly predicted, or surprising trials correspond to an incongruency between the given cue (S1) and the repetition/alternation occurrence during S2 (25% of the trials). The relationship between face gender and repetition probability was counterbalanced across participants (11 participants in one version and 12 in the other one), in a way that for the other half of the subjects ( N = 11) the gender cueing high repetition probability was male and the relative repetition probabilities were reversed accordingly. Participants were informed about the relative repetition/alternation probabilities as well as about their contingencies on the face gender of S1 prior to the scanning sessions. In addition, participants performed a 5-min long training session (using stimuli that were different from those used in the main experiment) immediately prior to the fMRI recordings.
Briefly, a trial started with a yellow fixation cross, which was presented for 1 or 3 s in the Immediate ISI condition and 3.75 or 5.75 s in the Delayed ISI condition. Participants were asked to fixate it. The cross was followed by the predictive cue, S1, which was shown for 250 ms. During the ISI a small white circle appeared on the screen. The ISI conditions correspond to Immediate and Delayed lengths of fix 500 ms and varying 1.75/3.75 s, respectively. Finally, the target, S2, was presented for 250 ms.
Moreover, following the method of , 20 (11.1% of the trials) additional blank trials were included in each run to enable the estimation of the average response time course to the target stimulus (S2) alone. In these trials, S1 was normally displayed and instead of S2, a blank screen was presented. This way, an estimate of the average response time course to S2 alone was obtained by performing a subtraction between the blank trials and the experimental conditions which included S2 and S1 as well. In order not to bias the predictions of participants, these trials had an equal amount of female and male faces for S1. Importantly, the overall probabilities for the correctly predicted and surprising conditions were 66.7 and 22.2%, respectively. As the introduction of the blank trials made the separation of subsequent trials perceptually more difficult, the color of the fixation cross was changed to yellow before the presentation of S1, to clearly mark the beginning of trials.
In total, we had five different experimental conditions, presented randomly within a run: expected repetition (E_Rep), expected alternation (E_Alt), surprising repetition (S_Rep), surprising alternation (S_Alt), and blank (Blank) trials. illustrates the experimental design.
Experimental design and conditions. Each face gender signaled different repetition/alternation probabilities (high or low) randomly for every participant. Here we present an example where the face gender signaling high repetition probability was female (E_Rep), while male faces cued high probability of alternations (E_Alt). Male/female faces signaled low probability of repetitions/alternations (S_Rep/S_Alt). Blank trials contained either female or male faces, randomly.
To control participants’ attention and to confirm that they are able to judge the stimulus gender effectively, 18% of the trials were target trials in which subjects had to report whether the S1 had been a female or male face by pressing a button ( ). Therefore, for these target trials, a choice-screen was presented for 2 s centrally showing either the text “ female? male ” or “ male? female ,” randomly. The choice-screen appeared 1 s after S2 was blanked out. A small color change of the fixation cross functioned as feedback regarding their answers (green for correct and red for incorrect responses).
### Imaging Parameters and Data Analysis
Imaging was done with a 3-Tesla MR scanner (Siemens MAGNETOM Prisma fit, Erlangen, Germany). T2 weighted images were collected using an EPI sequence (35 slices, 10° tilted relative to axial, TR = 2000 ms; TE = 30 ms; flip angle = 90°; 64 × 64 matrices; 3 mm isotropic voxel size). A high-resolution T1-weighted 3D anatomical image was acquired using an MP-RAGE sequence (TR = 2300 ms; TE = 3.03 ms; 192 slices; 1 mm isotropic voxel size).
Details of preprocessing and statistical analysis were described previously ( ). The functional images were realigned, normalized to the MNI-152 space, resampled to 2 × 2 × 2 mm resolution and spatially smoothed with a Gaussian kernel of 8 mm FWHM (SPM12, Wellcome Department of Imaging Neuroscience, London, United Kingdom). A separate functional localizer run (640 s long, 20-s epochs of faces, objects and Fourier randomized versions of faces, interleaved with 10 s of blank periods, 2 Hz stimulus repetition rate; 300 ms exposure; 200 ms blank) served as a basis for Regions of Interest (ROIs) detection. ROI creation was performed with MARSBAR 0.44 toolbox for SPM ( ). Only those individuals in whom the respective ROIs could be identified in both hemispheres were included in the further analyses. The FFA was determined individually as an area responding more intensely to faces than to objects and Fourier randomized versions of faces ( p < 0.0001 ). Its location could be identified reliably and bilaterally in 20 participants [average MNI coordinates (±SE): 41 (0.6), −54 (1.3), −19 (0.8), and −41 (1.4), −57 (1.7), −18 (0.7); average cluster size (±SE): 72(7), 52(5) voxels; for the right and left hemispheres, respectively].
A time series of the mean voxel value within the areas of interest was calculated and extracted from our event-related sessions using custom made scripts and Marsbar. The convolution of each of the five experimental conditions (E_Rep, E_Alt, S_Rep, S_Alt, Blank) with the canonical hemodynamic response function (HRF) of SPM12 (Welcome Department of Imaging Neuroscience, London, United Kingdom) was used to define predictors for a General Linear Model (GLM) analysis of the data. Target trials were not modeled separately, as there was sufficient time (1 s) between the end of the trial and the choice-screen presentation. Thus, the BOLD signal of the S2 was not affected by the button presses or by the choice-screens. Note that the subtraction between blank trials and the other experimental conditions (E_Rep, E_Alt, S_Rep, S_Alt) was executed to estimate the average response time course to S2 alone ( ). The peak values of the BOLD signal elicited by S2 only were submitted to the following statistical analysis. We performed repeated measures ANOVAs for the FFA activity separately with hemisphere (2), expectation level (2, E and S), trial type (2, Alt and Rep) and ISI condition (2, Immediate and Delayed) as factors. Post hoc analyses were executed using Fisher LSD tests. We also performed a t -test and calculated Bayes factor (e.g., ) to test the independence of RS/ES from the ISI length and denoted evidence according to the thresholds proposed by . We used the following prior hypothesis: RS and ES effects are larger in the Immediate ISI condition than in the Delayed one, therefore the reported results show how much more likely our hypothesis is when compared with the null hypothesis. In order to perform a t -test and directly compare the effects of repetition and expectation suppression for the two ISI conditions, we calculated the repetition suppression index (RSI = Alt-Rep) and the expectation suppression index (ESI = Sur-Exp).
As there is evidence that prediction error units of FFA can be activated by a positive prediction error (i.e., the occurrence of an unexpected face), but not by a negative one (i.e., the unexpected omission of a face; see ). We decided to test the influence of stimulus omission in this experiment by performing a repeated measures ANOVAs for the FFA activity separately with ISI condition (2, Immediate and Delayed) and omission level (2, Blanked and Non-blanked trials) as factors. Post hoc analyses were executed using Fisher LSD tests.
## Results
### Behavior
Participants required on average 981 ms (±SD: 45 ms) to determine the gender of the presented S1 faces. Reaction times did not differ significantly between trial types ( F (1,22) = 1.15, p = 0.29, η = 0.05), expectation levels ( F (1,22) = 0.24, p = 0.63, η = 0.01) or ISI conditions ( F (1,22) = 2.22, p = 0.15, η = 0.09). Similarly, only tendencies were observed for any of the interactions ( p > 0.08 for all comparisons). We found a tendency for an interaction between expectation levels and ISI conditions ( F (1,22) = 3.18, p = 0.088, η = 0.126), showing that correctly predicted trials differed between ISI conditions [being faster for Immediate trials ( M (± SD ) = 927 (39)ms) as compared to Delayed ones ( M (± SD ) = 1018 (35)ms), p = 0.003], while incorrect predictions did not show any difference.
Mean accuracy for gender judgment was 86% (±SD: 3%) across all experimental conditions. The participants’ accuracies did not differ between trial types ( F (1,22) = 1.53, p = 0.22, η = 0.07) and ISI conditions ( F (1,22) = 1.62, p = 0.22, η = 0.07). Further, no significant interactions were observed ( p > 0.08 for all comparisons). Interestingly, and confirming previous results ( ; ), there was weak evidence for a main effect of expectation level ( F (1,22) = 3.4, p = 0.08, η = 0.13), showing an enhanced accuracy for correctly predicted ( M (± SD ) = 88 (3)%) when compared to surprising ( M (± SD ) = 82 (5)%) trials.
The similar accuracy rates and response times suggest a similar allocation of attention to the different experimental conditions.
### Fusiform Face Area
Overall, the results confirmed those of our prior studies ( ; ). We observed a significant main effect of trial type (i.e., significant RS; ; F (1,19) = 25.09, p = 0.0008, η = 0.57) with an average signal reduction of 0.1% (equivalent to an average relative signal reduction of 27%). We also found a main effect of expectation level (i.e., significantly higher responses for surprising as compared to correctly predicted events: F (1,19) = 5.65, p = 0.028, η = 0.23). On average the correct predictions led to a signal reduction of 0.05% (corresponding to an average relative signal decrease of 16%) when compared to the incorrect predictions. No main effect of hemisphere was found ( F (1,19) = 1.27, p = 0.27, η = 0.06). Additionally, the effects of trial type and expectation level did not interact with each other ( F (1,19) = 3.08, p = 0.10, η = 0.14), but were additive ( ).
Effects of expectation and repetition on the FFA responses (averaged left and right hemispheres) for different ISI conditions. (A) Average response time course for Rep and Alt trials in expected (correctly predicted; left) and surprising (incorrectly predicted; right) events for the Immediate (up) and Delayed (down) ISIs. (B) Percent-signal changes (±SE) are presented separately for trials types, expectation levels and ISI conditions. p < 0.001; p < 0.05.
More important to the aims of the current study, we did not find a significant main effect of ISI condition ( F (1,19) = 1.68, p = 0.21, η = 0.08). There was neither an interaction of ISI condition with the effect of trial type ( F (1,19) = 0.37, p = 0.54, η = 0.02) nor with the effect of expectation ( F (1,19) = 1.2, p = 0.28, η = 0.06). The four-way interaction of the hemisphere, ISI condition, trial type and expectation was not significant either ( F (1,19) = 0.53, p = 0.48, η = 0.03). None of the remaining two-way and three-way interactions are significant. This suggests that both RS and ES are independent of the length of the ISI period. The Bayesian t -test revealed that both effects of neuronal response suppression, i.e., RS (B < 0.2) and ES (B < 0.2) are independent of the ISI length.
We found a significant main effect of omission level [i.e., larger BOLD responses to the non-blank trials when compared to blank trials; F (1,19) = 53.95, p = 0.000001, η = 0.59]. No interaction was found between the ISI condition and the omission level ( F (1,19) = 1.44, p = 0.23, η = 0.074).
Importantly, the two ISI conditions of this study differ in terms of ISI variability characteristics and predictability. Although we included blank trials in both ISI condition blocks, in the Immediate ISI condition, there is only one possible ISI length (500 ms), and therefore the stimulus onset is nearly fully predictable in time. While, in the Delayed ISI condition, there are two possible ISIs (long – 5.75 s and short – 3.75 s). In this condition, the longer one is nearly fully predictable, as it will occur whenever there was no stimulus after 3.75 s and the current trial isn’t a blank trial. The short ISI in the Delayed condition is only expected in 44, 45% of the trials. To test whether the results were affected by these differences in the variability and predictability characteristics of the ISI length of the Immediate (constant and fully predictable) and the Delayed ISI condition (variable and semi-predictable, i.e., long – 5.75 s and short – 3.75 s), we performed a repeated measures ANOVA to compare the BOLD responses of the two fully predictable conditions, i.e., the longer Delayed ISI lengths and the Immediate ISI condition. This extra analysis only revealed to be significant in two main effects: repetition suppression ( F (1,19) = 15.63; p = 0.0009; η = 0.45) and ISI (in a way that the Immediate ISI length elicited larger BOLD responses when compared with the longer Delayed ISI condition; F (1,19) = 18.88; p = 0.0004; η = 0.47).
### Whole-Brain Analysis
It is theoretically possible that expectation and repetition effects are encoded elsewhere in the brain. Hence, we performed a second level whole-brain analysis testing for the main effects of RS, ES, ISI and the interaction of these factors, using a fixed threshold of p < 0.05 , with a cluster size > 50 voxels. Testing the main effect of ISI ( Delayed > Immediate ) revealed an active cluster in the early visual cortex (MNI [ x , y , z ]: 4, −86, 20; cluster size: 288), while the opposite contrast ( Immediate > Delayed ) led to several regions with significant activations ( ). The whole-brain analysis did not reveal additional active clusters when testing for the main effects of RS and ES or the interactions between ES, ISI, and RS.
Results of the whole-brain analysis with a fixed threshold of p < 0.05 , with a cluster size bigger than 50 voxels for the following contrasts: Delayed > Immediate and Immediate > Delayed .
In order not to overlook any activation on the whole-brain level (however, see the recent discussion, initiated by about the inflated false-positive rates of such cluster analyses) we also applied a more liberal p < 0.0001 threshold with a smaller cluster size (>20 voxels). Both the Immediate > Delayed and the Delayed > Immediate contrasts showed some additional regions with significant activations ( ). Yet, once again, no additional active clusters were found when testing for the main effects of RS and ES or for the interactions between ES, ISI and RS, supporting the results of the ROI analysis. In principle, one would expect the FFA to be activated in the whole-brain analysis when testing for the main effects of RS and ES as well. Still, it is likely that the lower sensitivity of the whole-brain, when compared to the ROI based analysis ( ), as well as the large inter-individual difference in the peak location of the FFA ( ) accounts for the lack of such an observation.
Summary of significant activations in the whole-brain analysis.
## Discussion
We observed significant repetition and expectation effects in the FFA in the form of reduced responses for repeated and expected stimuli, respectively. These effects were found to be additive and independent of the length of ISI and imply that Immediate and Delayed predictive cueing produce similar effects of expectation related response suppression in the FFA, suggesting that the observed expectation effects survive a several second-long time-interval. The fact that RS and ES were found to be additive and thereby independent from each other for both ISI lengths confirms the results of recent studies that used short ISIs ( ; ; ).
### Repetition Suppression
Earlier RS studies, using different ISI lengths, have suggested that RS is stable over short cue-target periods (in the range of 250 ms to 4 s) for object stimuli in an fMRI experiment ( ; ), which is in accordance with our results showing no difference in RS across ISI lengths. However, if ISIs are prolonged further (maximum of 8 min) several studies propose that the neural mechanisms underlying RS with short ISIs (less than 3 s) are different from those underlying RS with long ISIs ( ; ).
For example, reported that the effect of ISI on RS for visual scenes measured in the fMRI depends on scene viewpoint (in the range of 500 ms to 8 min, for short and long ISIs, respectively), in other words, short-interval RS was only significant when scenes were repeated from the same viewpoint, while long-interval RS was less viewpoint-dependent. Also, long- and short-interval RS effects did not interact at all. Furthermore, used objects as stimuli and showed that RS varies quantitatively across time periods in the ventral temporal cortex. This study used ISI categories which are somewhat different from those used in the current study: the short and the long ISI periods were 500 ms to 3 s and of 1 to 174 s, respectively. Therefore, in the study of there was an overlap of durations in the short and long ISI conditions, which was not present in the current study. Additionally, the maximum length of their “short” ISI is comparable to our Delayed condition and they did not study RS on a trial by trial basis. Please note that , as well as and used object stimuli and therefore also tested different regions. All these facts make the comparison to the current study difficult.
Face studies have found that with long ISIs (in the range of 7 min), the effects of repetition depend on familiarity such that RS only occurs for familiar faces ( ). In this study, participants had to judge face familiarity. The results revealed that face-processing occurs even without perceptual awareness. Furthermore, different face viewpoints were also investigated for the short-lagged (subliminal priming) condition, yet no effects of viewpoint were found for either the familiar or unfamiliar faces. Note that the minimum duration for the long-lagged condition was 7 min in their study, which is considerably larger than the 3.75 s applied in the current study. Importantly, instead of a blank screen, in this study, a mask was presented between S1 and S2 to manipulate perceptual awareness. The use of shorter lengths and the absence of this mask in the ISI period might explain why we found RS effects with unfamiliar faces for the Delayed condition as well. Also, the current study did not include familiarity as a factor. It will be important to determine what role familiarity plays in expectations and RS with specifically designed future experiments that are comparable to those of the study of . Another study using face stimuli and examining the impact of different cue-target intervals is from . They investigated how probability-based expectations affect RS with longer ISIs and showed that P(rep) modulation of RS exists with longer (4 s) cue-target periods but that this effect depends on attention. These findings are in accordance with our results, despite the fact that induced expectations implicitly, based on the differential probabilities of trials within blocks, while here expectations were manipulated explicitly, with a cue, on a trial-by-trial basis. Please note, that the main goal in their study was to show the effect of attention on probability-based RS modulation.
A recent electrophysiological study has investigated not only how RS varies with different ISI periods but also how it is influenced by diverse stimulus presentation durations of S1 ( ). Their results indicate no effect of ISI period on the N170 amplitudes for faces or chairs. However, the amplitude of the positive P2 component was lowest when the ISI was short (200 ms). As is known, electroencephalography has better temporal resolution than fMRI, and this fact can possibly explain incongruences between that and the current studies. Also, the ISI periods of this study varied from 200 to 500 ms, which is in the range of our Immediate condition and makes comparison difficult. Anyway, further electrophysiological studies are also necessary to evaluate how expectation effects modulate RS in different cue-target stimulation periods.
### Expectation
Notably, no ISI effects on ES were observed on the behavioral data or on the BOLD signal in the current study, whereas reported reaction time facilitation for expected events that were presented with short cue-target stimulus periods. In other words, if expectations are fulfilled (the target can be predicted) and the cue and the target appear in a narrow time window response times are shortened (which fits the predictive coding framework). The discrepant results of and our current study can easily be explained by the lower number of trials in our study [360 vs. 500 in ] and the application of different stimuli (moving dots vs. faces). Also, used an exogenous cue, whereas we applied endogenous cues, signaling the appearance of subsequent images.
Another behavioral study inspected how time perception depends on different durations of stimulus presentation and ISI ( ). Following the paradigm of previous studies ( ; ), this behavioral study used the probabilities of repetitions in each block to manipulate expectations. Interestingly, repeats were judged longer than novel items for shorter ISIs, but this effect was more pronounced when the repetitions were rare. For the longer ISI condition repeated and novel images were judged the same.
The fact that we found similar ES for the Immediate and Delayed conditions is in line with theories of predictive coding ( , PC). PC explains the brain as a cascadic system of parallel feed-forward and feedback processes in which the sensory information is continuously compared to the current expectations of the system, based on prior experiences, and only the difference of the two, the predictive error, is propagated to higher-level areas ( ). The predictive error is calculated and updated continuously in such a system. Whether there is an upper time-limit of the influence of the predictive stimuli is still an open question. Our results, however, suggest that the effect of the calculated predictions is not only manifest for immediate subsequent phenomena but also extends to a time range of several seconds, increasing the stability of the system. Recently, the processing of sensory information and most of the neuronal phenomena, such as RS and ES is explained under the predictive coding framework more and more widely. This framework assumes that, for example, visual processing occurs in a hierarchical manner in which lower-level areas receive predictions about the incoming sensory input from higher-order areas through feedback connections ( ). Consequently, when the sensory input coincides with the created high-level expectations, there is a suppression of the predicted neural responses in lower level areas, due to an inhibited response of these neuronal populations in the form of an efficient encoding mechanism ( ).
Given the universal nature of PC, it is rather surprising that some recent findings disagree with the PC explanations of the neuronal response suppression. Evidence comes from studies that used non-face stimuli (fractals and chairs) and found no repetition probability modulations of RS in macaques’ inferior temporal ( ) and humans’ lateral occipital cortices ( ) (but see for a different conclusion). These results are in contrast to what had been found for faces and voices, i.e., a strong modulation of RS by P(rep) ( ; ; ; ). Therefore, the question if the observed similarity of short and long-term ISI on P(rep) in our study is a general property of the visual processing network, or its validity is limited to the areas processing faces is open and requires further studies. The above described differences in the capacity of PC explaining RS led to the following question: are there several neuronal mechanisms underlying the effect of P(rep) in the different cortical areas or are there other crucial factors determining these differences? One possible factor could be the level of expertise or prior experience with the given stimulus category. In fact, the results of suggest that expertise influences the magnitude of P(rep) modulation effect on RS, in a way that expectation effects only occur for familiar (real Roman characters) but not novel objects (false Roman characters). Since we assume that we are experts on faces, this could be a possible explanation for the different results. However, a more recent fMRIa study using face stimuli ( ) could show that expectation facilitates recognition behaviorally, but these modulatory effects could not be found in the BOLD signal of face-sensitive regions. Also, a recent study ( ) tested the effects of repetition probability in RS of the macaque inferotemporal cortex and found no interaction of P(rep) and RS on the spiking activity even though repetitions were task-relevant and repetition probability affected behavioral decisions. Again, in the current study, the sensory stimuli were of high expertise, i.e., faces. Therefore, future experiments will be needed in order to clearly understand whether the time window between cue and target stimuli lead to similar expectation effects for novel and familiar stimuli equally.
Furthermore, showed that perceptual expectation requires attention, specifically the P(rep) modulation effect on RS was only present if participants’ attention was directed toward the stimuli. Note that the experimental design of study focused on probabilistic, implicit expectations. Hence, it would be worthwhile to manipulate the subjects’ spatial and/or object-based attention, repetitions and expectations orthogonally, possibly applying a paradigm similar to or . It is likely that the attentional state of the participants and therefore the applied task also plays a role in the fact that different results were obtained in the previous studies.
However, as it has been mentioned above, did not observe P(rep) effects for every-day objects and the participants performed the same task (i.e., to signal the occurrence of a target trial, where the size difference between S1 and S2 was 55% by pressing a button) as in those prior studies, which reported significant P(rep) effects on RS for faces ( ; ). These findings suggest that differences in the attentional state alone are unlikely to induce such dissimilar perceptual expectation effects unless the given stimuli attract different attentional resources per se . In fact, there is evidence that faces recruit more attention than inanimate objects ( ; ), which might explain the differences in P(rep) modulation effects on RS previously observed ( ; ). Still, to the best of our knowledge, there is no evidence that real Roman characters draw more attention than false Roman characters. Also, could not find a P(rep) modulation effect on RS magnitude even though they used faces. Still, in this case, attentional effects could have caused the differences between the behavioral and neuroimaging data, as for the behavioral experiment attention was drawn to the images, whereas in the fMRI experiment participants performed an orthogonal task on target trials. Even though attention cannot be completely ruled out to explain previous findings, it is very unlikely as the source of the stability in terms of the ISI of expectation effect observed in the current study.
Importantly, in the current study there is a methodological asymmetry between the two expected experimental conditions, i.e., expected repetition (E_Rep) and the expected alternation (E_Alt). In the expected alternation condition the participants can only predict that the S2 is a previously unseen face, while in the expected repetition condition the predictions are that the S2 face is equal to S1. In other words, in the expected alternation condition participants can predict what the stimulus is not, but not what it is. Note that predictive theories argue that prediction updates occur repeatedly, and beliefs are gradually refined until the sensory system settles on the most likely interpretation of the inputs. Considering this, one can reason that if the statistical regularities of an environment are against our “default” predictions (i.e., learned based on experience), the strength of those predictions would be continuously diminished, due to constant updates. There is, therefore, a gap in the precision level of the predictions created in these two conditions. Still, there is an expectation effect on the alternation trials for both Immediate and Delayed ISI lengths (see ), in a way that the BOLD response is larger for surprising alternation than for the expected one. Following this line of thought, one question arises: what would happen if the alternation is predictable? employed the influential Summerfield paradigm (2008) with an additional alternating block type where alternations were predictable. Participants could predict the S2 face based on S1, as S2 was specifically paired with S1. The participants were not aware of the contingencies but learned those in a preceding training session on the day before the scanning session. The authors found that predictable alternation trials elicited reduced FFA responses, as compared to unpredictable faces. Interestingly, repetition trials showed similar neuronal activation when presented in alternation blocks to when presented in the predictable alternation blocks. In other words, even though these repetition trials are surprising FFA responses were more suppressed than for predictable. In fact, repetition is always expected as it is the default expectation and, therefore, even with the alternating trials being predictable and expected, default expectations of repetition maintain and are stronger than the experimentally induced perceptually expectations. Still, it is not yet known how the predictable alternation affects cue-based, explicit expectations. Thus, future training studies will be necessary.
The gender-identification task used in the current study requires attention to S1 and not to S2 ( ), which can lead to different attentional states between S1 and S2. Prior studies using this task ( ; ) revealed that subjects utilized different repetition probabilities to perform the task. In other words, even if they did not know or remember the gender of the first face, they could expect the faces to be repeated or alternating, congruent on the gender of S1. If so, then participants should show perfect performance and faster reaction times for expected trials. Interestingly, the results of the current study do not show any behavioral facilitation response for expected trials, a result similar to those of . One possible explanation is that the gender-identification of S1 is less dependent on the effects of expectation and surprise than that of S2. We decided the behavioral task relied on the discrimination of the S1 gender, to make sure participants directed their attention to the S1 in a way that expectations were induced and to ensure those expectations were not incorrect due to wrong discrimination of the S1 gender. It is possible that the chosen behavioral task might have directed attention toward S1 and way from S2, still, we found RS and ES effects in the FFA. A very recent study ( ), on the other hand, found a strong behavioral modulation of a priming effect (shorter RT for repeated as compared to alternating trials) depending on the likelihood of repetitions (larger modulation for expected as compared to unexpected trials). Surprisingly and contrary to many prior studies, the fMRIa was not modulated by expectation in this study, suggesting the relative independence of behavioral and neuroimaging correlates of expectation and urging further experiments, testing the issue.
### Possible ISI Variability Effects
In addition, we also know that the frequency or pace of events is a crucial parameter for the creation of expectations. A central timing system also referred to as “pacemaker,” describes that the pace/frequency of the event occurrences enables the creation of temporal perception units ( ). Furthermore, it has been proposed that these local temporal perception units feed information to central systems ( ) and probably have an important role in prediction generation as well. investigated in an electrophysiological study how the consistency of stimulus repetition influences the effects of expectation and RS using stable (30–40 trial long) and volatile (10 trial long) blocks of stimulus presentations. Note that expectation was manipulated using different repetition probabilities in these blocks. Their results showed that RS was modulated by expectations at central electrodes for the stable, long blocks, while no modulation was present for the volatile, shorter blocks. As stability over time (sometimes labeled as “time-variability,” see ) can play a role in forming expectations, it will be important to test possible effects of ISI variability and the ISI length, independently. This relates to the major limitation of the current experiment, i.e., the fact that the two ISIs had different variability characteristics, as we, due to methodological constraints, only had one long ISI but variable and one short and at the same time constant ISI condition. Furthermore, the possibility that activation differences found between the Immediate and the Delayed ISIs are dependent on the different synchrony levels cannot be excluded as the additionally longer ITIs we used for the Delayed condition might also contribute to different overall temporal patterns. Therefore, further experiments are necessary to disentangle these two distinct effects (variability and length).
### Whole-Brain Analysis
The results obtained by the whole-brain analysis are in line with the previous studies that propose different neuronal mechanisms for short and long lagged cue-target stimulation periods. The results show several brain activation differences between the Immediate and the Delayed ISIs. Yet no significant differences between these two conditions were found in the FFA. Moreover, the whole-brain analysis did not elicit main effects of trial or expectation conditions which were previously found by , . The lack of these effects in the present study might be due to the lower number of trials in comparison with the former studies. Furthermore, the possibility that activation differences found between the Immediate and the Delayed ISIs are dependent on the different variability levels (constant and variable) cannot be excluded.
## Conclusion
In conclusion, this study shows that RS and expectation effects in the FFA are independent and additive processes for both Immediate and Delayed ISIs. As no significant difference was found between the two ISI lengths in the FFA, we can conclude the effects of repetition and expectation are maintained for several seconds in the FFA.
## Data Availability Statement
The datasets generated for this study are available on request to the corresponding author.
## Ethics Statement
The studies involving human participants were reviewed and approved by the Ethics Committee of the Faculty of Social and Behavioural Sciences of the University of Jena. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this manuscript.
## Author Contributions
CA, MG, and GK designed the concept of the manuscript. MG and NW ran the experiments and analyzed the data together with CA. CA, S-MR, and GK wrote the manuscript.
## Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
|
MRI diffusion data suffers from significant inter- and intra-site variability, which hinders multi-site and/or longitudinal diffusion studies. This variability may arise from a range of factors, such as hardware, reconstruction algorithms and acquisition settings. To allow a reliable comparison and joint analysis of diffusion data across sites and over time, there is a clear need for robust data harmonization methods. This review article provides a comprehensive overview of diffusion data harmonization concepts and methods, and their limitations. Overall, the methods for the harmonization of multi-site diffusion images can be categorized in two main groups: diffusion parametric map harmonization (DPMH) and diffusion weighted image harmonization (DWIH). Whereas DPMH harmonizes the diffusion parametric maps (e.g., FA, MD, and MK), DWIH harmonizes the diffusion-weighted images. Defining a gold standard harmonization technique for dMRI data is still an ongoing challenge. Nevertheless, in this paper we provide two classification tools, namely a feature table and a flowchart, which aim to guide the readers in selecting an appropriate harmonization method for their study.
## Introduction
Diffusion-weighted magnetic resonance imaging (dMRI) is an MRI technique in which the image contrast is related to the diffusion of water molecules inside tissues. dMRI has brought great innovation to neuroimaging analysis, since it enables non-invasive probing of brain microstructure. Nevertheless, many studies using diffusion data rely on small sample sizes, leading to poor reproducibility of results. Fortunately, research is evolving toward large multicenter studies with the aim of increasing statistical power. However, the success of a joint analysis is highly dependent on the comparability of the multi-site data.
Diffusion data of the same subject obtained at different sites and/or acquired at different time points can be different due to local and/or temporal scanner characteristics resulting in a high inter- and intra-scanner variability ( ; ; ). These variabilities may arise from a range of factors, such as hardware (scanner manufacturer, field strength, transmitter/receiver coils, magnetic field inhomogeneities, etc.), reconstruction algorithms (SENSE, GRAPPA, etc.), acquisition parameters (voxel size, number of gradient directions, echo time, repetition time, etc.), and image quality [signal to noise ratio (SNR), etc.] ( ; ; ; ). All these factors affect the final diffusion signal intensity and consequently the diffusion metrics, preventing reliable multi-site and/or longitudinal diffusion studies ( ; ; ).
In literature, many conflicting inferences have been reported between studies, in which findings based on small distinct cohorts are used to generalize conclusions for an entire population, without considering intra- and inter-site differences ( ; ; ). To determine the site effects on diffusion data, a number of studies examined diffusion phantom data to detect scanner related variabilities ( ; ; ; ; ). Up to 7% of inter-site variability in diffusion metrics was demonstrated in phantoms ( ; ). However, using parameters obtained from phantom data to correct human data is not advised due to the structural complexity of human biological tissue ( ).
Previous research has established that inter-site variability is non-uniform across the white matter of the human brain, with a variability up to 5% in diffusion metrics of major brain tracts ( ; ; ). Recently, investigators have examined the reproducibility of multi-shell diffusion images in a multi-site study involving traveling subjects ( ). A 7.7% median inter-center coefficient of variation was estimated for the track density maps in whole white matter among the subjects. These inter-site variabilities in diffusion metrics are similar to the changes due to pathologies. For example, in the work of , it was shown that the variability in diffusion metrics in the corpus callosum between controls, mild Traumatic Brain Injury (TBI) and moderate TBI patients, are of the same order as intra-scanner changes. Furthermore, a quantitative study by Mahoney et al. reported longitudinal changes in diffusion metrics in dementia patients compared to controls in the same order of the site variabilities ( ). From these findings, we can infer that it is crucial to reduce the variability across multi-center diffusion data.
Inter-site variability can be reduced by acquiring data with scanners from the same manufacturer at each site and using similar acquisition parameters ( ; ; ). However, diffusion parameters of subjects scanned using the same acquisition protocol may still differ significantly across sites ( ; ; ). These differences may come from several sources, such as sensitivity of head coils, imaging gradient non-linearities, magnetic field inhomogeneities and other scanner related factors. Hence, there is a substantial need for robust harmonization techniques ( ; ). The overall concept of harmonization methods is to apply statistical or mathematical concepts to reduce unwanted site variability while maintaining the biological content. In the last decade a multitude of harmonization methods have been developed.
For this review, we have categorized the brain dMRI methods in two main groups depending on the data-format used as input for harmonization. The first category uses calculated diffusion (para)metric maps, such as Fractional Anisotropy (FA), Mean, Axial and Radial Diffusivity (MD, AD, and RD, respectively), Kurtosis Anisotropy (KA), Mean, Axial, and Radial Kurtosis (MK, AK, and RK, respectively), as input (i.e., diffusion parametric map harmonization; DPMH). While the second category uses diffusion weighted images (DWI) as input (i.e., diffusion weighted image harmonization; DWIH).
To the authors’ knowledge, no previous study has provided an extensive report of diffusion harmonization methods. In this review paper, a comprehensive overview of those methods is presented, including an investigative analysis of their strengths and weaknesses. DPMH and DWIH methods reported since 2009 are described. This paper is organized as follows. Section “Literature Search” describes the search mechanism used for selecting the literature on brain diffusion data harmonization. In Section “Requirements for Harmonization,” the requirements for harmonization are specified. Sections “Diffusion Parametric Map Harmonization Methods” and “Diffusion Weighted Image Harmonization Methods” depict the DPMH and DWIH harmonization methods reported in the literature. Section “Discussion” then presents an overview of the main characteristics of the methods and a guideline that helps the user to select an adequate harmonization method for her/his data. Finally, in Section “Conclusion” conclusions are drawn.
## Literature Search
Two authors (MSP and RP) independently performed a literature search across two databases (PubMed and Google Scholar) using combinations of the following search terms: “harmonization,” “harmonisation,” “normalization,” “normalisation,” “multi-site,” “multi-center,” “inter-site,” “intra-scanner,” “diffusion,” “MRI,” “DTI,” “meta-analysis,” “covariates,” “spherical harmonics,” “deep learning.” Besides the usual search engines, additional important papers were selected by checking the reference lists of identified relevant publications on data harmonization. After removing the duplicates, all identified articles were screened by title and abstract. Studies were included if they described diffusion harmonization methods and concepts.
## Requirements for Harmonization
For the majority of dMRI harmonization procedures, co-registration is of crucial importance. Co-registration of diffusion images aims to find spatial transformations to map different images to a common reference space, allowing direct comparison of various image properties. Prior to harmonization, a voxel-by-voxel correspondence between multiple diffusion volumes is needed, in order to minimize errors in subsequent calculations. In particular, voxel-wise DPMH and DWIH approaches require all subjects to be in the same space in order to extract common features that are site-related rather than anatomically specific. The common space can be a study-specific template or a standard brain atlas template, as for example, the ICBM152 template of the Montreal Neurological Institute (MNI) space . Many tools are available for registering diffusion images, such as Advanced Normalization Tools (ANTs; ), FMRIB Software Library (FSL; ), and elastix ( ; ).
Additionally, a dataset with a balanced number of subjects per site is advised for robust harmonization. Many DPMH and DWIH methods use these subjects to efficiently learn a set of so-called mapping parameters used to characterize the differences between the images across scanners. Additionally, an important requirement, especially for DWIH methods, is the availability of training data, i.e., matched subjects across sites for obtaining the mapping parameters between sites. Age, gender, handedness, and socio-economic status need to be matched among the subjects to remove the statistical differences at group level. Moreover, for some machine learning techniques, there is a need for DWI data of individual subjects that are scanned at different sites, within a small interval of time, to train a network to recognize site-related underlying inter-scanner/inter-site differences in the characteristics of the images to harmonize.
Overall, for all the methods, it is highly recommended to use a balanced dataset and to co-register the diffusion images or maps to a common template. The recommendations are to assure that statistical differences are only due to hardware, software and protocol differences, and ensure spatial compatibility intra- and inter-subjects during the harmonization procedure. Furthermore, each method has its own specifications and limitations that are described in the following sections.
## Diffusion Parametric Map Harmonization Methods
Diffusion parametric map harmonization methods perform particular transformations on the diffusion parametric maps that enable data pooling and reduction of unwanted intra- and inter-site variability. For a joint analysis of multi-site diffusion metric maps that have been estimated using a given diffusion model [e.g., diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), etc.], statistical or mathematical DPMH methods can be applied. The purpose of these methods is to perform joint statistical analysis on multi-site data. It can be performed in two ways: (1) without modifying the original diffusion parametric maps (see Subsection “Modeling Inter-Site Variability Within the Statistical Analysis”); (2) by modifying the parametric maps with a posteriori analysis (see Subsection “Harmonizing the Parametric Maps Based on Regression of Covariates”). DPMH methods allow to pool DWI parametric maps obtained from different diffusion acquisition schemes (diffusion directions, b -values, repetition time, echo time, etc.). The DPMH methods described below are meta-analysis, mega-analysis, and regression of covariates.
### Modeling Inter-Site Variability Within the Statistical Analysis
#### Meta-Analysis
Meta-analysis is a popular statistical analysis technique in biomedical research that combines results of independent multi-site and/or longitudinal studies. The general concept is to perform a group-wise statistical analysis separately for each site, followed by a weighted combination of effect size over the different studies to strengthen conclusions about the research question ( ). Meta-analysis is useful to pool retrospective data with sample sizes that are too small to draw valid conclusions independently ( ).
presents an example of meta-analysis in which statistical inferences are obtained independently per site from the FA maps of different groups of subjects. As a first step, an intra-site statistical analysis is performed. The resulting statistical scores (e.g., z -score) of the metric of interest (e.g., FA) can then be weighted by each site’s sample size or with respect to an estimate of precision, such as effect size ( ), to obtain the final statistical score. In contrast to this approach, the overall statistical score can also be obtained by modeling site as a random effect ( ; ; ). For example, in the work of , meta-analysis was used to investigate FA and MD differences between dementia patients and controls in a multi-site study, taking scanner effects into account. Voxel-based t -statistics were converted to z -scores after which a variance component analysis was applied, effectively reducing effects of site (random effect), age and gender (fixed effects).
Scheme of meta- and mega-analysis. FA measures from sites 1 and 2, for two groups of subjects: controls and patients. The FA frequency for each group is estimated for each of the sites. Meta-analysis performs the statistical evaluation between groups for each site separately, followed by a weighted combination of its statistical results, while in mega-analysis a weighted statistical evaluation is performed for all sites jointly.
One of the main advantages of meta-analysis is the possibility to pool data from small/underpowered studies to derive robust conclusions. It is also the only way to pool studies for which only aggregated data are reported (e.g., group difference statistics or the mean FA per region of interest) and for which the whole brain images are not available. However, one drawback is that if the statistics performed in the individual studies are biased by study size, the population estimate will be also affected. Another disadvantage is that the statistical analysis should first be performed separately for each diffusion metric of interest.
#### Mega-Analysis
In contrast to meta-analysis, mega-analysis refers to a technique of summarizing the statistics from the individual subjects of all sites to jointly evaluate population group differences ( ; ). As depicted in , in mega-analysis group-difference statistics are not calculated for each site separately. Instead, group differences are identified by a site-weighted combination of the statistical scores from all individuals jointly.
When the individual diffusion data (e.g., FA) is available per subject, the measures can be pooled to calculate the effect size across the entire group in a mega-analysis. To take into account the variability due to site differences, the site effect can be modeled using a mixed linear model statistical approach (or another statistical method to analyze the dataset), just as in meta-analysis.
While not directly harmonizing the imaging data itself, mega-analysis allows a joint analysis of two (or more) datasets to evaluate a common characteristic in the population ( ; ; ). Some limitations in this approach are that the size of the cohort may not be sufficient to capture the variance of the entire population, pre-processing steps could be very different for each site (if the FA maps are computed independently), and the statistical analysis has to be performed separately for each variable (e.g., FA, MD, AD, and RD).
Meta-and mega-analysis have successfully been adopted in the field of neuroimaging by the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium ( ; ). The general concept of the harmonization method proposed by the ENIGMA-DTI group is that each site preprocesses the diffusion metric maps (e.g., FA) separately. The statistical scores are harmonized using meta- or mega-analysis, to improve data comparability and robustness. Findings of the ENIGMA-DTI group indicate that results obtained by meta- and mega-analysis may differ, in favor of the latter. In multi-center studies with a moderate amount of variation between cohorts, a mega-analysis statistical framework appears to be the better approach to investigate structural neuroimaging data, showing greater stability and higher power for jointly analyzing the data ( ). Nonetheless, when the individual diffusion metric maps are not available, meta-analysis could serve as a valuable alternative. However, meta-analysis should be performed carefully and one should take into account cohort trends ( ).
### Harmonizing the Parametric Maps Based on Regression of Covariates
Covariates, also known as explanatory variables, are variables that may affect the estimate of the diffusion metric under study. These covariates can be variables of clinical interest or unwanted confounding variables, such as MR hardware (e.g., scanner manufacturer, field strength, and coils), software, acquisition parameters (e.g., echo time, repetition time, b -value, and gradient directions) or image quality. One way to handle unwanted variability due to confounding factors is the use of regression models ( ). This approach is illustrated in . After fitting a regression model to the diffusion values, adjusted values can be derived that no longer contain the effect of the covariates. The use of the regression of covariates harmonization approach to correct for variability in software and hardware has been reported extensively in the literature ( ; ; , ; ; ). Regression of covariates methods can be divided into two categories: global harmonization methods and voxel-wise harmonization methods. Both classes are described below.
General scheme of a voxel-wise regression of covariates harmonization approach. For these methods the voxel intensity of the diffusion metric maps ( y , the intensity for a specific site s , subject p and voxel v ) is modeled as a combination of a voxel-wise intercept (α ), a voxel-wise slope (β ) multiplied by a model-specific dependent variable ( x ), and an error component (ε ). Each of the regression of covariates approaches will have a different model and dependent variable to describe the biological and site-related effects on the diffusion metric intensities. Next, the estimated coefficients are used to compute the new harmonized diffusion intensity values ( ).
The methods present different options for harmonizing diffusion metric maps (e.g., FA and MD maps). For briefness, we use the notation to denote the diffusion metric measure y harmonized by a specific method , at site s , for subject p and voxel v .
#### Global Harmonization
##### Human-phantom based harmonization (HuP)
A straightforward approach for data harmonization is to apply scanner-specific correction factors derived from human phantom data (i.e., a group of individuals scanned at multiple scanners/sites within a short period of time) ( ). One scanner type is defined as the reference ( R ) and the other as the target ( T ). The goal is to correct the diffusion metric maps of the target site. For this purpose, a correction factor ( F ) is calculated as the ratio of the mean value (across the human phantoms) of the diffusion metric in the reference and target, respectively: , where and are the mean metric value across the white matter voxels for human phantom p at the reference and target site, respectively, and N is the number of human phantoms. Successively, once the scanner-specific correction factors are determined, metric maps y for subject p and voxel v scanned in the target scanner ( y ) are scaled by the appropriate correction factor in order to obtain the HuP-harmonized diffusion metric maps: .
The main advantages of the correction factor are its simple derivation and the fact that it has been demonstrated to correct for differences that are likely attributable to the MR system manufacturer ( ). However, human phantom datasets from multiple sites are required. Moreover, a unique correction factor per scanner type only partially reduces the harmonization problem due to its intrinsic non-linearity, i.e., scanner type differences are not uniform but vary in a highly non-linear fashion across the brain ( ).
##### Hardware-phantom based harmonization (HaP)
presented global multi-site harmonization models, using phantom data acquired at multiple centers in a longitudinal study. For this study, dedicated diffusion single-strand phantoms were developed by HQ Imaging (Heidelberg, Germany). The study aimed to build a comprehensive model for the variability of FA. Protocol-specific and site-specific effects were included in the models, considering hardware (scanner vendor and head coil), software, acquisition parameters (bandwidth, TE, and TR), image quality (signal-to-noise ratio and mean residual), as fixed predictor variables, and site as random predictor variable, taking into account that fixed predictors relate to effects that are constant across all individuals, and random predictors relate to effects that vary across individuals.
Different models were proposed to describe the diffusion metric values y of the phantoms p considering the differences between acquisitions and were evaluated via the combination of the fixed and random predictors ( x and z , respectively): , where β is the fixed intercept, β the fixed effects slope, b the random intercept per phantom, b the random slope per phantom, and ε the error. In order to find the most comprehensive model for the diffusion metric data, many linear mixed effects models were evaluated by the Akaike information criterion (AIC). The selection of model parameters was based on three model categories: protocol-specific intercept, protocol-specific intercept with quality effects, and protocol-specific intercept with protocol-specific quality effects. Each model was further divided into submodels depending on the included variables. AIC is used to select which model best describes the variations in the metric intensities. The results showed that scanner manufacturer, SNR, head coil, bandwidth and TE are the covariates that best describe the sources of variability in the inter-site phantom data, and should be used to harmonize the diffusion metric maps of multi-center studies.
The use of hardware phantoms for harmonization has several advantages. Hardware phantoms can be scanned multiple times, for a longer time, and their images do not suffer from motion artifacts. The phantom content is controllable and remains stable over time. Duplicated phantoms can be easily obtained by several sites, obviating transport. The main drawback of hardware phantom based harmonization is that such phantoms do not fully represent the complexity of the human brain, and therefore have different, intra- and inter-scanner variabilities. Obviously, voxel-wise harmonization (cf., Section “Voxel-Wise Harmonization”) of brain dMRI is not possible using phantom data.
##### Global scaling (GS)
In the global scaling method presented by , a linear model is used to correct the site effect on the diffusion metric maps ( ). The estimated location (θ ) and scale (θ ) model parameters, per site s , encapsule the variabilities in the diffusion metric maps due to site effects. They are estimated by fitting a linear regression model: , where is an n ×1 vector containing the average diffusion metric intensity per voxel for the number of voxels n computed over all subjects of site s , is an n x1 vector containing the average diffusion metric intensity per voxel for the number of voxels n computed over all subjects of all sites together (considered a reference), and ε is the residual error. From the estimated parameters, the harmonized diffusion metric maps are calculated as: .
The main advantage of global scaling is that it takes into account information from all sites. Some disadvantages are that the removal of site effects can also remove biological variability, and that it does not account for spatial heterogeneity of the site effects in the brain.
#### Voxel-Wise Harmonization
##### Removal of Artificial Voxel Effect by Linear regression (RAVEL)
The Removal of Artificial Voxel Effect by Linear regression (RAVEL) method ( ) uses voxels in the cerebrospinal fluid (CSF) voxels as control region. The CSF-voxels are used for harmonization because their diffusion metric intensities are unassociated with disease or other clinical factors and are theoretically only influenced by site-related variabilities. In this method, the voxel-wise intensity of the diffusion metric maps ( y ) is described as a combination of four components: the average intensity in the sample (α1 ), the known clinical covariates of interest (β X ), the unknown site-related factors (γ Z ) and a residual ( R ): y = α1 + β X + γ Z + R . Where the symbol t indicates the transpose operation, y is the v × p matrix containing the registered and normalized voxel intensities for v voxels and p subjects, α1 is a v × 1 vector containing the average voxel intensity per site, X is a p × k matrix containing for each subject p the correspondent biological covariates k , β is the coefficient matrix associated with X , Z is a p × m matrix containing for each subject p the associated m unwanted coefficient factors and γ is the coefficient matrix associated with Z .
The CSF voxels are used to estimate the unknown/unwanted factors ( Z ) by assuming that α and β are null for the CSF since there is no association between control voxels and clinical features. Thus, the CSF diffusion intensities ( ) are described as: . Singular value decomposition is used to obtain the first latent factors ( w ) from the CSF voxels, representing the site-related variability common to all voxels. Next, the voxel-wise RAVEL coefficients (ψ ) are estimated fitting the linear regression model to the voxel-wise diffusion intensities ( y ) and the first latent factors ( w ): y = α + ψ w + ε , where ε is the residual error. Lastly, the RAVEL-harmonized diffusion metric map intensities are computed: .
An advantage of the RAVEL method is that it is a voxel-wise harmonization method that uses intra-subject information that is not affected by disease (CSF control region) for improving comparability between subjects. However, if these control regions do not carry the information about the inter-site variability and/or are related to the parameter of interest, then the correction may remove relevant biological information, becoming a disadvantage to use this method in such cases.
##### Surrogate Variable Analysis (SVA)
Surrogate Variable Analysis (SVA) identifies and estimates unknown, unmodeled or unwanted sources of variation from the data ( ; ). The so-called batch effects can be defined as measurements of unwanted variability that have qualitatively different behavior across conditions and are unrelated to the biological or scientific variables in a study ( ). In the context of multi-site harmonization, SVA is particularly useful when it is not known which datasets belong to which site. Through singular value decomposition, the data is decomposed into a set of m surrogate variables ( z ,…, z ). Variables with the largest variance, and which are not covarying with a priori defined factors of interest such as age, gender or diagnosis, are then regressed out of the data. The voxel-wise SVA coefficients (Φ ) are estimated by fitting the surrogate variables ( z , for surrogate variable m , site s and voxel v ) and the original diffusion metric intensities ( y , for site s , subject p and voxel v ) to the linear regression model: , where α is the voxel-wise overall measure of the diffusion metric and ε is the residual error. Next, the SVA-harmonized diffusion metric map intensities ( ) are computed as: .
Surrogate variable analysis is implemented in the SVA package for R, and is applicable voxel-wise ( ). A strong point is that it estimates all common sources of latent variation, without needing to know their exact origin (e.g., site). Nonetheless, if this inherent variation is related to biological variability (e.g., patients in site A, controls in site B) then SVA is not appropriate.
##### Combined association test (ComBat)
The combined association test (ComBat) uses regression of covariates for data harmonization ( ). It started as a batch effect correction tool (similar to SVA) used in genomics, in which the batch effect is known ( ). It is a powerful and fast alternative for SVA in cases where site is an a priori known factor.
ComBat describes the non-harmonized diffusion metric in each voxel ( y , for site s , subjects p and voxel v ) by an adjustment model that consists of the following terms: an overall measure of the diffusion metric (α ), the product of a design matrix ( X ) containing the covariates of interest (e.g., gender and age) and the vector of corresponding regression coefficients (β ), a term representing the so-called additive site effects (γ ) and, finally, the product of a normally distributed error term (ε ) and a factor representing the so-called multiplicative site effects (δ ): y = α + X β + γ + δ ε . The site-specific parameters of the adjustment model are assumed to have parametric prior distributions, being a normal distribution for the additive factor (γ ) and an inverse gamma distribution for the multiplicative factor (δ ). The parametric distributions are estimated from the data, using an empirical Bayes framework to decrease the variance of the site effects. It assumes that all voxels share a common distribution, and are used to infer the properties of the site-effects. Subsequently, ComBat-harmonized diffusion parameter maps are created based on the estimated additive and multiplicative factors ( and , respectively): .
It was reported that the ComBat harmonization method preserves between-subject biological information ( ). However, a limitation of this method is that the optimization procedure assumes the site effect parameters to follow a particular parametric prior distribution (Gaussian and Inverse-gamma), which might not generalize to all scenarios or measures. Moreover, it is not clear how non-linearities in the signal due to site effects propagate through the preprocessing techniques, as well as model fitting procedures.
## Diffusion Weighted Image Harmonization Methods
Diffusion parametric map harmonization methods for data pooling and joint analysis, meta- and mega-analysis and regression of covariates, have been reported extensively in the literature. Nonetheless, the harmonization of diffusion metric maps has several drawbacks, as described in section 4 for each of the methods. Recall that one of the main drawbacks is the lack of knowledge on how the scanner-specific non-linearities propagate in the diffusion model fit, possibly affecting the harmonization procedure of the diffusion metric maps. Recently, the use of the dMRI intensity signal has been proposed to perform model-free harmonization approaches. These methods are categorized as DWIH ( ; ; ; ; ). DWIH methods rely on mapping the DWI images to a reference space. An overview of these DWIH approaches is given below. The methods described are the rotation invariant spherical harmonics method, machine learning algorithms, and the method of moments.
### Rotation Invariant Spherical Harmonics (RISH)
The use of rotation invariant spherical harmonics (RISH) for dMRI signal harmonization has been first proposed by and several improvements to this method have been presented since then ( , ; ).
The core idea of the RISH method is to map the diffusion weighted imaging (DWI) data from a target ( T ) site to a reference ( R ) site. The voxel-wise DWI signal intensity S = [ s , …, s ] , along g unique directions, can be compactly represented in a spherical harmonics (SH) basis: , composed by SH basis functions ( Y ) and their corresponding coefficients ( C ) of order i and degree j , with j = 1,2,…,2i + 1. The RISH features, per harmonic order, are extracted from the estimated SH coefficients as: .
The harmonization procedure, which is illustrated in , consists of two parts: (1) learning scale maps between sites from training data and (2) applying the learned scale maps to harmonize all DWI of the target site. The learning part is performed using training data that is a subset of subjects that are matched by age and gender for both sites. From the DWI, the RISH features are calculated and used to create a multivariate template, per b -value shell. In template space, the voxel-wise expected value per site s and per harmonic order i [ ] of RISH features is calculated as the sample mean over the number of training subjects ( N ): , where s represents the site, v the voxel location in template space and p the training subject. Then, voxel-wise scale maps (Φ ) are computed for each harmonic order i: . Next, in the application part, the scale maps are used to calculate the harmonized SH coefficients of the target data per harmonic order: . Next, the image is transformed from SH domain back to the intensity signal domain [ ] using the harmonized SH coefficients: .
Representation of the RISH harmonization approach. Consider the purpose of modifying the DWI acquired in a target site, to correspond to the DWI acquired in the reference site. In the learning part using matched subjects, the RISH features are computed in native space from the DWI for the two data sets separately: reference ( R ) and target ( T ) sites. Then RISH features are transformed to a common space, the expected values are calculated per site s and per harmonic order i ( ), after which the scale maps are calculated (Φ ). The scale maps, which are computed for each harmonic order i , represent the transformation of the RISH features from target to reference site. Next, in the application step, the SH coefficients from the target site are calculated, the scale maps are warped into native space and applied to the SH coefficients, creating harmonized SH coefficients in native space. Those are transformed back to the signal intensity domain, obtaining the harmonized DWI. Thus, harmonized DWI from the target site can be jointly analyzed with the ones from the reference site.
Rotation invariant spherical harmonics has many advantages, the most important one being that it harmonizes the raw dMRI signal in a model-independent manner. The mapping captures only site-related differences, preserving the between-subject biological variation and fiber orientation ( ). However, a limitation is that it requires dMRI data with similar acquisition parameters across sites. It also requires the same number of matched controls that are scanned in both reference and target sites to obtain the scale maps.
### Machine Learning
In the past decade, several diffusion data harmonization methods have been developed employing a machine learning approach, such as sparse dictionary learning (SDL) and deep learning (DL).
#### Sparse Dictionary Learning (SDL)
Sparse dictionary learning is a representation learning method aiming at representing the input data as a linear combination of elements (the sparse dictionary), thus reducing the complexity of the harmonization problem ( ). The dictionary elements are small patches of spatial and angular image features (e.g., 3 × 3 × 3 × 5 voxels) that are learnt from the data itself. From a large set of random features, SDL extracts the common features with which full images can be reconstructed. The idea behind applying SDL for harmonization is that when a sparse dictionary can be constructed from data originating from multiple sites, the learnt imaging features will not include features of inter-site variability, as those are not common across the input data. Reconstructing dMRI data with a sparse dictionary, would then effectively harmonize the data ( ; ).
An advantage of this method is that modeling a signal with such a sparse decomposition (sparse coding) is very effective in detecting salient regions that are related to the more informative areas. However, a disadvantage is that, depending on the interest points and the type/resolution of the image, sometimes only a few regions are detected.
#### Deep Learning (DL)
The DL approach, which is illustrated in , consists of two steps: (1) Training: the learning stage in which the network parameters are optimized using the DWI from the same subjects acquired in two sites (target and reference) and (2) Inference: the trained network is applied to harmonize all subjects of the target site.
Representation of a deep learning approach for diffusion data harmonization. The purpose of the method is to modify the DWI acquired at the target site, to correspond to the DWI of the same subject acquired at the reference site. In the training part, DWI from the target site is used as input and DWI from the reference site as ground truth, for patient X . Matched subjects are used to tune the weights of the harmonization network. During the forward phase, the network produces the predicted harmonized DWI that is compared with the corresponding expected DWI from the reference site. The difference between the predicted and the ground-truth (cost function) is back propagated into the network to update the weights in such a way that the loss decreases and the predicted harmonized DWI is closer to the ground truth. In the inference step, the trained network is used to generate the predicted harmonized DWI from unseen DWI data of the target site, which then become comparable to the DWI from the reference site.
The current deep learning algorithms for diffusion data harmonization are mainly based on spherical harmonic features. The aim is to bring all the images in the same SH domain, by modifying the SH coefficients of the target data creating harmonized DWIs of the target site that are comparable to the DWIs from reference site. To achieve this, the network is trained to generate the harmonized image starting from the image acquired at a target site, using the image acquired in the reference site as ground truth, as illustrated in . Hence, diffusion data from subjects that were acquired in both reference and target sites are used for training the network. Once it is trained, the inference can be done for other subjects from the target site, to create harmonized images.
presented a summary of four deep learning algorithms and one sparse dictionary learning harmonization algorithm used to evaluate two harmonization tasks in diffusion MRI: scanner-to-scanner mapping and angular- and spatial-resolution enhancement, i.e., mapping between standard and state-of-the-art acquisitions. Each of the algorithms was built with different net architectures and strategies. The deep learning algorithms that were evaluated by are: spherical harmonic network (SHNet), spherical harmonic residual network (SHResNet), spherical network (SphericalNet), and fully convolutional shuffling network (FCSNet). The used SH coefficients, on which the net is based, are obtained starting from the diffusion signal of the same subjects scanned in different scanners and with different acquisition schemes. Here we summarize some of these methods. A more extensive benchmark can be found in .
##### Spherical Harmonic Network (SHNet)
Spherical Harmonic Network is based on a classical Fully Connected Network (FCN) architecture, composed of a cascade of three fully connected layers, in which the rectified linear unity (ReLU) function is used as the activation function ( ; ). Next, a batch normalization layer is used to stabilize. The different weights of the neural network layers are tuned by using paired images from different sites. The net is trained by matching data between the target site and the reference site to obtain the harmonized image. Once the network is trained, it can be used to harmonize unseen datasets from the target site. The main advantage of this network is that it is a simple FCN approach to tackle the harmonization problem. However, it might not be sufficiently sensitive to learn all the complex features of an accurate harmonization procedure.
##### Spherical Harmonic Residual Network (SHResNet)
A Convolutional Neural Network (CNN) approach has been presented by . In this case, the network algorithm is based on the novel concept of residual structure by . This approach is based on the difference between the input and the ground truth (target signal). The main building blocks of SHResNet are so-called functional units consisting of three convolutional layers, where each functional unit predicts the coefficients of a single SH order ( ). The main advantage of using a residual network structure consists in the robustness against the degradation problem (decrease of accuracy due to the increased network depth) and hence enabling the use of a deeper network (more convolutional layers). Nonetheless, the harmonization is done per harmonic order of the SH signal, thus, the signal from both target and reference should have the same SH orders.
##### Spherical Network (SphericalNet)
SphericalNet is a novel deep learning approach based on spherical surface convolutions ( ). It transforms the signal from SH space into spherical surface space, and performs three spherical surface convolutions. After each of these convolutions, a sigmoid activation function is applied in order to limit the signal’s range between 0 and 1 ( ). The signal is converted back to SH space, followed by three 3-D convolutional layers with parametric ReLU as activation. Spatial information is combined in the last convolutional layer to project neighborhood info into one voxel. The advantage of this algorithm is that it uses spherical information during spatial convolution to improve accuracy in the harmonization procedure. However, for this algorithm the intensity signal has to be transformed twice (for SH domain and then to spherical surface domain), which could introduce additional complexity to the harmonization problem.
##### Fully Convolutional Shuffling Network (FCSNet)
Fully convolutional network is a patch-based deep learning harmonization algorithm inspired by . The architecture of this network contains four hidden convolutional layers with ReLU activation. Large patches are used as input, overlayed to cover the entire brain, and smaller patches are obtained as output. The last layer contains a “shuffle” operation and is composed of “skip” connections to increase the prediction accuracy. The cost function for this algorithm has two parts: channel-wise loss and loss on the function-value. The algorithm uses the patched-based fully convolutional network for diffusion data harmonization and resolution enhancement. One advantage of this approach is the use of large patches that inform about the local neighborhood and are beneficial for the harmonization procedure. On the other hand, neighborhood data could be biased and end up corrupting the harmonization algorithm.
Deep learning algorithms demonstrated the robust capability of solving non-linear problems such as data harmonization. However, some limitations are: (1) overfitting, i.e., when the model is more accurate in fitting known data but less accurate in predicting unseen data, (2) the need for a large amount of matched subjects scanned at different sites with similar acquisition sequences per site for training and (3) possible distortion of pathological information, if the net is trained with healthy subjects and then applied to patients.
### Method of Moments (MoM)
Method of Moments is a statistical harmonization approach that uses spherical moments to map DWI images from target to reference sites ( ). The first moment ( M ) corresponds to the spherical mean and the second central moment ( C ) corresponds to the spherical variance. The core idea is to match the spherical mean and spherical variance in order to correct for unwanted variability. Each voxel-wise n-th spherical moment ( M ) is defined as the diffusion signal at constant b -value ( S ) raised to the power of n integrated over all directions g : . MoM matches M and C per b-shell b using the mapping function ( f ): M [ R ] = M [ f ( T )] and C [ R ] = C [ f ( T )], where R is the diffusion signal acquired at the reference site, and T the signal at the target site. Considering the mapping function as f ( S ) = α S + β, α and β are the mapping coefficients calculated as and β = M [ R ]−α M [ T ]. The MoM parameters are calculated in template space and then warped back to native space of the target subjects and applied to the DWI images. The MoM-harmonized DWI signal is .
The MoM approach is illustrated in . In this method, M and C are computed in native space from the DWIs acquired in the reference and target sites. Next, the moment images are warped into a common space that is defined by the target data at the population level. Population moment median images across subjects are calculated for each of the moments for each of the sites. The mapping parameters (α and β) for the target site are obtained by matching the population median moments using the linear mapping function f . These parameters are warped to native space for each of the subjects of the target site and the mapping function is applied voxel-wise. Lastly, the harmonized DWI of the target data is obtained.
Representation of the method of moments harmonization pipeline. The purpose of the method is to modify the DWI of the target site, to correspond to the DWI acquired in the reference site. Initially, the diffusion signal in the reference ( R ) and target ( T ) are used to compute spherical means ( M [ R ] and M [ T ]) and spherical variances ( C [ R ] and C [ T ]) in native space for each b-shells ( b ). The spherical moments are warped to a common space, based on the target population. Then the moment medians are calculated across subjects ( M [ R ], C [ R ], M [ T ], and C [ T ]). Afterward, the mapping parameters (α and β ) are calculated per b-shell, by matching the population moments. The mapping parameters are warped to native space and applied voxel-wise to the DWI images of target site subjects, obtaining the harmonized DWI.
Advantages of the MoM are that it (1) allows direct harmonization of DWI images, without the need to represent them in any other space domain (e.g., SH space); (2) preserves directional information of the signal; (3) does not require that reference and target data have the same number of gradient directions; (4) does not require training data or matched populations with controls/patients, and (5) allows the harmonization of either a subject or a population of subjects. However, MoM as described in does not harmonize multi-site data with different spatial resolution or different b -values. Possible solutions to cope with different spatial resolutions and different b -values would be to resample the reference data to the resolution of the target data, and rescale the signal, respectively, both prior to harmonization.
## Discussion
Multi-center and/or longitudinal studies using diffusion MRI data are significantly affected by inter- and intra-site variability. Sources of variability include, but are not limited to, hardware, acquisition settings, reconstruction algorithms, incompatible data formats and data quality. To cope with this variability, regulations and strategies are needed to facilitate harmonization of multi-center diffusion MRI data. In that respect, MR scanner vendors and researchers have a responsibility regarding the access and storage of DWI data, and transparency on reconstruction algorithms, acquisition protocols and applied pre- and post-processing steps. Ideally, worldwide governments should ally to enforce regulations regarding calibration procedures to MR scanner vendors. The use of the same quantitative calibration phantom and a standard procedure would decrease inter-scanner variability ( ; ).
The need for harmonization has increased with the availability of large diffusion MRI multi-center datasets. Examples of these are the Human Connectome Project (HCP ), the Alzheimer’s Disease Neuroimaging Initiative (ADNI ), CENTER-TBI , and the Cross-scanner and cross-protocol diffusion MRI data harmonization ( ). For performing joint analysis of data that have been acquired with multiple acquisition settings, several statistical and mathematical harmonization approaches have been developed to reduce unwanted site variability while preserving the biological variability.
To overcome the challenges with respect to joint analysis of multi-center diffusion data, the scientific community has gathered to participate in challenges on data harmonization. The Diffusion MRI Data Harmonization 2017 and the Multi-shell Diffusion MRI Harmonization Challenge 2018 (MUSHAC ) were proposed with the aim to evaluate the performance of algorithms that enable the harmonization of DWI data. From the last challenge, presented a summary of results comparing the effects of DWIH methods on diffusion parametric maps. Different DWIH methods were used to harmonize the multi-shell DWI data. The algorithms range over three approaches: interpolation-based, regression-based and CNN algorithms. Diffusion parametric maps were calculated before and after the harmonization procedure, such as FA, MD, and MK. The results demonstrated that the harmonization algorithms are significantly effective in reducing the variability and maintaining the biological information.
In this paper, we have reviewed a variety of harmonization methods proposed in the literature. The decision as to which method to use depends on several aspects, such as the study design, the research question and the available data. In , we have categorized the reviewed methods in terms of their intrinsic properties. This categorization may help to select a harmonization method, given a certain diffusion MRI dataset and a specific research question. Additionally, shows a flowchart that could provide guidance for selecting the most appropriate harmonization strategy.
Overview of the harmonization methods presented in this review.
Flowchart describing a possible way to select a suitable harmonization method depending on the available data and research question at hand. In this flowchart, the first question to be answered is: Do you want to harmonize the DWI or the diffusion metric maps? For harmonization of the diffusion metric maps (right segment of the flowchart), the following question is: Do you want to create new harmonized metric maps? If so, the suggested harmonization approach would be one of the regression of covariates methods. In case of a negative answer, the next question is: Do you have individual measures available or a summary of statistics? If the user has a summary of statistics, the suggestion is to use a meta-analysis approach, otherwise, if one has individual diffusion measures, the suggestion is to harmonize the data using a mega-analysis approach. On the other hand, for harmonization of DWIs (left segment of the flowchart), the next question is: Do you have DWIs of the same subjects acquired in multiple sites? In case of an affirmative answer, the suggested approach is machine learning, which comprehends deep learning and sparse dictionary learning methods. In case of a negative answer, the following question is: Do you have DWIs of a cohort of subjects that is age- and gender-matched between the sites? If the user has matched data, the RISH method is suggested. Otherwise, the method of moments is the suggested approach.
For example, the flowchart can be applied to the study of , who assessed the relation between diffusion MRI indices and cognitive impairment in brain aging using the ADNI3 dataset. In this study, new harmonized metrics maps (FA, MD, AD, and RD) were created using the ComBat method to remove any site-effects from the results. Following the flowchart presented in , first, the research was related to the harmonization of diffusion metric maps, thus, the right segment of the chart is suggested to be followed. Next, the researchers aimed to create new harmonized maps, in this case the choice of a regression of covariates method was logical and appropriate. Along these lines, the suggested harmonization approach by our flowchart is in agreement with the decision from the authors.
In general, it is an ongoing challenge to define a gold standard for dMRI harmonization. A possible explanation for this might come from the complexity of removing the unwanted variability. The sources of unwanted variability may stem from differences in number of subjects acquired per site, MRI hardware, acquisition protocol (voxel size, repetition time, echo time, number of diffusion directions, number of b-shells, etc.), pre-processing steps and co-registration effects. In these circumstances, the preservation of expected biological variability is a useful criterion for evaluating the efficacy of harmonization methods, but this is only possible when the same subjects are scanned at different sites. When traveling human phantoms are included in the study design this provides a ground truth and allows for carefully evaluating the newly computed features and their accuracy and precision ( ). However, traveling human phantoms datasets are mostly absent from a scenario of multi-center studies, where distinct subjects are scanned at different sites. Additionally, a note of caution in both cases is due here since anatomical differences or co-registration deformations (to a common space) may cause significant errors in the harmonization.
Although DPMH approaches have demonstrated their ability to harmonize diffusion metric measures for joint analysis in multi-center studies, there are some drawbacks, which can be avoided by using DWIH methods. First, DPMH methods require different transforms to harmonize each of the diffusion metrics of interest. This may have implications for multivariate analyses, as it is not guaranteed that subject-specific patterns (e.g., high FA in combination with low MD) are preserved after both metrics are harmonized separately. Second, DWIH methods do not rely on a specific diffusion model, hence unwanted variation is not propagated (and as a result made more complex) through model fitting. Moreover, any diffusion metric estimated from DWIH harmonized DWIs will automatically be harmonized as well. In this regard, DWIH approaches are more promising for reliable harmonization.
In a recent study by , DWIH was applied to harmonize diffusion MRI multi-site data prior to detection of white matter abnormalities in schizophrenia patients. RISH was retrospectively applied to DWIs of 13 different sites to remove the site-related differences. For this, a reference site was chosen and the DWI data from the other 12 sites were harmonized accordingly. The harmonization performance was evaluated in a group of matched controls, using their FA maps before and after harmonization. It was shown that the statistical differences between sites were removed and the inter-subject biological differences were preserved.
Nonetheless, many challenges remain for diffusion data harmonization in multi-center studies. Ideally, novel harmonization methods should not require training data of subjects scanned in multiple centers, and be applicable to data acquired with different spatial resolution, number of b shells, or number of diffusion gradient directions. Moreover, the availability of easily implementable methods and open-source platforms are important assets to encourage researchers to perform diffusion data harmonization in multi-center and longitudinal studies.
Furthermore, harmonization methods should be generalizable to clinical cases. Up to now, serious challenges that limit voxel-wise harmonization of DWI data of clinical patients are the co-registration requirement, since disease-related anatomical alterations may severely complicate co-registration, and the condition that the pathological content (e.g., diffusion properties of lesions) should be harmonized while the expected biological variability should not be affected. To overcome these limitations, the use of clinical data during the training of DWIH harmonization approaches would be valuable.
## Conclusion
While dMRI is routinely used in clinical workflows, comparing the signal intensity of dMRI scans across sites and over time is challenging. Harmonization methods aim to overcome this by recalibrating/recalculating either the DWI signal intensities or the resulting diffusion metrics. In this article an overview of harmonization methods in the literature was presented, covering meta- and mega-analysis, regression of covariates, rotation invariant spherical harmonics, machine learning algorithms and the method of moments. The proposed feature table and flowchart present the main characteristics of the methods, assisting in the decision of which method to use depending on the study design and the available data. Future developments of diffusion harmonization methods may benefit from focusing on DWIH approaches, avoiding unwanted variation propagates through diffusion model fitting.
## Author Contributions
MP and RP wrote the manuscript with comments from TB, PVD, P-JG, BJ, AR, AdD, and JS. MP and RP contributed equally to this manuscript. All authors read and approved the final manuscript.
## Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
|
The oxytocin receptor (OXTR) is a G protein-coupled receptor with a diverse repertoire of intracellular signaling pathways, which are activated in response to binding oxytocin (OXT) and a similar nonapeptide, vasopressin. This review summarizes the cell and molecular biology of the OXTR and its downstream signaling cascades, particularly focusing on the vasoactive functions of OXTR signaling in humans and animal models, as well as the clinical applications of OXTR targeting cerebrovascular accidents.
## Introduction
Since its original cloning and characterization by , to more recent predictions of its three dimensional structure ( ), the human oxytocin receptor (OXTR) has garnered special attention for its role as a potential therapeutic target in a wide array of physiological and behavioral disorders. Several recent reviews have comprehensively covered the impact of OXTR signaling upon peripheral and central control of behavior and physiological functions including osmoregulatory, stress modulation, and memory ( ; ). By contrast, this review will survey the role of OXTR-mediated cellular and molecular pathways regulating vascular function, with a special focus on mechanisms of cerebrovascular disease and the receptor’s putative disease-modifying role in the post-stroke environment, which may be amenable to therapeutic targeting.
## The Oxytocin Receptor
The OXTR is a widely expressed G protein-coupled receptor (GPCR) that binds its endogenous nonapeptide ligand, oxytocin (OXT), with an affinity of about 1–10 nM ( ), as well as a structurally similar nonapeptide, vasopressin, with an affinity of about 100 nM–1 μM ( ). The OXT peptide and its full nine amino acid sequence was first detailed in 1953 by Du Vigneaud and colleagues through varied partial hydrolysis experiments combined with paper chromatography ( ). However, its existence was recognized as early as 1928 when researchers began testing the effects of OXT from pituitary extracts on peripheral reactions such as uterine contractions and blood pressure ( ; ; ; ). OXT has since been found to exert both central and peripheral effects via OXTR-mediated phospholipase C (PLC) activation and downstream Ca signal transduction ( ). OXT is synthesized in the hypothalamic magnocellular and parvocellular neurons, reaching the peripheral circulation through the posterior pituitary ( ), while central actions appear to occur through both axonal and possibly volume transmission through dendrites ( ). This volume transmission and its relative contribution to OXTR activation is an ongoing subject of debate, as more OXT neuronal projections to forebrain OXTR-expressing regions have become apparent in recent years ( ; ). The very first hint of a bioactive OXTR was indirectly demonstrated by Sir Henry Dale and focused on the induction of uterine contractions by posterior pituitary gland components ( ). Since that time OXTRs have not only been identified in the uterus ( ), but also the mammary glands ( ), heart ( ), blood vessels ( ), and brain ( ). While the presence and activity of the receptor in each of these regions underscores the importance of OXTR signaling in peripheral and central physiology, this review will focus primarily on those found in the brain. Regardless, the widespread expression of this receptor and its ligands underscores the continued relevance and necessity of research into its functional repertoire ( ), even after the 100 years that have passed since Dale engaged the receptor without knowing what it was ( ). Before turning to physiological and disease modifying possibilities for the receptor, its biology as revealed by basic research will be summarized. This section provides a synthesis of the current understanding of the OXTR gene and protein at the cellular level that will be necessary to fully grasp the potential of its therapeutic use.
### The Oxtr Gene
In 1994, Inoue and colleagues described the genomic sequence of the human Oxtr , identifying it as a ∼17 kb single gene on chromosome 3 ( ). The gene contains two exons corresponding to the OXTR’s promotor region and two exons corresponding to the receptor coding sequence itself, along with three introns of which the third displays the longest sequence at 12 kb ( ). Three transcripts of varying lengths were found in uterine tissues and differences were observed in antibody binding to the third intracellular loop ( ), suggesting the existence of receptor subtypes at the time. However, it is now recognized that the three mRNA transcript lengths are due to sequence differences in the untranslated region (UTR) flanking a single coding sequence, thus resulting in one receptor transcript that may be differentially regulated post-transcriptionally ( ; ). The Oxtr gene promoter also contains multiple response element sequences that contribute to differential expression of the receptor across age ( ), region ( ), and at parturition in females ( ; ). The promotor region of the receptor has three TG-dinucleotide repeats that, based on the known ability of calcium to alter DNA structure at these repeats, could also explain some site-specific differences in OXTR mediated activity ( ; ). Using kinase inhibitors, it has been demonstrated that regional differences exist in the brain as to whether protein kinase A or protein kinase C leads to increased Oxtr gene transcription ( ). The 5′-UTR and promoter region of the Oxtr also bears putative response elements to interleukin-6, acute-phase proteins, GATA-1, c-Myb, and activator proteins 1 and 2 ( ). Beyond the promotor region, interest has also alighted on potential regulatory elements in the large third intronic region that separates the amino acid coding exons. reported a region of hypomethylation in a central part of the intronic sequence in highly expressing myometrium and hypermethylation in low expressing leukocytes, suggesting epigenetic regulation of the receptor gene within the third intron. Furthermore, experience and exposure-induced epigenetic regulation of the expression of the Oxtr has been studied in relation to the emergence of characteristics of several conditions including autism spectrum disorders (ASD) and psychiatric conditions such as schizophrenia ( ; ; ).
### Protein Structure
Until recently, the tertiary structure of the OXTR had not been visualized with either X-ray crystallography or cryo-EM but rather by computer simulations, perhaps owing to its conserved homology with the rhodopsin family GPCRs in general and the beta-adrenergic receptors in particular ( ). Indeed, many studies have pointed to it being a prototypical Class A GPCR ( ). In a potentially exciting development, a crystal structure obtained by Waltenspühl and colleagues was recently reported that supports a canonical GPCR topology ( ). This supports the rigor of prior structural studies of the OXTR, especially in the context of β-arrestin recruitment ( ) and dimerization ( ), which have focused on comparisons to rhodopsin ( ). The OXTR is composed of seven membrane-spanning α-helices with three intracellular and extracellular loops, a N-terminal region in the extracellular space, and a C-terminus in the cytoplasmic space ( ; ). Important individual structural elements of the OXTR, such as ligand binding sites, the site of interaction with the G protein, transmembrane movements due to activation, and protein modifications have been revealed by computer simulation and mutation studies ( ; ; ; ; ; ; ).
Model of the oxytocin receptor A. (A) linear view of the receptor. (B) The top of the receptor as it forms a binding pocket. (C) The bottom of the receptor as it forms a binding pocket. (D) Color coded key. The general location of specialized sites are highlighted by color with key provided. Based on chimera, point mutation, and structural analysis studies as cited in the text. Created in Blender 2.8.
The OXTR binds both nonapeptides OXT and vasopressin, with OXT acting as a full agonist and vasopressin acting as a partial agonist ( ). Of most relevance to this review is the similarity of vasopressin V1a receptors to OXTRs in structure, the G proteins and kinases to which they couple, and the high concentration of these receptors in the CNS and vasculature ( ). The peptides are predicted to assume a similar shape at physiological pH, consisting of a six-residue ring with a three-residue linear region ( ). The binding pocket for the ligands most likely lies in a cleft surrounded by the transmembrane domains. Chini and colleagues used a coordinated method of ligand and receptor mutations to find residues on the first extracellular loop of the receptor and transmembrane domains V and VI that accounted for the specificity of the receptor for OXT versus vasopressin ( ; ). This occurs through aromatic-aromatic and hydrophobic-aromatic interactions with the isoleucine (I3) and leucine (L8) residues of OXT, which are the only two amino acids that differ between the two neuropeptides ( ). Therefore, any disruption in these residues would be expected to reduce the ability of the OXTR to respond to its natural agonist. The increase in availability of nonpeptide agonists and antagonists continue to contribute to the molecular modeling of this pore-based docking site, showing that its surface region promotes agonism and that deeper binding impairs conformational changes promoting antagonism ( ). More broadly, the linear region of the peptide appears to closely interact with the first extracellular loop, and the cyclic region of OXTR appears to interact with the second extracellular loop with intermittent connectivity with transmembrane domains II-VII ( ). A conserved aspartic acid residue in the second transmembrane domain (Asp 85) is demonstrably important for trafficking the OXTR to the plasma membrane ( ).
On the intracellular receptor face, research has focused on identifying the sites of G protein subunit interactions, as well as the site of β-arrestin recruitment for receptor internalization. Selective replacement of the intracellular loops shows that intracellular loops 2 and 3 are vital for the ability of the receptor to effect G protein signaling, since no increase in phospholipase C is observed in their absence ( ). Furthermore, disruption of the large α-helix within the fourth intracellular domain also subsequently disturbs the ability of the receptor to bind G ( ). V1aRs also couple to Gq/11 proteins and stimulate calcium-dependent signaling cascades ( ). The use of a X-ray free electron laser on crystalized rhodopsin in complex with arrestin revealed that phosphorylation at certain residues are vital to recruitment ( ). Briefly, the intracellular C-terminus of the receptor is phosphorylated at a β-sheet that attracts the N-terminus of arrestin, which allows it to undergo a conformational change or domain twist ( ). As such, a similar mechanism is likely to be involved in OXTR internalization and regulation of signaling activity. Additional OXTR posttranslational modifications include three N-glycosylation sites at residues N8, N15, and N26 in the N-terminus and sites for palmitoylation at cysteine residues C346 and C347 in the C-terminus; however, so far no vital role for these modifications have been defined ( ; ; ).
Recent GPCR research has revealed the ability of many GPCRs to dimerize or even oligomerize to maximize the strength of intracellular signaling ( ). Busnelli and colleagues recently demonstrated a likely presence of high affinity dimers of OXTR. Moreover, by using alkane spacers of varying lengths between ligands, they were able to identify a likely place of dimerization on the OXTR ( ). The linkage probably occurs at the position of transmembrane helix 1 to transmembrane helix 2 which would accommodate bivalent ligands and allow for a reduction in entropy cost that would favor the formation of dimers ( ). While methods need to advance before we can confirm the relative occurrence of single receptors, dimers, or oligomers, it provides an intriguing possibility for discrete manipulation of the OXT system, perhaps even for some of the conditions discussed below ( ). See for a visual summary.
### Signaling and Cellular Function
As noted above, activation of the OXTR typically stimulates intracellular Ca mobilization through a PLC-dependent mechanism ( ; ). While the OXTR is reported as coupling predominantly to G type G protein subunits, it is now established that the OXTR also couples to G /G type G protein complexes ( ; ). Recently, an ambitious synthesis of previous reports of OXTR intracellular signaling pathways was completed by Chatterjee and colleagues. To date, this open-source resource on NetPath is the most comprehensive overview of the OXTR signaling cascade ( ). Several of the more well-established pathways and their functional role at the cellular level will be described below. G -transduced signaling is mediated by PLC-stimulated hydrolysis of the phospholipid phosphatidylinositol 4,5-bisphosphate (PIP ) to diacylglycerol (DAG), which in turns activates protein kinase C (PKC) and inositol 1,4,5-trisphosphate (IP ), which stimulates the release of intracellular Ca stores via IP receptors and also activates PKC among other Ca -activated kinases ( ; ).
Phospholipase C stimulates the phosphorylation of PI3K and AKT leading to the activation of endothelial nitric oxide synthase (eNOS) influencing cellular migration and vasodilation ( ; ). Additionally, the involvement of Rho kinases in smooth muscle uterine contractions suggests that OXTR activation of these kinases can lead to the production of phospholipase A2 and, in turn, cyclooxygenase 2 ( ). The modulation of levels of Rho GTPases by OXTR leads to shifts in cell adhesion molecules, particularly in neurons ( ). OXTR-stimulated PKC signaling has been shown to lead to the dephosphorylation of eukaryotic translocation factor eEF2, which aids in cellular proliferation through peptide chain elongation ( ). Several other kinases are reported to be activated through the G cascade of OXTR activation including the mitogen-activated protein kinases (MAPKs) ERK1/2, which induce c-fos and c-jun expression as an early mediator of proliferation, and ERK5, which is more specific to cellular differentiation ( ; ). Whereas the suspected G /G pathway is less well-defined, it has been demonstrated that G signaling leads to p38 MAPK activation and aids cells in adaptive processes to physiological stressors through transcriptional activators and direct effects on cell stabilizing proteins ( ). Potential hyperpolarizing effects through G could occur through interactions with Ca -dependent K channels, as proposed based on recent studies using alternative peptides and chelators ( ). Hence, in addition to the well-established role for OXT as a contraction influencing hormone, it appears to be involved in cellular differentiation, migration, proliferation, responses to stressors, dilation, and inflammation.
The response to OXTR signaling varies in a cell type-dependent manner ( ). In smooth muscle cells and neurosecretory cells this results in contraction and excitability, respectively ( ; ). Importantly, smooth muscle cells respond to Ca mobilization by triggering calmodulin to activate myosin-light chain kinase ( ), most prominently at parturition leading to uterine contractions ( ). OXTR-secreting neurons of the hypothalamus express presynaptic OXTRs that are self-excitatory, which may impact the glutamatergic properties of subpopulations of these neurons ( ). Due at least partially to the neuromodulatory effect of OXT at OXTRs on neurons of multiple subtypes ( ), the general excitatory or inhibitory effects throughout the brain appears to differ and is further confounded by an influence on ionic channel expression ( ). For instance, it has excitatory effects at the hippocampal formation ( ), and presynaptic inhibitory effects in the SON ( ). Some studies have suggested an effect of OXTR activation on astrocytes including a modulation of glutamatergic signaling and GFAP induction that might be important for plasticity ( ; ; ). Additional effects of OXTR signaling, whether stimulated by OXT or AVP, on CNS physiology and behavior will be discussed in greater detail below.
Proposed intracellular signaling pathways for the oxytocin receptor for vascular and cerebral cells. The oxytocin receptor is a vasoactive influencer and promotes proliferation and migration for multiple cell types in the tightly connected vascular and nervous systems. Created in Blender 2.8.
### Signaling Partners
As a receptor with a divergent, context-dependent signaling cascade, OXTR activity is influenced by interactions with a variety of additional cell and tissue-specific signaling partners and modulators. Many of these OXT and vasopressin binding-independent activities have involved a parallel expansion on the roles of known cellular components; among these are β-arrestins, cholesterol, and steroid hormones. Therefore, these connections have expanded not only our understanding of OXTR physiology, but also of GPCR-mediated cell biology.
Once thought to be only involved in the receptor desensitization process, the role of arrestins in receptor activation has recently been more fully elucidated. The β-arrestins have been suggested to promote alternative GPCR signaling pathways when ubiquitinated by sterically hindering interactions between the receptor and canonical pathway signaling partners and acting as a scaffold for enzymes driving alternative pathways ( ). One such scaffolding activity that has been defined is the role of β-arrestin in recruiting cRAF-1, ERK2, and MEK1 to form the ERK2 signalosome, which can control nuclear transcription factors through phosphorylation ( ). Another such β-arrestin role in signal transduction is its recruitment of the tyrosine kinase c-SRC for the purpose of mitogenic activity ( ). While it is uncertain whether these pathways are also associated with the OXTR signaling repertoire, its known interactions with the β-arrestins suggest additional signal transduction properties initiated by internalized receptors.
Cholesterol is a lipoprotein long held to be an important building block of the plasma membrane, but GPCR research has also identified it as a potential factor in receptor activation. Work by Gimpl and colleagues identified a dual role for cholesterol in modulating OXTR signaling ( ). When analyzing the role of varying cholesterol content on the binding of OXT to its receptor, they found a cooperative and facilitating role for cholesterol as an allosteric molecule for endogenous OXTR signaling ( ). Specifically, chimeric approaches using the cholecystokinin type B receptor possessing a critical C-terminus region of the OXTR (amino acid residues C347-A389) suggest that the binding site for multiple molecules of cholesterol is located on the N-terminus of the receptor ( ) or alternatively, due to specific residues on transmembrane domains 5 and 6 ( ). Further debate on the location of the cholesterol binding site may arise with the publication of the crystal structure of the OXTR, which suggests it is located within a pocket of transmembrane domains 4 and 5 ( ). Secondarily, they reported that cholesterol protected OXTRs from degradation under high heat conditions ( ). Further allosteric molecules include Mg , which is a positive allosteric molecule ( ), and Na , which lowers the affinity of OXTR for its ligand in a concentration dependent manner ( ).
Estrogen may also play an essential role in OXTR signaling. In the uterus, the OXTR increases in density around parturition and decreases swiftly afterward ( ). This increase is believed to be the consequence of a surge in the concentration of estrogen, with subsequent decreases due to progesterone ( ). Further, predictable variances have also been noted around the estrous cycle in rats when estrogen and progesterone levels vary ( ). Experiments using the protein synthesis inhibitor cycloheximide bolstered the belief that estrogen-induced OXTR expression is de novo synthesis-dependent and its decline in expression is due to progesterone’s antagonistic effect ( ). In this regard, an estrogen response element (ERE) was found in the OXTR promoter region, and it has been reported that the addition of estrogen to the brain in pregnant rats resulted in increased OXTR expression in several brain regions ( ). Consistent with this, in vitro studies showed that cells transfected with the full palindromic ERE increased Oxtr promotor activity and increased protein levels, while a truncated ERE did not ( ). Interesting, this effect of estrogen on Oxtr expression was not found in virgin dams, suggesting that additional factors associated with pregnancy synergize to upregulate the Oxtr gene ( ). While the complete story of steroid hormone influence on the OXTR remains to be elucidated, in vivo studies using mice deficient in estrogen receptor α ( ) suggest it is vital for OXTR increases due to estrogen. Studies of virally labeled OXTR in female ovariectomized rats with subsequent hormone replacement point to estrogens as potentially driving sexually dimorphic expression in certain regions where estrogen receptor α is co-expressed ( ). Also, in vitro binding studies suggest progesterone’s inhibition of OXTR binding density is due to a direct interaction with the OXTR ( ); however, progesterone’s antagonistic effect toward estrogen also cannot be ignored ( ).
### Cellular Processing
Once recruited from the endoplasmic reticulum to the cell surface ( ), it is believed that usually the OXTR undergoes endocytosis and recycling upon stimulation ( ). In order to trace the dynamic processing of the OXTR throughout the cell, we will focus on the process of receptor desensitization, internalization, trafficking, and recruitment.
Desensitization is a very important process to protect and preserve GPCR signaling as it both protects the cell from overstimulation, while also allowing for the recycling of receptors back to the cell surface for multiple responses over time, a discovery based on early studies of β-adrenergic receptors ( ; ; ). Varying time frames have been estimated for peak desensitization of the OXTR upon agonist stimulation. A range of 4–6 h for peak desensitization has been reported in myocytes expressing OXTR ( ), while others have reported it taking up to 20 h ( ). These differences can mostly be attributed to the concentration and tissue dependent aspects of receptor desensitization ( ).
Oxytocin receptor is considered a Class A GPCR with respect to its interaction with β-arrestins, meaning that it maintains a strong connection with β-arrestin to regulate its endocytosis into secretory or degradative pathways ( ). The interaction of OXTR with β-arrestins, and therefore its desensitization, is initiated by phosphorylation of the receptor by G protein receptor kinase 6 ( , ). Studies on OXTR tagged with GFP suggest that many receptors are recycled back though the secretory pathway, indicating a more sustained need for OXTR pathway signaling. Indeed, fluorescence microscopy studies revealed the colocalization of fluorescently tagged OXTR with transferrin after stimulation and a subsequent return of the signal to the plasma membrane 4 h later independent of protein synthesis, suggesting prominent receptor recycling ( ). Further detection of OXTR co-labeling with Rab5 and Rab4, small GTPases involved in “short cycle” trafficking back to the membrane, also support this conclusion ( ). While this may be the typical cycle, this is not always the case, as β-arrestin-independent internalization and recycling loss has also been reported, particular with analogs of OXT ( ). Some nuclear trafficking of the OXTR in concert with β-arrestins, Rab5, importin-β, and transportin-1 has been reported in mouse osteoblasts, but any transcription modifying effects remain speculative ( ).
Comparatively less is known about the trafficking of the receptor to the plasma membrane post-translation. Three N-linked glycosylation sites within the N-terminus of the OXTR suggest the importance of cell surface targeting of the receptor for cell function, although whether these sites are used for such a role is disputable ( ). As mentioned above, when the conserved residue Asp 85 is mutated in the OXTR, very little receptor makes it to the plasma membrane post-translation, suggesting a vital role for this amino acid in some stage of trafficking ( ). It is also unclear which Rab GTPases are involved in the initial trafficking from ER to Golgi to plasma membrane after translation ( ). Given the established role the secretory pathway plays in controlling the number of receptors available at the cell surface, and therefore signaling magnitude ( ), further research into OXTR trafficking dynamics will shed insights into its potential multifactorial role in cell and tissue function. Possible variances in desensitization and trafficking of the OXTR could have therapeutic implications based on drugs and individual differences, such as the divergence in pathways by different agonists (e.g., carbetocin vs. OXT) ( ). These variables may impact our understanding for OXTR-mediated functions with respect to vascular health, as discussed below.
## Oxtr Signaling in Vascular Health and Disease
### Vasoactive Agent
OXTR-regulated vasogenic activity has been well established from the earliest use of its peptide ligand in research ( ), when OXT was observed to lower blood pressure. This long-term decrease in blood pressure has subsequently been confirmed in rats ( ) and humans ( ). Subsequent studies in pregnant women and rats have shown that while blood pressure drops, heart rate increases with peripheral administration of OXT ( ; ). The interest in OXTR as a candidate in the etiology of and treatment for cardiovascular conditions has recently been revived due in large part to the work of , which supports the concept of a protective effect for OXTR signaling in tissue response to infarctions that will be detailed below.
Oxytocin receptors are localized in the heart contributing to the release of atrial natriuretic peptide (ANP) and a decrease in cardiac output ( ; ). OXT can induce vasodilation when acting on endothelial cells through eNOS activation ( ; ), but can also promote vasoconstriction when acting on smooth muscle cells ( ; ). Further, there is reason to believe this might not be due to OXTR signaling, but through OXT acting on vasopressin receptors ( ; ). These divergent findings might also be vessel-dependent, as small artery vasodilation upon OXT administration has been reported ( ), while larger peripheral arteries may respond instead with vasoconstriction ( ). As Petersson points out, this could be explained by the alternate effects of OXTR activation upon endothelial versus smooth muscle cells, their relative distribution in large and small vessels, and the administration method ( ). OXTRs in the cerebrovasculature seem to maintain many of the same attributes as central receptors such as upregulation following circulating estrogen ( ), and interactions with arrestins and the same G proteins ( ). However, the blood pressure lowering effects of OXT seems to be based on receptors in the periphery and not those in the CNS, as peripherally but not centrally administered OXT lowers blood pressure ( ). A systemic rise in OXT concentration, even with intranasal delivery, leads to a decrease in regional cerebral blood flow, primarily in the amygdala ( ). Alternatively, this same route of delivery under an fMRI using cerebral blood volume reported an increase in the hippocampus and frontal cortex ( ). Although vasopressin can also activate OXTR receptors at high concentrations, vasoconstriction and increased heart rate via central administration of vasopressin is thought to be mediated by V1a receptors ( ; ; ). Hence, the cross reactivity between these ligands and receptors do seem to be independently distinguishable, and OXTR signaling clearly influences vascular activity in a context-dependent manner.
### Oxidative and Inflammatory Stress
Oxidative stress and inflammation are common to many neurodegenerating conditions, including those induced by an ischemic injury ( ; ). An increase in antioxidant enzymes, activation of reactive oxygen species (ROS) producing enzymes, or decreased ROS directly are taken as evidence of a potential protective effect ( ; ). Levels of pro- and anti-inflammatory cytokines as well as upstream activators are a means of identifying if there is evidence for inflammatory modulation ( ; ). There is evidence from in vitro and in vivo studies that signaling through OXTR influences antioxidant and anti-inflammatory outcomes.
I n vitro studies have shown that exogenously applied OXT decreases the production of reactive oxygen species (ROS) initiated by H O application to lymphocytes ( ). This observation is supported by in vivo studies finding reduced ROS production with chronic OXT treatment in a mouse model of ASD ( ) as well as reduced oxidative stress status in OXT-treated naïve Wistar rats ( ), ischemia-reperfused Sprague-Dawley rats ( ), and naïve zebrafish ( ). Candidate downstream enzymes mediating OXT’s effects on ROS production include MAPK/ERK1/2, superoxide dismutase (SOD), and glutathione as antioxidant promoting pathways ( ; ; ). Moreover, NADPH oxidase-mediated production of ROS is observed to be dampened with the addition of OXT, indicating an effect of attenuating prooxidative pathways ( ; ). Alternatively, OXTR knockdown in fibroblasts led to a decrease in oxidative stress and an increase in antioxidative enzymes ( ). Critically, in a parallel but inverse set of discoveries, the use of OXTR antagonists lead to an increase in markers of oxidative stress in cardiac tissue ( ). In cardiac cells and in vivo rat hearts a protective effect for vasopressin, and specifically vasopressin acting at V1aR and OXTRs has been reported to reduce oxidative stress ( ; ). So in regard to OXT and OXTR roles in oxidative stress, complete independence from the arginine-vasopressin system cannot be assumed. While the bulk of the evidence available points to a decrease in ROS following OXT administration or OXTR engagement, it is unclear whether encouraging antioxidative signals is the primary cause or a reduction in prooxidative ones, and whether enzymatic subtypes, cell type, or injury state matter.
With respect to the role of OXTR signaling in inflammatory modulation, complementary in vitro and in vivo studies have shown that OXT administration reduces the production of pro-inflammatory cytokines such as IL-6, TNF-α, and IL-1β ( ; ; ), and increases anti-inflammatory cytokines such as IL-10 and TGF-β ( ; ). This dampening of proinflammatory cytokines is at least partially attributed to actions at NF-kβ ( ). Alternatively, stimulating the OXTR leads to a several fold reduction in the receptor for advanced glycation end-products (RAGE), which stimulates macrophage cells to produce proinflammatory cytokines ( ). Additionally, cytokines like IL-6 and IL-1β appear to feed forward and increase the expression of OXTR ( ; ), suggesting a protective feed-back loop. While less studied than OXT, the role of vasopressin in inflammation is highly variant in either enhancing or dampening inflammatory responses and this could be due to multiple receptor subtypes ( ; ; ). However, as far as specific inflammatory cell types, OXT seems to generally promote the activation of peripheral immune responses while tempering central immune activation ( ; ). For instance, OXT increases the production of spleen leukocytes, enhances the differentiation of thymus immune cells ( ; ), and reduces inflammation-related transendothelial cell migration ( ; ), whereas OXT appears to mitigate microglial activation in the brain ( ; ; ). The elucidation of the full extent of OXTR signaling involved immune activation and the consequences of this activation on OXTR activity is an ongoing subject of research.
### The OXTR in Brain: Cerebrovascular Function and Post-Stroke Potential
The potential role for OXTR signaling in cerebrovascular protection is grounded in evidence for its protective role in the periphery. With respect to cardiovascular disease, OXTR stimulation in the heart causes the release of ANP and decreased heart rate ( ). In addition, a decrease in pressure in the chambers of OXT-treated hearts has also been reported ( ). Stress-induced increases in blood pressure that can prove deleterious over time are also mitigated with higher plasma OXT in mothers ( ). Since ischemic heart disease is a leading cause of death worldwide ( ), the idea that OXTR-mediated effects might prove effective as a management strategy or as an acute rescue agent has gained traction ( ). OXT treatment in rodents has been shown to induce stem cells to adopt a cardiomyocyte phenotype ( ), which could lead to exciting prospects in cardiac regeneration. Some experiments have even tested non-invasive ways to increase OXT after heart surgery, including massage and music interventions ( ). In addition to these acute treatments, long term OXT increases are thought to be a primary agent through which social ties reduce the risk for cardiovascular disease ( ). In rats, treatment with OXT lowered blood pressure in a hypertensive strain ( ), and prevented the occurrence of hypertension subsequent to hypoxic injury ( ), whereas hypertension induced by angiotensin-II was found to be exacerbated by OXT administration ( ). In regards to cardiac sympathetic tone, one study examining myocardial infarction found a negative effect of OXT based increased sympathetic tone ( ), while another examining ventricular hypertrophy and subsequent heart failure found a beneficial effect to this same OXT linked sympathetic tone based modulation ( ). This is an interesting paradox that could perhaps suggest a strong environmental effect to the effects of OXT. It could be that the response to OXT in an ischemic environment versus one of pathological remodeling could differ substantially. Regardless, well-controlled longitudinal studies are needed to assess whether OXTR manipulation might lead to improved cardiovascular or even cerebrovascular outcomes.
Oxytocin receptors might be uniquely positioned to respond to vascular insults due to their localization on microvascular endothelial cells ( ; ). OXT has been shown to induce proliferation of endothelial cells, most likely through a PI3K and Src kinase dependent production of nitric oxide by eNOS ( ; ). Beyond these pro-angiogenic effects, the receptor appears to have potent anti-inflammatory and antioxidant properties. It both reduces the activity of NADPH oxidase isoforms on endothelial cells and innate immune cells ( ; ) and reduces the production of pro-inflammatory cytokines in favor of anti-inflammatory cytokines ( ; ). OXTRs also potentiate the uptake of glucose during hypoxia ( ; ).
Notably, these are some of the same pathways that are thought to be beneficial in the recovery of surviving tissue after an ischemic injury ( ; ; ; ; ). OXTR activation has been mechanistically linked to the amelioration of tissue damage following cardiac infarction ( ), renal infarction ( ), hepatic infarction ( ), and cerebral stroke ( ; ; ). Cardiomyocytes can also be protected from ischemia and reperfusion injury through a reduction in mitochondrial-sourced ROS and a shift in cell signaling away from pro-apoptotic Bax toward anti-apoptotic Bcl-2 ( ). In examining cerebral ischemic stroke more directly, Karelina and colleagues ( ) used social housing, OXT treatment, and OXTR antagonists to demonstrate a protective role for OXT in reducing tissue loss and deleterious inflammation while enhancing antioxidative enzyme expression following middle cerebral artery occlusion. This observation has been extended to show that the neuroprotective effect of nursing in cerebral ischemia can be mimicked with exogenous OXT administration in mice, reducing ROS production and apoptotic neuron death ( ; ). Effects on cognitive changes post-stroke in animal models and human studies are limited. Only one human case study post-stroke has been published, wherein the authors speculated that a patient’s rapid recovery from post-partum stroke may have been due to OXT administered to reduce postpartum bleeding and increased endogenous OXT release upon contact with her newborn ( ). Cognitive effects have been limited to post-stroke depression and anxiety-like behavior in animals, and supposition in humans ( ; ). Post-stroke memory impairments are a relatively unexplored target.
In this regard, a de novo up-regulation of OXTRs in astroglia within the peri-infarct space was demonstrated in patients who died with a clinical pathologic diagnosis of vascular dementia, suggesting a druggable target for quick intervention ( ). This is supportive of the detection of functional OXTR on astroglial cells in culture that can bind appropriate radioligands and trigger a release of TGF-β ( ; ), as well as reports of post-ischemic increases in OXTR for CNS tissue ( ), though the opposite has been found in post-ischemic heart tissue ( ). In cases of birth-related ischemic injury, OXT administration improved viability of immature hippocampal cells and reduced markers of oxidative stress ( ; ; ), which may be linked to associated changes in GABAergic chlorine channels in addition to possible hemodynamic alterations ( ; ). By contrast, other studies have found that OXT administered to dams of pups undergoing birth-related ischemic injury might actually exacerbate injury due to a vasodilatory reaction leading to exacerbated birth anoxia ( ). Critically, an ischemic environment might switch the vasodilatory effect of OXT to a vasoconstrictive one based on studies of isolated cerebral arterioles ( ). In the case of long term management of vascular health, OXT has been found to reduce atherosclerosis in mice, rabbits and rats prone to the development of such plaques ( ; ; ). Interestingly, vasopressin might be protective against ischemic injury ( ), but V1aRs are thought to be deleterious and their antagonism may present a route of intervention ( ). While this work suggests that the OXTR is a valid target for recovery of cerebrovascular insults, including stroke related to cognitive impairment and dementia ( ), further mechanistic and validation studies are warranted, especially in reference to ischemic conditions.
### Cell Survival
Another current strategy to improve outcomes post-ischemia is the enhancement of cell survival through inhibiting apoptosis ( ; ; ), reducing excitotoxicity ( ; ), and improving metabolism ( ). The general measures of reduced apoptosis are taken as a reduction in caspase activation and a higher ratio of Bcl-2 to Bax ( ; ). Combatting excitotoxicity often ultimately focuses either on reducing glutamate signaling, mostly through NMDA receptors, or reducing intracellular calcium accumulation and waves in connected cells ( ; ). The improvement of metabolism is closely tied to glucose uptake to sustain cells in the absence of production ( ). While current methods to enhance cell survival through these means include pharmacological intervention, pre-conditioning, and non-invasive neuronal stimulation, there is reason to believe that enhancement of cell survival in post-ischemic environment could also be enhanced through targeting of the OXTR as evidenced by overlap with the survival mechanisms targeted in these studies. Some of these mechanisms include the favoring of anti-apoptotic proteins over pro-apoptotic ones, potential suppression of NMDA receptor activation, and the enhancement of glucose uptake in some cell types.
Oxytocin treatment increases the expression of the pro-survival Bcl-2 in cases of ischemia/reperfusion injury, at least in cardiac tissue ( ; ). A reduction in the pro-apoptotic Caspase-3 and Bax also supports the anti-apoptotic function of OXTR signaling ( ; ). Oxytocin administration dampens the basal levels of glutamatergic excitatory activity in the frontal cortex of mice, and the use of inhibitors suggests this occurs at the NMDA receptors ( ). Lastly, OXT administration enhances the uptake of glucose both peripherally in skeletal and cardiac cells ( ; ), and centrally in non-human primates after intranasal administration ( ). Another option is, again, the observed pro-survival and glucose metabolism supporting effects as evidenced by vasopressin administration and V1aR knockout animals ( ; ). While the existing evidence is minimal, and little has been tested centrally, there are reports of some enhancement of cell survival, including under ischemic conditions, for OXT and OXTR signaling.
### Synaptic Plasticity and Neurogenesis
When it comes to CNS repair after injury, enhancing plasticity for the strengthening of remaining connections and neurogenesis for recovery are strong areas of focus ( ; ; ; ). The support of plasticity is assessed through the activation of kinases involved in the induction of long-term potentiation (LTP) and the modulation of synaptic receptors. Of the kinases involved in LTP studied in ischemic stroke the mitogen-activated protein kinases (MAPKs) are often cited ( ; ), and the synaptic receptors like GABA Rs ( ; ). Neurogenesis in stroke studies is assessed both directly through markers like BrdU ( ), or through the production of growth factors ( ). Studies of OXT and the OXTR signaling pathways have revealed effects on plasticity and neurogenesis both in and outside of ischemic conditions. By contrast, vasopressin exerts little effect on neurogenesis outside of early development ( ), but an enhancement of plasticity in hippocampal subfields has been reported ( ; ).
Signaling cascades through the OXTR have been associated strongly with MAPKs, particularly ERK1/2 and ERK5 ( ). The signaling of OXTR through these mechanisms are strongly associated with cellular proliferation ( ). Importantly, in hippocampal fields, a common site for the investigation of LTP, a facilitation of LTP by OXTR has been demonstrated to be dependent on MAPKs ( ; ). An important caveat to consider is the potential divergent effects of these signaling partners based on the cellular localization of receptors, namely whether they are present in caveolin domains or not ( ; ). From the time of its role in the GABAergic switch to an inhibitory one around birth, OXT is tied to GABAergic modulation ( ). Now there is increasing interest in the OXT mediation of GABA R signaling, particularly under ischemic stress ( ). While the protective effects observed have been attributed to a counter to excitotoxicity ( ), the role of GABA Rs, especially reducing tonic inhibition of these receptors, in enhancing neuroplasticity is an intriguing alternative ( ).
In regards to neurogenesis, OXTR positive neurons in the CA2 and CA3 subfields of the hippocampus undergo neurogenesis as detected by BrdU upon OXT stimulation, while the deletion of the receptor impairs survival ( ). A similar effect is found in the hypothalamus, while an opposite effect is observed in the olfactory bulb upon OXT stimulation ( ). Differences in effects within regions of the dentate gyrus are also observed as well ( ). This suggests that the role of OXT and OXTR signaling in neurogenesis could be context dependent. Two potential effectors for this neurogenesis through OXTR signaling could be through Akt/PI3K signaling, which is tied to neurogenesis ( ; ) and or through support of growth factors cited as determinants of neurogenesis and angiogenesis ( ). One avenue of OXTR signaling is through phosphorylation and activation of Akt/PI3K ( ). Several growth factors appear to be upregulated upon OXT stimulation such as brain-derived neurotrophic factor and insulin-like growth factor ( ; ). In turn, OXT and OXTR expression is regulated by growth factors, especially insulin-like growth factor 1 ( ). While the interactions are complex and likely conditionally dependent there is reason to investigate OXTR signaling as an influencer of neurogenesis and plasticity.
## The OXTR as a Therapeutic Target for Ischemic Injury
Oxytocin receptor signaling has long been exploited for therapeutic purposes, such as for inducing labor or halting preterm contractions, but its potential clinical applications might go far beyond that. For example, as discussed above, there might be CNS applications for OXTR signaling in ameliorating CVD related tissue loss, enhancing repair, and/or protecting cognition. However, this goal is hindered by the inability of current agonists and antagonists to cross the blood brain barrier (BBB) and the lack of specific compounds that do not also target vasopressin receptors. Despite these hindrances, the history of successfully targeting GPCRs, which represent the majority of drug targets ( ), and the well-established safety profile of OXT ( ; ). suggests that there is translational potential in targeting the OXTR for vascular disease.
As a GPCR, the OXTR is a member of a class of the most widely utilized therapeutic targets, as 35% of drugs on the market target GPCRs ( ). On the other hand, several hindrances have emerged that have slowed clinical and basic research aimed at targeting the OXTR. For example, endogenous OXT does not cross the BBB in large amounts, making central actions of the receptor difficult to manipulate ( ). Additionally, many OXTR agonists or antagonists are not specific as they also show cross-reactivity with vasopressin receptors ( ). Finally, disparate findings over the relative efficacy of peptide vs. non-peptide agonists for OXTR targeting have added a layer of complexity to development of novel therapeutic strategies ( ; ; ). These challenges need to be overcome to truly test the extent to which OXTR signaling might modify the progression of cerebrovascular lesion spread, possible cognitive dysfunction, and/or additional functional impairments.
The BBB is relatively impenetrable except for gaseous and small molecules, especially to larger peptides or transmitters, though transporters exist to allow the gated passage of many other substances such as nutrients ( ; ). Some studies have suggested that labeled OXT does cross the BBB, but comparisons of methods and results reveal that about 1 in 10,000 units of peripherally administered OXT reaches the CNS ( ; ). A recent finding by Yamamoto and colleagues that RAGE on vascular endothelial cells is the main transporter responsible for this CNS bioavailability presents an enticing possible means to enhance this penetrance ( ). Though, the issues with targeting such a pro-inflammatory receptor as RAGE raises multiple troubling caveats ( ). Similar problems arise with the administration of intranasal OXT, as some studies have found beneficial effects when the peptide is administered in this way ( ; ), but again, CNS penetration is relatively low ( ). For a hormone with known peripheral and central properties, delivering supraphysiological doses to the periphery to ensure central activation likely results in off-target effects. Perhaps this concern is not such a hindrance, as MacDonald and colleagues found a serviceable safety profile with the use of up to 40 IU OXT ( ). The reported side effects were extremely rare, with the only severe side effect being water intoxication reported twice among over 1500 cases ( ). A promising alternative is the use of aerosolized OXT that, like intranasal oxytocin, relies on the nasal epithelium for absorbance, but can cover relatively more of the surface ( ; ). Significantly, several studies have reported its ability to not only increase plasma OXT levels, but sustained increases in CSF OXT levels, as well ( ; ). Some suggestion has been made of using ultrasonic disruption of the BBB by focused pulses. While rodent and non-human primate studies have found no long-term negative consequences, the delivery of OXT or an analog has not been tested and the chance of hemorrhagic transformation in stroke means it could be highly dangerous to use for this specific application ( ; ). Finally, the use of nanoparticles as a carrier method to enhance BBB penetrance of peptides is an exciting addition to therapeutic research that could revolutionize CNS drug delivery including OXT or an OXTR agonist ( ; ). While further investigation is needed, there is hope this can provide an effective, noninvasive, and safe option for OXTR modulation.
An additional challenge in OXTR targeting is that these receptors share around 40–50% homology with vasopressin receptors ( ); moreover, these receptors can heterodimerize with each other ( ). OXT and vasopressin differ at only two of their nine residues ( ; ). Vasopressin is also a partial agonist at OXTRs with only two residues in the binding pocket conferring a higher sensitivity to OXT ( ). OXT can also act as a partial agonist at vasopressin receptors due to these similar binding sites ( ). An excellent review of this issue and other considerations like bivalent agonists and G protein-specific ligands provides more detailed insight ( ). More recently, truncated versions of OXT peptides have shown potency as agonists without off-target and dangerous V1a receptor agonism ( ). Surprisingly, the reduced molecular weight did not lead to an increase in permeability for the small cyclic analog ( ). These analogs then are a potential breakthrough for specific targeting of the OXTR in the periphery but does not solve the problem of BBB permeability for modulating CNS OXTRs.
The half-life of OXT has been studied extensively, but all show relatively short periods of action. In blood and plasma the half-life of OXT is only 4–5 min and in pregnant women even lower at 2–3 min ( ). The half-life of OXT was reported to be higher in CSF or after an intracerebral injection reported at around 20 min ( ). Regardless, the short window of action is likely not desirable for a drug meant to create long term behavioral modifications. One suggestion has been to use non-peptide agonists or antagonists for the receptor. Non-peptide agonists and antagonists can have a longer half-life as endogenous peptidases do not recognize such molecules ( ). Many of the non-peptide OXT analogs tested to date have shown good to strong efficacy and affinity profiles ( , ). However, there are currently no selective non-peptide OXTR agonists in clinical trials, although the WAY 267 464 agonist is available for basic research ( ). Since the recent discovery that methylated WAY 267 464 becomes an antagonist at the OXTR instead of its typical agonistic effect, this has opened a possibility to further predictive models for specific nonpeptide agonists and antagonists ( ; ). Synthetic OXT peptides such as pitocin or syntocinon behave very similarly to endogenous OXT, whereas peptide analogs such as carbetocin have shown a longer half-life than OXT and fared better clinically ( ). Another alternative that can prolong the half-life of the peptide is lipidation. In fact, Cherepanov and coworkers have created several analogs of OXT with palmitoyl groups added to various residues ( ). These analogs were able to induce behavioral changes out to 24 h indicating longer half-lives, though the ability to induce intracellular calcium uncaging through the OXTR was much less than the endogenous peptide ( ). With respect to receptor antagonism, OXTR antibodies bound to the surface of liposomes have been tested, but only in targeting myometrium at this point ( ; ).
In summary, the complex nature of OXTR biology, with diverse, context-dependent cellular processing, homology with the vasopressin receptors, and ubiquitous expression peripherally and centrally, present challenges to the implementation of promising treatments. Nevertheless, that should not discourage the continuing search for specific and safe options. Certainly, the multitude of potential conditions, including ischemic injury, that might benefit from targeting the OXTR, and the history of successfully targeting GPCRs for treatments in general, should only serve to inspire more promising strategies and results in the future.
## Conclusion
Oxytocin was an early forbearer in peptide hormone research. As such, its “receptive substance” as proposed by Langley’s receptor theory ( ; ) has inspired scientific interest in the OXTR for decades. Within this review, we have examined the foremost research on the functions of the OXTR at the cellular level and the consequences of this GPCR at the organismal level for both humans and animals, particularly with respect to vascular health and cerebrovascular dysfunction including stroke. These findings are summarized in . As translational research has come into its own, the structural, mechanistic, and behavioral data arising from OXTR studies support the utility of targeting these receptors in preclinical studies of cerebrovascular insults. In particular, there is therapeutic rationale for targeting the OXTR in the treatment and management of ischemic stroke and, potentially, vascular dementia. Secondly, the significant and ongoing amount of basic research into OXTR function should provide optimism that understanding the mechanistic role of this receptor in health and disease will continue to refine therapeutic strategies for these disorders.
Potential effects of oxytocin receptor activation in cerebrovascular and post-stroke environment.
## Author Contributions
EM researched and served as the principle author of the review. SC served as the editor and secondary author of the review. Both authors contributed to the article and approved the submitted version.
## Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
|
Heart rate variability (HRV) is the fluctuation in time between successive heartbeats and is defined by interbeat intervals. Researchers have shown that short-term (∼5-min) and long-term (≥24-h) HRV measurements are associated with adaptability, health, mobilization, and use of limited regulatory resources, and performance. Long-term HRV recordings predict health outcomes heart attack, stroke, and all-cause mortality. Despite the prognostic value of long-term HRV assessment, it has not been broadly integrated into mainstream medical care or personal health monitoring. Although short-term HRV measurement does not require ambulatory monitoring and the cost of long-term assessment, it is underutilized in medical care. Among the diverse reasons for the slow adoption of short-term HRV measurement is its prohibitive time cost (∼5 min). Researchers have addressed this issue by investigating the criterion validity of ultra-short-term (UST) HRV measurements of less than 5-min duration compared with short-term recordings. The criterion validity of a method indicates that a novel measurement procedure produces comparable results to a currently validated measurement tool. We evaluated 28 studies that reported UST HRV features with a minimum of 20 participants; of these 17 did not investigate criterion validity and 8 primarily used correlational and/or group difference criteria. The correlational and group difference criteria were insufficient because they did not control for measurement bias. Only three studies used a limits of agreement (LOA) criterion that specified a priori an acceptable difference between novel and validated values in absolute units. Whereas the selection of rigorous criterion validity methods is essential, researchers also need to address such issues as acceptable measurement bias and control of artifacts. UST measurements are proxies of proxies. They seek to replace short-term values which, in turn, attempt to estimate long-term metrics. Further adoption of UST HRV measurements requires compelling evidence that these metrics can forecast real-world health or performance outcomes. Furthermore, a single false heartbeat can dramatically alter HRV metrics. UST measurement solutions must automatically edit artifactual interbeat interval values otherwise HRV measurements will be invalid. These are the formidable challenges that must be addressed before HRV monitoring can be accepted for widespread use in medicine and personal health care.
## Introduction
The purpose of this review article is to critically examine the criteria used in studies of ultra-short-term (UST) heart rate variability (HRV) and to identify challenges of criterion, concurrent, and predictive validity, and measurement artifacts.
Section “Heart Rate Variability” explains HRV from the perspectives of the neurovisceral integration mode and vagal tank theory. We underscore that HRV metrics are associated with regulatory capacity and health, providing an indication of how HRV predicts health crises such as fetal distress before the appearance of symptoms or mortality. Further, these metrics describe the correlation between low HRV, disease, and mortality.
Section “Length of the HRV Recording Period” describes long-term, short-term, and UST HRV recordings, and it emphasizes that long-term measurements best predict health outcomes, and provides a description of time domain, frequency domain, and non-linear metrics. We explain that short-term measurements poorly correlate with long-term values, and stress that we cannot use long-term and short-term norms interchangeably. We caution that short-term measurements are proxies of long-term measurements and that their predictive validity is uncertain. Finally, we characterize UST measurements as proxies of proxies and call for research into their predictive validity.
Section “Why Is There Interest in UST HRV Measurements?” discusses the reasons for the limited use in HRV measurements in medicine, the challenges to their integration into routine medical care, the opportunity created by wearable products for consumer HRV monitoring, and the research required before the widespread adoption of HRV metrics in fitness and wellness applications.
Section “Criterion Validity Ensures Measurement Integrity” explains criterion validity, which can be established using the concurrent and predictive validity approaches. These approaches depend on a high-quality criterion that is relevant, reliable, and valid.
Section “UST HRV Research” provides an overview of 28 studies that have reported UST HRV features. We argue that comparison approaches using correlational coefficients, coefficients of determination or regression, and group mean or median comparisons approaches cannot establish criterion validity because they do not control for measurement bias, which is the difference between novel and validated measurements. Section “Correlation Coefficients” explains that although correlation coefficients can identify potential surrogates, they cannot establish criterion validity. Correlations show association but cannot establish equivalence. A proxy measurement can be perfectly correlated with a reference standard measurement while falling outside an acceptable range (e.g., ±10% of the reference standard’s range). Section “Coefficient of Determination or Regression” argues that neither method is appropriate for demonstrating equivalence. The coefficient of determination shares the same limitations as correlation coefficients and use of regression for this purpose violates its underlying statistical assumptions. Section “Group Mean or Median Comparisons” challenges the claim that two methods are comparable if they yield a non-significant group mean or median difference because this does not ensure validity and can be confounded by insufficient statistical power. Lastly, Section “Limits of Agreement (LOA) Solutions” describes how this approach establishes criterion validity when accuracy standards are specified a priori .
Section “UST HRV Studies Reporting Limits of Agreement Solutions” summarizes four studies that have reported LOA and compares findings from three reports ( ; ; ) that utilized LOA as a selection criterion for valid UST measurements. Finally, Section “Practical Recommendations” outlines four steps for determining the shortest period that can estimate a 300-s measurement.
## Heart Rate Variability
Heart rate and HRV are calculated from the time intervals between successive heartbeats and HRV is associated with executive function, regulatory capacity, and health ( ; ; ; ). Heart rate , the number of heart beats per minute (bpm), is an UST (<5 min) metric that is widely used in medicine, performance, and daily fitness assessment using wearables. HRV is the organized fluctuation of time intervals between successive heartbeats defined as interbeat intervals ( ; ). The complexity of a healthy heart rhythm is critical to the maintenance of homeostasis because it provides the flexibility to cope with an uncertain and changing environment ( ). “A healthy heart is not a metronome” ( ). From the perspective of the neurovisceral integration model ( ), increased HRV is associated with improved executive function and may strengthen descending medial prefrontal cortex regulation of emotion ( ). have proposed the vagal tank theory as an integrative model of cardiac vagal control or vagus nerve regulation of heart rate. Cardiac vagal control indexes how efficiently we mobilize and utilize limited self-regulatory resources during resting, reactivity, and recovery conditions ( ). HRV metrics are important because they are associated with regulatory capacity, health, and performance ( ) and can predict morbidity and mortality.
A decline in HRV can signal dangerous health changes and low HRV values are associated with an increased risk of illness and death. HRV reductions precede heart rate changes in conditions of fetal distress ( ) and sensory disturbances in diabetic autonomic neuropathy ( ). Low HRV correlates with anxiety ( ), asthma ( ; ), cardiac arrhythmia, chronic obstructive pulmonary disease ( ), depression ( ), functional gastrointestinal disorders ( ), hypertension, inflammation, myocardial infarction ( ; ; ), post-traumatic stress disorder ( ), and sudden infant death ( ). Low HRV also correlates with all-cause mortality ( ; ). For example, low power in the very-low-frequency (VLF) band (0.0033–0.04 Hz) more strongly predicted all-cause mortality (higher Z -scores and relative risk) than low-frequency (LF; 0.04–0.15 Hz) and high-frequency (HF; 0.15–0.4 Hz) bands, and is associated with arrhythmic death ( ).
## Length of the HRV Recording Period
Heart rate variability recording periods range from under 1 min to over 24 h. Long-term recordings (≥24 h) constitute the reference standard for clinical evaluation due to their predictive validity , which is the ability to predict future outcomes ( ). For example, 24-h measurements of the standard deviation (SD) of the interbeat intervals of normal sinus beats (SDNN) predict cardiac risk ( ). Acute myocardial infarction patients with SDNN values under 50 ms are unhealthy, between 50 and 100 ms have compromised health, and over 100 ms are healthy ( ). Acute myocardial infarction patients with SDNN values over 100 ms have been reported to have a 5.3 lower mortality risk at a 31-month mean follow-up than those under 50 ms.
While long-term, short-term (∼5 min), and UST (<5 min) recordings calculate HRV metrics using the same mathematical formulas, they are not interchangeable, reflect different underlying physiological processes, and achieve different predictive powers. HRV in long-term recordings may be attributed to changes in the circadian rhythm, fluctuations in core body temperature and the renin–angiotensin system, and the sleep cycle ( ; ). Long-term recordings monitor cardiorespiratory regulation across diverse situations, physical workloads, and anticipatory central nervous system (CNS) reactions to environmental stimuli. These extended recording periods reveal the sympathetic nervous system (SNS) component of HRV ( ; ). HRV in short-term recordings is produced by four interdependent sources that operate on a briefer time scale and are defined by: (1) the complex interaction between the sympathetic and parasympathetic branches; (2) respiration-mediated increases and decreases in heart rate via the vagus nerve, termed respiratory sinus arrhythmia (RSA); (3) the baroreceptor reflex that regulates blood pressure using negative feedback; and (4) rhythmic adjustments in blood vessel diameter ( ). Short-term values correlate poorly with their long-term counterparts ( ). Basic research is needed to identify the major HRV generators in UST recordings.
Although long-term, short-term, and UST HRV recordings are characterized using the same time-domain, frequency-domain, and non-linear indices, they differ in predictive power. Time-domain metrics calculate the amount of variability in a series of interbeat intervals. Frequency-domain measurements compute absolute or relative power distribution across four bands: ultra-low-frequency (ULF; ≤0.003 Hz), VLF (0.0033–0.04 Hz), LF (0.004–0.15 Hz), and HF (0.15–0.40 Hz). Non-linear indicators measure the interbeat interval time series’ unpredictability ( ; ). ST recordings achieve lower predictive power than long-term recordings ( ; ; ). To summarize, long-term recordings represent the reference standard for predicting health outcomes. For this reason, long-term and short-term norms cannot be used interchangeably. Short-term values are proxies of long-term values with unknown predictive validity; therefore, UST measurements are proxies of proxies. Basic research is also needed to determine the predictive validity of UST recordings.
Short-Term HRV metrics adapted from and .
## Why Is There Interest in UST HRV Measurements?
There is a potential role for UST HRV measurements in medical assessment, research involving brief (e.g., <30 s) experimental tasks, and personal wellness assessment once researchers validate their accuracy and predictive power. Despite the availability of short-term normative HRV values for adults ( ; ) and elite athletes ( ), HRV is not widely used in medical assessment outside of cardiology and obstetrics. For example, nurses do not routinely monitor HRV as a vital sign during general practice visits. Short-term HRV assessment’s time cost is one of many barriers to its integration in routine medical practice: “…a 5-min HRV assessment is prohibitively long when compared with routine office or home measurements of blood glucose, blood pressure, core body temperature, heart rate, oxygen saturation, and weight” ( , p. 215). If researchers were to validate the accuracy and predictive power of UST HRV measurements, and provide age- and sex-related normative values, manufacturers could add this modality to widely used instruments like electrocardiographs and pulse oximeters.
Research studies in diverse areas (e.g., clinical and social psychology) may involve brief experimental tasks that require UST HRV measurements. For example, short-term HRV monitoring would be inappropriate for a 30-s task designed to induce frustration. As with medical applications, researchers need to validate the accuracy and meaning of UST HRV measurements.
Consumers increasingly monitor their physiology using dedicated tracking devices and smartwatches that incorporate electrocardiographic (ECG) and photoplethysmographic (PPG) sensors of heart rate and HRV. ECG sensors detect the R-spike and PPG sensors identify the peak of the pulse wave to determine when a heartbeat has occurred ( ). The ECG method is more accurate than PPG during paced breathing ( ) and when increased sympathetic tone results in vasoconstriction in monitored fingers ( ; ). UST measurements are ideal for these ambulatory fitness and wellness applications if investigators can demonstrate their accuracy under non-stationary and stationary conditions, their predictive validity, and normative values.
## Criterion Validity Ensures Measurement Integrity
Criterion validity confirms that test scores accurately estimate scores of validated measures or metrics and depends on the identification of a high-quality criterion ( ). Researchers use concurrent and predictive validity approaches to provide evidence of criterion validity. In the concurrent approach, investigators obtain test and criterion scores simultaneously ( ). The UST HRV studies reviewed in this article illustrate this strategy. Here, the test scores are UST and the criterion scores are short-term HRV values. In the predictive approach, researchers obtain test scores to estimate future outcomes or performance. The success of both strategies depends on the existence of a high-quality criterion , which is relevant, valid, and reliable ( ). Relevant means that we can objectively assess the criterion (e.g., SDNN). Validity means that the criterion (e.g., 5-min SDNN) accurately measures the metric of interest (e.g., SDNN). Finally, reliability means that criterion scores (e.g., 5-min SDNN values) obtained from the same individuals under identical conditions are consistent. Although valid measures are always reliable, reliable measures are not valid unless they accurately assess a given construct (e.g., SDNN).
## UST HRV Research
We evaluated 28 studies that reported UST HRV features with a minimum of 20 participants ( ). Seventeen studies did not investigate criterion validity. Eight studies primarily used correlational and/or group difference criteria to demonstrate the criterion validity of UST (test scores) with respect to short-term values (criterion scores; ; ; ; ; ; ; ; ). Correlation coefficients, the coefficient of determination or regression, and group mean or median comparisons are insufficient to establish criterion validity because they do not control for measurement bias —the difference between UST and short-term measurements.
Studies that reported UST HRV measurements and their primary criterion validity criteria.
### Correlation Coefficients
Although correlation analysis can help researchers identify potential surrogates, they cannot measure criterion validity ( ). Many researchers make the mistake of applying a correlation coefficient, typically Pearson’s r , to conclude that two methods are sufficiently comparable or in agreement. The Pearson r quantifies the direction, magnitude, and probability of a linear relationship between two continuous variables, x and y . The magnitude of the Pearson r ranges from −1 to +1 ( ). A correlation coefficient, however, is merely a measure of association and does not provide evidence that one method agrees with or is comparable to another method ( ). In fact, it is possible for two methods to have a perfect correlation of r = 1 but no agreement or comparability between the measurements ( ). For example, consider the situation where Method A and Method B both measure heart rate, but only Method A does this accurately. If Method B yields readings that are consistently 10 bpm higher than Method A, they would be perfectly correlated ( r = 1) but their measurements would disagree by 10 bpm ( ).
Hypothetical scatterplot of UST and ST heart rates (bpm) depicting a perfect correlation ( r = 1), but no agreement (points do not fall along the line of equality where y = x ). Credit: Center for Applied Psychophysiology.
The American National Standards Institute criterion ( ) for heart rate accuracy is the larger of ±10% of all values or ±5 bpm. If we set the allowable heart rate difference at ±10% of Method A’s range, Method B would report heart rates far beyond acceptable measurements as shown by a Bland–Altman plot ( ).
Hypothetical Bland–Altman difference plot of UST and ST heart rates (bpm). Credit: Center for Applied Psychophysiology. The line at 0 represents the line of equality or y = x (the diagonal line from ). When measures achieve absolute agreement, they will all fall along that line at 0.
Additionally, a significant correlation between two different methods “is generally useless because two methods designed to measure the same quantity will rarely be uncorrelated” ( , p. 218). For these reasons, researchers conclude that a “correlation coefficient … is of no practical use in the statistical analysis of comparison data” ( , p. 53).
### Coefficient of Determination or Regression
Some method comparison studies use the coefficient of determination ( r ) or simple regression analysis to claim two methods are comparable via intercepts or slopes ( ). The coefficient of determination estimates the percentage of variability of variable y that can predicted by x . Denoted as r , the coefficient of determination is identical to the square of the Pearson r coefficient. For example, a Pearson r coefficient of 0.50 corresponds to an r value of 0.25, meaning that 25% of the variability in y is accounted for by variability in x . The magnitude of r ranges from −1 to +1. Simple regression analysis estimates a straight line with a slope (B ) and height at which the line crosses the vertical axis (B ) to predict the value of y , given x ( ). These measures are also inappropriate for demonstrating agreement. The coefficient of determination estimates the proportion of variance that Method A and Method B share but present the same pitfalls as the correlation coefficient ( ). In addition, the coefficient of determination calculates how well a regression equation or model fits the observed data. This is problematic for method comparison studies as measurements from each method are dependent variables, each possessing their own measurement error. Linear regression models make an implicit assumption that some portion of the variance in a dependent variable (Y) is being explained by variance in an independent variable (X). Therefore, a simple linear regression assumes that the procedure measures X without error. This method is not appropriate when comparing two dependent measures and may produce a biased regression coefficient ( ; ). If regression is used, both variables should be treated as possessing measurement error. In these cases, Deming regression (parametric) or Passing–Bablok regression (non-parametric) are more appropriate alternatives ( ).
Deming regression ( ) is a type of total least squares regression that accounts for measurement error in both X and Y variables, as opposed to ordinary least squares regression which merely accounts for error in the dependent variable. Deming regression assumes that errors are independent and normally distributed, but the procedure is sensitive to outliers. Passing–Bablok regression ( , ) is a robust non-parametric rank method that also accounts for error in both X and Y and produces an unbiased slope estimate by calculating the median of all possible slopes ( ). Passing–Bablok regression is less sensitive to outliers and does not have assumptions about the distribution of errors, but it does require that the two variables measured do not significantly deviate from linearity ( ).
### Group Mean or Median Comparisons
Another statistical approach misused in method comparison studies is to claim that two methods are comparable if they yield a non-significant group mean or median difference via parametric or non-parametric tests. For example, a two-sample t-test is a parametric statistic that evaluates whether the difference between pairs of normally-distributed scores can be explained by chance. A Kruskal–Wallis test is a non-parametric procedure that determines whether samples were obtained from a single distribution ( ). There are several issues with such an approach. First, the goal of comparing two different methods of measurement is not to have an equivalent overall group agreement (mean or median), but rather that the methods appropriately agree across individual observations. Such logic would imply that having greater measurement error would be more favorable because it decreases the probability of finding a significant difference ( ). Non-significant group differences do not indicate whether two methods agree or have acceptable bias. Second, significance is related to the power and sample size of the study ( ), and so a non-significant mean or median difference between two methods could be the result of an underpowered study or one without a large enough sample. Third, because many HRV measures are non-normally distributed, some studies inappropriately use a parametric t -test or ANOVA on data that have not been log-transformed or fail to use a non-parametric test instead ( ).
### Limits of Agreement (LOA) Solutions
To overcome the aforementioned issues with analyzing agreement between methods, the authors recommend the use of LOA in Bland–Altman plots ( ; ). An important caveat is that Bland–Altman plots and LOA do not indicate whether or not the agreement between measures is sufficient. The researcher must decide a priori the extent to which two measures must agree for them to be comparable. Although there are industry standards for the accuracy of blood pressure and heart rate measurement ( , ), there are no comparable standards for HRV short-term measurements such as SDNN. The degree of precision may depend upon the specific question being asked and may vary by discipline ( ).
Bland–Altman plots are a graphical approach to assessing the extent to which two methods agree with each other by plotting the difference between the two methods (Method A – Method B) on the y -axis against the mean of the two methods ([Method A + Method B]/2) on the x -axis. If the two methods agree completely, the mean difference ( ) between them will be zero, and all the points on the Bland–Altman plot would fall along a line of y = 0. Because perfect agreement between two methods rarely occurs, the distance between an ideal of zero and the observed is an index of bias. The greater the bias—the distance of from zero—between the two methods, the less the two measures tend to agree. Assuming that the differences are normally distributed, the SD of the differences can then be multiplied by 1.96 and added/subtracted from the mean difference . This calculation produces a lower LOA ( – 1.96 s ) and an upper LOA ( + 1.96 s ), representing the range where 95% of the differences should fall; the lower LOA represents the 2.5th percentile and the upper LOA represents the 97.5th percentile.
Researchers should construct confidence intervals and statistically determine whether the disagreement between the two methods falls within the LOA. They should construct 95% confidence intervals around the mean difference and the lower/upper LOA to take variability into account ( ; ). Next, they should perform a statistical analysis to determine whether the differences between the two methods fall within the appropriate LOA ( ). Finally, they should follow with an equality test (H : μdifference = 0) such as the Student’s t -test. Bland–Altman plots do not require the raw measurements from the two methods to be normally distributed, but the differences between the two methods should be normally distributed. Researchers should take appropriate steps if the differences are not normally distributed or the differences are proportional to the size of the measurement (e.g., greater differences between the two methods as the measurements get larger). They can logarithmically transform the raw data or the ratios or percentages ([Method A – Method B]/Mean%) before constructing a Bland–Altman plot. This transformation can provide superior results to plotting a simple difference between the methods against the average ( ; ). In addition to assessing agreement, Bland–Altman plots can also be used to detect outliers ( ).
## UST HRV Studies That Report Limits of Agreement Solutions
Of the 28 UST HRV studies that we reviewed, four reported LOA plots whether used as a selection criterion or not ( ; ; ; ) ( ).
UST studies that reported limits of agreement adapted from .
obtained resting PPG measurements from 467 healthy participants (249 men and 218 women; aged 8–69 years). They compared 10-, 20-, 30-, 60-, 90-, 180-, 210-, 240-, and 270-s values with 300-s measurements. Their criteria for selecting the shortest UST period were a significant Pearson r and non-significant ( p > 0.05) Kruskal–Wallis statistic. Although they illustrated their results with Bland–Altman plots (mean difference ± 1.96 SD ), the authors did not use them to draw conclusions.
acquired ECG measurements from 23 male collegiate athletes (aged 19–21 years) for 10 min while supine before a treadmill test and for 30 min post-exercise. They analyzed the last 5 min of each rest period and compared log-transformed 10-, 30-, and 60-s with 300-s root mean square of the successive differences (RMSSD) values. They compared intra-class correlations (ICCs) and Bland–Altman plots (mean difference ± 1.96 SD ) across the three UST periods and concluded that that 60 s yielded the largest ICC and most stringent LOA. Whereas the ICC test identified 60 s as a potential surrogate, a Bland–Altman plot confirmed its criterion validity with respect to 300-s RMSSD measurements.
recorded beat-to-beat middle finger pressure using a Portapres device from 3387 participants (1660 men and 1727 women; aged 44–63 years) in the Prevention of Renal and Vascular End-Stage Disease study. They obtained recordings over a 15-min period while resting in the supine position. The authors analyzed the last 4–5 min of data that exhibited a stationarity pattern and compared the log-transformed 10-, 30-, and 120-s with 300-s RMSSD and SDNN values. They compared ICC, Pearson r values, and Bland–Altman plots across the three UST periods. The authors concluded that a minimum of 10 s was required to measure RMSSD and 30 s to calculate SDNN.
obtained 5-min EEG recordings from 38 healthy undergraduates (20 men and 18 women; aged 18–23 years) while sitting upright under resting conditions with their eyes open. They acquired 10-, 20-, 30-, 60-, 90-, 120-, 180-, and 240-s epochs from the 5-min recordings. Following manual removal of artifacts, they calculated the time domain, frequency domain, and non-linear HRV metrics outlined in . The authors identified potential surrogates using a Pearson r with a conservative criterion ( r ≥ 0.90). They applied Bland–Altman’s LOA technique using an allowable difference of ±5% of the range of the 5-min value and a Student’s t -test to confirm the equality of UST and ST values. The results of LOA analyses are summarized in . These findings were consistent with who also reported that a time interval of 60 s was required to estimate 5-min RMSSD. However, the finding that a 60-s sample is required to measure RMSSD and SDNN was inconsistent with the study by who reported minimum periods of 10 and 30 s, respectively. This disagreement may have been due to the more stringent LOA requirement (±5% of the range of the 5-min measurement) and smaller sample in the study.
Minimum time period required to estimate 5-min HRV metrics adapted from .
## Practical Recommendations
Recommendations for analyses of data from method-comparison studies differ. As previously mentioned, correlation/regression analyses quantify the degree of association between variables but do not denote agreement ( ). As such, we recommend using LoA solutions to assess whether two methods produce comparable results. Although oft-cited guidelines recommend correlation/regression analyses in addition to the LoA solutions ( ), most researchers incorrectly consider them to be supplemental ( ; ). Although correlation/regression analyses may answer certain questions that are relevant in method-comparison studies (e.g., whether two measures are not associated), there is a strong argument against their inclusion in favor of only reporting the LoA and their respective confidence intervals ( ; ). Prior to conducting method-comparison studies, researchers should consider whether conducting correlation/regression analyses is appropriate.
Assuming that researchers obtain 10-s, 20-s, 30-s, 60-s, 90-s, 120-s, and 180-s RMSSD values and want to determine the shortest period that can estimate a 300-s RMSSD measurement, they should consider the following steps:
Determine whether the RMSSD measurements are normally distributed. If not, use a logarithmic transformation like log(e) or the natural log (ln).
Determine a priori the largest acceptable difference between 30-s and 300-s RMSSD values.
Prepare difference plots like Bland–Altman using a 95% confidence interval and then conduct an equality test (e.g., Student’s t- test) to confirm that the 30-s and 300-s RMSSD values are identical.
If the 30-s RMSSD measurement passes the equality test, then a suitable surrogate has been found. If it fails the test, perform the same analysis with the 60-s measurement, and so on.
## Conclusion
Eight of the 11 HRV criterion validity studies we reviewed used correlational and/or group difference criteria that did not control for measurement bias. Because these criteria do not require a maximum acceptable difference (e.g., 5 bpm), they could yield an UST heart rate value that was 10 bpm higher or lower than its 5-min counterpart. Therefore, minimum recording length prescriptions from studies that used these criteria ( ; ; ; ; ; ; ) should be treated with caution and confirmed by studies that use a LOA criterion and confirmative equality tests. As succinctly stated, “A correlate does not a surrogate make” (p. 605).
The routine use of UST HRV measurements in medicine, performance, and personal fitness assessment awaits advances in six key areas. First, HRV monitoring with automatic artifact correction needs to be added to existing hardware (e.g., activity trackers, pulse oximeters, and smartwatches). Second, researchers should identify the short-term HRV metrics (e.g., RMSSD) most strongly associated with health and performance outcomes. Third, researchers should determine the minimum UST time periods required to estimate these short-term HRV features with respect to age and sex. We recommend a LOA criterion based on the a priori determination of the largest acceptable difference between UST and short-term values confirmed by an equality test. Fourth, researchers should demonstrate that UST HRV metrics themselves can forecast real-world health or performance outcomes. UST measurements are proxies of proxies. They seek to replace short-term values, which, in turn, attempt to estimate reference standard long-term metrics. This criterion validity requirement is the most intractable and may prove insurmountable. Fifth, researchers should establish UST HRV norms stratified by age and sex. Sixth, researchers and manufacturers need to educate healthcare professionals and the public about what HRV means, its importance to their health and performance, how it should be measured, and the strategies that can increase it. These six breakthroughs are necessary before HRV monitoring can be more widely used in medicine, performance, and personal health care.
## Author Contributions
FS reviewed the literature, wrote the initial manuscript, and made subsequent revisions following feedback and editorial suggestions for all drafts from ZM and CZ. ZM reviewed the literature, created and managed the UST literature database, and summarized and critiqued the UST studies. CZ reviewed the method agreement literature and wrote the methodological critique section. All authors contributed to the article and approved the submitted version.
## Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
|
Recent evidence increasingly associates network disruption in brain organization with multiple neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS), a rare terminal disease. However, the comparability of brain network characteristics across different studies remains a challenge for conventional graph theoretical methods. One suggested method to address this issue is minimum spanning tree (MST) analysis, which provides a less biased comparison. Here, we assessed the novel application of MST network analysis to hemodynamic responses recorded by functional near-infrared spectroscopy (fNIRS) neuroimaging modality, during an activity-based paradigm to investigate hypothetical disruptions in frontal functional brain network topology as a marker of the executive dysfunction, one of the most prevalent cognitive deficit reported across ALS studies. We analyzed data recorded from nine participants with ALS and ten age-matched healthy controls by first estimating functional connectivity, using phase-locking value (PLV) analysis, and then constructing the corresponding individual and group MSTs. Our results showed significant between-group differences in several MST topological properties, including leaf fraction, maximum degree, diameter, eccentricity, and degree divergence. We further observed a global shift toward more centralized frontal network organizations in the ALS group, interpreted as a more random or dysregulated network in this cohort. Moreover, the similarity analysis demonstrated marginally significantly increased overlap in the individual MSTs from the control group, implying a reference network with lower topological variation in the healthy cohort. Our nodal analysis characterized the main local hubs in healthy controls as distributed more evenly over the frontal cortex, with slightly higher occurrence in the left prefrontal cortex (PFC), while in the ALS group, the most frequent hubs were asymmetrical, observed primarily in the right prefrontal cortex. Furthermore, it was demonstrated that the global PLV (gPLV) synchronization metric is associated with disease progression, and a few topological properties, including leaf fraction and tree hierarchy, are linked to disease duration. These results suggest that dysregulation, centralization, and asymmetry of the hemodynamic-based frontal functional network during activity are potential neuro-topological markers of ALS pathogenesis. Our findings can possibly support new bedside assessments of the functional status of ALS’ brain network and could hypothetically extend to applications in other neurodegenerative diseases.
## Introduction
In recent years, there has been growing interest in associations between disruptions in brain network topology and a range of neurodegenerative diseases ( ; ; ), including amyotrophic lateral sclerosis (ALS) ( ; ), a rare terminal neurodegenerative condition generally characterized by progressive deficits in motor neurons. This tendency stems from a conceptual shift from a reductionist view of brain organization as a mere sum of independent constituent regions toward a more integrative network view, which has been propelled to applying network science in neuroimaging studies ( ; ). Particularly for ALS, this interest grows out of a change in our perception of the disease from a mere motor system pathology to a multifaceted disease, which includes non-motor disruptions involving behavioral and cognitive functions ( ; ). Up to 50% of people with ALS are reported to have cognitive impairments and behavioral disorders, which include frontotemporal lobar degeneration (FTLD), affecting 15% to 41% of patients ( ). Among cognitive impairments, executive dysfunction is the most prevalent deficit, affecting about 40% of non-demented ALS patients ( ; ). report that 13% of ALS patients they assessed displayed symptoms of dementia, while 37% demonstrated non-demented executive impairments. Executive dysfunctions in ALS patients are typically associated with impairments in verbal fluency, working memory (WM) processing, dual tasking functions, sustained and selective attention, cognitive inhibition, and visual attention ( ; ). As executive dysfunctions are generally related to deficits in the frontal cortex ( ), recognizing the underlying structural and functional neurocorrelates of these type of impairments in the frontal regions of the cortex would advance our understanding of the pathological and prognostic patterns of the disease and lead to more efficient diagnostic and treatment methods.
Although advanced non-invasive neuroimaging methods have recently been proposed to fulfill this aim, little understanding has been gained about the cortical organizations underlying executive dysfunction in ALS patients. A common finding of structural changes in the non-motor cortical regions of ALS patients is a reduction in frontal and prefrontal white matter density ( ; ; ). Notably, structural and functional connectivity degeneration are reported to be coupled in these cohorts and mutually affected by the degeneration associated with the disease. Accordingly, there is a rising interest in determining the functional connectivity underpinning the pathogenesis of ALS. Several studies have adopted resting-state functional connectivity (RSFC) to characterize potential neurophysiological biomarkers of ALS. However, divergent outcomes have hindered a clear consensus on both the functional connectivity markers and their proper interpretations in ALS ( ; ; ). Therefore, there is a strong need for complementary approaches to improve our understanding of network alterations underlying disease-related dysfunctions, particularly frontal markers in the presence of non-motor impairments.
One shortcoming of connectivity studies is that they primarily focus on the comparison of individual connections and thus are inadequate in providing a global and integrative perspective on the brain network ( ). Graph theory has recently shown promise in compensating for this shortfall and bridging between network disruptions and neurodegenerative diseases ( ; ) by modeling the brain as a whole network with recording channels, or regions of interest, as nodes and their functional interaction, or structural interconnections, as links. By employing integrative network metrics, these methods provide a ground to reconstruct and compare the global and local characteristics of the brain network’s organizations between different groups and experimental conditions. With regard to ALS, a handful of studies ( ; ) have adopted graph-theoretical methods to address structural, but not functional, disruptions in patients’ brain networks by constructing the brain network in terms of the interlinking white matter tracts between segmented regions. Notably, these studies have commonly reported a loss of local connectivity in the motor area, which expands to the frontal and parietal regions.
However, a remaining challenge for conventional graph-theoretical methods is the limited comparability between brain network characteristics across groups or conditions in different studies, as they involve arbitrary choices in the normalization stage. These arbitrary choices project an intrinsic bias to these methods, and thus, lead to inconsistent findings across different studies ( ; ). One suggested method to compensate for this problem is the minimum spanning tree (MST) analysis, which avoids methodological biases. The MST network is a sub-graph that traverses all nodes by minimizing the cost (link weights) without forming a loop ( ; ). Link weights in neuroimaging studies are typically attributed in terms of functional connectivity measures. The MST is, in principle, not sensitive to scaling effects, as its structure depends merely on the order rather than the absolute values of link weights ( ). Although in converting a fully connected weighted network to the unweighted MST, we may lose some information, it has been frequently shown that the MST sub-network can preserve essential network properties ( ; ) and be equally sensitive to topological alterations as conventional network analysis methods, such as clustering and path length ( ).
Initiated by to characterize patients with temporal epilepsy, MST analysis has been recently adopted in an increasing number of neuroimaging studies ( ; ; ; ). As an MST subnetwork is derived from a weighted connectivity network, it is insensitive to the nature of the connectivity measure or the imaging modality. This offers great potential for the method to be employed in a variety of neuroimaging studies investigating different populations and conditions. However, only a few studies have used this method to gain a better understanding of the underlying disruptions in the brain networks of ALS patients. In an electroencephalography (EEG) resting-state network study, report significant differences in the topological properties of MSTs constructed over functional connectivity matrices of ALS patients compared to controls. They have also observed significant correlations between network metrics and disability scores in patients. In another resting-state magnetic resonance imaging (MRI)-magnetoencephalography (MEG) study using MST analysis, , report more connected and scale-free brain networks as the disease develop in ALS patients.
However, in addition to being limited in numbers, there are some concerns about expanding the topological findings reported in these few studies as neurological markers associated with the prognosis and progress of the disease, particularly in the presence of non-motor and executive dysfunctions. First, these studies, like the majority of works adopting network and connectivity analysis, are focused only on resting-state experimental paradigms, which lack the potential to identify disruption of brain network organizations while subjects actively perform tasks. Although resting-state paradigms are advocated to be insensitive to performance variability across subjects ( ) and, accordingly, a better candidate for structural impairments, they cannot completely mirror the inter-regional functional deficits underlying the disease during daily life activities. In particular, when it comes to addressing the cognitive and executive dysfunctions associated with motor impairments in ALS patients, there is a strong need to investigate topological disruptions during a cognitive task. Second, non-portable and bulky neuroimaging systems such as MRI or MEG are not compatible with the specific physical conditions of ALS patients, particularly as the disease progresses and the patients lose their mobility. Therefore, there is an increasing interest in more portable and flexible neuroimaging equipment for use at patients’ bedsides ( ; ). EEG is generally a suitable candidate to fulfill this goal with its high temporal resolution, which provides the foundation for functional connectivity analysis in different frequency bands ( ; ). However, due to its relatively low spatial resolution and its low signal-to-noise ratio (SNR), likely due to its high sensitivity to artifacts, it is not competitive, especially when the region of interest includes the prefrontal and anterior frontal channels that can include artifacts from eye-blink and forehead muscle movement. With regard to ALS, this modality does not prove to offer an adequate competitive advantage when the aim of the study is to address executive dysfunction in the frontal lobe. For instance, in the aforementioned MST-based EEG study pursued by , several prefrontal channels were excluded from further analysis due to probable contamination from muscular or ocular artifacts. Therefore, more suitable portable neuroimaging methods are required to capture network disruptions associated with executive dysfunction in the ALS cohort. Functional near-infrared spectroscopy (fNIRS) has been recently introduced as a non-invasive portable neuroimaging system to mirror hemodynamic perturbations in a range of neurodegenerative diseases, including ALS ( ; ; ). Compared to EEG, fNIRS systems provide higher spatial resolution and lower sensitivity to artifacts. In connectivity analysis, fNIRS nullifies spurious inter-regional functional relations and thus is less affected by volume conductance ( ). Interestingly, fNIRS systems have shown promising results in mirroring alterations in cerebral oxygenation in response to the activation of the prefrontal and frontal cortices through mental tasks such as mental arithmetic operations ( ; ). Although an increasing number of studies have employed resting-state fNIRS connectivity analysis to characterize neurodegenerative diseases (either channel-wise or clustered using graph theory) ( ; ), few have been conducted to investigate connectivity or network patterns in an active paradigm while subjects perform a task. Moreover, to the best of our knowledge, no fNIRS study has adopted MST network analysis to address topological properties in the brain network.
In this study, to capture the frontal topological disruptions associated with executive dysfunctions in ALS patients, for the first time, we have adopted MST analysis to map the neuroimaging data recorded through fNIRS during a proposed visuo-mental paradigm. Following our previous work that shows frontal channel-wise differences over the first order hemodynamic properties in ALS patients during the same proposed task ( ), in the present study, we hypothesize that there are probable network disruptions related to executive dysfunctions in ALS patients reflected in MST graphs constructed over hemodynamic-based functional connectivity metrics. We will also demonstrate that applying MST mapping to fNIRS-based hemodynamic signals through a cognitive task is a feasible and fruitful process. We will further investigate whether the global properties gained through network analysis are associated with the clinical records of ALS patients. The outcomes from this study can extend further to develop bedside fNIRS-based systems to explore neuro-topological markers of disease pathogenesis and prognosis.
## Materials and Methods
### Participants
Nineteen participants were recruited for this study and were divided into two groups: 9 individuals diagnosed with ALS (age: 58.2 ± 12.9, seven males) and 10 age-matched healthy controls (HC) (age: 60.5 ± 11.6, four males). The demographic and clinical information of the ALS group, including age, sex, disease duration, disability score, and education level, are listed in . Revised ALS functional rating scale (ALSFRS-R) scores, a validated screen for the dysfunctional progression of the ALS disease, averaged 19.2 ± 15.0 on a 48-point scale, where the highest score (48) reflects normal function in activities of daily living (ADL), and the lowest score (0) represents a complete loss of function ( ). Their disease durations were 4.9 ± 4.0 years on average. Three patients (ALS-1, 2, and 4) had gastrostomies as well as tracheostomies. All participants in both groups had at least some post-secondary education. Healthy controls acknowledged no history of visual, mental, or substance-related disorders. One of the healthy controls showed insufficient channel quality in the calibration stage and was, therefore, excluded from further analysis. All procedures were in accordance with the study protocol approved by the Institutional Review Board (IRB) of the University of Rhode Island (URI). All participants provided informed consent or assent prior to the experiment and were financially compensated. All participants in the ALS group were tested in either their homes or care centers, while the healthy cohort participated in the experiments in the NeuralPC lab at URI.
ALS participants’ demographic information.
### Data Acquisition
Functional near-infrared spectroscopy data were recorded using the NIRScout system (NIRx Inc.) with two near-infrared wavelengths (760 nm and 850 nm) and digitized at a sampling rate of 7.81 Hz. shows the optode placements and channel configuration. We used six emitters (green) and five detectors (orange) constituting 14 channels over the pre/frontal cortical areas, commonly used in fNIRS studies for a variety of mental tasks, including mathematical operations ( ; ). The emitters were located at Fpz, AF3, AF4, F3, Fz, F4, while the detectors were placed at Fp1, Fp2, AFz, F1, and F2, according to the modified combinatorial nomenclature (MCN) positioning system. A calibration test was performed prior to each recording to assess signal quality for each channel separately.
Schematic of the montage, data, and graph analysis. (A) The fNIRS recording montage, including detectors (orange), emitters (green), and 14 constructed channels (black). (B) Exported HbO2 time series for each channel (four of which are shown for illustrative purposes only). (C) Segmented 20 s epochs (red) corresponding to each channel with respect to a target stimulus (blue) consisting of 12 s post-stimulus, 4 s pre-stimulus, and 2-s margins at each edge. (D) PLV adjacency matrix (14 × 14) based on the average pairwise normalized PLV over the post-stimulus time for each subject. (E) MST adjacency matrix (14 × 14) extracted from the PLV adjacency matrix through the Kruskal algorithm. (F) List of topological metrics computed from the MST analysis.
### Experimental Protocol
All subjects first participated in a familiarization session to be trained on the experimental protocol and then participated in a main experimental session. In this study, we used a novel visuo-mental (VM) paradigm as our experimental protocol, following our previous works, which showed its efficacy to evoke hemodynamic responses in ALS patients as well as healthy participants ( , ). The VM paradigm incorporates mental calculation alongside commonly used visual stimulation in brain–computer interface (BCI) communication systems. This augmentation of mental arithmetic has been reported to compensate for ALS patient’s incompetence in performing visual tasks, particularly in the later stages of the disease ( ). Subjects were exposed to visual stimuli through a 23″ LCD monitor. For ALS patients, a holder kept the display before them at their bedsides. The VM stimulation paradigm was designed and presented through BCI2000 software ( ).
Participants each had two successive runs of the VM task in the main experimental session. In each run, participants were instructed to perform mental calculations using the 2 × 2 matrix of numbers intensified over the target character. The calculation included a simple addition/subtraction either diagonally (at the first flash) or vertically (at the second flash) within the intensified matrix, picking the greater value and multiplying it by two, as explained in more detail in our previous works ( , ). The stimulation time was set to 300 ms, and the inter-stimulation interval (ISI) was set to 6 s. In total, for two runs of the VM paradigm, there were 28 target characters (14 for each run), with one row/column flashes (single-trial) for each target character.
### Data Analysis
#### Signal Preprocessing
The modified Beer-Lambert law was used to calculate concentration changes for oxygenated hemoglobin (HbO2) and deoxygenated hemoglobin (HbR) in terms of recorded alterations in reflected light attenuation (see ). fNIRS data were then band-pass filtered at 0.01–0.15 Hz to mitigate physiological noises caused by respiratory (∼0.2–0.3 Hz) ( ) and cardiac activities (∼0.8–2 Hz) ( ), and to remove high-frequency noise (above 2 Hz). The exported data were then segmented into [−6 to 14] sec epochs relative to target stimulus onset. The total length of each epoch for phase analysis was therefore 20 s, consisting of 12 (post-stimulus) + 4 (pre-stimulus) + 2 × 2 (margins) (see ). Including the 4 s pre-stimulus further supported baseline normalization for the connectivity analysis, in which the baseline period was selected from 4 to 1 s prior to target stimulus onset. The extra 2-s margin periods were selected to cancel edge effects in the later phase analysis and included at the beginning and ending of each epoch.
As HbO2 signals have been reported to reflect stronger effects in fNIRS connectivity analysis ( ) and also are more sensitive to the cerebral vascular changes than HbR signals ( ), the HbO2 epochs were selected for further connectivity analysis.
#### Functional Connectivity
##### Phase-locking value (PLV) analysis
The Hilbert transform was used to calculate the instantaneous phase (φ[ n ]) for each epoch ( x [ n ]). The Hilbert transform of x [ n ] in the frequency domain ( X ) is defined as ( ):
where X ( e ) is the Fourier transform of x [ n ] and sgn (ω) is the sign function with a value of +1 and −1 for positive and negative frequencies, respectively. Then, by calculating the inverse Fourier transform of X as x , we constituted the analytical signal x [ n ] as below:
The instantaneous phase φ [n]was then calculated as:
Following similar fNIRS-based connectivity studies ( ; ), the functional connectivity metric used in this study was the PLV ( ; ) that quantifies the synchronization level between the phases of two signals. For each pair of channels { k , l }, each time point ( n ), PLV [ n ] was calculated by averaging the instantaneous phase differences between the two channels over all the trials ( N ) as follows:
where φ [ p , n ] is the instantaneous phase of the signal in channel k , trial p , and time-stamp n . Note that N , here, is 28, i.e., the total number of target trials (epochs) for each session.
To extract only the task-related connectivity, the pairwise PLV index was then normalized with respect to the baseline period (−4 to −1 s), which is expected to cancel the stationary phase synchronization unrelated to the task ( ; ). To carry out this normalization, we subtracted the mean PLV value of the baseline period from each pairwise PLV and then divided the result by the standard deviation of the same baseline. The normalized PLV (zPLV) calculation was formulated as below:
where μ and σ are, respectively, the mean and standard deviation of the PLV in the baseline period between channels k and l . The use of normalized phase synchronization measures, i.e., zPLV, as the functional connectivity metric in our study, also makes the analysis less biased to the level of hemodynamic responses, which can vary across different subjects, performances, and recording environments ( ; ).
### Graph Analysis
The pairwise normalized PLV index was used to construct weighted graphs, extract the topological properties, and then, accordingly, investigate between-group topological differences in the graphs’ characteristics. demonstrates the schematic pipeline for the graph analysis performed for each subject.
#### PLV Adjacency Matrix
To construct the PLV adjacency matrix, the normalized PLV (zPLV) index was averaged over all post-stimulus time-points for each channel pair, i.e., over [0–12] sec after the target stimulus onset. The total pairwise phase-locking value (tPLV) was formulated as below:
where M is the total number of samples for the 12-sec post-stimulus period (here M = 94 ∼ 12 (sec) × 7.81 ( sampling rate ). Accordingly, for every subject, all pairwise combinations of tPLVs resulted in a square PLV adjacency matrix (14 × 14), with the number of channels (14) as its dimension.
#### MST Adjacency Matrix
For each session and each participant, an undirected weighted graph was constructed by assigning each pairwise tPLV to the link (edge) between the corresponding channel (node) pair, as its weight. Kruskal’s algorithm ( ) was then applied to the graph to obtain its MST. The MST network is a unique sub-graph that traverses all nodes by minimizing cost (edge distances) without forming a loop ( ; ). In this work, following other connectivity-based MST network studies ( ; ), the maximum connectivity tree was derived from each PLV adjacency matrix, mathematically equivalent to the MST constructed by Kruskal’s algorithm. In brief, we first sorted the links, in ascending order, with respect to their tPLV values. Then, starting with the edge with the highest tPLV, we continued assigning an unweighted edge to the tree, unless adding the link formed a cycle, where we skipped that edge and selected the next link in the sorted array. To detect cycles, we implemented the deep-first search (DFS) algorithm ( ), in which for every visited node ‘a,’ if there was an adjacent node ‘b’ that was already visited and was not the parent of ‘a,’ then a cycle was formed in the graph. A visited node is a node we have traversed before, and the parent node is the node for which the algorithm is currently tracing adjacent nodes. The final MST is thus an undirected unweighted graph, including all N nodes (i.e., 14) with N − 1 links (i.e., 13). Equivalently, the resulting MST adjacency matrix would be a binary matrix with values either 1 or 0 for a connected or disconnected node pair, respectively.
#### Topological Analysis
The topology of the extracted MSTs was characterized using network metrics obtained from the MST adjacency matrix analysis. These metrics were categorized into two main groups: global properties and local (nodal and edge) characteristics. The former provides global measures to compare networks as a whole, while the latter includes individual measures of relative nodal importance ( ; ). We compared network metrics (both global and local) between ALS and HC subjects at both the group and individual levels (i.e., group MST analysis and individual MST analysis). In the group MST analysis, we constructed the MST and its associated properties from the average PLV matrix over the subjects in each group, while in the individual MST analysis, first, we constructed individual MSTs from the individual PLV matrices of each subject in each group, and then we extracted network metrics from each individual MST. Before describing the topological metrics, we define the following basic terms: (a) degree ( k ), the number of edges connected to a node; (b) leaf node, a node with degree one, i.e., with a single connecting edge; (c) hub node, with multiple connected edges and, therefore, a degree greater than one. Two extreme networks are generally considered to investigate tree properties: (1) line-like tree (snake), which is a line traversing all the nodes. All nodes in a line-like network have degree two, except two starting and ending nodes with degree one, i.e., leaf nodes. (2) Star-like tree, in which one node is in the center of the network, connected to all other nodes. The central hub node has degree N − 1, where N is the total number of nodes in the network, and all other nodes have degree one. Below several network metrics used in this study are explained:
##### Local (node and edge) properties
The nodal measures used in this study are as follows: (1) degree ( k ). (2) Betweenness centrality ( BC ), a measure of how often each node sits on the shortest path between two other nodes. The shortest-path for each nodal pair is the minimum path between them. The BC for a node r is defined as below ( ):
where n ( r ) is the number of shortest paths between nodes p and q , which pass through the node r . N represents the total number of shortest paths between nodes p and q. BC is 0 for a leaf node and is 1 for a central node in a star-like tree. (3) Eccentricity ( Ecc ), which represents node centrality and is the length of the longest ‘shortest-path’ from a reference node to any other node in a network. Eccentricity takes its minimum possible value of one for a central node in a star-like tree.
As in the group analysis, the individual characteristics of nodes and edges could not be completely revealed ( ), we further investigated the occurrence of edges and hubs among individual MSTs from subjects in each group. In this way, individual occurrence information complements the outcomes of group analysis.
##### Global properties
The global MST network metrics demonstrate the characteristics of the entire network across the brain. The global measures we used here include (1) leaf fraction ( L ), which is the number of nodes with only one connected edge ( k = 1), i.e., the number of leaf nodes ( L ), divided by the maximum possible number of leaves for a graph with N nodes, i.e., N − 1. For a star-like network, the leaf fraction is 1, while for a line-like network, it would be 2/( N − 1). (2) Maximum degree ( k ), which is the maximum degree of a node in a network. (3) Maximum BC ( BC ), which is the maximum BC value among the nodes of the network. (4) The Ecc of a whole MST network, which is defined as the difference between the largest and the smallest eccentricity values among the nodes in the tree. (5) Kappa or degree divergence, which is the measure of the variance of the degree distribution and is calculated as below ( ):
where k is the degree associated with the node i . (6) Diameter ( D ), which is defined as the length of the longest shortest-path, normalized by ( N − 1). The greater the diameter, the less central the network. (7) Tree hierarchy ( T ), which characterizes a hypothesized optimal topology of an efficient organization while preventing information overload of central nodes. It can be interpreted as the balance between hub overload prevention and large-scale integration ( ). T ranges between 0 and 1 and is calculated as below:
For a line-like network, T tends to 0, while in a star-like tree, it is 0.5 ( L = N − 1).
(8) Similarity analysis, in which the fraction of edge overlap between individual MSTs and the reference MST (MSTref) was compared for both ALS patients and healthy controls. The MSTref was constructed from the average connectivity matrix of all healthy controls ( ; ).
In addition to network topological properties, we calculated the global PLV (gPLV) for each individual subject by averaging the tPLVs over all elements (pairwise tPLV) of the PLV adjacency matrix for further group-comparison analysis.
### Statistical Analysis
The statistical significance of between-group differences for each global network metric was evaluated with the non-parametric Mann–Whitney U test (Wilcoxon rank-sum test). Moreover, for the ALS cohort, the Spearman correlation analysis was conducted to explore relationships between the global properties, as described above, and the ALS clinical scores, including disease duration and disability score (ALSFRS-R).
## Results
### Functional Connectivity
The global PLVs (gPLVs) that were calculated by averaging the PLVs over all channel pairs for each subject within each group were given to Wilcoxon rank sum statistical analysis. No significant between-group differences were observed for the gPLV ( p -value = 0.447, see ).
Means (M) and standard deviations (SD) for the global network metrics in ALS and HC groups.
### Group MST Comparison
illustrates the outcomes of the graph analysis for each group (HC and ALS). Specifically, shows the PLV adjacency matrices constituted from the average normalized PLVs over all subjects in each group. shows the weighted undirected graphs constructed based on the PLV adjacency matrices in each group. For visualization purposes only, we have shown the links with weights greater than 15% of the maximum normalized PLVs in each group. This threshold was arbitrarily selected to project a general schematic of the corresponding weighted graphs. By applying the Kruskal algorithm to these weighted graphs, unweighted (binary) MST adjacency matrices were extracted from each group ( ). Corresponding MST graphs, which will henceforth be called ‘group MST,’ were projected over a cortical brain area in . The radius of each node represents its degree, i.e., the numbers of edges connected to that node. Leaf nodes (degree = 1) are blue, while hub nodes (degree > 1) are red. In the group MST for ALS group, we observed the highest degree at channels AF4-Fp2 and Fpz-Fp2 with degrees 7 and 5, respectively, i.e., Kmax = 7. In the group MST for healthy controls, the nodes with the highest degree were observed in the channels AF4-Fp2 and Fpz-Fp1, both with degree 4, i.e., Kmax = 4. TH and BCmax in the group MST for ALS were 0.97 and 0.40, respectively, compared to respective values of 0.94 and 0.33 in the group MST for controls. The comparatively higher values of Kmax and BCmax in the group MST for ALS are associated with more load on the corresponding nodes compared to controls. The group MST for ALS included ten leaf nodes ( Lf = 0.77), compared to eight leaf nodes ( Lf = 0.62) in the group MST for healthy controls, which indicates more centralization in the ALS cohort’s group MST. The diameter and global eccentricity in the group MST for ALS were 0.38 and 2, respectively, compared to respective values of 0.54 and 3 in the controls’ group MST. These metrics provide additive evidence of a more centralized network in the group MST for ALS than for controls. Furthermore, the six hubs of the group MST for controls were distributed more evenly over both pre/frontal hemispheres, while three of four hubs in the group MST for ALS resided in the right pre/frontal hemisphere. The group MST for ALS had only three edges (i.e., 23.08% of the edges in the ALS group) in common with the group MST for controls, which was later used as the reference for the dissimilarity analysis (MSTref).
The group MST graph analysis for both the healthy control (HC) (left) and ALS (right) groups. (A) Average PLV adjacency matrices over all subjects in the group. (B) The weighted graphs constructed based on group average PLVs with an arbitrary threshold of 15% of the highest value. (C) Unweighted MST adjacency matrices extracted through the Kruskal algorithm. (D) Corresponding MST graphs. The radius of each node represents its degree. Leaf nodes (degree = 1) are blue, while hub nodes (degree > 1) are red.
### Individual MST Analysis
#### Comparison of Global Metrics
The box plots in shows how the global metrics for individual MSTs were distributed in both the ALS and HC groups and lists the corresponding means and standard deviations of the global metrics obtained from individuals in both cohorts. The non-parametric Mann–Whitney U test revealed significant differences between ALS and HC for the majority of the global MST topological metrics. Specifically, we observed significantly higher values of maximum degrees ( Kmax, p -value = 0.030) and leaf fractions/numbers ( Lf, p -value = 0.029) in the ALS cohort compared to controls. Complementary to the group analysis, these results show more centralized global networks in the patient group. The Kappa value was also significantly ( p -value = 0.005) lower in controls, which indicates less variability in the degree distribution over the nodes in individual MSTs for healthy subjects. The diameter ( D , p -value = 0.040) and global eccentricity ( Ecc , p -value = 0.006) were significantly higher in healthy controls than in patients. Overall, all global network measures, except for overlap, showed higher standard deviations in patients than in controls, indicating less variability and more robust networks in the controls. Additionally, similarity analysis showed marginally significant higher overlap ( p -value = 0.051) in the individual MSTs of controls compared to the patients, as an additive support for more consistent topological variation over the healthy cohort.
Boxplots for global network properties in both groups. In each box, the central red line denotes the median value, and the lower and upper limit of the blue box, respectively, denote the 25 and 75 percentiles. The outliers are shown with red plus signs. o : represents data points for individual ALS patients and healthy controls, significant ( p -value < 0.05), marginally significant ( p -value ∼ 0.05), ns, non-significant.
#### Comparison of Local Properties
(top) shows the occurrence of edges and hubs in individual MSTs for the ALS and HC subjects. Nodes, which appear more frequently as hubs for subjects in that group, are shown as circles with larger radii, while less frequently occurring hubs have smaller radii. Similarly, thicker edges indicate the more frequent presence of that edge in individual MSTs for that group. The most frequent MST hubs in ALS subjects were channels AF4-Fp2 and Fpz-Fp2, with respective occurrences of 77.8% and 66.7%. In healthy controls, the hubs were distributed more evenly over the frontal cortex, with the most frequent hubs appearing at channels AF4-Fp2 with 70.0% and Fz-F2, AF3-F1, and AF3-Fp1 with 60.0% occurrence.
Top: Comparison of hub and edge occurrences between the two groups of ALS and healthy control (HC). Bottom: Nodal average degree (left). Nodal average betweenness centrality (BC) values (right).
To remove visual complexity, the edges shown are limited to those that occur in more than 25% of the subjects in each group. In the ALS cohort, the most commonly occurring edge was in the right prefrontal area, between channels Fpz-Fp2 and AF4-Fp2, appearing in 66.7% of the patients’ MSTs, i.e., 6 out of 9. In the healthy controls, we observed a more even distribution of edge occurrence over the frontal cortex with most commonly occurring edges connecting channels AF3-Fp1 and Fz-AFz, channels Fpz-Fp1 and Fz-F1, channels AF3-F1 and AF4-Fp2, and channels Fz-F1 and AF4-Fp2 in 40% of the MSTs of healthy controls, i.e., 4 out of 10.
(bottom) illustrates the average degree and BC of each node (channel) over all the subjects in each group. In the ALS cohort, we observed the highest average degree and BC at channel AF4-Fp2, in the right prefrontal cortex, with respective values of 4.78 ± 3.03 and 0.27 ± 0.19. In the HC group, we observed the highest average degree and BC at channel AF3-Fp1, in the left prefrontal cortex, with 2.60 ± 1.78 and 0.16 ± 0.18, respectively.
shows the topological representation of the nodes with the occurrence of maximum BC ( BCmax ) and degree ( Kmax ) in individual MSTs for both groups. In the ALS group, both BCmax and Kmax occurred most frequently at channel AF4-Fp2, in the right prefrontal cortex, in 55.6% of the subjects for each. However, in the controls, the highest occurrence of BCmax and Kmax was at channel AF3-Fp1, in the left prefrontal cortex, in 44.4% and 33.3% of the subjects, respectively.
The topological mapping of nodal occurrence of maximum BC (top) and maximum degree (bottom) in each node in each group. The maps are superimposed on a standard 3D brain model for illustrative purposes only. Dots show the corresponding fNIRS channel over the cortex.
### Associations Between Global Network Properties and Clinical Data
illustrates the significant results derived from the Spearman correlation analysis between the global network properties and clinical scores in the ALS group. Although no significant correlation was observed between global MST metrics and disability score (ALSFRS-R) in the ALS cohort, we observed a significant correlation between global PLVs (gPLVs) and disability scores ( p -value = 0.044). Moreover, among the network metrics, leaf fraction ( Lf , p -value = 0.020) and tree hierarchy ( TH, p -value = 0.047) showed significant correlations with disease duration.
Scatter plots showing the significant correlations between clinical scores in ALS group and global network properties, including global PLV (left), leaf fraction (middle), and tree hierarchy (right). The respective correlation’s rho and p -value are represented on the top left of each plot.
## Discussion
In this study, we applied MST network analysis to fNIRS-based hemodynamic responses to a visuo-mental task to compare frontal functional brain network topology between ALS patients and healthy controls and further link the outcomes to executive dysfunctions reported across ALS studies. We used PLV-based phase synchronization connectivity measures to calculate functional inter-channel relations over which the individual and group MSTs were constructed. This study also investigated how patients’ clinical scores are associated with their global connectivity and topological metrics. The MST network analysis employed in the present study revealed both global and local disruptions in frontal network properties in ALS patients in relation to controls. Compared to more commonly used network analysis methods, MST has been shown to be less influenced by spurious connectivity due to its intrinsic dimensionality reduction of the links ( ). So, the construction of the MST results in a unique and more robust network representation of brain network organization and captures the core topological properties essential for a less-biased comparison of brain network organizations across different groups, conditions, and studies ( ; ).
Globally, both group and individual MST analyses suggested a shift toward a more centralized frontal network organization for the ALS patients compared to the controls. A more centralized network is, in general, a more star-like configuration, which has been interpreted as more random or dysregulated as opposed to a more regular network ( ; ). Tending to a more random structure in the ALS group is an indicator of lower clustering and a shorter path length ( ), which is aligned with the observed smaller diameter and higher leaf fraction in this cohort than in the HCs. The higher centralization in the more star-like patient MSTs maintains that the information traverses along fewer limited and overloaded nodes that disrupts the quality of information exchange. This is also an indicator of the tendency toward a more ‘scale-free’ type of network ( ; ), which similarly denotes the existence of hubs with a high density of connections (degree). This is corroborated by the higher maximum degree and betweenness in the patients’ cohort, meaning that most information is exchanged between only a few central hubs. In addition, aligned with other metrics of centralizations, higher values of kappa (degree divergence) in patients suggest the existence of high-degree nodes, causing a more rapid synchronization, i.e., linking more likely to the higher density nodes, and at the same time making the network more vulnerable to noise ( ). Our observed global pattern of a more centralized network organization in patient MSTs is aligned with the findings reported by in a recent MEG–based MST network analysis study on a cohort of ALS patients that reported a shift toward a more centralized brain network in all frequency bands as the disease progressed. This shift toward a more centralized and random network has also been reported in MST-based studies on other neurological diseases such as schizophrenia ( ) and major depressive disorder (MDD) ( ). Topological disruption in the global MST networks of ALS patients compared to healthy controls has also been reported by in an EEG-based RSFC study, although it was not consistent with the aforementioned centralization trend within the beta-band. The inconsistency in the global beta-band network characteristics in the previous work might be attributed to the local topological difference related to motor functions associated with ALS ( ; ). The inconsistency may also be related to a possible difference in the clinical and cognitive characteristics of the patient population ( ) and/or methodological sensitivity to the selection of regions of interest (ROI) ( ). Our work differs from the previously reported MST-based studies on ALS as first, our paradigm is activity-based in contrast to the previous resting-state studies, and second our study is frontally focused, unlike previous widespread reports on whole brain organization. Thus, considering the conflicting trends reported in MST-based global network analyses in ALS, integrating further local and nodal network analysis in the frontal area of the cortex might lead to new findings on cognitive aspects to compensate for the inconsistency and play a complementary role for the existing motor-related network findings.
Our nodal analysis characterized the main local hubs, which are highly connected nodes that serve as relay stations and are responsible for transferring information across dispersed brain modules for each group ( ). The importance of identifying the hubs here stems from their critical contribution to cognitive processes ( ) and performance ( ). These hubs are reported as common targets of neurodegenerative conditions ( ). Here, in the healthy controls, hubs are distributed more evenly over the frontal cortex with a slightly higher occurrence in the left prefrontal cortex (PFC), while in the patients, the most frequent hubs were asymmetrically observed, particularly in the right PFC. The high-density nodes in the right PFC and the highest occurrence of maximum degree and betweenness centrality jointly identified these nodes as central hubs in the ALS cohort. Overall, compared to controls, it can be implied that efficiency and node strength in ALS patients decreased in the left PFC area and increased in the right PFC area. This lateralized processing in the prefrontal cortex is in accord with what has been reported in a positron emission tomography (PET) neuroimaging ALS study by with a verbal fluency task and an event-related potential (ERP) study by , where ALS patients performed a dual-task constituted from a spatial and a WM n-back task. These two studies linked PFC-related deficits in ALS to specific task-related executive dysfunctions in these cohorts, which, rather than merely requiring passive storage of data, actively engaged WM to manipulate incoming information. This supports our current observation of the PFC alteration, as our proposed dual-task required active manipulation of numbers in the mental arithmetic component of the task, which has been broadly linked to WM processing ( ; ) and particularly associated with hemodynamic activity in the PFC ( ; ). This also aligns with our previous findings ( ), where we observed a significant contrast, primarily in the PFC region, in ALS patients compared to the controls, using a simple first-order feature (i.e., the integral of hemodynamic activities) in response to the same paradigm. One explanation for this DLPFC asymmetric processing and general frontal topological alteration is “task-specific” executive dysfunction in the ALS cohort, in particular deficits in task-related WM processing. This interpretation is in accord with other behavioral studies reporting executive dysfunctions in ALS, particularly WM and fluency impairments ( ; ). Specifically, as the right PFC is suggested to contribute to executive control, including inhibitory functions ( ) and memory retrieval monitoring ( ), our derived high-density hubs in the right prefrontal lobe in ALS patients might be attributed to excessive compensatory executive attempts in these cohorts to maintain control over task-related WM processing. Moreover, as the right prefrontal cortex has also been reported to be involved in spatial WM tasks ( ), our observed right PFC hyper-connectivity in the ALS group can be attributed to their dysfunctions in handling the spatial aspect of our tested dual task. This frontal lateralization has also been reported by in ALS patients, as well as other neurological diseases including schizophrenia ( ) and MDD ( ), with higher activation in the right frontal lobe. Similarity with executive neuro-topological markers of the depressive disorder has an additive explanatory value, as there is a consistent body of literature associating depressive symptoms with ALS ( ; ; ). Another explanation for the observed frontal network alterations in ALS is the plausible attribution of functional dysfunction to reported ‘ALS-specific’ structural atrophies, mainly reported in ‘task-negative’ RSFC studies ( ; ). For example, in a network-based resting-state MRI-DTI study, concluded that executive dysfunction in ALS patients is related to reduced white matter integrity and associated with deviations in global and local network properties with a high frontal and temporal preference. Specifically, they report associations between behavioral verbal fluency errors and clustering coefficient alterations, primarily in the right frontal and temporal lobes. Similarly, reported suppression in the right fronto-parietal network in ALS, possibly due to the patients’ frontal dysfunction and right-lateralized patterns of regional atrophy. However, the direction of connectivity alteration is not consistent across resting-state studies, reporting both increases and decreases in connectivity measures or associated network metrics ( ; ). Nonetheless, we can only connect task-negative (resting) functional connectivity findings to our current activity-based paradigm in which structural alterations can affect the functional network, which can be consequently reflected in both task-negative and task-positive conditions. Accordingly, we can follow the interpretive line suggested by RSFC studies that cortical connectivity alterations, in particular hyper-excitability, are a mechanism to compensate for structural atrophies ( ). This hyper-connectedness has also been attributed to a progressive loss of inhibitory cortical neurons as part of ALS pathogenesis ( ). Additionally, these local atrophies can underlie the dysfunction of task-related cognitive executive networks, including the executive control network (ECN), fronto-parietal network (FPN), and dorsal attention network (DAN), which all cover the frontal regions and are believed to contribute to a range of cognitive dysfunctions in ALS patients ( ; ).
Although our obtained global PLV values (gPLV) did not show any significant between-group differences, we observed a significant association in patients between their gPLVs and their disability scores (ALSFRS-R). While two other EEG-MST and MEG-MST studies on the ALS cohorts ( ; ) did not report any significant association between clinical scores and their phase synchronization measure, i.e., phase lag indexes (PLI), here the global normalized PLVs derived from the hemodynamic responses showed a significant association with patients’ disability scores. This suggests that fNIRS-based gPLVs, as a global network characteristic, can, per se , be introduced as a potential neurological marker for ALS pathogenesis, even before any MST being derived from it. Furthermore, the marginally significant difference in the overlap of individual MSTs in each group with the MSTref, together with the less variability of topological metrics in the healthy group, implies that MSTs can potentially provide a clinically relevant reference graph to assess possible ALS pathogenesis. Among the topological properties extracted from the MSTs, leaf fraction, and tree hierarchy were positively correlated with the duration of disease. The former metric leads to the speculation that as the disease continues, the frontal network tends toward a more centralized and dysregulated organization. The latter association, i.e., tree hierarchy with disease duration, suggests that the longevity of the disease leads to a suboptimal balance between hub overload and network integration ( ). This is partly aligned with the association between tree hierarchy and clinical scores reported in the two aforementioned studies, though they reported associations with ALSFRS-R scores rather than disease duration. As in a broader sampling of the ALS patients, functional disability scores are reported to be associated with disease duration ( ), our reported association with disease duration and not with ALSFRS-R scores might be due to our relatively small sample size.
### Limitations and Future Work
One limitation of this study can be attributed to the probable information loss due to the intrinsic nature of the MST-based network analysis in ignoring lower ordered connections and the dimensionality reduction in the connectivity matrices. Although MST methods can capture core network properties and are less likely to be influenced by connectivity strength and network density, this possible information loss can consequently make some network properties more sensitive to the network size. This suggests that our findings should be replicated with a higher density of optodes distributed widely over the brain. Moreover, methodically, this work was limited to MST as an unweighted and undirected graph analysis to allow unbiased comparison of the topological outcomes and also, in general, to investigate the feasibility of adopting this approach for hemodynamic responses in ALS patients as opposed to healthy controls. Nonetheless, it would be informative to examine conventional weighted network analysis such as the clustering coefficient and the average shortest path length measures ( ; ) to comparatively evaluate the efficacy of MST based approach employed here. A parallel investigation of the structural relevance of our findings together with more cognitively oriented behavioral batteries, such as the Edinburgh Cognitive and Behavioral ALS Screen (ECAS) or Cognitive and Behavioral Screen (CBS), can also help extend our findings to explore the underlying behavioral and structural executive dysfunctions reported sporadically in similar ALS studies. Moreover, the statistical power of our study was limited by its relatively small sample size due to the rare nature of the disease and the difficulty of conducting an activity-based paradigm with partly/completely locked-in ALS patients. For future work, replicating our results with larger sample sizes would facilitate the generalizability of the observed neuropathological and prognostic markers of the disease. The other limitation of our work was the gender gap between the two cohorts, which is partly influenced by the gender imbalance reported in the ALS patients ( ), and may have influenced our results. Thus, future works should consider the statistical effect of gender on our topological outcomes.
## Conclusion
In the present study, for the first time, we have demonstrated that MST analysis can mirror frontal changes in functional network topology in ALS patients during activity-based tasks. This frontally focused analysis can specifically reveal executive impairments associated with the disease and can generally be integrated with other ALS-targeted studies to play a complementary role with existing motor-related network findings. We also showed the feasibility and clinically relevant applicability of MST network analysis in fNIRS-based hemodynamic responses.
In summary, our analyses demonstrated a shift toward a more centralized and asymmetric frontal network organization in ALS cohorts compared to controls. Furthermore, it was demonstrated that the global PLV synchronization metric is associated with disease progression, and a few topological properties, including leaf fraction and tree hierarchy, are linked to disease duration. These findings suggest that hemodynamic-based network analysis during activity can possibly provide new neuro-topological markers for the bedside assessment of the functional status of ALS patients. Moreover, the methodologies developed in this study can be further extended to explore network disruption in other neurodegenerative diseases.
## Data Availability Statement
The data analyzed in this study is subject to the following licenses/restrictions: The data are restricted to be publicly available, as they contain confidential information that may conflict with the privacy of the research participants. Requests to access these datasets should be directed to .
## Ethics Statement
The studies involving human participants were reviewed and approved by Institutional Review Board (IRB) of the University of Rhode Island. The patients/participants/authorized witnesses provided their written informed consent to participate in this study.
## Author Contributions
YS had supervised all the aspects of this project, including the IRB process, patient and healthy control recruitment, data recording, data analysis, the interpretation of the results, and the manuscript preparation. SB had conducted the data recording, performed all the necessary computational analyses, interpretation of the results, and primarily completed the manuscript. JM had primarily assisted in the subjects’ recruitment, data recording, and proofreading of the manuscript. KM had assisted in fNIRS-related technical aspects of the study and proofreading of the manuscript. All authors contributed to the article and approved the submitted version.
## Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
|
## Background
Dysfunctions in the renin-angiotensin system (RAS) seem to be involved in the pathophysiology of several mental illness, including schizophrenia and mood disorders. We carried out a cross-sectional study assessing the levels of RAS-related molecules among bipolar disorder (BD) patients compared to healthy controls.
## Methods
our sample consisted of 30 outpatients with BD type 1 (10 males, 20 females, age = 35.53 ± 10.59 years, 14 euthymic, 16 experiencing mood episodes) and 30 healthy controls (10 males, 20 females, age = 34.83 ± 11.49 years). Plasma levels of angiotensin-converting enzyme (ACE), angiotensin-converting enzyme 2 (ACE2), angiotensin-II (Ang II), and angiotensin (1–7) [Ang-(1–7)] were determined by ELISA.
## Results
BD patients experiencing ongoing mood episodes had significantly lower ACE levels compared to controls (median: 459.00 vs. 514.10, p < 0.05). There was no association between the levels of these biomarkers and clinical parameters.
## Conclusion
Our findings support the involvement of RAS dysfunction in the pathophysiology of BD. Considering the potential therapeutic implications linked to a better understanding of the role of RAS dysfunction in BD, studies allowing a better characterization of RAS-related molecules level and activity across different mood states are of high interest.
## Introduction
Bipolar disorder (BD) is a chronic and potentially severe mental illness that affects 2–5% of the American population ( ). It is implicated in a high rate of morbidity and important functional impact ( ).
The pathophysiology of BD has not yet been completed elucidated but seems to involve multiple dimensions, including dysfunctions in the brain circuits associated with the processing of emotions, neurodevelopmental disruptions, and systemic processes ( ; ; ; ; ). More recently, it has been hypothesized that dysfunctions in the renin-angiotensin system (RAS) might play a role in the pathophysiology of BD ( ; ).
Systemically, the RAS plays a crucial role in blood pressure regulation and in the maintenance of homeostasis ( ). The existence of a brain RAS is well-established, and angiotensin-II receptors can be found in different areas of the brain, including amygdala, hippocampus, and prefrontal cortex ( ). Angiotensin II (Ang II) is produced through the conversion of Angiotensin I by the action of the angiotensin-converting enzyme (ACE). Abnormal levels of ACE in the cerebrospinal fluid (CSF) and plasma of patients with schizophrenia ( ; ; ), have been previously reported ( ). These findings seem to be of particular importance, in light of the possible role of the RAS in regulating the inflammatory response and the large amount of evidence supporting the involvement of inflammation in the pathogenesis of different mental illnesses, such as major depressive disorder (MDD), schizophrenia, and BD ( ). Furthermore, in Alzheimer’s disease, decreased ACE levels in plasma and CSF have been previously described ( ), and it has been hypothesized that these decreases are directly related to the accumulation of amyloid plaques and, ultimately, neuronal damage ( ).
Specifically with respect to mood disorders, retrospective data point to lower levels of depressive and anxious symptoms among hypertensive patients receiving ACE inhibitors, as well as to lower rates of antidepressant use among patients with hypertension or diabetic nephropathy treated with ACE inhibitors or angiotensin receptor antagonists ( ; ). On the other hand, decreased ACE serum levels have been previously described among patients with MDD ( ).
Considering the potential involvement of the RAS in the pathophysiology of BD and its potential therapeutic implications, we carried out a cross-sectional study analyzing the levels of RAS-related molecules- ACE, angiotensin-converting enzyme 2 (ACE2), angiotensin-II (Ang II), and angiotensin (1–7) [Ang-(1–7)]- among patients with BD compared to healthy controls (HC).
## Methods
### Participants
The individuals who participated in the present study were recruited from the outpatient clinics of the Department of Psychiatry of the University of Texas Health Science Center at Houston, as well as through flyers placed on the community. Our sample was composed by 30 BD patients (10 males, 20 females, age = 35.53 ± 10.59 years) and 30 matched healthy controls (10 males, 20 females, age = 34.83 ± 11.49 years). For both groups, the established inclusion criteria were: age equal or superior to 18 years, no family history of hereditary neurological disorder, no neurological or major medical condition, no current substance abuse or dependence, and a negative urine drug screening. In addition, specifically in the case of healthy controls (HC), individuals with a positive family history of mental disorders in first-degree relatives were excluded.
The diagnosis of BD among patients and the absence of psychiatric disorders in HC was established through the administration of the Structured Clinical Interview for DSM-IV Axis I Disorders-SCID ( ). All patients met DSM-IV criteria for BD type I. At the time of their inclusion in the study, 14 BD patients were euthymic, while eight were depressed, five were manic, one was hypomanic, and two met criteria for a mixed mood state. The participants’ mood state were defined according to their answers to the SCID. Euthymic mood was defined as an absence of criteria for an acute mood episode at the time of the assessment, based on the SCID and, in addition, according to the scores of the MADRS and YMRS. The following cut offs were adopted: MADRS score of at least 7 (for the characterization of depressive mood) and YMRS of at least 12 (for mania/hypomania). Most patients ( n = 26) were receiving one or more psychiatric medications at the time of their inclusion in the study (seven patients were on lithium, 16 on anticonvulsant mood stabilizers, 12 on antidepressants, and 17 on antipsychotics). There were no statistically significant differences between euthymic and non-euthymic patients with respect to medication status. This study was approved by the respective Institutional Review Board (HSC-MS-09-0340). Informed consent was obtained from all participants.
### Measurement of the Levels of RAS-Related Molecules
Blood samples were collected in heparin-coated collection tubes and centrifuged twice at ∼20°C for 10 min, one at 1,800 rpm and the other one at 3,000 rpm. Plasma samples were stored at −80 °C for further processing. The levels of plasma ACE (catalog # MBS727096), ACE2 (catalog # MBS723213), Ang II (catalog # MBS764273), and Ang-(1–7) (catalog # MBS084052) were assessed using Enzyme-Linked Immunosorbent Assay (ELISA), according to the manufacturer’s instructions (MyBioSource, Inc., SanDiego, CA, United States). A competitive ELISA method was used for ACE, while a sandwich ELISA procedure was used for ACE2, Ang II, and Ang-(1–7). Concentrations were measured in pg/mL (ACE, ACE2, and Ang II) and in ng/ml [Ang (1–7)]. The sensitivity of the assays was 1.0 pg/mL for ACE and ACE2, 18.75 pg/mL for Ang II, and 0.02 ng/mL for Ang (1–7). The analyses were performed blind to subject group (patients vs. controls).
### Statistical Analysis
The statistical analyses were performed using IBM SPSS statistical software (IBM SPSS, version 19, Armonk, NY) and STATA ( ). The sociodemographic features of the groups were compared using exact chi-square tests (for categorical variables) and the Student’s t -test (for continuous variables). The levels of Angiotensin II, ACE, and other markers of RAS activity in patients and controls were compared using the Mann–Whitney U test. Among patients, we also looked into possible correlations (using the Spearman correlation coefficient) between the levels of RAS-related molecules and mood-rating scales, namely the Montgomery–Åsberg Depression Rating Scale (MADRS) and the Young Mania Rating Scale (YMRS).
## Results
Both groups were matched according to age and sex, and did not differ with respect to other sociodemographic features ( ). No statistically significant differences between groups were observed with regards to the plasma levels of ACE, ACE2, Ang-(1–7), and Ang II ( ). However, a secondary analysis, including only non-euthymic BD patients and HC ( ) revealed significantly lower ACE levels among patients compared to controls (median:459.00 vs. 514.10, p < 0.05). Differences regarding ACE2 (median: 20.73 vs. 15.91, p = 043), Ang II (median: 374.90 vs. 415.10, p = 0.35), and Ang-(1–7) [median: 0.34 vs. 0.25, p = 0.12) remained non-significant. Similarly, among bipolar patients, we found no statistically significant correlations between the plasma levels of the molecules of interest and the scores of the MADRS [ACE: r = 0.12, p = 0.53; ACE2: r = −0.01, p = 0.94; Ang II: r = −0.17, p = 0.37); Ang (1–7): r = 0.21, p = 0.26] or YMRS [ACE: r = −0.16, p = 0.40; ACE2: r = −0.14, p = 0.45; Ang II: r = 0.08, p = 0.69); Ang (1–7): r = 0.12, p = 0.59].
Sociodemographic features and plasma levels of renin-angiotensin system (RAS)-related molecules in bipolar disorder patients (BD) and healthy controls.
Plasma levels of renin-angiotensin system (RAS)-related molecules in non-euthymic bipolar patients and healthy controls. Non-euthymic bipolar disorder patients ( n = 16) presented with significantly lower plasma levels of angiotensin-converting enzyme (ACE) than healthy controls ( n = 30). Differences regarding the levels of angiotensin-converting enzyme 2 (ACE2), angiotensin-II (Ang II), and angiotensin (1–7) [Ang-(1–7)] were not statistically significant.
## Discussion
Our results indicate that patients with BD in non-euthymic state had lower plasma levels of ACE when compared to healthy controls. No statistically significant differences were observed with respect to the levels of ACE2, Ang II, and Ang-(1–7). While these results may be, at least in part, interpreted as evidence against the involvement of the RAS in the pathophysiology of BD, their putative pathophysiological significance needs to be carefully discussed.
The relationship between RAS activity and mental disorders seems to be complex, with evidence suggesting a close association between RAS, inflammation, and psychiatric disorders ( ; ; ). It has been proposed that the RAS is composed by two arms, which have opposite actions in terms of inflammatory activity and effects on the pathophysiology of mental illnesses. The first arm, composed by ACE, Angiotensin II, and Angiotensin receptor type I (AT1), displays proinflammatory actions. Increments in this pathway are hypothesized to contribute to the development of mental disorders ( ). On the other hand, the second arm, comprised of ACE2, Ang (1–7), and Angiotensin receptor type II (AT2) seems to have anti-inflammatory effects and a putatively protective effect against the development of neuropsychiatric disorders ( ). Nevertheless, there are inconsistent patterns of findings on the possible involvement of the RAS across different psychiatric conditions.
While part of these conflicting findings may be secondary to methodological issues, including the characteristics of the subjects, it is possible that these discrepancies are related to variations in the pathophysiological factors involved in different subtypes of mood disorders. For example, some patients with mood disorders seem to have a more prominent involvement of immune factors and stress-related hyperactivation of the HPA axis. The relationship between the RAS and HPA axis is well-described and seems to be bidirectional, with the ACE contributing to the activation of the HPA axis and, in contrast, elevated cortisol levels leading to compensatory decreases in ACE levels ( ). Therefore, while our finding of decreased ACE levels in BD patients may sound counterintuitive, it may suggest that, in certain groups of psychiatric patients (including patients with mood disorders and schizophrenia), abnormalities in the RAS system may occur as a downstream consequence of increases in inflammatory and HPA activity. This possibility might also explain the fact that we found significantly decreases in ACE levels among non-euthymic patients but not in euthymic patients. In other words, according to this hypothesis, decreased ACE might represent a potential state biomarker for BD but not an endophenotype/marker of vulnerability for that condition.
Our study has some methodological limitations that need to be acknowledged. First, our small sample size may, in part, explains the lack of statistically significant differences between groups regarding other RAS-related molecules. Second, our sample included only outpatients, and it is unclear whether similar findings would be applicable to a sample of patients with more severe forms of the disease. Third, most of the bipolar patients in our sample were medicated, and literature data indicates that antipsychotics are associated with increases in the CSF levels of ACE ( ). While it is uncertain whether other drugs, such as lithium, other mood stabilizers, and antidepressants have similar effect, this possibility exists. Finally, we measured ACE and ACE 2 levels but not enzyme activity.
Moreover, RAS abnormalities may be involved in the pathophysiology of other mental disorders, whose symptoms might potentially overlap with the ones from BD. In spontaneous hypertensive rats, which show behavioral features correlated with hyperactivity and impulsivity and have been proposed as an animal model for attention-deficit disorder (ADHD) ( ; ), associations between RAS and HPA activity have previously been described ( ). These finds suggest that RAS dysfunctions might play a role in the pathogenesis of ADHD. Given the commonly observed challenges involved in the differentiation between BD and ADHD due to phenotypical overlap between both conditions, one could hypothesize that some of our negative findings are related to the inadvertent inclusion of ADHD patients in our sample. While the possibility in question cannot be completely ruled out, it is unlikely that was the case, as the diagnosis of BD was confirmed by a structured psychiatric interview (SCID). Furthermore, we recruited only BD I patients, whose higher severity in terms of lifetime psychopathological features allows for a more clear distinction between BD and ADHD.
One last methodological limitation of our study is related to potential concerns about the accuracy and specificity of commercial ELISAs for the measurement of Ang II, and Ang-(1–7), which can produce results at range values distinct from those obtained through other approaches ( ). That issue may create difficulties in the comparison of our results with the ones obtained by other groups. Our findings should be considered preliminary and must be confirmed and validated by future studies utilizing different methods.
In summary, our results raise some hypothesis about the potential involvement of RAS dysfunction in the pathophysiology of BD, particularly as a putative marker of activity of that illness. The molecular mechanisms underlying this involvement, as well as its possible modulation by clinical factors such as mood state, illness severity, and treatment status among patient with BD, correspond to a promising and relatively unexplored area of study. Given the potential therapeutic implications linked to a better understanding of the role of RAS dysfunction in BD, studies with larger samples and longitudinal designs, allowing the measurement of RAS-related molecules level and activity across different mood states are of high interest.
## Data Availability Statement
The datasets for this article are not publicly available. Requests to access the datasets should be directed to MS.
## Ethics Statement
The studies involving human participants were reviewed and approved by the CPHS-UT Health Science Center at Houston. The patients/participants provided their written informed consent to participate in this study.
## Author Contributions
MS participated in the research design, data collection and analysis, data interpretation and in the drafting, revision, and approval of the final manuscript. GC participated in the data analysis and interpretation and in the drafting, revision, and approval of the final manuscript. VC and TB participated in the data collection and in the revision and the drafting, revision, and approval of the final manuscript. DR participated in the drafting, revision, and approval of the final manuscript. JS participated in the research design, data interpretation, and in the drafting, revision, and approval of the final manuscript. AT participated in the research design, data analysis and interpretation, and in the drafting, revision, and approval of the final manuscript. All authors contributed to the article and approved the submitted version.
## Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
|
The aim of the present study was to elucidate the effect of resveratrol (natural polyphenol) on seizure activity, production of ROS, brain damage and mitochondrial function in the early phase of status epilepticus (SE), induced in immature 12 day-old rats by substances of a different mechanism of action (Li-pilocarpine, DL-homocysteic acid, 4-amino pyridine, and kainate). Seizure activity, production of superoxide anion, brain damage and mitochondrial function were assessed by EEG recordings, hydroethidium method, FluoroJadeB staining and Complex I activity measurement. A marked decrease of complex I activity associated with the acute phase of SE in immature brain was significantly attenuated by resveratrol, given i.p. in two or three doses (25 mg/kg each), 30 min before, 30 or 30 and 60 min after the induction of SE. Increased O production was completely normalized, brain damage partially attenuated. Since resveratrol did not influence seizure activity itself (latency, intensity, frequency), the mechanism of protection is likely due to its antioxidative properties. The findings have a clinical relevance, suggesting that clinically available substances with antioxidant properties might provide a high benefit as an add-on therapy during the acute phase of SE, influencing also mechanisms involved in the development of epilepsy.
## Introduction
Existing data clearly indicate that seizures and status epilepticus (SE) are associated with oxidative stress ( ; ; ; ; ; ; ). Our recent studies have demonstrated that oxidative stress (demonstrated by the increased production of O in various brain regions) ( , ) and by the elevation of mitochondrial oxidative damage markers, 3-nitrotyrosine, 4-hydroxynonenal and protein carbonyls) ( ) and mitochondrial dysfunction (particularly a marked inhibition of respiratory chain complex I activity ( , , ) occur also in immature brain and may thus be considered a general phenomenon ( ; ). Recent studies suggest that targeting oxidative stress can ameliorate alterations associated with the acute phase of SE and improve also disease outcome (e.g., ).
Many efforts have been aimed at developing the substances capable of detoxifying reactive oxygen and nitrogen species (ROS and RNS) and their damaging effects ( ; ). Synthetic metalloporphyrin catalytic antioxidants (small molecule mimics of SOD and/or catalase) have appeared as a novel neuroprotective agents ( ; ; ). Oxidative stress and neuronal damage associated with status epilepticus in adult animals could be attenuated by some of these compounds ( ; ). We have shown that both superoxide anion formation and the deficiency of complex I activity associated with SE in immature rats could be prevented or substantially attenuated with SOD mimic Mn (III) tetrakis (1-methyl-4-pyridyl) porphyrine pentachloride (MnTMPYP, from Calbiochem), a nitroxide antioxidant and the superoxide dismutase mimic 4-hydroxy-2,2,6,6-tetramethylpiperidine-1-oxyl (Tempol, from Sigma) and by a peroxynitrite scavenger and decomposition catalyst 5,10,15,20-tetrakis (4-sulfonatophenyl) porphyrinate Iron (III) (FeTPPS from Calbiochem) ( , , , ; ). In addition, treatment with these antioxidants resulted in a partial amelioration of neuronal degeneration associated with SE in immature rats ( , ).
It is well-recognized, that acquired epilepsy develops in otherwise healthy brain after the initial “epileptogenic insult” (such as status epilepticus, hypoxic-ischemic insults, infection, trauma, stroke etc.). It triggers vast cascade of multilevel processes which in some individuals finally result in occurrence of spontaneous recurrent seizures, i.e., epilepsy. Epileptogenic insult, epileptogenesis and epilepsy most likely represent independent pharmacological targets. Recently we have observed protective effect of a natural polyphenolic compound present in red wine, resveratrol (RSV) (3,5,4’-tri-hydroxy-trans-stilbene), during epileptogenesis, i.e., long period, up to 4 weeks, following the epileptogenic insult, namely Li-Pilocarpine status epilepticus in immature rats ( ).
The aim of the present study was to discover effect of resveratrol on seizure activity, production of ROS, neuronal damage and mitochondrial function in the early phase of epileptogenic insult itself, i.e., status epilepticus induced in immature rats. For induction of status epilepticus we have utilized 4 substances of a different mechanism of action, namely DL-homocysteic acid, 4-aminopyridine, Li-pilocarpine and kainate, offering the possibility for general conclusions on resveratrol effect during epileptogenic insult.
## Materials and Methods
### Animals
Immature 12-day-old male Wistar rats were used for these experiments. Twelve-day-old rats were chosen because of the level of brain maturation which is comparable to the early postnatal period in human infants ( ). The rat pups were removed from their dams 1 h before the experiment. They were kept in plastic observation chambers on an electrically heated pad at 34°C (i.e., the temperature of the nest), with the exception of surgery. The protocol of experiments was approved by the Animal Care and Use Committee of the Institute of Physiology, Academy of Sciences of the Czech Republic, in agreement with Animal Protection Law of the Czech Republic, which is fully compatible with the guidelines of the European Community Council directives 86/609/EEC. The Institute possesses The Statement of Compliance with Standards of Humane Care and Use of Laboratory Animals #A5228-01 from NIH. All efforts were made to minimize animal suffering and to reduce the number of animals used.
### Surgery
The animals were anesthetized with isoflurane and fixed in a stereotaxic apparatus, modified for rat pups ( ). For DL-HCA and 4-AP application, bilateral stainless steel guide cannulae (26-gauge, 4 mm in length, Plastics One, Germany) were stereotaxically implanted 1 mm above the lateral ventricles (AP:0.7 mm caudal from the bregma; L: ± 1.5 mm; V: 3.3 mm from the skull surface). Cannulae were fixed to the skull with dental acrylic. After the surgery animals were returned to their mothers in home cages to recover.
### EEG Recordings and Analysis
After surgery, animals recovered 2 h and then were connected to the EEG system (Pentusa, TDT, United States) and continual EEG were recorded on 1 kHz and stored for offline analysis. The EEG recording covered baseline (20 min) and whole period of SE development up to 90 min duration. Offline analyses were performed in Spike2 (CED, United Kingdom) and Matlab (Mathworks, United States) software. Epileptic spikes were detected as suprathreshold events and quantified in 60 s long bins.
### Seizure Induction
DL-HCA (from Aldrich, Germany) and 4-AP (SigmaAldrich) were dissolved in sterile saline and the pH adjusted to ∼7.0, only freshly prepared solutions were used. Bilateral i.c.v. infusions of DL-HCA (600 nmol/side), 4-AP (100 nmol/side) or saline were made in a volume of 0.5 μl at a rate of 0.17 μl/min using a SP200i infusion pump (WPI, United States) through stainless steel internal cannulae (33 gauge, 5 mm in length, Plastics One, Germany), each connected by a polyethylene tube to a 10 μl Hamilton syringe. To induce Li-Pilo SE, LiCl (SigmaAldrich) was dissolved in redistilled water and administered i.p. to PD11 immature rats (127 mg/kg). After 24 h, pilocarpine (SigmaAldrich), dissolved in redistilled water was given i.p. (35 mg/kg) to lithium pretreated pups. To induce kainate SE, KA (Tocris Bioscience, Bristol, United Kingdom) was dissolved in saline and given i.p. (6 mg/kg). Control animals received corresponding volumes of the appropriate vehicles.
### The Effect of Resveratrol
For assessing a potential protective effect of a natural polyphenolic compound resveratrol (RSV), RSV (from Sigma Co.) was dissolved in DMSO and then diluted with PBS (final concentration of DMSO ∼5%). Only freshly prepared solutions, kept in dark, were used for applications. Resveratrol was given i.p. in two or three doses (25 mg/kg each), 30 min before, 30 or 30 and 60 min after induction of SE. The schema of experimental design of the current study can be seen in .
Schema of experimental design.
### Superoxide Anion Determination
Production of superoxide anion (O ) in different brain regions in situ was determined using hydroethidium (Het) method ( ), adopted for immature rats, as described in detail in our previous work ( ). Het was given by i.p. injection immediately before infusion of DL-HCA or 4-AP and ∼15 min after i.p. administration of Li-Pilo or KA (final concentration 10 mg/kg). Sixty minutes after the application of Het, rat pups were deeply anesthetized with 20% (w/v) urethane and transcardially perfused with 0.01 M phosphate buffered saline (PBS), pH 7.4, followed by a fixative solution [4% (w/v)] paraformaldehyde in 0.1 M phosphate buffer, pH 7.4. The brains were removed from the skull, postfixed for 3 h at 4°C in the same fixative, then cryoprotected in sucrose of increasing concentrations (10, 20, and 30% (w/v), respectively) in 0.1 M phosphate buffer, pH 7.4 and finally frozen in dry ice. Coronal sections (50 μm) were cut through the brain in a cryostat and mounted onto the gelatinated slides. All procedures were performed under the reduced light.
The level of the oxidized products of Het was assessed microscopically by detection of their fluorescence (>600 nm). Pictures of the selected regions of interest (hippocampal fields CA1, CA3, and DG, primary somatosensory cortex and dorsal thalamus) of the same size and orientation, were captured (AP −3.5 to −4.0 according to Paxinos and Watson ( ), with cooled camera mounted onto upright microscope (10 x magnification lens). Camera settings remained unchanged throughout the evaluation of the current set of tissue sections of animals from one experimental day, treated with the same solution of Het. The group comprised always at least three saline-treated controls, three animals with convulsant drug alone and three with convulsant drug plus resveratrol. Fluorescence signal (represented as integral intensity of the given region) was normalized by values of the control animals of the current set. Results are expressed as percentage of saline-treated animals.
### Brain Damage Analysis
Brain damage was evaluated in Li-Pilo model of SE. At 24 h following SE, rat pups from Li-Pilocarpine ( n = 9) and Li-Pilocarpine + resveratrol ( n = 8) groups were subjected to fixation procedure (see its detailed description in the section “Superoxide Anion Determination”). Coronal 50 μm thin slices were cut and stained with Fluoro-Jade B (Histochem, United States) as previously used and described in details by our group ( , ). To assess neurodegeneration and potential protective effect of resveratrol, we have performed semiquantitative grading (using a score) of number of Fluoro-Jade B positive cell in regions of interest (ROI), spatially corresponding to regions evaluated by ethidium method. Neurodegeneration was assessed in hippocampal regions CA1, CA3 and dentate gyrus (DG), sensorimotor cortex (Cx), and mediodorsal thalamic nuclei (Thal). Position of ROIs, selected consistently through all animals, are illustrated in . A semiquantitative scale was used to assess the brain damage; score 0: <7 neurons, score 1: 7–15 neurons, score 2: 16–25 neurons, score 3: 26–40 neurons, score 4: >40 neurons.
### Isolation of Mitochondria
Mitochondrial fractions were isolated according to Liang et al. ( ), as described in detail in our previous works ( , ). All procedures were performed at 4°C. Cerebral cortices (weighing ∼250 mg) were used for each mitochondrial preparation. 10% (w/v) homogenates in ice-cold isolation buffer (70 mM sucrose, 210 mM mannitol, 5 mM Tris-HCl, 1 mm EDTA, pH 7.4) were prepared with Elvehjem-Potter type glass-Teflon homogenizers manually by twenty slow up-and-down strokes. Homogenates were centrifuged at 600 × g for 5 min at 4°C, the postnuclear supernatant was centrifuged at 17,000 × g for 10 min at 4°C. Mitochondrial pellet was resuspended with 100 μl 50 mM Tris-HCl (pH 7.4). Fresh isolated mitochondria were used for protein determination. Aliquots of mitochondria frozen in liquid nitrogen and stored at −70°C were used for determinations of complex I and citrate synthase activities (performed within 1 week).
### Enzyme Assays
Activities of mitochondrial respiratory chain complex I and citrate synthase were measured at 30°C in a total reaction volume of 1 ml using Shimadzu 1601 spectrophotometer. Duplicate determinations were carried out with each mitochondrial sample.
#### Complex I
NADH-ubiquinone oxidoreductase (EC 1.6.5.3) activity was determined as the rotenone-sensitive rate of NADH oxidation at 340 nm. The reaction mixture contained: 25 mM potassium phosphate (pH 7.2), 10 mM MgCl , 1 mM KCN, 0.25% fatty acid-free bovine serum albumin (BSA), 0.1 mM NADH and approximately 50 μg of mitochondrial protein. The reaction was initiated by the addition of CoQ10 (decylubiquinone, final concentration 50 μM). After 2 min, 2 μl of rotenone were added (final concentration 5 μM) and the inhibited rate was followed for further 2 min ( , ).
#### Citrate Synthase
Citrate synthase (EC 4.1.3.7) activity was determined as the rate of color change of 5,5’-dithiobis-(2-nitrobenzoic) acid (DTNB) at 412 nm. The reaction mixture contained 100 mM Tris-HCl (pH 8.1), 0.2 mM DTNB, 0.1% Triton X-100, 0.1 mM acetyl-CoA and ∼20 μg of mitochondrial protein. The reaction was initiated by the addition of 20 μl of 10 mM oxaloacetate (final concentration 0.2 mM) ( , ).
Activity of complex I was expressed as nmol/min/mg protein. To correct for the potential variations in mitochondrial contents in the samples, activities can also be expressed as a ratio to citrate synthase.
#### Protein Determination
Mitochondrial protein concentration was estimated by Bradford’s method, with bovine serum albumin as a standard.
### Statistics
Statistical analysis was performed in SigmaPlot 13 software (Systat Software Inc., United States). The data were evaluated by one-way ANOVA with Newmann-Keul’s post hoc test or by t -test where appropriate. The level of statistical significance was set to 5%.
## Results
### The Behavioral Pattern of Seizures
All four convulsants induced SE that was characterized by generalized clonic-tonic seizures in DL-HCA and 4-AP models and by generalized clonic seizures in Li-Pilo and KA model, in the latter case accompanied by mild tonic extensions. Detailed description is given in our previous studies ( , ).
### Effect of Resveratrol on Behavioral Pattern and on Electrographic Activity in Li-Pilo SE
In all four models studied, latency to the first behavioral seizure and character of SE were not influenced by resveratrol. Effect of resveratrol on electrographic pattern has been analyzed in Li-Pilo model. Latency to the first electrographic seizure and severity of SE, as assessed by number of spikes during first 90 min, were not influenced by resveratrol treatment ( ).
(A) EEG activity during Li-Pilo status epilepticus has been determined as number of epileptic spikes in 60 s bins. Time zero is the time of pilocarpine application. Red line, convulsant agent alone; Blue line, convulsant agent plus resveratrol. (B) Latency to the first electrographic seizure does not differ after resveratrol treatment ( P = 0.753). Red column, convulsant agent alone; Blue column, convulsant agent plus resveratrol. (C) Total number of detected epileptic spikes during 90 min of SE duration was not influenced by resveratrol suggesting no anticonvulsant effect ( P = 0.668). Red column, convulsant agent alone; Blue column, convulsant agent plus resveratrol.
### Generation of Superoxide Anion During Seizures
As can be seen in , fluorescent signal of the oxidized products of Het (reflecting O production) significantly increased in all the studied brain structures, namely CA1, CA3, and DG of hippocampus, cerebral cortex and thalamus of immature rats after SE lasting 60 min in all four models, with the exception of DG in DL-HCA model.
(A) Fluorescence of the oxidized products of hydroethidium (reflecting superoxide anion production), assessed microscopically by fluorescence (>600 nm), in various brain structures following 60 min lasting SE induced by Li-Pilocarpine (Li-Pilo), homocysteic acid (DL-HCA), 4-aminopyridine (4-AP) or kainic acid (KA). Upper image for each model, convulsant agent alone; lower image for each model, convulsant agent plus resveratrol. (B) Effect of resveratrol on superoxide anion formation at 60 min following the onset of SE, induced in immature rats by Li-Pilo, DL-HCA, 4-AP, or KA. White columns, saline-treated controls; black columns, convulsant agent alone; cross-hatched columns, convulsant agent plus resveratrol. Results are expressed in percent, compared to 100% in the control animals. Mean values for 5–6 animals ± SEM. * P < 0.05 as compared with saline; P < 0.05 as compared with convulsant agent alone. CA1 and CA3, hippocampal fields; DG, dentate gyrus; CX, cerebral cortex; Thal, dorsal thalamus.
Effect of resveratrol on brain damage. (A) Fluoro-Jade B staining revealed neurodegeneration in the explored regions (white arrows). Resveratrol treatment provided neuroprotective effect in CA3 region (red arrows). (B) Schema of investigated regions. (C) Brain damage evaluated by semiquantitative grading of FJB positive cells (description of score employed is given in the section “Materials and Methods”). * P < 0.05 as compared with Li-Pilo alone.
### Effect of Resveratrol on O Production
(cross-hatched columns) demonstrates that RSV provided a complete protection in Li-Pilo, 4-AP and KA models and significantly reduced the fluorescence signal during SE induced by DL-HCA (see also , lower row of images).
### Effect of Resveratrol on Brain Damage
Status epilepticus induced by Li-Pilocarpine resulted in a perceptible neuronal damage as revealed by Fluoro-Jade B staining at 24 h after SE. Structures of hippocampal formation, namely CA1 and CA3, as well as dentate gyrus (DG) and mediodorsal thalamic nuclei have been bilaterally affected and various number of Fluoro-Jade B positive cells has been identified under both control (Li-Pilo only) and resveratrol treated conditions. Sensorimotor cortex, however, revealed only a minimal or none neuronal damage. Semiquantitative evaluation of neuronal damage in selected regions of interest revealed partial but still significant neuroprotection in CA3 field of hippocampus (3.8 ± 0.15 vs. 2.9 ± 0.3, P = 0.023) while other evaluated regions did not show signs of neuroprotection ( ).
### Effect of Resveratrol on Inhibition of Complex I Activity
Our previous studies have demonstrated that SE induced in immature rats by DL-HCA ( ) or by 4-AP, Li-Pilo, and KA ( ) leads to a marked deficiency of complex I activity, corresponding to more than 50%. As evident in , the inhibition of complex I activity was in all four models studied significantly attenuated by the treatment with RSV. Although significant, the protection was only partial since the activities of complex I after treatment with RSV remained significantly lower as compared with the appropriate controls. The same decrease of complex I activity and the same extent of protection provided by resveratrol was evident when the activity was expressed both as the specific activity ( ) and as a ratio to citrate synthase (data not shown).
Effect of resveratrol on mitochondrial complex I activity at ∼20 h of survival after SE induced in immature rats by Li-Pilo, DL-HCA, 4-AP, or KA. White columns, controls; black columns, convulsant agent alone; cross-hatched columns, convulsant agent plus resveratrol. Results are expressed as nmol/min/mg protein. Mean values for 4–6 animals ± SEM. * P < 0.05 as compared with controls; P < 0.05 as compared with convulsant agent alone.
## Discussion
The important finding of the present study is the proof that resveratrol, naturally occurring polyphenolic compound, was able significantly reduce mitochondrial dysfunction associated with the acute phase of SE in immature rats.
The crucial question arises whether the protective effect of RSV could not be due to an anticonvulsant effect. However, behavioral pattern of seizures (severity, frequencies, duration) observed in all the studied models did not differ between groups with convulsant agent and resveratrol and those with convulsant compound alone. In addition, lack of an anticonvulsant property of RSV was confirmed in Li-Pilo model by EEG recordings. Our findings are thus in agreement with recent reports of . These authors employing three different seizure models in adult mice did not observe an obvious anticonvulsant effect of RSV, only a trend toward a delay in seizure latency. Thus, our findings are compatible with the statement that the protective effect of RSV is most likely due to its antioxidant properties.
Neuroprotective effect of RSV has been observed in various models of neurological disorders in adult animals ( ; ; ; ; ; ; ; ). Its employment has some advantages, since RSV enters the brain after a peripheral administration and it does not seem to have adverse effects ( ). It seems likely that substances interacting with multiple targets can achieve a better effect than single target therapies. Thus, RSV besides the direct antioxidant effect ( ; ) has multiple cellular effects, interfering with several signaling pathways ( ; ; ; ). Recently, it has been reported that RSV is able to activate Nrf2 (nuclear factor erythroid 2-related factor 2) which is an essential transcription factor regulating the expression of numerous endogenous antioxidant and anti-inflammatory genes and plays a crucial role in cellular defense against oxidative stress ( ; ). Importantly, our recent findings indicate that neuroprotective effect comparable to that observed with RSV can be detected in immature rats during Li-pilocarpine SE after treatment with sulforaphane, established activator of Nrf2 (manuscript in preparation). Furthermore, an increased expression ( ) or activation of Nrf2 ( ) have been reported recently to provide a marked protection in experimental epilepsy models in adults. Nevertheless, the precise mechanism of RSV action in our study remains to be clarified by future studies.
Whatever the mechanism, RSV prevented completely the generation of O associated with the acute phase of SE. It should be mentioned that the Het assay used for the evaluation of O formation has several limitations as discussed recently ( ; , ). These mainly concern the difficulty to distinguish microscopically the fluorescent red signal belonging to 2-hydroxyethidium (a specific product of Het reaction with O ) and ethidium (a product of Het reaction with other ROS and/or oxidants). Importantly, our recent findings demonstrating complete prevention of the increased fluorescent signal after the treatment animals with SOD mimetic MnTMPYP, support the involvement of superoxide anion ( , ).
Mitochondrial dysfunction, especially a deficiency of complex I activity has been demonstrated in humans with temporal lobe epilepsy ( ; ) and in several experimental models of epilepsy in adult ( ; ; ; ) as well as immature animals during SE ( , , ). In agreement with our previous studies, more than 50% decrease of complex I activity was observed in all four models of SE induced in immature rats ( ). The question arises what may be the underlying mechanism. We showed that in DL-HCA model, the decrease of complex I activity was not associated with changes in the size of the assembled complex I or with changes in mitochondrial content of complex I ( ). We have thus proposed that inactivation, namely oxidative modification of complex I, may be responsible for the deficiency of complex I activity. This assumption is supported by an extreme sensitivity of this enzyme to both oxidative and nitrosative stress ( and references therein). Furthermore, the increased ROS production detected in all four models can create conditions favoring oxidative modifications of sensitive targets. Several posttranslational oxidative modifications of complex I can occur, such as carboxylation, nitration of tyrosine (and/or tryptophane) residues, S-nitrosation of some of its protein thiols etc. ( ; ; ; , ). Indeed, the oxidative modification (nitration or carboxylation) of only a few subunits from the total 46 was reported to result in a pronounced inhibition of complex I activity ( ; ). Potential role of other factors beside oxidative inactivation cannot, however, be excluded. Nevertheless, the involvement of oxidative modification is supported by our recent findings demonstrating that deficiency of complex I activity could be significantly attenuated by SOD mimics Tempol ( ) or MnTMPYP ( , ), by a selective peroxynitrite scavenger and decomposition catalyst FeTPPS ( , , ) and, as the present findings clearly indicate, by resveratrol.
It should be kept in mind that complex I besides being a target for ROS and RNS is also the important source of their production, especially when partially inhibited ( ; ; ; ; ). It can thus be assumed that the enhanced production of ROS and/or RNS as a consequence of complex I inhibition, may lead to a potential impairment of sufficient energy production, contribute to neuronal injury and/or epileptogenesis ( ; ; ).
## Conclusion
The present study clearly demonstrates that treatment with resveratrol significantly attenuates early mitochondrial dysfunction (evident as a marked preservation of complex I activity) during the acute phase of status epilepticus in immature rats. The protective effect of resveratrol was evident in all four models, induced in immature rats with substances of a different mechanism of action and, can thus represent a general phenomenon associated with SE in immature brain. Since resveratrol does not influence seizure activity itself, the mechanism of protective action is most likely due to its antioxidative properties (as documented by diminished O production). The findings have a clinical relevance suggesting that clinically available substances with antioxidant properties might provide a high benefit as an add-on therapy during the acute phase of status epilepticus interacting with mechanisms involved in development of epilepsy.
## Data Availability Statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics Statement
The animal study was reviewed and approved by the Ethical and Use committee of the Institute of Physiology CAS, Prague.
## Author Contributions
JF: conceptualization and writing—original draft. JF and JO: formal analysis, supervision, visualization, and writing—review and editing. JO: funding acquisition and project administration. JF, PJ, and JO: investigation and methodology. All authors contributed to the article and approved the submitted version.
## Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
|
Previous literature shows that deep learning is an effective tool to decode the motor intent from neural signals obtained from different parts of the nervous system. However, deep neural networks are often computationally complex and not feasible to work in real-time. Here we investigate different approaches' advantages and disadvantages to enhance the deep learning-based motor decoding paradigm's efficiency and inform its future implementation in real-time. Our data are recorded from the amputee's residual peripheral nerves. While the primary analysis is offline, the nerve data is cut using a sliding window to create a “pseudo-online” dataset that resembles the conditions in a real-time paradigm. First, a comprehensive collection of feature extraction techniques is applied to reduce the input data dimensionality, which later helps substantially lower the motor decoder's complexity, making it feasible for translation to a real-time paradigm. Next, we investigate two different strategies for deploying deep learning models: a one-step (1S) approach when big input data are available and a two-step (2S) when input data are limited. This research predicts five individual finger movements and four combinations of the fingers. The 1S approach using a recurrent neural network (RNN) to concurrently predict all fingers' trajectories generally gives better prediction results than all the machine learning algorithms that do the same task. This result reaffirms that deep learning is more advantageous than classic machine learning methods for handling a large dataset. However, when training on a smaller input data set in the 2S approach, which includes a classification stage to identify active fingers before predicting their trajectories, machine learning techniques offer a simpler implementation while ensuring comparably good decoding outcomes to the deep learning ones. In the classification step, either machine learning or deep learning models achieve the accuracy and F1 score of 0.99. Thanks to the classification step, in the regression step, both types of models result in a comparable mean squared error (MSE) and variance accounted for (VAF) scores as those of the 1S approach. Our study outlines the trade-offs to inform the future implementation of real-time, low-latency, and high accuracy deep learning-based motor decoder for clinical applications.
## 1. Introduction
Upper-limb amputation affects the quality of life and well-being of millions of people in the United States, with hundreds of thousand new cases annually (Ziegler-Graham, ). Neuroprosthetic systems promise the ultimate solution by developing human-machine interfaces (HMI) that could allow amputees to control robotic limbs using their thoughts (Harris, ; Johannes, ; Schultz, ; Cordella, ). It is achieved by decoding the subject's motor intent with neural data acquired from different parts of the nervous system. Proven approaches include surface electromyogram (EMG) (Sebelius, ; Fougner, ; Jiang, ; Amsuss, ; Zuleta, ; George, , ), electroencephalogram (EEG) (Hu, ; Zeng, ; Sakhavi, ; Kwon, ), cortical recordings (Mollazadeh, ; Hochberg, ; Irwin, ), and peripheral nerve recordings (Micera, ; Davis, ; Vu, , ; Wendelken, ; Zhang, ; Nguyen and Xu, ).
However, implementing an effective HMI for neuroprostheses remains a challenging task. The decoder should be able to predict the subject's motor intents accurately and satisfy certain criteria to make it practical and useful in daily lives (Vujaklija, ; Krasoulis, ). Some criteria include dexterity , i.e., controlling multiple degrees-of-freedom (DOF) such as individual fingers; intuitiveness , i.e., reflecting the true motor intent in mind, and real-time , i.e., having minimal latency from thoughts to movements.
In recent years, deep learning techniques have emerged as strong candidates to overcome this challenge thanks to their ability to process and analyze biological big data (Mahmud, ). Our previous work (Nguyen and Xu, ) shows that neural decoders based on the convolutional neural network (CNN) and recurrent neural network (RNN) architecture outperform other “classic” machine learning counterparts in decoding motor intents from peripheral nerve data obtained with an implantable bioelectric neural interface. The deep learning-based motor decoders can regress the intended motion of 15 degrees-of-freedom (DOF) simultaneously, including flexion/extension and abduction/adduction of individual fingers state-of-the-art performance metrics, thus complying with the dexterity and intuitiveness criteria.
Here we build upon the foundation of Nguyen and Xu ( ) by exploring different strategies to optimize the motor decoding paradigm's efficiency. The aim is to lower the neural decoder's computational complexity while retaining high accuracy predictions to make it feasible to translate the motor decoding paradigm to real-time operation suitable for clinical applications, especially when deploying in a portable platform. This paper does not aim to solve the real-time problem but to study different approaches, highlighting their advantages and disadvantages, which will inform future implementation of the motor decoding paradigm in real-time.
First, we utilize feature extraction to reduce data dimensionality. By examining the data spectrogram, we learn that most of the signals' power concentrates in the frequency band 25–600 Hz (Nguyen and Xu, ). Many feature extraction techniques (Zardoshti-Kermani, ; Phinyomark, , ; Rafiee, ) have been developed to handle signals with similar characteristics. Feature extraction aims not only to amplify the crucial information, lessen the noise but also to substantially reduce the data dimensionality before feeding them to deep learning models. This could simultaneously enhance the prediction accuracy and lower the deep learning models' complexity. Here we focus on a comprehensive list of 14 features that consistently appear in the field of neuroprosthesis.
Second, we explore two different strategies for deploying deep learning models: the two-step (2S) and the one-step (1S) approaches. The 2S approach consists of a classification stage to identify the active fingers and a regression stage to predict the trajectories of digits in motion. The 1S approach only has one regression stage to predict the trajectories of all fingers concurrently. In practice, the 2S approach should be marginally more efficient because not all models are inferred at a given moment. The models in the 2S approach are only trained on the subset where a particular finger is active, while all models in the 1S approach are trained on the full dataset. Here we focus on exploring the trade-offs between two approaches to inform future decisions of implementing the deep learning-based motor decoder in real-world applications.
The rest of this paper is organized as follows: Section “Data Description” introduces the human participant of this research, the process of collecting input neural signals from the residual peripheral nerves of the participant, and establishing the ground-truth for the motor decoding paradigm using deep learning models. Section “Data Preprocessing” elaborates on how to cut raw input neural data into trials and extract their main features in the temporal domain before feeding to deep learning decoding models. Section “Proposed Deep Learning Models and Decoding Strategies” discusses the two approaches to efficiently translate motor intent from the residual peripheral nerves of the participant into motor control of the prosthesis as well as the architecture and the hyper-parameters of the deep learning models used in each approach. Section “Experimental Setup” is about the three machine learning models used as the baseline and how input neural data are allocated to the training and validation set. Section “Metrics and Results” presents the metrics to measure the performance of all models used in the motor intent decoding process and discusses the main results of both proposed approaches. Section “Discussion” discusses the role of feature extraction in reducing the deep learning motor decoders' complexity for real-time applications, how to further apply it in future works, and the advantages of machine learning and deep learning motor decoders in different scenarios where input dataset's size varies. Finally, section “Conclusion” summarizes the main contributions of this paper.
## 2. Data Description
### 2.1. Human Participant
The human experiment is a part of the clinical trial DExterous Hand Control Through Fascicular Targeting (DEFT), which is sponsored by the DARPA Biological Technologies Office as part of the Hand Proprioception and Touch Interfaces (HAPTIX) program, identifier No. NCT02994160 . The human experiment protocols are reviewed and approved by the Institutional Review Board (IRB) at the University of Minnesota (UMN) and the University of Texas Southwestern Medical Center (UTSW). The amputee voluntarily participates in our study and is informed of the methods, aims, benefits, and potential risks of the experiments prior to signing the Informed Consent. Patient safety and data privacy are overseen by the Data and Safety Monitoring Committee (DSMC) at UTSW. The implantation, initial testing, and post-operative care are performed at UTSW by Dr. Cheng and Dr. Keefer, while motor decoding experiments are performed at UMN by Dr. Yang's lab. The clinical team travels with the patient in each experiment session. The patient also completes the Publicity Agreements where he agrees to be publicly identified, including showing his face.
The participant is a transradial male amputee who has lost his hand for over 5 years ( ). Among seven levels of upper-limb amputation, transradial is the most common type that accounts for about 57% of upper-limb loss in the U.S. (Schultz, ; Cordella, ). Like most amputees, the subject still has phantom limb movements; however, such phantom feelings fade away over time. By successfully decoding neural signals from the residual nerves of an amputee who has lost his limb for a long time, we would offer a chance to regain upper-limb motor control for those who are sharing the same conditions.
Photo of the (A) amputee and (B) the data collection software during a training session. The patient performs various hand movements repeatedly during the training session. Nerve data and ground-truth movements are collected by a computer and displayed in real-time on the monitor for comparison.
The patient undergoes an implant surgery where four longitudinal intrafascicular electrode (LIFE) arrays are inserted into the residual median and ulnar nerves using the microsurgical fascicular targeting (FAST) technique ( ). The electrode array' design, characteristics, and surgical procedures are reported in Cheng ( ) and Overstreet ( ). The patient has the electrode arrays implanted for 12 months, during which the conditions of the implantation site is regularly monitored for signs of degradation.
Overview of the human experiment setup and data acquisition using the mirrored bilateral training. The patient has four FAST-LIFE microelectrode arrays implanted in the residual ulnar and median nerve (Overstreet, ). Peripheral nerve signals are acquired by two Scorpius neural interface devices (Nguyen and Xu, ). The ground-truth movements are obtained with a data glove.
The patient participates in several neural stimulations, neural recording, and motor decoding experiment sessions. He initially has weak phantom limb movements due to reduced motor control signals in the residual nerves throughout the years. However, the patient reports that the more experiment sessions he takes part in, the stronger his phantom control and sensation of the lost hand become. This suggests that training may help re-establish the connection between the motor cortex and the residual nerves, resulting in better motor control signals.
### 2.2. Nerve Data Acquisition
Nerve signals are acquired using the Scorpius neural interface ( )–a miniaturized, high-performance neural recording system developed by Yang's lab at UMN. The system employs the Neuronix chip family, which consists of fully-integrated neural recorders designed based on the frequency shaping (FS) architecture (Xu, , ; Yang, , , ). The specifications of the Scorpius system are reported in Nguyen and Xu ( ). The system allows acquiring nerve signals with high-fidelity while suppressing artifacts and interference. Here two Scorpius devices are used to acquire signals from 16 channels across four microelectrode arrays at a sampling rate of 40 kHz (7.68 Mbps, 480 kbps per channel). The data are further downsampled to 5 kHz before applying a bandpass filter in 25–600 Hz bandwidth to capture most of the signals' power. This results in a pre-processed data stream of 1.28 Mbps (80 kbps per channel).
### 2.3. Ground-Truth Collection
The mirrored bilateral training paradigm (Sebelius, ; Jiang, ) is used to establish the ground-truth labels needed for supervised learning ( ). The patient performs various hand gestures with the able hand while simultaneously imagining doing the same movement with the phantom/injured hand. During the ground-truth collection, the virtual hand and/or motorized prosthesis hand can follow the glove's movement to provide additional visual cues for the amputee. The ground truth is recorded while the patient poses his arms in different positions, including holding arms overhead, spreading to two sides, reaching the front, and resting along the body. The gestures include bending the thumb, index, middle, ring, little finger, index pinch, tripod pinch, grasp/fist, and resting. We record the training data in multiple sessions. During each session, the amputee performs one hand gesture 100 times, each time 4 s altering between resting and flexing. Peripheral nerve signals are acquired from the injured hand with the Scorpius system, while ground-truth movements are captured with a data glove (VMG, 30, Virtual Motion Labs, TX) from the able hand. The glove can acquire up to 15 DOF; however, we only focus on the main 10 DOF (MCP and PIP) corresponding to the flexion/extension of five fingers.
## 3. Data Preprocessing
### 3.1. Cutting Raw Neural Data
Raw neural data are cut using a sliding window to resemble online motor decoding ( ). Here the window's length is set to 4 s with an incremental step of 100 ms. At any instant of time, the decoder can only observe the past neural data. The pseudo-online dataset contains overlapping windows from a total of 50.7 min worth of neural recordings. Each of these 4 s neural data segments serves as an input trial of the motor decoding process later.
(A) Illustration of the sliding windows to cut neural data to create a pseudo-online dataset that resembles conditions in online decoding. (B) Illustration of the process to compute the feature data.
### 3.2. Feature Extraction
Previous studies have shown that feature extraction is an effective gateway to achieve optimal classification performance with signals in the low-frequency band by highlighting critical hidden information while rejecting unwanted noise and interference. Here we select 14 of the most simple and robust features that are frequently used in previous motor decoding studies (Zardoshti-Kermani, ; Phinyomark, , ; Rafiee, ). They are chosen such that there is no linear relationship between any pair of features. All features can be computed in the temporal domain with relatively simple arithmetic, thus aiding the implementation in future portable systems.
summaries the descriptions and formula of the features. illustrates the process to compute feature data. x is the 4 s neural data segments, which are further divided into windows of 100 ms with N is the window length. Two consecutive windows are 80% overlapped, which is equivalent to a 20 ms time step. This results in a data stream of 224 features over 16 channels with a data rate of 179.2 kbps (11.2 kbps per channel), which is more than 40 times lower than the raw data rate. presents an example of the feature data in one trial that shows a clear correlation between the changes of the 14 extracted features and the finger's movement. The amplitude of each feature is normalized by a fixed value before feeding to the deep learning models.
List of features, descriptions, and formula.
An example of feature data in one trial which shows clear correlation with the finger's movement. A trial includes the finger's movement from resting to fully flexing and back to resting. Each color represents one of the 16 recording channels. The amplitude of each feature is normalized by a fixed value.
## 4. Proposed Deep Learning Models and Decoding Strategies
### 4.1. Two-Step (2S) Strategy
Each finger exists in a binary state: active or inactive, depending on the patient's intent to move it or not. There are 32 different combinations of five fingers corresponding to 32 hand gestures. Only a few gestures are frequently used in daily living activities, such as bending a finger (“10000”, “01000”, …, “00001”), index pinching (“00011”), or grasp/fist (“11111”). Therefore, classifying the hand gesture before regressing the fingers' trajectories would significantly reduce the possible outcome and lead to more accurate predictions. The movement of inactive fingers could also be set to zero, which lessens the false positives when a finger “wiggles” while it is not supposed to.
shows an illustration of the 2S strategy. In the first step, the classification output is a [1 × 5] vector encoding the state of five fingers. While the dataset used in this study only includes nine possible outcomes, the system can be easily expanded in the future to cover more hand gestures by appending the dataset and fine-tuning the models. In contrast, many past studies focus on classifying a specific motion, which requires modifying the architecture and re-training the models to account for additional gestures.
Illustration of the (A) two-step (2S) and (B) one-step (1S) strategy for deploying deep learning models.
In the second step, the trajectory of each DOF is regressed by a deep learning model. Ten separate models regress the trajectory of 10 DOF (two per finger). The models associated with inactive fingers are disabled, and the prediction outputs are set to zero. As a result, the dataset used to train each model is only a subset of the full dataset where the corresponding DOF is active. While all models use the same architecture, they are independently optimized using different sets of training parameters such as learning rate, minibatch size, number of epochs, etc., to achieve the best performance. An advantage of this approach is that if one DOF fails or has poor performance, it would not affect the performance of others.
### 4.2. One-Step (1S) Strategy
shows an illustration of the 1S strategy. It is the most straightforward approach where the trajectories of each DOF are directly regressed regardless of the fingers' state. As a result, the full dataset, which includes data when the DOF is active (positive samples) and idle (negative samples), must be used to train each DOF. Because the number of negative samples often exceeds the number of positive samples from 5:1 to 10:1, additional steps such as data augmentation and/or weight balancing need to be done during training. This also leads to more false-positives where an idle DOF still has small movements that could affect the overall accuracy.
Although the 1S procedure is more straightforward than the 2S's, its time-latency and efficiency are not necessarily better than the 2S. While our hands are at rest most of the time, the 1S approach has to continuously predict all fingers' trajectories regardless of their activeness status. The 2S approach goes through a classification step to identify the active DOF before predicting the trajectories of those DOF, which helps disable several deep learning models depending on the hand gestures, direct resources to the active DOF, and results in lower overall latency and computation in most implementations where there is only one processing unit (GPU or CPU) in the second step. It is shown in Table 3 that a simple RF can help achieve comparable classification outcomes of accuracy above 0.99 to those from the two deep learning techniques. The regression steps of both approaches utilize the same deep learning model.
### 4.3. Deep Learning Models
shows the architecture of the deep learning classification and regression models. They include standard building blocks such as convolutional, long-short term memory (LSTM), fully-connected, and dropout layers of different combinations, order, and set of parameter values. The architecture is optimized by gradually adding layers and tuning their parameters while tracking the decoder's efficacy using 5-fold cross-validation. As the performance converges, additional layers would tend to result in over-fitting.
Architecture of the deep learning models: (A) CNN for classification, (B) RNN for classification, (C) CNN for regression, (D) RNN for regression.
There are 10 copies of the regression model for 10 DOF, each of which is trained separately. We use Adam optimizer with the default parameters β = 0.99, β = 0.999, and a weight decay regularization . The mini-batch size is set to 38, with each training epoch consists of 10 mini-batches. The learning rate is initialized to 0.005 and reduced by a factor of 10 when the training loss stopped improving for two consecutive epochs.
As shown in , the input of both 1S and 2S networks is a matrix [224 x 200] where 224 is the total number of features, and 200 is the time vector. During the data preprocessing step, raw neural data are cut into several 4 s segments. Each of these 4 s segments goes through the feature extraction step, during which it is further cut into 100 ms segments with an incremental step of 20 ms as illustrated in . There are 14 features to be extracted from each channel as mentioned in . Hence, a 4 s segment of raw neural data from 16 channels results in a matrix [224 × 200] where 224 is the total number of extracted features from 16 channels [16 × 14 = 224] and 200 is the total number of time steps cut from 4 s segments [4 s/20 ms = 200].
shows a rough comparison between the deep learning models used in this and our previous work. Note that for regression, the number of learnable parameters is a total of 10 models for 10 different DOF. The addition of feature extraction, thus dimensional reduction, allows significantly lowing the deep learning models' size and complexity. This is essential for translating the proposed decoding paradigm into a real-time implementation for portable systems.
Comparison between this work and Nguyen and Xu ( ).
## 5. Experimental Setup
In this research, we investigate the performance of two main deep learning architectures: the CNN and RNN for both classification and regression tasks. Besides, the deep learning models are benchmarked against “classic” supervised machine learning techniques as the baseline. They include support vector machine (SVM), random forest (RF), and multi-layer perceptron (MLP).
For baseline techniques, the input of the classification task is the average of 224 features across 200 time-steps, while the input of the regression task is the 30 most important PCA components. The SVM models use the radial basis function (RBF) for classification and polynomial kernel of degree three for regression, with parameter C = 1. The RF models use 5 and 10 trees for classification and regression, respectively, with a max depth of three. The MLP model for classification is created by replacing the convolutional layer of the CNN model with a fully-connected layer of 200 units. The MLP model for regression has four layers with 300, 300, 300, and 50 units, respectively.
The 5-fold cross-validation is used to compare the performance of the classification task. For the regression task, the dataset is randomly split with 80% for training and 20% for validation. The split is done such that no data windows from the training set overlap with any data windows from the validation set.
The data processing is done in MATLAB (MathWorks, MA, USA). The deep learning networks are implemented in Python using the PyTorch library. The deep learning models are trained and evaluated on a desktop computer with an Intel Core i7-8086K CPU and an NVIDIA TITAN Xp GPU.
## 6. Metrics and Results
### 6.1. Metrics
This subsection introduces the metrics to measure the performance of five models, including the two discussed deep learning models and three other supervised machine learning techniques as benchmarks in both classification and regression tasks.
The performance of the classification task is evaluated using standard metrics including accuracy and F1 score derived from true-positive (TP), true-negative (TN), false-positive (FP), and false-negative (FN) as follows:
We use the definition of accuracy with an equal weight of sensitivity and specificity because the occurrence of class-1 (active finger) is largely outnumbered by the occurrence of class-0 (inactive finger).
The performance of the regression task is quantified by two metrics: mean squared error (MSE) and variance accounted for (VAF). MSE measures the absolute deviation of an estimated from the actual value of a DOF, while VAF reflects the relative deviation from the actual values of several DOF. They are defined as follows:
where N is the number of samples, y is the ground-truth trajectory, ȳ is the average of y , and ŷ is the estimated trajectory. The value of y and ŷ are normalized in a range [0, 1] in which 0 represents the resting position.
Although the MSE is the most common metric and effectively measures absolute prediction errors, it cannot reflect the relative importance of each DOF to the general movements. For example, if the average magnitude of DOF A is hundreds of times smaller than that of DOF B, a bad estimation of A still can yield lower MSE than a reasonable estimation of B. The VAF score is more robust in such scenarios; thus, it could be used to compare the performance between DOF of different magnitude. The value of the VAF score ranges from (-∞, 1], the higher, the better. This research presents the MSE and VAF scores of different neural decoding models from different approaches to choose the best one for future clinical applications in real-time. A neural decoder that results in negative VAF scores is definitely not the best one. In fact, SVM is the only decoding architecture that occasionally shows negative VAF scores. Therefore, ignoring its negative VAF scores would neither affect the comparison nor change the final decision of the best model. For practicality, negative VAF values are ignored when presenting the data.
### 6.2. Classification Results
shows the average 5-fold cross-validation classification results of all the techniques. The predictability of each finger is largely different from one another. The thumb, which produces strong signals only on the median nerve (first eight channels), is easily recognized by all techniques. Overall, CNN offers the best performance with accuracy and an F1 score for all fingers exceeding 99%. RF closely follows with performance ranging from 98 to 99%. While deep learning still outperforms classic techniques, it is worth noting that RF could also be a prominent candidate for real-world implementation because RF can be more efficiently deployed in low-power, portable systems.
Classification performance.
### 6.3. Regression Results
presents the MSE and VAF scores for both strategies. In the 1S approach, the deep learning models, especially RNN, significantly outperform the other methods in both MSE and VAF, as shown in . To verify the significance of differences between RNN and the other decoders, we conduct paired t-tests with a Bonferroni correction. The results indicate that the performance differences in MSE and VAF are all statistically significant with p < 0.001. In the 2S approach, the performance is more consistent across all methods, where classic methods even outperform deep learning counterparts in certain DOF as shown in . Between the two strategies, the 1S approach generally gives better results; however, the high performance can only be achieved with RNN.
Regression performance in term of MSE (A,B) and VAF (C,D) .
## 7. Discussion
### 7.1. Feature Extraction Reduces Decoders' Complexity
Both deep learning architectures investigated in this study, namely CNN and RNN, deliver comparable motor decoding performance to our previous work (Nguyen and Xu, ) while require much lower computational resources to implement. The average VAF score for most DOF is 0.7, with some exceeding 0.9. Such results are achieved with relatively shallow deep learning models with 4–5 layers, a significant reduction from the previous implementation with 26 layers. While an extra step of feature extraction is required, all feature extraction techniques are specifically designed to be efficiently computed with conventional arithmetic processors (e.g., CPU) and/or hardware accelerators (e.g., FPGA, ASIC).
Another direction for future works would be including additional features. Here we apply the most common features in the temporal domain for their simplicity and well-established standing for neuroprosthesis applications. There are other features, such as mean power, median frequency, peak frequency, etc., in the frequency domain that have been explored in previous literature. The purpose of this study is not to exhaustively investigate the effect of all existing features in motor decoding but to prove the effectiveness of the feature extraction method in achieving decent decoding outcomes. The success of this method will open up to future research on simultaneously applying more features in multiple domains for better outcomes.
Furthermore, it is worth noting that the use of feature extraction excludes most of the high-frequency band 600–3,000 Hz, which is shown in our previous work that could contain additional nerve information associated with neural spikes. A future direction would be extracting that information using spike detection and sorting techniques and combine them with the information of the low-frequency band to boost the prediction accuracy. However, the computational complexity must be carefully catered to not hinder the real-time aspect of the overall system.
### 7.2. Classic Machine Learning v.s. Deep Learning
The classification task can be accomplished with high accuracy using most classic machine learning techniques (e.g., RF) and near-perfect with deep learning approaches (e.g., CNN). Along with other evidence in Nguyen and Xu ( ), this suggests that nerve data captured by our neural interface contain apparent neural patterns that can be clearly recognized to control neuroprostheses. While the current dataset only covers 9/32 different hand gestures, these are still promising results that would support future developments, including expanding the dataset to cover additional gestures.
The regression results are consistent with the conclusion of many past studies that deep learning techniques only show clear advantages over classic machine learning methods when handling a large dataset. This is evident in the 1S strategy, where each DOF is trained with the full dataset consisting of all possible hand gestures. In contrast, in the 2S strategy, where the dataset is divided into smaller subsets, deep learning techniques lose their leverage. However, as the dataset is expanded in the future, we generally believe that deep learning techniques should emerge as the dominant approach.
### 7.3. Improving Motor Decoder Effectiveness
This paper focuses on exploring different approaches to build a new decoding paradigm that is simpler and requires less computational power than that of the previous research for applying in real-time applications. However, for such real-time applications, we also need to test the deep learning decoders' stability in the long run. Future research would apply the same deep learning architectures but train and validate on data recorded on different days. The time gap between the training and validation data may also be varied to investigate the trade-off between the re-training frequency and the decoding outcomes' accuracy.
## 8. Conclusion
This work presents several approaches to optimize the motor decoding paradigm that interprets the motor intent embedded in the peripheral nerve signals for controlling the prosthetic hand. The use of feature extraction largely reduces the data dimensionality while retaining essential neural information in the low-frequency band. This allows achieving similar decoding performance with deep learning architectures of much lower computational complexity. Two different strategies for deploying deep learning models, namely 2S and 1S, with a classification and a regression stage, are also investigated. The results indicate that CNN and RF can deliver high accuracy classification performance, while RNN gives better regression performance when trained on the full dataset with the 1S approach. The findings layout an important foundation for the next development, which is translating the proposed motor decoding paradigm to real-time applications, which requires not only accuracy but also efficiency.
## Data Availability Statement
Further information and requests for resources and reagents should be directed to and will be fulfilled by Zhi Yang ( ) and Qi Zhao ( ) in accordance with the IRB protocol.
## Ethics Statement
Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.
## Author Contributions
ZY and EK conceptualized the system and experiments. DL, MJ, and QZ designed, trained and benchmarked the deep learning models. AN and JX designed the integrated circuits and carried out hardware integration. EK designed the microelectrode array. JC and EK performed the implantation surgery and post-operative care. DL, AN, JX, and MD conducted experiments, collected nerve data, and performed data analysis. DL and AN prepared the manuscript. All authors contributed to the article and approved the submitted version.
## Conflict of Interest
ZY is co-founder of, and holds equity in, Fasikl Inc., a sponsor of this project. This interest has been reviewed and managed by the University of Minnesota in accordance with its Conflict of Interest policy. JC and EK have ownership in Nerves Incorporated, a sponsor of this project. The remaining authors declare that this study received funding from Fasikl Inc. The funder had the following involvement with the study, including study design, data collection and analysis, and preparation of the manuscript.
|
Aggregation of the microtubule-associated protein tau into paired helical filaments (PHFs) and neurofibrillary tangles is a defining characteristic of Alzheimer’s Disease. Various plant polyphenols disrupt tau aggregation in vitro but display poor bioavailability and low potency, challenging their therapeutic translation. We previously reported that oral administration of the flavonoid (−)-epicatechin (EC) reduced Amyloid-β (Aβ) plaque pathology in APP/PS1 transgenic mice. Here, we investigated whether EC impacts on tau pathology, independent of actions on Aβ, using rTg4510 mice expressing P301L mutant tau. 4 and 6.5 months old rTg4510 mice received EC (∼18 mg/day) or vehicle (ethanol) via drinking water for 21 days and the levels of total and phosphorylated tau were assessed. At 4 months, tau appeared as two bands of ∼55 kDa, phosphorylated at Ser262 and Ser396 and was unaffected by exposure to EC. At 6.5 months an additional higher molecular weight form of tau was detected at ∼64 kDa which was phosphorylated at Ser262, Ser396 and additionally at the AT8 sites, indicative of the presence of PHFs. EC consumption reduced the levels of the ∼64 kDa tau species and inhibited phosphorylation at Ser262 and AT8 phosphoepitopes. Regulation of the key tau kinase glycogen synthase kinase 3β (GSK3β) by phosphorylation at Ser9 was not altered by exposure to EC in mice or primary neurons. Furthermore, EC did not significantly inhibit GSK3β activity at physiologically-relevant concentrations in a cell free assay. Therefore, a 21-day intervention with EC inhibits or reverses the development of tau pathology in rTg4510 mice independently of direct inhibition of GSK3β.
## Introduction
Currently, there is no preventative treatment strategy for Alzheimer’s disease (AD), the most common cause of dementia. A well-tolerated intervention is urgently needed: one that can target multiple aspects of the heterogenous disease pathology and can be delivered at a population level, throughout adult life. Given this, there is considerable interest in lifestyle interventions based around polyphenol-rich diets, which have been shown to improve cognition and reduce AD biomarker burden ( ; ).
Alzheimer’s disease is a neurodegenerative disease characterized by the deposition of amyloid plaques and neurofibrillary tangles (NFTs) in the brain, formed by aggregation of Amyloid-β (Aβ) peptides and tau, respectively. Around 15–20 years prior to symptom onset Aβ begins to accumulate, activating downstream pathological cascades and triggering the development of tau pathology. Aβ has been at the forefront of drug development since the inception of this amyloid cascade hypothesis. However, Aβ-targeting drugs continue to disappoint at clinical trials probably because they are being delivered too late in the disease process, when Aβ-initiated pathways are well underway and significant synaptic and neuronal damage has already occurred ( ; ).
The microtubule associated protein tau may be a more tractable target, as tau pathology typically appears at a later stage and correlates much better with neuronal loss and cognitive deterioration than Aβ ( ; ). Physiologically, tau is a natively unfolded, soluble protein showing little tendency for aggregation ( ). However, in AD, tau is hyperphosphorylated by several kinases including glycogen synthase kinase-3β (GSK3β), MAP kinases and CDK5 ( ). Hyperphosphorylated tau misfolds, leading to the formation of toxic oligomeric tau species which trigger neuronal dysfunction and seed self-propagation ( ; ; ). Blocking tau phosphorylation, aggregation and propagation thus has the potential to provide additional disease-modifying benefit beyond direct targeting of Aβ pathology.
The polyphenol (−)-Epicatechin (EC) is a dietary flavonoid of the flavan-3-ol subgroup, found in relatively high concentrations as a monomer in cocoa beans and more widely distributed in oligomeric form as a proanthocyanidin. EC has well characterized signaling actions in neural cells ( ; ) and is a promising intervention for AD due to its low toxicity profile ( ) and potential for multi-modal targeting ( ). EC has been shown to improve vascular function and cognition in humans ( ; ), reduce oxidative stress and up-regulate neuroprotective pathways ( ; ; ; ; ; ). With respect to AD pathology, intervention with EC has been shown to inhibit APP processing in primary cortical neurons ( ) and reduce Aβ burden in APP /PS1 mouse models ( ; ).
There is some evidence to suggest that flavan-3-ol administration could influence tau pathology, as grape seed and lychee extracts reduce tau phosphorylation in vivo ( ; ; ), and EC reduced basal tau phosphorylation in aged mice ( ). The key tau kinase GSK3β is a promising target for EC as it can increase Akt phosphorylation ( ), which regulates GSK3β activity by inhibitory phosphorylation at Ser9. EC intervention in aged ( ) and APP/PS1 ( ) mice has been shown to increase phosphorylation of GSK3β via Akt, but this has not been explored in a mouse model of tauopathy.
It is not clear if there is a direct causal relationship between EC administration and the emergence of pathological tau or the extent to which this might be driven by actions at GSK3β. Tau pathology can be modeled in mice using a single point mutant that is sufficient to cause frontotemporal dementia with parkinsonism-17, such as P301L tau ( ). rTg4510 mice overexpress htau with the P301L missense mutation and exhibit an age-dependent increase in tau phosphorylation, aggregation, and associated pathology ( ; ).
Using rTg4510 mice as a model of tauopathy, we sought to determine whether addition of EC to drinking water for 21 days could inhibit tau pathology. Our results suggest that oral intervention with EC reduced the appearance of phosphorylated, high molecular weight forms of tau, but this could not be correlated with any inhibitory actions at GSK3β.
## Methods
### Materials
(−)-Epicatechin (≥90%; E1753) was purchased from Sigma Aldrich for in vivo studies. Quercetin dihydrate (≥99%; 1135), (−)-Epicatechin (≥99%; 0977), and Epigallocatechin gallate (EGCG; ≥98%; 0981) were purchased from Extrasynthese for in vitro studies. All culture reagents and media were purchased from Gibco. Primary and secondary antibodies as stated in .
Antibodies used for immunoblotting.
### Animals
All procedures were carried out in accordance with the United Kingdom Animal (Scientific Procedures) Act 1986 and were approved by the Universities of Exeter (PPL P29FAC36A) and Bath Animal Welfare and Ethical Review Body.
rTg4510 mice were gifted from Eli Lilly. The rTg(tet-o-TauP301L)4510 mouse model ( ; ) was bred on a mixed FVB/NCrl + 129S6/SvEvTa background and delivered to the University of Exeter via Envigo (Loughborough, United Kingdom). Male rTg4510 mice were housed on a 12-h light/dark cycle with ad libitum access to food and water. rTg4510 mice overexpress the human four-repeat tau gene containing the P301L mutation that has been linked with familial frontotemporal dementia. Transgenic gene expression is under the control of the Ca -calmodulin kinase II promoter and can be repressed with doxycycline.
### EC Dosing Regime
Four month ( n = 15 for each group) and 6.5 month ( n = 10 for each group) old, male mice were administered EC (3 mg/ml) or vehicle (0.1% ethanol v/v) in their water supply for 21 days prior to sacrifice. Average intake of EC per day was 17.27 ± 1.20 mg/mouse/day (SEM) for the 4-month group and 19.03 ± 2.54 mg/mouse/day (SEM) for the 6.5-month group. There were no differences in drinking volumes between the EC and vehicle only groups. At the end of the treatment phase, animals were sacrificed and the brains rapidly removed and snap frozen on dry ice before processing for biochemistry.
### Brain Processing
Mouse brain was homogenized in 50 mM Tris, pH 8.0, 274 mM NaCl, 5 mM KCl, 2 mM EGTA, 2 mM EDTA, and cOmplete , EDTA-free protease inhibitor cocktail. Homogenates were centrifuged at 15,000 × g for 15 min, and the supernatant was collected as a total soluble fraction for immunoblotting. Protein concentrations were determined using the Bradford assay (BioRad).
### Primary Neuron Culture
Primary cortical neuronal cultures were prepared as described previously ( ). Cortices were dissected from embryonic day 15 CD1 mouse embryos and mechanically dissociated using a fire-polished Pasteur pipette, coated in heat inactivated Fetal Bovine Serum, in PBS-Glucose [6 mM -glucose (Sigma), Ca and Mg free]. Neurons were plated into 6 well tissue culture plates (Nunc) that were precoated with 20 mg/mL poly- -lysine (Sigma). Neurons were maintained in Neurobasal medium minus phenol red, supplemented with B-27, 2 mM glutamine, 100 mg/mL streptomycin, and 50 mg/mL penicillin (Invitrogen), at 37°C in a humidified atmosphere of 95% air and 5% CO2.
### EC Treatment of Primary Cortical Neurons
Stock EC was prepared in 0.5% acetic acid (v/v) in ethanol:H O (8:2; v/v). The media from DIV14 primary cortical neurons was replaced with conditioned media prior to treatment with EC or vehicle for 15 min. Neurons were lysed in radioimmunoprecipitation buffer [150 Mm NaCl, 25 mM Tris, 0.5% Sodium deoxycholate, 0.1% SDS, 1% Non-idet P-40, pH 7.4 made complete with 2 mM EDTA (pH 8), cOmplete , EDTA-free protease inhibitor cocktail, PhosSTOP phosphatase inhibitor] and detached by cell scraping. Lysates were centrifuged at 20,000 × g for 20 min at 4°C and the supernatant retained. Samples were diluted 1:4 in Laemmli sample buffer (10% 2-mercaptoethanol) and boiled for 2 min.
### Immunoblotting
Samples were resolved by 10% (GSK3β detection) or 12% (Tau detection) Tris–glycine SDS-PAGE before transfer to 0.45 μm nitrocellulose membrane (GE Healthcare). Following transfer, the membranes were blocked in TBS + 5% milk for 30 min at rtp. Membranes were washed briefly with TBS-T before incubation with the primary antibodies (1:1000) in TBS-T + 1% milk overnight at 4°C. Membranes were then washed with TBS-T and incubated with secondary antibodies (1:2500) in TBS-T + 1% milk for 1 h at rtp. Following washing with TBS-T and then TBS, bound antibodies were detected using Amersham ECL Western Blotting Detection Reagent (GE Healthcare). Western blots were imaged and quantified using the Fusion-SL Chemiluminescence System (Vilber Lourmat).
Primary antibodies used for immunoblotting ( ). Anti-Mouse IgG Antibody, (H + L) HRP conjugate and Anti-Rabbit IgG Antibody, HRP conjugate secondary antibodies (1:2500; Millipore), were used as in accordance with the source species.
### Luciferase Assay
Glycogen synthase kinase 3β Kinase Enzyme System was used with the ADP-Glo assay (Promega) in accordance with manufacturers guidelines. This assay utilizes recombinant full-length human GSK3β, a GSK3 substrate [YRRAAVPPSPSLSRHSSPHQ(pS)EDEEE], derived from human muscle glycogen synthase and a luminescence detection system. Final assay reagent concentrations: 4 μM ATP, 0.04 ng/μL GSK-3β, 20 μg/mL glycogen synthase (substrate). For concentration-response analysis, flavonoids were initially dissolved in 100% dimethyl sulfoxide (DMSO) at a 1 mM concentration before dilution to working concentrations (5% DMSO). The GloMax -Multi + Detection System (Promega) was used to measure luminescence. 4 independent assays were undertaken for each flavonoid.
### Statistical Analysis
Data were analyzed using GraphPad Prism software (Version 8). Immunoblotting data were analyzed by two-way ANOVA with Bonferroni’s Multiple Comparison Test. GSK3β luciferase assay data were analyzed by one-way ANOVA with Bonferroni’s Multiple Comparison Test.
## Results
### Short Term Intervention With (−)-Epicatechin to rTg4510 Mice Inhibits Tau Phosphorylation
Previous studies using the rTg4510 mouse model have shown that between 4 and 6 months there is an increase in hyperphosphorylated, oligomeric tau ( ; ; ). Additionally, at 5.5 months significant neuronal loss can be observed in both the hippocampus and the cortex ( ; ). With the aim of considering early disease intervention around these critical age-points, 4 and 6.5-months were chosen to assess whether EC treatment could impact on the development and progression of tau pathology. A schematic of the experimental protocol is shown in .
(−)-Epicatechin inhibits age-dependent tau phosphorylation in rTg4510 mice. (A) Schematic summarizing the (−)-epicatechin (EC) dosing regime. rTg4510 male mice at 4 and 6.5 months of age were given free access to drinking water supplemented with either EC (3 mg/ml) or vehicle (0.1% ethanol) for 21 days. (B) Immunoblots of whole brain homogenates (20 μg) from 4 and 6.5 month old mice exposed to either vehicle (V) or EC (E) probed with antibodies against full length Tau (Tau 5 and Tau 46), the dual phosphorylation sites Ser202/Thr205 (AT8), phosphorylation at Ser396 (p396), and phosphorylation at Ser262 (p262). The arrow indicates the higher molecular weight band at ∼64 kDa. (C–E) Quantification of changes in levels of phosphorylation status as a ratio of total Tau (Tau 5). Vehicle (Clear bars), EC (Black bars). Data presented is mean of relative intensity ± SEM ( n = 5). *** p < 0.001; **** p < 0.0001. Schematic created with .
Western blots probed with Tau5 and Tau46 antibodies to detect total tau showed an age-dependent increase in the appearance of a higher molecular weight (HMW) band of tau at 64 kDa, which was detectable at 6.5 months but not at 4 months ( ). This HMW band, which was most notable when probed with the Tau46 antibody, has been characterized previously as representing hyperphosphorylated, oligomeric tau ( ). EC treatment in 6.5-month old mice decreased the levels of 64 kDa tau ( p < 0.001), as emphasized by a clear redistribution to the lower molecular weight forms of tau ( ). EC did not, however, affect overall tau levels at either 4 months or 6.5 months giving us confidence that this change did not result from off-target effects at the doxycycline sensitive tetracycline transactivator. Overall, this initial series of observations strongly suggest that short term oral intervention with EC reduces the levels of oligomeric tau.
Blotting with phosphorylation-state specific antibodies against tau demonstrated high basal levels of p396 and p262 at 4 months, but much lower levels of AT8 (pS202/pT205). EC treatment did not affect phosphorylation of tau in 4-month old mice at any of these sites ( ). Only tau phosphorylation at the AT8 phosphoepitope was significantly increased between 4 and 6.5 months ( p < 0.0001; ), although tau phosphorylated at both AT8 and p262 was redistributed to the HMW band in 6.5-month old mice ( ). At 6.5 months, EC intervention significantly inhibited tau phosphorylation at AT8 (Ser202/Thr205; p < 0.0001) and at p262 ( p < 0.001) compared with vehicle controls ( ). The overall levels of phosphorylation at Ser396 were not affected by EC, although there was a redistribution to the 55 kDa band from the 64 kDa band in line with total tau changes ( ). This suggests that Ser396 phosphorylation is not essential for formation of the 64 kDa species.
### (−)-Epicatechin Does Not Inhibit GSK3β
To investigate whether EC targeted the key tau kinase GSK3β in this model, the levels of pGSK3β were measured at 6.5 months to determine if changes in tau phosphorylation could be correlated with alterations in activity of GSK3β. The levels of pGSK3β were quite variable between the samples but oral intervention with EC did not increase inhibitory GSK3β phosphorylation in rTg4510 mice compared to vehicle controls ( ). To further explore whether acute treatment with EC could increase Ser9 phosphorylation of GSK3β in a system with a less variable background, primary cortical neurons were treated with a range of concentrations of EC known to increase pAkt. No significant increase in GSK3β phosphorylation was measured at any of the concentrations of EC tested ( ).
(−)-Epicatechin does not inhibit GSK3β activity either in vivo or in vitro . (A) Immunoblots of whole brain homogenates (20 μg) from 6.5 month-old rTg4510 male mice exposed to either vehicle (V) or EC (E) probed with antibodies against GSK3β and GSK3β phosphorylated at Ser9 (GSK3βp ). (B) Scatter plot showing quantification of changes in phosphorylation status of GSK3β as a ratio of total GSK3β for vehicle (open squares) and EC (black squares) treatments, no significant differences ( n = 4). (C) Immunoblots of primary cortical neurons treated for 15 min with vehicle (V) or increasing concentrations of EC (0.03–3 μM) and probed with antibodies against GSK3β and GSK3β phosphorylated at Ser9 (GSK3βp ). (D) Quantification of changes in the phosphorylation status of GSK3β as a ratio of total GSK3β. Data is mean ± SEM ( n = 3). (E) Sensitivity of GSK3β catalytic activity to increasing concentrations of flavonoids (1 nM–10 μM). Data presented as% activity related to vehicle control (100%). Quercetin (black circles), EC (black squares), EGCG (open triangles), mean ± SEM ( n = 4). (F) Structures of Quercetin (1) and EC (2).
The dietary flavonoid quercetin, of the flavonol subgroup, ( ) binds to and inhibits GSK3β directly ( ; ), however, this has not been studied with respect to EC and other flavan-3-ols. To investigate this, we used an in vitro luciferase assay to test whether EC and EGCG, which disaggregates tau, inhibit the catalytic activity of human full length GSK3β. Quercetin inhibited GSK3β in a concentration-dependent manner ( p < 0.0001, IC = 0.236 μM) with an almost complete loss of activity at the highest concentrations tested ( ). Both EC (IC = 54.4 μM) and EGCG (IC = 51.4 μM) caused a much more modest (∼25%) concentration-dependent inhibition of GSK3β activity ( p < 0.01), with significant inhibition compared to controls only observed at 10 μM ( p < 0.05). Neither EC nor EGCG inhibited GSK3β significantly at concentrations that could be considered physiologically relevant (<1 μM).
Therefore, oral administration of EC inhibited tau phosphorylation in the rTg4510 mice and reduced the levels of oligomeric tau independently of either direct or indirect inhibition of GSK3β.
## Discussion
Hyperphosphorylated, oligomeric, soluble tau species are thought to be responsible for tau-driven neuronal toxicity ( ) and there is interest in identifying therapeutics that could target these processes. The rTg4510 mouse is a useful model for phenocopying the hyperphosphorylated tau aggregates that are characteristic of human tauopathies, although the accompanying neurodegeneration may be due in part to genomic disruption rather than tau overexpression per se so caution is needed when concluding on mechanisms ( ). The 64 kDa band of tau found in soluble lysates from rTg4510 mouse brain has been previously well characterized – a result of hyperphosphorylated, oligomeric tau species which correlate better with neuronal loss than Sarkosyl-insoluble tau in this model ( ). Our results from 6.5 month old mice showed a reduction of tau at the 64 kDa band, as well as reduced tau phosphorylated at AT8 and Ser262 phosphoepitopes ( ). This suggests that short term oral intervention with EC can reduce the levels of these potentially toxic tau species in the rTg4510 mouse.
AT8 and Ser262 are two key phosphorylation sites associated with AD. Phosphorylation at Ser262 inhibits tau binding to microtubules ( ) and in a recent proteomics analysis of AD brains, Ser262 phosphorylation was identified as one of the earliest and most distinct tau modifications compared to controls ( ). AT8-targeting antibodies are commonly used to label paired helical filaments (PHFs), and it has been suggested that S202/T205 phosphorylation increases the propensity of tau for aggregation ( ). The ability of EC to inhibit phosphorylation at both Ser262 and AT8 is therefore compelling, as it suggests the potential to target early tau pathology and decrease the propensity for tau aggregation. However, further studies in additional tau models will be necessary to confirm this. Interestingly, only phosphorylation at the AT8 phosphoepitope increased with age in these mice ( ). Other studies have shown that phosphorylation at Ser396 and Ser262 residues increase with age in the rTg4510 mouse model ( ; ; ) possibly reflecting changes in the insoluble fraction which were not studied here.
As GSK3β is a key tau kinase and EC is known to promote activity of its regulator, Akt ( ), we hypothesized that reduced tau phosphorylation could have resulted from EC-induced inhibition of GSK3β. However, EC did not increase inhibitory phosphorylation of GSK3β at Ser9 either in vivo ( ) or in vitro ( ). This contrasts with reported EC upregulation of the Akt/GSK3β pathway in aged ( ) and in APP/PS1 mice ( ). The lack of effect in rTg4510 mice could be related in some-way to the use of tauopathy models, as Grape seed polyphenolic extract (GSPE) also failed to increase activity of the Akt/GSK3beta pathway in TMHT tau mice ( ). However, the tau background would not explain the apparent lack of effect in primary neurons. Although EC induced a predicted bell-shaped concentration response in neurons the signal to noise was insufficient to detect a robust and significant increase in GSKβ S9 phosphorylation.
Reverse screening in silico has confirmed GSK3β to be a direct target of quercetin ( ), which, alongside a range of other citrus flavonoids, has been shown to inhibit GSK3β directly ( ). This could, therefore, be a possible mechanism for EC inhibition of tau phosphorylation. Indeed, we showed that EC and EGCG dose dependently inhibited GSK3β activity directly ( ). However, both compounds only inhibited GSK3β activity significantly at micromolar levels which are highly unlikely to be achievable in vivo particularly in brain ( ). Therefore, it can be concluded that direct inhibition of GSK3β by EC is unlikely to be the mechanism by which EC inhibited tau phosphorylation in the rTg4510 mouse model. While GSK3β is a prominent tau kinase, many other kinases contribute to tau phosphorylation ( ) such as CDK5 ( ), microtubule associated protein kinases ( ; ) and MAPKs ( ). Likewise, GSK3β is not the only tau-associated kinase that can be modulated by flavonoids ( ). Further investigation is needed to establish which other kinase pathways may have been affected by EC intervention in this study.
Alternatively, EC could have inhibited tau phosphorylation and oligomerization by binding to tau directly. GSPE which contains EC and other flavan-3-ols has been shown expand the width of PHFs from AD patients and reduce antibody labeling at several phosphoepitopes including AT8 and Ser262 (12E8; ). However, EC itself was not able to inhibit heparin-induced tau aggregation in vitro ( ) and it is more likely that the observed effects with GSPE were due to the known anti-amyloidogenic actions of EGCG ( ). While there are several intraneuronal mechanisms through which EC could be affecting tau phosphorylation, the possibility that the effects seen were a result of modulation of the extra-neuronal environment cannot be ignored. EC is known to improve vascular function, and this has been shown to improve cognition in both mice and humans ( ; ; ). Improved vascular function could potentially promote clearance of pathological tau species as well as boosting neuronal function to ameliorate tau phosphorylation and aggregation.
A consideration that is important for future mechanistic studies is that EC itself may not be the bioactive constituent. The EC metabolome has been characterized in both humans and rats and it is now known that EC is metabolized to at least 20 metabolites including structurally-related metabolites (GI tract metabolism) and valerolactones (microbiota metabolism; , ; ). Chronic supplementation of EC to rats resulted in higher concentrations of structurally-related metabolites in the brain than EC (valerolactones were not analyzed; ). Therefore, it is possible that metabolites of EC may be responsible for the changes in tau phosphorylation observed in this study rather than the native aglycone which could, therefore, be considered as acting as a pro-drug supplement. Indeed, the levels of EC administered to mice in this study could not be achieved in humans through dietary consumption alone and should not therefore, be extrapolated to form any dietary dose recommendations for dementia. Dietary flavan-3-ols have received increasing interest recently, with clinical trials underway to determine the effects of EGCG on cognitive decline in carriers of the AD risk gene APOE4 (PENSA; NCT03978052) and cocoa flavonoid intervention on cognitive decline in the aging population (COSMOS-Mind study; NCT03035201).
This study showed that short term, oral intervention with a high dose of EC inhibited tau phosphorylation and appears to have reduced the levels of potentially toxic oligomeric tau species. While the mechanisms underpinning the observed affect remain unclear and validation in additional tau mouse models and ultimately humans is required, the findings presented here further add to the existing evidence that EC has promise as a multi-modal AD therapeutic potentially as a supplement to a flavanol-3-ol rich diet.
## Data Availability Statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics Statement
All procedures were carried out in accordance with the United Kingdom Animal (Scientific Procedures) Act 1986 and were approved by the Universities of Exeter (PPL P29FAC36A) and Bath Animal Welfare and Ethical Review Body.
## Author Contributions
LS, JB, and RW conceived and designed the study. KH and RW analyzed the data and wrote the manuscript. LS performed the in vivo intervention. GM and RW processed the tissue and performed biochemical analysis. KH performed the in vitro and GSK3β work. KH, JB, JM, and RW interpreted the findings and edited the manuscript. RW, JB, and JM supervised the project. All authors read and approved the final manuscript.
## Conflict of Interest
RW has received an unrestricted grant from Mars Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
|
Despite the wide range of proposed biomarkers for Parkinson’s disease (PD), there are no specific molecules or signals able to early and uniquely identify the pathology onset, progression and stratification. Saliva is a complex biofluid, containing a wide range of biological molecules shared with blood and cerebrospinal fluid. By means of an optimized Raman spectroscopy procedure, the salivary Raman signature of PD can be characterized and used to create a classification model. Raman analysis was applied to collect the global signal from the saliva of 23 PD patients and related pathological and healthy controls. The acquired spectra were computed using machine and deep learning approaches. The Raman database was used to create a classification model able to discriminate each spectrum to the correct belonging group, with accuracy, specificity, and sensitivity of more than 97% for the single spectra attribution. Similarly, each patient was correctly assigned with discriminatory power of more than 90%. Moreover, the extracted data were significantly correlated with clinical data used nowadays for the PD diagnosis and monitoring. The preliminary data reported highlight the potentialities of the proposed methodology that, once validated in larger cohorts and with multi-centered studies, could represent an innovative minimally invasive and accurate procedure to determine the PD onset, progression and to monitor therapies and rehabilitation efficacy.
Graphical Abstract
## Introduction
Parkinson’s disease (PD) is one of the most common neurodegenerative disorders occurring in the elderly, associated with the inactivation of dopaminergic neurons in the substantia nigra and with the appearance of Lewy bodies made of abnormal α-synuclein ( ; ). Epidemiologically, PD is the second most relevant neurodegenerative disorder after Alzheimer’s disease (AD), with an increasing burden in aging society ( ). The PD diagnosis mainly relies on clinical motor symptoms, which occur generally decades after the pathology onset ( ; ). This lag time hampers the detection of the earliest phases of the disease and the time at which the treatment with neuroprotective drugs could have the greatest effect ( ). The further onset of cognitive impairment of variable degrees, common in various neurodegenerative disorders, before and during PD progression, lead to the worsening of the clinical diagnosis and prolonging the diagnostic period for a clear PD identification ( ; ). In this frame, researchers are focused on the identification of a measurable and easily collectable PD biomarker able to identify the disease onset also in the preliminary pathological phases [levels 1 and 2 of Braak’s staging ( )], to monitor the therapeutic and motor-neuronal rehabilitation efficacies and also to clearly distinguish the different forms of parkinsonism ( ). Up to now, most of the potential biomarkers have been identified in the cerebrospinal fluid (CSF) and in peripheral blood (serum and plasma) ( ). α-Synuclein, and the autosomal enzymes involved in synuclein degradation, are actually the most promising biomarkers and have been widely studied in CSF and blood, playing a central role in PD and other synuclein aggregation disorders ( ; ; , ; ). Other molecules involved directly in PD onset or in the organism response to the disease have been characterized and proposed as potential biomarkers, including amyloid species, microRNA, specific cytokines expression patterns and molecules associated to the damages of radical oxygen species (ROS) ( ; ; ; ; ; ; ; ; ; ). The main limitations related to all the described molecules concern the invasiveness of the sample collection procedure (i.e., CSF), the sharing of several biomarkers with other similar pathologies (PD and AD) and to the techniques used for their characterization ( ). The application of methodologies such as ELISA, blotting assays or mass spectroscopy focalize the investigation on one, or few, targets for each analysis with long preprocessing steps and high-cost procedures and materials ( ; ; ). These limitations make difficult the characterization of the whole biomarkers presence inside the chosen biofluid, hiding the complex expression patterns that can precisely identify the onset of a neurological disorder, such as the entire proteome involved in the inflammatory response or the biological product of the ROS pathway ( ; ; ). Therefore, the identification of a fast methodology able to determine a global biomarker with a minimally invasive procedure is of crucial importance for the PD diagnosis and for the monitoring of therapeutic and rehabilitative efficacy. Saliva is a complex biofluid collectable with a minimally invasive procedure, containing several biological molecules (i.e., protein, enzymes, lipids, nucleic acids, carbohydrates, metabolites, and hormones) shared with blood or CSF due to the physiological transport processes ( ; ). Among these molecules, different studies reported the presence of potential biomarkers directly correlated with PD onset, including heme-oxygenase-1 and cysteine protease DJ-1 ( ; ; ; ). Therefore, the characterization of the entire pattern of biomolecules contained in saliva of PD patients could be of crucial importance in order to assess differences in composition and attributing at the same time the origins of these differences. To overcome the detection and velocity limits of the methodologies used nowadays, one of the most promising approaches regards Raman spectroscopy (RS) that is able to detect the concomitant presence, concentration, mutation, environment, and interactions of different biological species inside a target biofluid ( ). The output of the RS analysis consists in a complex spectrum containing the complete and sensitive (10 to 10 M) biochemical information in the so called “Raman fingerprint” ( ). RS has already been proposed for the diagnosis of neurodegenerative diseases ( ; ) and the RS analysis of saliva has been applied for forensic purposes, chemotherapy monitoring, drug abuse evaluation, and for the diagnosis of different pathologies including tumors, viral infections, amyotrophic lateral sclerosis, AD, and for the detection of salivary α-synuclein in PD patients ( ; ; ; ; ; ; ; ; ; ; ). The aim of the present work regards the application of the RS for the analysis of saliva collected from PD patients and compared with related healthy subjects and other pathological controls to verify statistical differences able to create an automatic classification model. The methodology involves the modification of a previous RS protocol for the analysis of saliva ( ), in which the time for sample preparation has been reduced, whereas the total amount of biological molecules that can be monitored has increased. The collected data were used for the characterization of the PD Raman fingerprint by means of the multivariate analysis (MVA), creating a classification model able to discriminate the membership of the single spectra. In order to enhance the discriminative power from the single-spectra level to a patient-level enabling the deployment of the predictive framework for diagnostic purposes, more complex non-linear interactions need to be represented. For this reason, machine learning (ML) and deep learning (DL) models have been investigated, further automating the data analysis for unveiling hidden patterns that correlate with the pathology. In particular, DL has emerged as one of the most focused research aimed at learning features and directly building predictive models from large-scale raw datasets ( ), with excellent performances in many biochemical fields including spectroscopy ( ), metabolomics ( ), and genomics ( ). In fact, DL based methods are well adapted to highlight the complex connections within high dimensional data provided by RS ( ; ). In this work, the Raman data collected from PD patients, AD and mild cognitive impairment (MCI) patients and healthy controls (CTRL) were processed creating a MVA-based classification model able to individuate the single Raman fingerprint of PD patients with sensitivity, specificity, and accuracy of respectively, 97, 98, and 98% for the single-spectrum classification model. The consecutive application of convolutional neural network (CNN), combined with a data augmentation strategy to enrich the training dataset and a sequential model-based optimization (SMBO) for the computation of the hyper-parameter configuration ( ; ), revealed sensitivity, specificity, and accuracy of 90, 94, and 89% in the identification of PD patients between the considered experimental groups. The extracted parameters from the Raman database correlated with the clinical scores used for the diagnosis and monitoring of PD progression.
## Materials and Methods
### Materials
All the materials were purchased from Sigma-Aldrich (United States) and used as received if not differently specified. Salivette swabs for the saliva collection were purchased from Sarstedt (Germany, catalog number S1 1534). Mini Bin aluminum foils (Sigma-Aldrich, United States, catalog number Z691569-1EA) were used as Raman substrate. All the materials were used following the manufacturer’s instructions without further purification steps. All the described procedures were performed in accordance with relevant guidelines, regulations, and ethical standards. The procedures were approved by the Ethical Committee of the institution in which the experiments were done or in accord with the Helsinki Declaration of 1975.
### Patients Selection
All the recruited participants for the exploratory study provided written informed consent and the study was approved by the institutional review board at IRCCS Fondazione Don Carlo Gnocchi ONLUS on March 12th, 2018. The study was not pre-registered and no randomization and no blind methods were applied. PD patients and CTRL recruitment took place at the Neurology Unit of IRCCS Fondazione Don Carlo Gnocchi ONLUS in Milan (Italy), between June 2019 and June 2020. AD participants were recruited in the Department of Neurology-Stroke Unit of IRCCS Istituto Auxologico Italiano in Milan (Italy). PD patients were diagnosed according to the Movement Disorder Society Clinical Diagnostic Criteria for PD ( ), excluding patients affected by vascular parkinsonism (with cerebrovascular disease, as indicated by brain imaging computed tomography or magnetic resonance imaging, or by the presence of symptoms that are consistent with stroke). Moreover, brain tumor, drug-induced parkinsonism, other known or suspected causes of parkinsonism (e.g., metabolic, etc.), or any suggestive features of a diagnosis of atypical parkinsonism, severe speech problems and poor general health, concomitant neurologic, and/or psychiatric diseases were also excluded. The Hoehn and Yahr (H&Y) and the Movement Disorder Society – Unified Parkinson’s Disease Rating Scale motor part III (UPDRS III) criteria were adopted for the evaluation of the disease stage and the symptoms severity.
Levodopa equivalent daily doses (LEDD) prescribed at the time of saliva collection were registered for each patient. Patients with AD patients and MCI due to AD were diagnosed according to the clinical criteria described by and , excluding individuals with neurological or major psychiatric comorbidities. CTRL were matched for age and gender to the AD and PD patients in order to limit sex hormone variability in saliva that can affect the Raman signature ( ). Exclusion criteria for CTRL, PD, and AD were a continuous drug administration (e.g., anti-hypertensive) and the presence of chronic or/and inflammatory oral or systemic diseases or infections. For this study, a total number of 23 PD ( n = 23), 10 AD ( n = 10), and 33 CTRL ( n = 33) were selected for the saliva collection. The number of PD patients was enrolled in the study without a preliminary sample size calculation. Statistical comparison between the groups was performed using the ANOVA two-tailed t -test and Chi-square test. For all the participants, demographic, personal, and clinical data were recorded. All the demographic and clinical information regarding the subjects involved in the study are reported in .
Number, age, male sex percentages, Hoehn and Yahr (H&Y), Unified Parkinson’s Disease Rating Scale motor part III (UPDRS III), levodopa equivalent daily doses (LEDD) of subjects affected by Parkinson’s disease (PD), by Alzheimer’s disease (AD), and healthy subjects (CTRL).
### Saliva Collection and Raman Analysis
The saliva collection procedure was performed following the instructions reported for Salivette . Briefly, the swab was placed in the mouth of the subject and chewed for 1 min in order to stimulate salivation. To limit the results variability, the salivary collection time was fixed at an appropriate period from the last meal (2 h) and from teeth brushing (2 h) in the morning, keeping the same collection time for all the participants. Storage time and temperature (4°C), time between the collection and Raman analysis, smoking and dietary habits, oral and respiratory infections, gingivitis or periodontitis, and recent dental surgeries were recorded. The swab was then centrifuged at 1,000 g for 2 min in order to recover saliva. The Raman acquisition procedure and part of the data processing were adapted from previously published works ( , ). Raman spectra were acquired using the Raman microscope Aramis (Horiba Jobin-Yvon, France), equipped with a laser source at 785 nm at 512 mW power emission. The analysis was performed after instrument calibration with the reference band of silicon at 520.7 cm , using 30 s acquisition time. A drop (3 μL) of saliva was casted on an aluminum foil and dried at room temperature in order to achieve the surface enhanced Raman spectroscopy (SERS) effect ( ; ). Raman analysis was performed using a square-map (80 μm × 60 μm) close to the center of the drop, with the acquisition of at least 30 points for each subject. The acquisition range was set between 400 and 1600 cm . All the analyses were performed using a 50× objective (Olympus, Japan) and with a spectral resolution of 0.8 cm /step. The laser grating was set at 600 while the hole was kept at 400.
### Data Processing, Statistical Analysis, and Single Spectrum Classification Model
For successfully applying both MVA and ML, the spectral preprocessing step is crucial as it can strongly affect the classification performances. The raw acquired spectra were fitted with a fifth-degree polynomial baseline and normalized (unit vector) using the incorporated acquisition software LabSpec 6 (Horiba, France). With the same software, all the data were despiked and resized, aligning the spectra to the peak at 1001 cm . The contribution of the aluminum substrate was subtracted from each spectrum. For spectral representation, the second-degree Savitzky–Golay smoothing method was applied. Artifact spectra produced due to high fluorescence (saturation) or no signal (laser Z -axis shift) were identified and removed using the incorporated software LabSpec 6 (Horiba, France). At least 20 spectra for each subject were maintained for further statistical analysis. The MVA analysis was performed using principal component analysis (PCA) and linear discriminant analysis (LDA) on the three experimental groups, reducing the dimension of the data and highlighting the most relevant trends. The first 15 Principal Components (PCs) were used to create the LDA-based classification model to discriminate the data maximizing the variance between the groups, and to avoid data overfitting analyzing the cumulative loading of PCs of 78.3%. The error rate, accuracy, sensitivity, and specificity in spectra attribution of the model were tested using leave-one-out cross-validation (LOOCV) and confusion matrix. The receiver operating characteristic (ROC) curve was calculated using the MVA results on the classification model ( ). The Matthews correlation coefficient (MCC) was calculated to assess the quality of the binary classification (PD versus no PD). ANOVA test and Chi-square test were applied to verify the statistical relevant differences between the experimental groups. The correlation between the MVA results and clinical and paraclinical tests or scores was calculated using Pearson’s correlation, while the partial correlation coefficient was used to assess the effect of the covariates (age and sex) on the final scores. Correlation results were considered statistically relevant for p -values < 0.05. This section of the statistical analysis was performed using OriginPro 2018 (OriginLab, United States).
### Machine Learning Data Preparation
The applied pipeline included four different phases: data preprocessing and augmentation, model selection, model tuning, and model evaluation. In the preprocessing steps, we performed the removal of the outlier not-informative spectra (artifacts) and the realignment of the Raman shift axis, by means of a linear interpolation ( ), resampling each spectrum on a given grid of 900 points between 400 and 1600 cm . The signal given by the intensity of the aluminum substrate was subtracted from the original signal and the background noise given by the fluorescence of the samples, was removed by baseline fitting with a sixth-degree polynomial.
Despiking was performed by means of Whitaker–Hayes algorithm ( ) allowing the removal of any spike given by cosmic rays (threshold: 3.5; size of the neighborhood: 11). Finally, we tested Standard Normal Variate, Max value, Min–Max, and L2 techniques for intensity normalization. The results obtained showed substantial robustness of the classification models with respect to these normalization methods. Data augmentation was performed generating new synthetic data samples by applying variations and distortions to the original data (injection of Gaussian noise, offset in Raman shift and slope) to maximally exploiting their intrinsic invariances and partially overcoming data scarcity ( ). Since in our data set the PD–AD classes appear to be unbalanced in favor of the CTRL class, new patients’ data have been generated according to a different multiplication factor based on the degree of imbalance.
### Application of Learning Models for the Patient Classification Model
Support vector machine (SVM) and random forest (RF) were implemented as baseline ML algorithms and compared with different DL models, namely fully connected neural networks (FCNN) and 1D CNNs. A grid search approach was applied to find suitable hyper-parameters configurations in ML models, while Tree Parzen Estimator (TPE) sampling and SMBO has been used to optimize our FCNN and CNN, respectively. The DL-related hyper-parameters optimization task was performed through the Optuna framework ( ) in combination with Scikit-Optimize Library ( ). This Bayesian optimization framework allows the integration, in addition to the native TPE sampler, an SMBO module provided by Scikit-Optimize. The hyper-parameters optimization involved two main phases: the overall DL model structure optimization (selecting the number of convolutional or dense layers) and the tuning of the fine hyper-parameters. SMBOs were applied with Gaussian processes and RF regressions as base estimators along the process, both in combination with specific acquisition function. All the optimizations were performed in 10-folds cross-validation and the objective function was minimized on the average classification error. The models performances were evaluated on their ability in discriminating between PD, AD, and CTRL subjects according to the majority label assigned to their spectra. To control overfitting, we regularized our DL model by the intensive use of dropout masks in the fully connected layers responsible for the classification. In addition, we applied early stopping to avoid overtraining that could potentially harm generalization in our settings. Furthermore, to overcome any classification bias given by an arbitrary test-set choice, we applied the leave-one-patient-out cross-validation (LOPOCV), a robust and stable procedure where each test-fold is composed by the entire set of spectra from a single patient that guarantees a most accurate estimation of the model performances. The entire pipeline was coded in Python, and while ML algorithms have been implemented through the Scikit-Learn Library ( ), for the DL ones we exploited Keras, the Tensorflow high-level API ( ).
## Results
### Raman Analysis of Saliva
The modification of a previous optimized protocol was adopted for the analysis of saliva collected from 23 PD patients, 10 AD patients, and 33 CTRL ( ). The principal modification regarded the removal of sample filtration step with 3-kDa filters, resulting in a wider range of molecules detected and in a faster analytical procedure. The average spectra obtained from all the collected CTRL samples ( n = 33) is presented in . The detailed signal provides an overview on the species that mostly contribute to the Raman spectrum, with attributed peaks at 517, 532, 578, 619, 715, 750, 870, 920, 978, 1001, 1047, 1077, 1102, 1125, 1203, 1244, 1268, 1346, 1415, and 1444 cm ( , black arrows). The highlighted peaks and bands regard the typical Raman signal provided by salivary samples analyzed using aluminum substrates ( ; ). The most important signal attribution regards the peak at 750 cm related to the O–O stretching vibration in oxygenated proteins. The peaks at 870 and 1125 cm can be attributed to the C–N stretching and to the CH rocking in protein backbone, respectively, with the peak at 1001 cm related to the ring breathing of aromatic amino acids and the signal at 1444 cm assigned to the C–H stretching of glycoproteins, mostly obtained from mucins ( ; ). The peaks at 1002 and 1346 cm regard, respectively, the secondary bands of Amide I and the principal band of the Amide III, which are directly correlated with the secondary structures of the protein contained in saliva ( ). The associated standard deviation reveals high variability among the tested samples, probably due to the different physiological and pathological states of the subjects involved in the study ( , gray band). The great part of attributed modes regards the vibration of molecules belonging to the protein, lipid, nucleic acid, and saccharide families, providing a global overview on the distribution of the species inside the biofluid ( ).
Average Raman signal obtained from the collected CTRL salivary samples ( n = 33). Black arrows indicate the identified peaks. The gray band represents the standard deviation.
Attribution of the most prominent peaks obtained from Raman salivary analysis (±8 cm ), based on reported literature ( ; ; ).
The proposed procedure allows the collection of detailed spectra using a very fast protocol that foresees a minimal sample preparation. The molecular content, as well as the information provided by the spectra, are detailed and repeatable giving an optimal point of view on the biochemical species present in saliva. For these reasons, we analyzed 33 CTRL, 23 PD, and 10 AD saliva samples following the procedure described previously. shows the Raman signals collected from the saliva of CTRL ( n = 33; ), PD ( n = 33; ), and AD ( n = 10; ) groups. Considering the intra-group variability, the preliminary analysis highlights a higher standard deviation value of the CTRL group ( ) respect to the pathological groups ( ), indicating a potential specific biochemical equilibrium during the pathological onset. As it is possible to notice from the overlapped average Raman spectra, the main differences between the groups can be attributed only to variations in intensities and presence of specific peaks, with negligible signal shifts. The remarked differences in peak intensities are probably due to variations in the molecular concentration and distribution between the physiological and pathological states ( ). In order to highlight the principal spectral discrepancies between the experimental groups and to investigate potential new discriminatory regions, subtraction spectra were obtained by comparing the signal intensities of CTRL, PD, and AD at different wavelengths ( ). All the differences in intensity (ΔI) were considered for values of ΔI ≥ 0.01. The main differences between CTRL and PD ( ) are due to the peaks at 496, 595, 678, 715, 770, 829, 850, 939, 1001, 1047, 1102, 1244, 1346, 1415, 1444, and 1571 cm . Similarly, the main differences between CTRL and AD were identified at 472, 593, 641, 750, 770, 870, 920, 1047, 1372, 1415, and 1444 cm ( ), while regarding the comparison between PD and AD peaks at 643, 750, 767, 870, 920, 1001, 1047, 1181, and 1326 cm showed the main differences ( ). These differences compared with the already attributed peaks ( ) are mainly due to the potential modifications of proteins, lipids, and carbohydrates and confirm previous observations on the overlapped spectra ( ). In particular, in the subtraction spectrum between PD and AD patients ( ), the most prominent peaks in PD were related to protein (643, 750, 1001, and 1580 cm are peaks related to single amino acids while the peak at 1540 cm regards the Amide II band) and to phosphatidylinositol (770 cm ). The same difference was encountered between PD and CTRL ( ), but in this case the resultant intensity with the relative error propagation (ΔI ≤ 0.01) was not considered statistically significant.
Average salivary Raman spectra of the (A) CTRL ( n = 33), (B) PD ( n = 23), and (C) AD ( n = 10) experimental groups. The gray bands represent the associated standard deviations. (D) Overlapped average Raman spectra of the three analyzed groups.
Subtraction Raman spectra of (A) the average CTRL versus the average PD signals, (B) the average CTRL versus the average AD signals, and (C) the average PD versus the average AD signals.
### Classification Models
#### Single Spectra Classification Model
In order to verify if the observed differences could lead to the creation of a classification model able to discriminate the signals collected from CTRL, PD, and AD subjects, we performed the PCA–LDA on the collected spectra. The results are reported in . The scatterplot of the first three loadings obtained by means of the PCA (cumulative PC scores = 46.2%; ) shows a partial overlap of the data dispersion associated to the three defined groups represented on the base of the first three PCs with the higher loading (PC1 = 30.6%; PC2 = 8.9%; and PC3 = 6.5%, ). The subsequent LDA on the first 10 PCs demonstrated a distinct dispersion of the Canonical Variables (CVs), with the CTRL, AD, and PD group means widely spaced ( ). Only a partial overlapping was observed between CTRL and PD data. Taking into consideration the dispersion of CV1, the differences between each group were proved to be statistically significant ( p < 0.001, one-way ANOVA test, ), indicating the potential role of RS in the discrimination of the salivary spectra acquired from PD respect to CTRL and AD.
(A) Principal component analysis (PCA) in three axis distribution ( X = PC1; Z = PC2; Y = PC3), covering the 45.83% of the loadings. Linear discriminant analysis (LDA) and spatial distribution of (B) the Canonical Variables 1 and 2 and (C) box plot of values of the Canonical Variable 1 with the statistical groups including CTRL ( n = 33), PD ( n = 23), and AD ( n = 10). *** p < 0.001, one-way ANOVA test. (D) Receiver operating characteristic (ROC) curve with the relative confidence interval (97–99%).
The LDA analysis was used to perform a LOOCV and to create a classification model, using the acquired data for the training of the machine. The discriminatory performances of the model are reported in . The error rate for cross-validation of data was 1.05%, with calculated values of sensitivity, specificity, and accuracy of respectively, 97, 98, and 98%. The quality of the binary classification (PD versus no PD) was evaluated through the MCC with a related value of 0.97. The ROC curve, showed in , presents an area under the curve of 0.98 with standard error of 0.002 and confidence interval of 97% (significance level p < 0.001).
Sensitivity, specificity, accuracy, Matthews correlation coefficient (MCC), error rate for the cross validation and area under the curve for the receiving operators characteristic (AUC ROC) curve for the single spectrum Parkinson’s disease leave-one-out cross-validation (PD-LOOCV) classification model.
#### Patient Level Classification Model
The application of DL techniques to spectral data involves a series of challenges. As result of the high complexity (and capacity) of such DL models, the available data volume should be large enough to create a more uniform and coherent dataset, and for this reason a data augmentation protocol was applied resulting in the generation of synthetic spectral examples to favor the network training and to boost the classification performances, allowing for better generalization capability. Furthermore, higher model complexity leads to a large number of possible configurations. Therefore, a suitable DL architecture must be selected by searching for the optimal composition of the various layers. Especially for CNNs, the configuration design requires a great effort in selecting the hyper-parameters. To this extent, we used SMBO to optimize the CNN architecture and fine-tune its hyper-parameters. shows the final configuration of the model.
Graphic representation of the best 1D-CNN model configuration obtained through the hyper-parameters optimization process.
Our CNN architecture consists of three 1D convolutional layers for the feature extraction and three fully connected layers for the classification. Comparing our DL model against the ML baseline introduced in section “Application of Learning Models for the Patient Classification Model,” we found that the ML models were systematically outperformed by the CNN-based model. This can be explained by the capability of a convolution-based model to capture the local structure in high dimensional complex Raman data, correctly elaborating the peak-related local correlations and information. Our findings about the suitability of DL models for Raman spectral analysis seems to be confirmed also by and . Given the difficulty of the learning problem, characterized by a low-volume high-dimensional dataset, both the ML and DL models have been trained on pre-processed data. In addition, we performed classifications on raw unprocessed data, and we verified, in good agreement with , that DL models, in particular CNNs, are capable of gaining competitive performances without the need of any preprocessing on the data. The DL architecture is trained on the three-class classification problem outputting a probability distribution associated with the class choices, where the highest probability is used to predict the label. The model has been tested using LOPOCV and performance breakdowns are reported in the confusion matrix in . We firstly build a single-spectrum classification model (spectral-level) and then, having acquired multiple spectra for each patient, we aggregated the classification result at the patient-level according to their majority label. The average spectral-level sensitivity, specificity, and accuracy were respectively of 80, 89, and 80% using the CNNs ( , spectral-level). In comparison, the more common ML techniques of SVM and RF achieved discriminatory power not comparable with the scores reached by the CNN.
Confusion matrices obtained in LOPOCV by the proposed CNN. (A) Spectra-level and (B) patient-level.
Accuracy, sensitivity, and specificity in patient- and spectra-level obtained by the proposed CNN in leave-one-patient-out cross-validation using a 10× data augmentation.
The labeling decision for a patient can be taken based on its entire spectral set (since numerous spectral samples are acquired from the same individual). Classification metrics can thus be condensed into a new confusion matrix grouped by patients ( ), where the average sensitivity, specificity, and accuracy score of the CNN model were respectively of 90, 94, and 89%. Again, the scores of the tested ML methods were not comparable with the values reported for the CNN model.
#### Correlations
Data extracted from the MVA of the Raman database were correlated with clinical and paraclinical parameters collected from the PD patients including UPDRS III, H&Y, and LEDD. In order to assess the independency of the Raman method, all the data were correlated using as correcting covariates the demographical data of the subjects, including age and sex. The results are reported in . Interestingly, all the coefficients extracted from the Raman database using the MVA approach, show strong correlation with at least one of the indicated parameters. In particular, the clinical scales UPDRS III and H&Y demonstrated the influence on all the CVs and PCs correlated ( ). The levels of levodopa influence mostly the PCs distributions, with strong positive correlation for PC1, 2, and 3, but not of the obtained CV1 and 2 ( ) that represent the new set of coordinates in order to maximize the differences between the samples. A possible explanation for this result could be found in the influence of the drug therapy, which is able to influence the biochemical composition of saliva but not determining a direct influence on the final set of CVs used to build the classification model. The PCs represent independent directions, with their own specific weights (loadings, ), applied in order to maximize the variance between the variables considered during the PCA. The data reported in indicate a strong dependency of the Raman data with the clinical status and stage of the PD patients.
Heat map representing the partial correlation (Pearson’s coefficients) with the relative significance of Canonical Variables 1 and 2 (CV1 and CV2) and Principal Components 1, 2, and 3 (PC1, PC2, and PC3) correlated with levodopa equivalent daily doses (LEDD), Hoehn and Yahr (H&Y) stages and Unified Parkinson’s Disease Rating Scale (UPDRS) motor scales (III). Age, sex, and behavioral parameters were used as control covariates for the partial correlation. * p < 0.05, ** p < 0.01, and *** p < 0.001, Pearson’s test.
## Discussion
The proposed pilot study paves the way to the possibility to use the entire salivary Raman spectrum of PD patients to assess the pathology onset and progression. The preliminary data presented here have proved that the Raman analysis of saliva can distinguish PD patients with sensitivity, specificity, and accuracy, respectively of 90, 94, and 89%. The importance of biomarkers in the great part of pathologies relies on the possibility to assess the disease onset, to shorten the diagnostic delay, to evaluate the disease progression and to perform a continuous monitoring of the efficacy of both therapeutic and rehabilitation strategies. Due to the various different forms of PD-related pathologies and to the overlapping of symptoms with other neurodegenerative diseases, the necessity to discover a specific biomarker, able to identify the early pathological onset, is of crucial importance. In the last decade, various studies proposed different potential biomarkers, collectable from peripheral blood or CSF with invasive procedures, but their potential application is still unclear ( ). One of the reasons for the controversial results can be found in the pleiotropic role of the candidate molecules that can be associated to neurodegeneration in multiple neurological diseases, leading to a complex situation for the determination of a single biomarker uniquely associated to the PD onset and progression ( ). Previous studies have already demonstrated the applicability of the RS as diagnostic, prognostic, and therapeutics/rehabilitative monitoring tool using different biofluids for various neurodegenerative diseases ( ). In this work, we optimized a RS-based analysis for the spectroscopic characterization of saliva collected from PD patients. The optimized procedure allowed the collection of a highly informative signal from saliva, with information on proteins, lipids, carbohydrates, and nucleic acids, using a fast (20 min from the saliva collection to the Raman results), cheap (the only consumable reagent is commercially available aluminum), reproducible and minimally invasive procedure. Moreover, refining our previous sample preparation procedure and removing the filtration step ( ), we were able to concomitantly amplify the range of analyzable molecules and increase the technique velocity. Concerning the economic burden of Raman spectroscopy use in clinics, we would like to mention that portable instruments are already commercially available on market, with different degrees of spectral resolution depending on the methodological needs and affordable for any diagnostic laboratory. We foresee that, once the methodology will be verified with the benchtop instrument in a larger cohort, the transferability of the method to a portable cost-effective platform will be evaluated.
By adopting the described technique, we were able to characterize the salivary Raman fingerprint of PD patients and to identify evident differences compared to CTRL and AD subjects, principally regarding the peaks and bands related to proteins, nucleic acids, saccharides, and lipids. In detail, the differences between PD and CTRL regard peaks related to proteins (829, 939, and 1001 cm are signals from specific amino acids, while 1102 and 1346 cm due to the Amide bands), nucleic acids (1244 cm ) and glycoproteins/saccharides (850 and 1444 cm ) ( ; ). The protein signal alterations can potentially derive from the pathological species present in saliva during the PD progression or due to the secondary effects caused by the pathological state (e.g., inflammatory responses or ROS damages). For example, high concentrations of α-synuclein, heme-oxygenase-1, protein damaged by ROS and the cysteine protease DJ-1 were found in the saliva of PD patients, which could explain the higher signals related to protein found in the subtraction spectra between CTRL and PD ( ; ; ; ). Besides, the marked saccharides alteration can be related to the altered metabolism of glucose and related carbohydrates in PD patients, leading to an incremented amount of circulating saccharides ( ). Examining the spectral differences between PD and AD patients, those related to protein can be explained by the inflammatory species and aggregated proteins found in the circulating protein patterns in the two pathological states ( ). On the other hand, the phosphatidylinositol accumulation in PD patients is probably due to the alteration of the phosphatidylinositol transfer protein expression responsible for the transport and metabolism of the phospholipid in PD ( ). The LDA model applied to the three experimental groups (PD, AD, and CTRL) revealed two CVs dispersed with a distinct trend, which allows to statistically discriminate the Raman single spectrum on the base of the CV. These results mean that the differences between the Raman spectra collected from the saliva of PD patients are significant enough to determine a classification model able to discriminate them from the CTRL or AD with accuracy, sensitivity, and specificity of more than 95%. The ML and DL approaches were refined and performed in order to automatize the signal preprocessing and, more importantly, to create a classification model able to discriminate not only the signal coming from a single spectrum, but also the entire Raman spectral set associated to the subject. This approach is one of the most advanced procedures for the creation of a fast and sensitive Raman-based diagnostic and monitoring tool close to the clinical application. The deep convolutional model that obtained the best performances has been trained on the pre-processed data. Nevertheless, competitive performances have been also reached by training the DL model directly on raw data, in good agreement with . This can be explained by the fact that CNN models have local connectivity and translational invariance properties well suited for dealing with spectral data, where a vertical and horizontal translational invariance (e.g., small changes in intensity and in Raman shift) play important roles. These properties allowed a better handling of raw spectral data through CNNs, preventing the introduction of biases and information filtering during the manual data manipulation. The ML/DL pipeline resulted in the proper classification of almost all of the considered subjects. The attribution of part of the total number of PD spectra to the CTRL group (8.3% in the spectral-level confusion matrix) and consequently of four PD patients to the same group (patient-level confusion matrix), could be derived from the patients’ pathological progression. In fact, these particular patients had the lowest H&Y and UPDRS III clinical scores, determining the attribution by the classification models in the CTRL group, despite the regular assumption of the dopaminergic therapy. The results of this pilot study highlight the potentialities of the model not only to discriminate the pathological onset, but also to potentially identify the shades of the different PD stages. This feature must be validated with the study of new samples collected from a wider population of PD patients recruited at different pathological stages. Moreover, the introduction of new experimental groups and sub-groups related to the same pathology (e.g., different pathological stages and comorbidities), will be of fundamental importance to further train the classification mechanism, such as patients in the prodromal phase or parkinsonism followed in a longitudinal study and collecting the saliva samples at specific time-points corresponding to the pathological evolution. In the same way, data regarding the rehabilitation and drug therapies could provide an indication of the effectiveness of the prescribed procedures. In fact, one of the greatest advantages of this approach consist in the possibility to easily train the model with new and different data related to the target pathologies, leading to a refinement in the discriminatory power and to the classification of new groups, including PD stages of progression, overlapping comorbidities and PD prodromal phases. It has to be noted that the main limitations of the present work rely in the number of patients and in the involvement of a single laboratory for the RS analysis: as recently reviewed, multi-centered studies are needed to assess the actual robustness of RS methodology prior to their clinical application because of the profound influence of the setup-dependence in RS ( ; ). For this reason, next steps in the validation of the proposed methodology will include the inclusion of larger cohorts of patients including different pathological stages and comorbidities, use of multiple instruments and involvement of different research teams to avoid experimental setup influence and biases in the validation phase.
Finally, our results are the first reported data that suggest the reliability of the Raman salivary analysis through the correlation of the MVA results with the clinical parameters of PD patients. In particular, despite the limited number of patients recruited, CV1 and CV2 demonstrated a statistically significant correlation ( p < 0.001) with the two most relevant scales used nowadays for PD diagnosis and monitoring. The H&Y scale describes the motor symptoms correlated with PD progression, whereas the UPDRS provides a comprehensive tool to monitor PD related disability and impairment concerning a series of clinical manifestations including mental state, difficulty in performing daily activities, motor skills, dyskinesia, and others. Specifically, UPDRS part III refers to the motor function evaluation scale. The correlation of the CVs with UPDRS III and H&Y revealed a close relationship between the salivary biochemical content and the clinical state of the patient. It is worth noting that both scales have been recently reported to correlate also with the Raman biochemical fingerprint of serum extracellular vesicles in PD patients, assessing the close relationship between the biochemical equilibrium in biofluids and the disease onset ( ). This observation, together with the well-known physiological mechanisms of saliva production and the direct correlation of salivary composition to that of blood or CSF, make us hypothesize a possible involvement of the vesicular components in the salivary fingerprint of PD patients, although this issue requires further studies to be ascertained. Besides the partial correlation with the same clinical scales, the PC1, PC2, and PC3 also revealed a strong correlation with LEDD. These parameters could be exploited to individuate the optimal dose of dopaminergic therapy necessary to the specific PD patient, clinically framed by the associated Raman fingerprint. In order to investigate the molecular bases of these relationships and to validate the correlations between MVA data and clinical scales and therapies, further studies on larger and various cohort of PD are needed, including different PD progression stages and longitudinal studies.
Collectively, the MVA approach on the single Raman spectra allowed us to make a preliminary estimate of the diagnostic potential of the Raman analysis that was shown to reach an excellent accuracy level of 98.5%. Even more interestingly, the innovative ML/DL approach at the patient-level reached an accuracy of 89%. Once validated, this approach would represent a competitive diagnostic tool that could even surpasses previously proposed assays for PD ( ). In conclusion, our pilot study demonstrated the potential application of the Raman analysis for the simultaneous identification of a large range of molecules present in saliva, obtaining high discrimination performances. The complex signal obtained from the salivary spectra was approached using a combined ML and DL method able to characterize and validate the Raman signature of PD and to assess with high discriminant ability the clinical state of the considered subject. Despite the small cohort of considered subjects, the potentialities of the proposed method were corroborated by the statistically significant correlation obtained between the MVA coefficients and the clinical data collected from the patients. The proposed methodology, once validated in larger cohorts and with multi-centered studies, could represent an innovative, cost-effective, minimally invasive, and accurate procedure to determine the early PD onset, progression and to monitor therapies and rehabilitation efficacy. Having in mind the future steps required for the final validation, we believe that this method has the potentiality to be transferred to the clinical setting, thus, the Raman analysis of saliva could provide clinicians and researchers with a powerful instrument for the PD managing.
## Data Availability Statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics Statement
The studies involving human participants were reviewed and approved by institutional review board at IRCCS Fondazione Don Carlo Gnocchi ONLUS. The patients/participants provided their written informed consent to participate in this study.
## Author Contributions
CC, MB, AG, and SP conceived and designed the study. PB, MM, FV, NT, and VS collected the samples and clinical data. CC collected the Raman data. CC, SP, FR, and AG performed the statistical analysis. DB, MA, and EM created the classification model. CC, AG, MA, and DB wrote the original manuscript. All authors revised the manuscript and were involved in the drafting review, approving the decision to submit for publication.
## Conflict of Interest
VS received compensation for consulting services and/or speaking activities from AveXis, Cytokinetics, Italfarmaco, and Zambon. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s Note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
|
Schizophrenia is a chronic psychotic disorder characterized by the disruption of thought processes, perception, cognition, and behaviors, for which there is still a lack of objective and quantitative biomarkers in brain activity. Using functional magnetic resonance imaging (fMRI) data from an open-source database, this study investigated differences between the dynamic exploration of resting-state networks in 71 schizophrenia patients and 74 healthy controls. Focusing on recurrent states of phase coherence in fMRI signals, brain activity was examined for intergroup differences through the lens of dynamical systems theory. Results showed reduced fractional occupancy and dwell time of a globally synchronized state in schizophrenia. Conversely, patients exhibited increased fractional occupancy, dwell time and limiting probability of being in states during which canonical functional networks—i.e., Limbic, Dorsal Attention and Somatomotor—synchronized in anti-phase with respect to the rest of the brain. In terms of state-to-state transitions, patients exhibited increased probability of switching to Limbic, Somatomotor and Visual networks, and reduced probability of remaining in states related to the Default Mode network, the Orbitofrontal network and the globally synchronized state. All results revealed medium to large effect sizes. Combined, these findings expose pronounced differences in the temporal expression of resting-state networks in schizophrenia patients, which may relate to the pathophysiology of this disorder. Overall, these results reinforce the utility of dynamical systems theory to extend current knowledge regarding disrupted brain dynamics in psychiatric disorders.
## 1. Introduction
Brain activity at “rest” captured using functional magnetic resonance imaging (fMRI) reveals the recurrent emergence and dissolution of connectivity patterns that overlap with functional networks typically activated during task (Beckmann et al., ; Fox and Raichle, ; Smith et al., ; Deco and Jirsa, ). These resting-state networks (RSNs) have been consistently detected and extensively analyzed across neuroimaging studies (Damoiseaux et al., ; van den Heuvel and Hulshoff Pol, ) and their characterization in the temporal domain—referred to as dynamic functional connectivity (dFC)—has been suggested to provide potential biomarkers of several neurological and psychiatric disorders (Sakoğlu et al., ; Hutchison et al., ; Preti et al., ). In fact, the discovery of biomarkers in dFC is crucial not only for more efficient diagnosis but also to inform computational models in order to gain insight into the large-scale organizational principles of brain activity in health and disease (Cabral et al., , ; Stefanovski et al., ; Courtiol et al., ; Kringelbach and Deco, ).
Schizophrenia (SZ) is a chronic brain disorder affecting 1 in 300 humans worldwide and, if left untreated, its symptoms can be persistent and disabling (James et al., ). The cognitive and behavioral symptoms observed in patients with SZ are hypothesized to arise from the disrupted functional integration of segregated brain areas (Friston et al., ; Liang et al., ; Lynall et al., ; Skudlarski et al., ). Furthermore, neuroimaging studies suggest that SZ patients have aberrant functional connectivity in brain networks and these abnormalities are related to disease symptoms (Wang et al., ). As such, neuroimaging data-sharing initiatives have been developed to potentiate the discovery of biomarkers of schizophrenia in fMRI signals, allowing to test novel analysis methods which may provide further insights into the pathophysiology of this disease and, potentially, lead to the discovery of new treatments for the diseased brain (Aine et al., ).
To date, previous studies investigating dFC have suggested that compared to healthy controls (HCs), patients with SZ spend more time in FC states characterized by weak connectivity (Rabany et al., ) and less time in FC states which represent strong, large-scale brain connectivity (Damaraju et al., ; Dong et al., ; Sanfratello et al., ). Furthermore, when SZ patients transition into the FC state of strongest connectivity, they switch states very rapidly (Rabany et al., ). Overall, SZ patients have been found to exhibit fewer changes between connectivity patterns compared to HCs (Miller et al., ). Notably, most research on dFC in SZ has been carried out using independent component analysis to extract time courses of networks which were subsequently used to estimate dFC through sliding-window analysis (SWA) (Sakoğlu et al., ; Preti et al., ). However, the choice of the window length affects the temporal resolution of the SWA approach—raising questions over its validity (Preti et al., ). In this study, to overcome this weakness, the Leading Eigenvector Dynamics Analysis (LEiDA) method, based on phase coherence of fMRI signals, is used to investigate dFC at an instantaneous level (Glerean et al., ; Cabral et al., ). It must be noted that methods based on phase coherence may fail to capture the non-linear stochastic nature of neuronal network dynamics to its full extent—prompting the use of metrics such as multi-scale-entropy (Courtiol et al., ). Nevertheless, methods based on phase coherence have demonstrated a particular sensitivity to alterations in psychiatric symptoms (both clinical and pre-clinical) motivating its use in the current work (Cabral et al., ; Figueroa et al., ; Lord et al., ; Alonso Mart-nez et al., ; Larabi et al., ).
The main aim in this study was to investigate if patients with SZ exhibit alterations in the dynamical exploration of functional networks during rest detected using LEiDA. Furthermore, this work examined the validity of the partitions resulting from the clustering procedure and investigated the influence of using the K-medoids algorithm instead of the K -means algorithm to differentiate SZ patients from HCs. This work hypothesized to find abnormal dFC in SZ patients characterized by reduced excursions to an FC state possibly involved in the integration of segregated functional connections and increased excursions to a number of FC states which represent functionally segregated networks.
## 2. Materials and Methods
### 2.1. Neuroimaging Data
Neuroimaging data was obtained from the publicly available repository (Calhoun et al., ; Bellec, ). The neuroimaging data included preprocessed resting-state fMRI (rs-fMRI) data from 72 SZ patients and 74 HCs, in which participants passively stared at a fixation cross (Aine et al., ). The rs-fMRI data featured 150 echo planar imaging volumes obtained in 5 min, with repetition time (TR) = 2 s, echo time = 29 ms, acquisition matrix = 64 × 64 mm , flip angle = 75° and voxel size = 3 × 3 × 4 mm . The acquisition and preprocessing of the fMRI data are fully described in detail in Bellec ( ). Preprocessing included slice-timing correction, coregistration to the Montreal Neurological Institute (MNI) template and resampling of the functional volumes in the MNI space at a 6 mm isotropic resolution. No confounds were regressed from the data, because this procedure may extract fMRI signal variance whose contribution to functional networks remains under debate (Murphy et al., ; Bright and Murphy, ; Nalci et al., ; Chen et al., ). In addition, the fMRI volumes were not spatially smoothed since the subsequent parcellation induces a level of smoothing. Furthermore, temporal filters were not applied since previous works considering the whole-frequency spectrum have been shown to improve within-subject reliability regarding the temporal expression of FC patterns (Vohryzek et al., ).
Inspection of the fMRI data for each subject resulted in the exclusion of one subject whose data did not include all 150 volumes. Therefore, the final dataset used in this analysis included 71 SZ patients (57 males) and 74 HCs (51 males). A goodness of fit χ test did not reject the null hypothesis of independence between gender and group ( p = 0.1167). Both groups had an age range of 18–65 years old. A two-sided Wilcoxon Rank-Sum test with Bonferroni correction did not identify a significant difference between the mean age of the groups ( p = 0.4253). The framewise displacement (FD) provided a quantitative indication of each subject's head motion during the scanning period (Power et al., ). The same statistical test detected a significant intergroup difference in the group mean FD ( p < 0.001). Specifically, on average, the fMRI signals of SZ patients were characterized by larger amounts of head motion (FD). Given the statistically significant difference in the mean FD, the impact of head-motion in the group-level results was investigated (see ).
### 2.2. Parcellation
The entire brain of each participant was parcellated into 90 cortical and sub-cortical non-cerebellar regions using the Anatomic Automatic Labeling (AAL) template. Accordingly, for each region in the brain template, the fMRI signals were averaged over all voxels belonging to that brain area. For each subject, this resulted in an N × T dataset, where N = 90 is the number of brain areas and T = 150 is the number of volumes in each scan.
### 2.3. Computation of Dynamic Functional Connectivity
To compute the phase relationship between each pair of AAL regions, first the instantaneous phase of the fMRI signals across all brain regions n ∈{1, …, N } for each time t ∈{2, …, T −1}, θ( n, t ), were estimated by computing the Hilbert transform of their regional time courses (Glerean et al., ). Here, the first and last TR of each fMRI scan were excluded due to possible signal distortions induced by the Hilbert transform (Vohryzek et al., ). The Hilbert transform enables the capture of the time-varying phase of a fMRI signal at each time, t , by converting it into its analytical representation [see (top left)] (Glerean et al., ; Cabral et al., ). To obtain a whole-brain pattern of phase synchrony, the phase coherence between areas n and p at each time t , dFC ( n, p, t ), was estimated using Equation (1):
where phase coherence values range between -1 (areas n and p in anti-phase at time t ) and 1 (areas n and p have synchronized signals at time t ), as shown in (bottom left). This computation was repeated for all pairwise combinations of brain areas ( n, p ), with n, p ∈{1, …, 90}, at each time point t , with t ∈{2, …, 149}, and for all subjects. For each subject, the resulting dFC was a three-dimensional tensor with dimension N × N × T ′, where T ′ = 148, i.e., 148 dFC ( t ) matrices were estimated.
Graphical illustration of the estimation and characterization of the temporal trajectories of recurrent FC states obtained by using Leading Eigenvector Dynamics Analysis (LEiDA). (A) Phases of all N = 90 brain areas in the complex plane at time t (top left); Phase coherence matrix at time t , dFC ( t ) (bottom left); Vector representation of the leading eigenvector, V ( t ), of dFC ( t ) (middle); Matrix representation of V ( t ) (top right); Network representation of V ( t ), with links between the areas with positive elements in V ( t ) plotted in red (bottom right). (B) The leading eigenvectors are computed for each time point and from all fMRI scans. (C) The pooled leading eigenvectors are partitioned into K clusters using a clustering algorithm. The cluster centroids/medoids are assumed to represent recurrent patterns of phase coherence (FC states). (D,E) The leading eigenvector at each TR is represented by the centroid/medoid of the cluster to which it was assigned by the clustering procedure. This originates time courses of FC states for each fMRI session. The time courses are then characterized using tools from dynamical systems theory. (F) Each FC state can be represented as a N × N matrix (outer product) and as a network in cortical space (elements with positive sign linked by red edges).
### 2.4. Functional Connectivity Leading Eigenvector
To characterize the evolution of the phase coherence matrix over time with reduced dimensionality, the current study employed the LEiDA method which considers only the leading eigenvector, V ( t ), of each dFC ( t ) matrix (Cabral et al., ). In detail, as observed in (middle), the leading eigenvector, V ( t ), is an N × 1 vector that captures the dominant connectivity pattern of phase coherence at time t , i.e., V ( t ) represents the main orientation of the phases over all brain areas (Cabral et al., ). Under this framework, for each time t , the associated leading eigenvector partitions the N brain areas into two communities by separating the elements with different signs in V ( t ) (Newman, ; Cabral et al., ). When all elements of V ( t ) have the same sign, the phases between brain regions are coherent, which is indicative of a global mode of phase coherence governing all fMRI signals. This implies that all brain regions belong to the same community. Contrarily, if the elements of V ( t ) have different signs (i.e., positive and negative), the connectivity pattern between brain regions is not coherent. As a result, each brain area is assigned to one of the two communities according to their phase relationship. Additionally, the absolute value of each element in the leading eigenvector weighs the contribution of each brain area to the assigned community (Newman, ; Cabral et al., ). The dominant FC pattern of the dFC matrix at time t can also be reconstructed back into matrix format by computing the ( N × N ) outer product , as shown in (top right). Given that if V ( t ) is a leading eigenvector, so is − V ( t ), following the procedure of Figueroa et al. ( ); Lord et al. ( ); Vohryzek et al. ( ), it was ensured that most of the elements in V ( t ) had negative values. This is because by assigning positive values to the brain areas whose phases did not follow the global mode, functional brain networks were distinctly detected, as seen in (bottom right). Importantly, this approach was found to explain most of the variance of observed phase coherence data variation, while substantially reducing its dimensionality. In fact, the leading eigenvector accounted for more than 50% of the variance in phase coherence at all time points and for all subjects.
### 2.5. Estimation of FC States
Upon computing the leading eigenvector of the phase coherence matrix for each recording frame, the next step in the analysis was to characterize the evolution of the dFC over time by identifying recurrent FC states in the data, as illustrated in (Cabral et al., ).
The dataset of all leading eigenvectors computed across all 145 participants at the set of volumes {2, …, 149}, totalling 148 × 145 = 21, 460 leading eigenvectors, was clustered using: (1) the K -means algorithm; and (2) the K -medoids algorithm (Aggarwal and Reddy, ). Here, both algorithms were run with a value of K from 2 to 20, i.e., dividing the set of leading eigenvectors into K = {2, 3, …, 20} clusters. Furthermore, in both clustering analyses, the cosine distance was used as the distance metric for minimization and the algorithms were run 1,000 times to minimize the chances of getting trapped in a local minima (Cabral et al., ; Figueroa et al., ; Lord et al., ; Vohryzek et al., ).
Independently of the algorithm, the LEiDA clustering procedure outputs one optimal clustering solution for each value of K clusters. Specifically, each clustering solution contains K clusters C = { C , …, C }, with K ∈{2, …, 20}—decomposing the N -dimensional phase space of pooled leading eigenvectors into a K -dimensional state space. Each cluster C (α∈{1, …, K }) is represented by a vector of dimension N × 1, V , which represents a recurrent FC state, as depicted in . It must be noted that the K -means and K -medoids algorithms provide distinct interpretations regarding the functional meaning of the mentioned FC states. According to the K -means algorithm, the prototypes of each cluster, designated as centroids, are given by the mean of the leading eigenvectors belonging to each cluster. As such, centroids may not correspond to actual data points from the set of leading eigenvectors. On the other hand, the K -medoids algorithm chooses actual leading eigenvectors as the prototypes of the clusters which are designated as medoids. Whilst at the cost of higher computational complexity, the robustness of the K -medoids algorithm means it is better suited to manage outliers than the K -means algorithm when detecting recurrent FC states (Aggarwal and Reddy, ). Assuming that some of the leading eigenvectors belonging to SZ patients were outliers, by employing the K -medoids algorithm, their influence would be underestimated when detecting FC states—resulting in more representative functional networks of the set of leading eigenvectors from both groups.
### 2.6. Characterization of FC State Trajectories
For each clustering solution, the set of estimated K FC states was used to obtain, for each participant, time courses of FC states (as represented in ). This was accomplished by representing each V at time t by the FC state (centroid/medoid) of the cluster to which it was assigned by the clustering algorithm, depicted as a matrix and as a network in cortical space in . Specifically, following the conceptual framework proposed by Vohryzek et al. ( ), resting-state fMRI time series were assumed to temporally evolve through a finite state trajectory of recurrent patterns of phase coherence. Following this rationale, each clustering solution with K FC states was assumed to define a finite state space S = {1, …, K }. Furthermore, for a clustering solution with K clusters, the cluster (FC state) to which V was assigned at time t , denoted by V , was assumed to define a stochastic process, { V : t ∈{2, …, 149}}, with an associated finite state space given by S . Consequently, considering the Markov property (Kulkarni, ) holds, each temporal trajectory of FC states was assumed to define a time-homogeneous Discrete Time Markov Chain (DTMC). Importantly, it must be noted that, although brain activity is an uninterrupted process, the restricted fMRI scanning windows implied the state trajectories were temporally limited—resulting in a number of DTMCs not spanning the entire state space.
A number of descriptive measures were considered to characterize the properties of the temporal trajectories of FC states observed in SZ patients and HCs. Notably, these measures have been shown to provide relevant insights on dynamic brain activity in previous LEiDA analyses (Cabral et al., ; Figueroa et al., ; Lord et al., ).
#### 2.6.1. Fractional Occupancy
The fractional occupancy (probability of occurrence) of an FC state α represents the proportion of times V is assigned to cluster C throughout a scan (Vohryzek et al., ). The fractional occupancy of FC state α for the fMRI scan of subject s , , is calculated (estimation) as follows:
where T ′ = 148 is the number of time points (first and last volume of each scan were excluded), 1 is the indicator function and is the FC state to which V ( t ) was assigned at time t . For each clustering solution, this measure was estimated for each of the K FC states separately for each fMRI scan.
#### 2.6.2. Dwell Time
The dwell time (mean duration) of an FC state represents the mean number of consecutive epochs spent in that state throughout the duration of a scan (Vohryzek et al., ). The dwell time of FC state α, , is defined (estimation) as:
where is the number of consecutive periods in which was assigned to cluster C and R is the duration of each of the periods. For each clustering solution, the dwell time was estimated for each of the K FC states separately for each fMRI scan.
#### 2.6.3. One-Step Transition Probability Matrix
Considering a clustering solution with state space S = {1, …, K }, the probability of being in FC state α at time t and transition to FC state β at time t +1 is given by the following expression:
with α, β∈{1, …, K } (Vohryzek et al., ). From Equation (4), for a clustering solution with K FC states, it follows that the Transition Probability Matrix (TPM) of the fMRI scan of subject s , P , is defined (estimation) as:
with α, β∈{1, …, K }. For the tentative optimal clustering solution, a TPM was estimated separately for the DTMC of each fMRI scan.
#### 2.6.4. Limiting Probability
In this study, the limiting distribution was only estimated for irreducible and aperiodic DTMCs (Kulkarni, ), with finite state space given by the tentative optimal state trajectories. Therefore, for every subject, s , with a DTMC satisfying the aforementioned criteria, it followed that:
where the estimate of the row vector denoting the stationary distribution of the DTMC (Kulkarni, ), , with dimension 1 × | S |, is given by:
where 1 is a 1 × | S | vector of ones, I is the identity matrix with rank | S |, P is the TPM of subject s and ONE is an | S | × | S | matrix all of whose entries are one. Due to the inclusion criteria imposed on the DTMCs defined by the optimal state trajectories, i.e., irreducibility and aperiodicity, only 37 and 46 DTMCs from the HC and SZ groups, respectively, were analyzed. For a given FC state β, π (element β of the row vector π ) was the measure to be used to perform intergroup comparisons. Importantly, since only aperiodic DTMCs were considered, π can be understood as the limiting probability that the DTMC is in FC state β and as the long-run fraction of time the DTMC spends in FC state β. It must be noted that intergroup comparisons between the estimated stationary distributions were not performed in this study.
### 2.7. Intergroup Comparisons
In this research, hypothesis tests to compare the group mean of the properties calculated from the temporal state trajectories observed in SZ patients and HCs were performed using Monte Carlo permutation tests (Pesarin and Salmaso, ) by adapting the procedure used by Cabral et al. ( ); Figueroa et al. ( ); Lord et al. ( ). To produce an accurate approximate estimation of the permutation distribution, these tests were conducted using B = 10, 000 permutations (Marozzi, ). Here, depending on the result of a Levene's test (Levene, ) (used to assess the homogeneity between the group variances) the Monte Carlo permutation tests were performed based on one of the following two statistics (under the null hypothesis):
where and are the random sample means, S and S are the random sample standard deviations, and n and n are the sample sizes for the HC and SZ groups, respectively. The pooled random standard deviation, S , is given by . Under the null hypothesis, the statistic from Equation (8) used to perform the statistical test was subsequently used to obtain the value of the statistic under each of the B permutations of the sample data. In this study, the standard deviation of the difference of the group means was estimated using 500 bootstrap samples within each permutation sample. This was performed so that the estimation of this quantity was conducted independently of the calculated means difference.
To complement the statistical hypothesis tests and understand the magnitude of the detected intergroup differences independently of the sample size, the effect size was estimated using Hedge's g statistic (Hedges, ). The use of this measure was based on its appropriateness to measure the effect size for the difference between means and on the fact that this measure takes into account the size of the sample from each group.
### 2.8. Comparison to Resting-State Functional Networks
The functional relevance of the estimated FC states was investigated by assessing whether there was a significant spatial overlap between the centroids/medoids and any of the seven reference RSNs defined by Thomas Yeo et al. ( ). This was accomplished by employing the procedure used by Lord et al. ( ); Vohryzek et al. ( ). Specifically, the seven RSNs were transformed into seven non-overlapping vectors with dimension 1 × 90, where each entry of the vectors corresponded to the proportion of voxels of each AAL brain area that were assigned to each of the seven RSNs. Finally, the Pearson correlation coefficient was used to assess the spatial overlap between these seven RSNs and the centroids/medoids V with α∈{1, …, K } (all negative values of V were set to zero so that only areas thought to define relevant functional networks were considered).
### 2.9. Unsupervised Internal Cluster Validation Criteria
The quality of clustering solutions outputted by the clustering algorithms was evaluated using the average Silhouette coefficient and the Dunn's index (Aggarwal and Reddy, ).
### 2.10. External Validation Clustering Agreement Measures
Clustering outputs from distinct algorithms were compared using the Adjusted Rand Index (ARI) and the Variation of Information (VI) clustering agreement measures (Aggarwal and Reddy, ).
### 2.11. Clustering Stability Evaluated by K-Fold Cross-Validation
The stability of clustering solutions was assessed according to a 10-fold cross-validation procedure adapted from Martins and Cardoso ( ). Firstly, the sample of the pooled leading eigenvectors was split into two subsamples, referred to as training and test samples. Secondly, a clustering algorithm was applied to the training sample—yielding partition P . Subsequently, a Nearest Centroid classifier assigned each observation of the test sample to the cluster of partition P , whose centroid was nearest—resulting in the class set P of the test sample. The same clustering algorithm was then applied to the test sample—producing the cluster set P . Finally, partitions P and P were compared based on the ARI, VI and percent agreement (fraction of objects correctly assigned). This procedure was repeated for each of the 10 cross-validation folds.
### 2.12. Software
This analysis used MATLAB R2019b (MATLAB, ), the Statistics and Machine Learning Toolbox and the Econometrics Toolbox .
## 3. Results
### 3.1. Intergroup Differences Across Partition Models Detected by the K-Means Algorithm
The collection of clustering solutions was investigated to search for FC states whose fractional occupancy and dwell time most significantly and consistently differed between SZ patients and HCs. For a partition model with K clusters, K hypothesis tests were performed. Consequently, to account for the increased probability of false positives, the significance threshold α = 0.05 was adjusted to α = 0.05/ K using a Bonferroni correction. Additionally, a conservative significance threshold of was considered to encompass both dependent and independent null hypotheses across clustering solutions.
presents, for each clustering solution, the K two-sided p -values obtained from evaluating whether the group mean fractional occupancy of an FC state differed between SZ patients and HCs. From the inspection of , it is apparent that, across all partition models, the clustering procedure consistently returned FC states whose mean fractional occupancy differs significantly between groups—falling below the corrected significance thresholds α and α .
Intergroup comparisons of the mean fractional occupancy of each FC state for each clustering solution. (A) Barplot of the estimated mean fractional occupancy with associated standard error of each FC state for each group. For each FC state, the color of the bars indicates whether the null hypothesis of no intergroup differences in the mean fractional occupancy was rejected (two-tailed tests). The standard error of each bar was calculated as the standard deviation of the sample data divided by the square root of the sample size. (B) Two-sided p -values obtained for the intergroup comparisons of the mean fractional occupancy of each FC state for each partition model. FC states (clusters) are ranked according to their probability of occurrence, where cluster 1 consists of the largest number of objects and cluster K consists of the least number of objects. The red, green and blue dotted lines correspond to a 0.05, 0.05/ K and significance threshold, respectively.
Closer inspection of shows there are significant intergroup differences in the mean fractional occupancy of FC state 1 for a range of clustering solutions ( p < α , two-tailed tests). In fact, the mean fractional occupancy of this state was found to be significantly decreased in SZ patients compared to HCs ( p < α for K ∈{2, …, 17}, one-tailed tests), as suggested in . Interestingly, for all partition models, the centroid associated with FC state 1 revealed this recurrent FC pattern represents a globally synchronized state of phase coherence (all elements of the centroid had the same sign). Hence, FC state 1 is referred to as the Global Mode.
As depicted in , across all clustering solutions, further non-global FC states are characterized by significant intergroup differences in the group mean fractional occupancy ( p < α , two-tailed tests). Interestingly, all these states were typified by a higher mean probability of occurrence in the SZ group compared to the HC group ( p < α , one-tailed tests), as presented in . Visual inspection of these non-global FC states revealed they represent varying forms of the same underlying connectivity patterns. Specifically, states detected for lower values of K could be obtained by combining the fine-grained FC patterns identified in partition models with larger values of K —evidencing the dependence among the hypothesis tests performed across clustering solutions.
The analysis of mean dwell time estimates of detected FC states suggested that this measure did not allow as much consistent and clear differentiation between groups compared to the estimates of the fractional occupancy of FC states, as observed in . In fact, the mean dwell time of the Global Mode was reduced significantly in SZ patients compared to HCs in only 8 clustering solutions ( p < α , one-tailed tests). Conversely, across all partition models, the mean dwell time was identified as significantly increased in the SZ group compared to the HC group in only two FC states ( p < α , one-tailed tests). Notably, these non-global states were highly correlated (Pearson's r = 0.996)—reinforcing the fact that significant intergroup differences were consistently detected across similar FC patterns.
### 3.2. Overlap With Reference Functional Networks
Investigation of the overlap between the centroids of the detected FC states and the seven canonical functional networks defined by Thomas Yeo et al. ( ), depicted in , confirmed that intergroup differences were consistently detected in a number of varying forms of the same FC patterns. Interestingly, FC state 1 did not significantly overlap with any of the seven reference RSNs, as shown in —indicating this global state does not reveal the activation of any particular subset of functionally coupled brain regions. Additionally, across partition models, the non-global FC states with a significantly increased mean fractional occupancy in SZ were found to repeatedly overlap with the Somatomotor, Dorsal Attention and Limbic networks, as illustrated in . The mean dwell time of FC states related to the Dorsal Attention network was significantly increased in the SZ group compared to the HC group. Accordingly, FC states with functional activity possibly related to that of the aforementioned canonical RSNs recur more often (and lasted for larger consecutive periods of time) in SZ patients.
### 3.3. Internal Validation of K-Means Clustering Solutions
As shown in , the highest average Silhouette coefficient and Dunn's index were obtained for clustering solutions with a low number of FC states, which are of limited interest for the present study. Contrarily, for clustering solutions with more than 12 clusters, both validation measures remained relatively constant at low values—indicating such partitions are also of limited interest.
Internal validation of K -means clustering results. Average Silhouette coefficient and Dunn's index used to evaluate the quality of clustering solutions.
Notably, for clustering solutions with K between 7 and 11, the average Silhouette coefficient decreased smoothly and the Dunn's index remained approximately constant, as observed in —suggesting these partition models are of potential interest for further analysis.
### 3.4. Selection of the Optimal Clustering Solution
For the subsequent analysis, the partition model with 11 FC states was selected as the optimal K -means clustering solution. This decision was based upon the ability to identify a collection of FC states with properties that significantly differed between groups and the quality and stability of the actual partition of the data.
The collection of 11 phase coherence patterns, their fractional occupancy and dwell time values are presented in . The non-global FC states were found to be significantly correlated with six of the seven RSNs estimated by Thomas Yeo et al. ( ), as shown in . From , it is apparent that the 11 FC states represent phase coherence between distinct subsets of brain areas. Furthermore, significant intergroup differences were identified in the mean fractional occupancy of 4 FC states, as observed in . Closer inspection of reveals that these 4 FC states represent distinct functionally meaningful networks. The mean fractional occupancy of FC state 1 was significantly decreased in SZ patients compared to HCs ( p < α ; Hedge's g = 0.694, medium to large effect size), with estimates 29.8 ± 19.8% and 39.8 ± 14.9% (mean ± std) for the SZ and HC groups, respectively. Furthermore, the mean fractional occupancy of FC states 5, 9, and 10 was significantly increased in SZ patients compared to HCs ( p < α ; Hedge's g = {0.611, 0.630, 0.629}, respectively, medium to large effect size), with values 7.61 ± 7.78%, 5.67 ± 6.34%, and 5.14 ± 4.09% for the SZ group and 4.02 ± 3.15%, 2.52 ± 3.24%, and 2.94 ± 2.81% for the HC group, respectively (mean ± std). Finally, the mean dwell time of 2 FC states was found to be significantly different between groups, as shown in . Specifically, the mean dwell time of the Global Mode was significantly decreased in SZ patients compared to HCs ( p < α ; Hedge's g = 0.618, medium to large effect size). The mean dwell time value of this state was 4.986 ± 2.306 s and 6.574 ± 2.803 s (mean ± std) for SZ patients and HCs, respectively. The mean dwell time of FC state 9 was significantly increased in SZ patients compared to HCs ( p < α ; Hedge's g = 0.519, medium to large effect size), with values 2.287 ± 0.763 s for the SZ group and 1.830 ± 0.977 s for the control group (mean ± std).
Repertoire of FC states defined from phase coherence obtained from the clustering solution with K = 11 clusters. The FC states are arranged (left-to-right) according to decreasing estimated fractional occupancy. Each FC state is represented by a N × 1 centroid V , with α∈{1, …, 11}. (A) Cortical rendering of all brain areas with positive values in V . The functional network defined by Thomas Yeo et al. ( ) with which V most significantly overlapped is indicated as subtitle. (B) Vector representation showing the N elements in V , representing the contribution of each brain area to FC state α. (C) Boxplot of the fractional occupancy values for each FC state for the SZ and HC groups. (D) Boxplot of the dwell time values for each FC state for the SZ and HC groups. Single and double asterisks indicate significant intergroup differences with p < α and p < α (one-tailed tests), respectively. Green points represent outliers, according to the Tukey criterion (Tukey, ).
Furthermore, the percent agreement, ARI and VI obtained for each fold of the 10-fold cross-validation procedure are provided in . The results suggest respectively, good levels of association and paired agreement between partitions of the test sample and that the amount of information that was lost in changing from the class set P to the cluster set P of the test sample was relatively low. Consequently, the optimal clustering solution is considered valid and appropriate for further analysis.
### 3.5. State-to-State Transitions of the Optimal State Trajectories
With respect to the optimal clustering solution, for all participants, the individual DTMC defined by the temporal trajectories through the finite state space S ′ = {1, …, 11}, was characterized by its estimated TPM. For each probability of transitioning from state α to state β (α → β, α, β∈ S ′), a two-sided p -value was obtained by testing whether its group mean differed between groups. The state-to-state transition probabilities that were significantly affected in SZ patients compared to HCs are depicted in .
Transition diagram of the state-to-state transitions significantly altered in SZ patients compared to HCs. Arrows represent a mean transition probability that was significantly increased (green) or decreased (red) in SZ patients compared to HCs. Single and double asterisks indicate, respectively, significant intergroup differences with p < 0.05/11 and p <0.05/(11 × 11) (one-tailed tests).
As shown in , the mean probability of remaining in FC state 1 was significantly reduced in SZ patients compared to HCs (Hedge's g = 0.726, medium to large effect size). Furthermore, the mean probability of remaining in FC states 2 and 7 was significantly reduced in the SZ group compared to the HC group (Hedge's g = {0.449, 0.515}, respectively, small to medium effect size). Lastly, the mean probability of transitioning from FC state 1 to FC states 5 and 10 (Hedge's g = {0.452, 0.513}, respectively, small to medium effect size) and from FC state 9 to FC state 10 (Hedge's g = 0.461, small to medium effect size) were significantly increased in SZ patients compared to HCs. Overall, a number of mean transition probabilities were found to be altered in SZ patients.
### 3.6. Limiting Probabilities of the Optimal FC States
For the subgroup of 37 HCs and 46 SZ patients with irreducible and aperiodic DTMCs, the estimated mean long-run proportion of TRs spent in FC state 1 was, respectively, 0.309 ± 0.104 and 0.272 ± 0.111 (mean ± std). Surprisingly, no intergroup differences were found in the mean limiting probability of this state (two-tailed test; Hedge's g = 0.342, small to medium effect size). Only the mean limiting probability of FC states 5 and 10 were identified as significantly increased in the SZ subgroup compared to the HC subgroup ( p < 0.05, one-tailed tests; Hedge's g = {0.464, 0.449}, respectively, small to medium effect size).
### 3.7. Influence of Using the K-Medoids Algorithm Instead of the K-Means Algorithm
The application of the K -medoids algorithm was found to enable the detection of FC states with a mean fractional occupancy and a mean dwell time that consistently and significantly differ between groups. Similarly with the findings produced by the K -means algorithm, the K -medoids algorithm identified an FC state which represents a globally synchronized state whose mean fractional occupancy and dwell time was significantly decreased in SZ patients compared to HCs. Additionally, the mean fractional occupancy of a number of non-global FC states related to the reference Somatomotor, Dorsal Attention and Limbic RSNs was found to be significantly increased in SZ patients compared to HCs. Finally, the mean dwell time of FC states related to the Dorsal Attention and Limbic networks was found to be significantly increased in SZ patients compared to HCs.
The ARI and VI showed that the clustering solutions with the same number of FC states detected by each of the clustering algorithms were dissimilar. Interestingly, for each K , with K ∈{2, …, 20}, the FC states (centroids/medoids) detected by each of the clustering algorithms with significant intergroup differences in the mean fractional occupancy and mean dwell time ( p < α , two-tailed tests) were found to be highly correlated. Therefore, both the K -means and the K -medoids algorithms were found to effectively identify similar FC states whose properties provide the capacity to differentiate SZ patients from HCs.
## 4. Discussion
This study investigated differences in resting-state brain activity between schizophrenia patients and healthy controls. This was done from the perspective of dynamical systems theory by considering the exploration of functional networks as trajectories through a state space—providing an insightful framework to interpret brain activity alterations in schizophrenia.
Overall, SZ patients were found to spend less time in a globally synchronized state, or Global Mode, in line with previous studies using different analytical approaches (Damaraju et al., ; Rabany et al., ; Sanfratello et al., ). Conversely, a repertoire of non-global FC states, involving the phase synchronization of brain areas belonging to the Somatomotor, Dorsal Attention and Limbic RSNs, were shown to recur more often in SZ patients. These non-global FC states have been previously referred to as “ghost” attractor states since they appear briefly and erratically, yet consistently and recurrently across subjects (Vohryzek et al., ). The detection of increased excursions to a subset of these “ghost” attractor states in schizophrenia is suggestive of alterations in the energy landscape of brain activity. In particular, the RSNs that recurred more often have been previously associated with schizophrenia symptoms. In fact, the Somatomotor network was related to motor and negative symptoms of schizophrenia (Bernard et al., ), the Limbic network was related to positive symptoms (such as paranoid ideation; Walther et al., ) and to disorganization (Lin et al., ) and the Dorsal Attention network was related to the regulation of attention Kandilarova et al. ( ), as well as to positive and negative symptoms' improvement after antipsychotic treatment (Kraguljac et al., ).
Regarding state-to-state transitions, SZ patients were found to be less likely to remain in the globally synchronized state, in line with previous studies (Rabany et al., ). Importantly, this state has been linked to greater neural flexible switching via integration or segregation of different functional connections (Cabral et al., ; Nomi et al., ). Therefore, the reduced ability of SZ patients to access and remain in this state could be hypothesized to provoke impaired integration of functionally meaningful networks (Dong et al., ). Additionally, SZ patients were found to have a higher probability of transitioning from the Global Mode to states related to the Somatomotor and Limbic RSNs and from a Dorsal Attention-related network to a Limbic-related network—reinforcing the role of brain activity alterations across these “ghost” attractor states in schizophrenia. Another interesting finding was the reduced ability to remain in a state related to the Default RSN in SZ patients. In fact, this RSN has been linked to core processes of human cognition (Greicius et al., ; van den Heuvel and Hulshoff Pol, )—support the view of schizophrenia as a disorder affecting cognitive function. Finally, the reduced ability of SZ patients to remain in an Orbitofrontal Network—a network hypothesized to be involved in sensory integration, monitoring the reward value of reinforcers, decision making and expectation (Kringelbach and Rolls, ). This could potentially explain some of the positive and negative symptoms associated with this disorder. These findings provide additional evidence of the altered energy landscape in schizophrenia. However, how these alterations translate into distorted cognition and behavior remains completely unclear and future investigations should gather a diverse panel of experts to explore how these findings could be applied to improve our understanding of schizophrenia.
Despite the lack of a full understanding of the relationship between connectivity patterns observed in Electroencephalography (EEG) and fMRI, these findings could be speculated to portray a temporal dynamics related to that observed with EEG microstates measured at a different time resolution. In fact, in line with the aforementioned findings, EEG studies have reported an increased occurrence of a microstate associated with the Limbic RSN (Ramos da Cruz et al., ) and unexpectedly more transitions from a microstate associated with the Attention RSN to a microstate associated with the Limbic RSN in schizophrenia (Rieger et al., ).
On the question of the influence of using the K -medoids algorithm to conduct an LEiDA analysis, this study found that similar intergroup differences are captured by employing either the K -medoids algorithm or the K -means algorithm. This finding suggests that the choice of an optimal clustering algorithm should rely not only on statistical and cluster validation analyses, but also on concepts and methods from dynamical systems theory (Deco and Jirsa, ; Cabral et al., ; Vohryzek et al., ). On the one hand, from the definition of the K -means algorithm, the detected FC states (centroids) are not necessarily observations from the input dataset, but could rather be interpreted as averaged recurrent unobserved FC patterns; hence their designation as “ghost” attractor states (Vohryzek et al., ). However, the definition of the K -medoids algorithm implied the detected FC states (medoids) are observed recurrent FC patterns. Research questions pertaining to the functional meaning of the detected FC patterns underline the need to employ tools from dynamical systems theory to provide further insights into the dynamical regime of brain activity (Deco and Jirsa, ; Cabral et al., ; Vohryzek et al., ).
Developing on from previous LEiDA analyses, this study proposes examining the limiting probability of FC states. This property offers valuable insights into the long-run proportion of time that a DTMC spends in each state. Specifically, this measure is computed from the TPMs which characterize the state trajectories—capturing dynamic behavior of brain activity to a greater extent than fractional occupancy. However, considerably more research will need to be conducted to determine its utility. Furthermore, the measurements of this property are derived from the estimation of the stationary distribution of the state trajectories, defined as irreducible and aperiodic DTMCs. A natural progression of this work is to examine whether intergroup differences in the stationary distributions provide further insights into the limiting dynamic behavior of brain activity in diseased and healthy populations. This could be achieved by employing the two-sample goodness of fit χ test.
One shortcoming of this study which could have affected the measurements of both the state-to-state transition and state limiting probabilities is the low temporal resolution of the neuroimaging data (TR = 2 s). This was most clearly observed from the inconsistencies found across state trajectories obtained from the optimal clustering solution where, oftentimes, the occurrence of all FC states was not guaranteed. In fact, Magnetoencephalography (MEG) studies have suggested that brain functional connectivity dynamics occurs at time scales of approximately 200 ms (Baker et al., ; Vidaurre et al., ). Accordingly, future work should utilize data with higher temporal resolution to enable the capture of more rapid dynamics—improving the utility of these properties as possible biomarkers of schizophrenia.
Another limitation of this study is that the detected FC states were strongly constrained by the selected parcellation atlas (AAL). Despite having shown consistent results across studies employing LEiDA (Cabral et al., ; Figueroa et al., ; Lord et al., ; Larabi et al., ; Vohryzek et al., ), the AAL template is based on an anatomical parcellation and, therefore, may not provide an adequate framework to conduct an analysis of dFC. Accordingly, future studies could extend this analysis to other fMRI-derived anatomical or functional parcellations.
The effect of variables such as age, gender and clinical history of patients were not taken into account while assessing intergroup differences. Specifically, intergroup differences were attributed exclusively to the effect of the group. Further research is required to determine whether these variables or their interaction could explain the variability found between groups. Another unaddressed issue was whether not applying nuisance regression strategies influenced the LEiDA method and therefore, the observed intergroup differences. Future investigations on this question could contribute with valuable insights into this controversial preprocessing step. Despite these weaknesses and supported by the complementary analysis presented in , based on a large sample, this study provided unbiased and statistically rigorous evidence for differences between patients with schizophrenia and healthy controls—leading to increased confidence in the biomarking value of the findings from this research.
## 5. Conclusion
Resting-state dynamic functional connectivity comparisons were conducted between schizophrenia patients and healthy controls by employing and extending the LEiDA method. Through the characterization of the temporal expression of different FC states, this study found that schizophrenia patients exhibit an altered energy landscape of brain activity. An implication of this is the possibility that, even in the absence of any explicit task, schizophrenia patients transition more frequently to network patterns that are commonly activated during specific tasks.
## Data Availability Statement
The imaging data was collected and shared by the Mind Research Network and the University of New Mexico funded by a National Institute of Health Center of Biomedical Research Excellence (COBRE) grant 1P20RR021938-01A2. The datasets analyzed for this study can be found in the following repository: .
## Ethics Statement
The studies involving human participants were reviewed and approved by National Ethics Committee. The patients/participants provided their written informed consent to participate in this study.
## Author Contributions
MF carried out the analysis and wrote the main manuscript. JC contributed with codes. JC and CA verified and advised the theoretical methods and supervised the whole project. PM was responsible for funding acquisition. MF, JC, and PM contributed in the final writing of the manuscript. All authors participated in the discussion of the ideas, read, and approved the submitted version.
## Funding
This work was funded by FLAD Science Award Mental Health 2021. This work was partially funded by National funds, through the Portuguese Foundation for Science and Technology (FCT)—projects UIDB/50026/2020 and UIDP/50026/2020. JC is funded by FCT grant CEECIND/03325/2017.
## Conflict of Interest
PM has received in the past 3 years grants, CME-related honoraria, or consulting fees from Angelini, AstraZeneca, Bial, Biogen, DGS-Portugal, FCT, FLAD, Janssen-Cilag, Gulbenkian Foundation, Lundbeck, Springer Healthcare, Tecnimede, Viatris, and 2CA-Braga outside of this study. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's Note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
|
## Objective
Central glucagon-like peptide-1 (GLP-1) is a target in treating obesity due to its effect on suppressing appetite, but the possible downstream key genes that GLP-1 regulated have not been studied in depth. This study intends to screen out the downstream feeding regulation genes of central GLP-1 neurons through bioinformatics analysis and verify them by chemical genetics, which may provide insights for future research.
## Materials and methods
GSE135862 genetic expression profiles were extracted from the Gene Expression Omnibus (GEO) database. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were carried out. STRING database and Cytoscape software were used to map the protein-protein interaction (PPI) network of the differentially expressed genes (DEGs). After bioinformatics analysis, we applied chemogenetic methods to modulate the activities of GLP-1 neurons in the nucleus tractus solitarius (NTS) and observed the alterations of screened differential genes and their protein expressions in the hypothalamus under different excitatory conditions of GLP-1 neurons.
## Results
A total of 49 DEGs were discovered, including 38 downregulated genes and 11 upregulated genes. The two genes with the highest expression scores were biglycan ( Bgn ) and mitogen-activated protein kinase activated protein kinase 3 ( Mapkapk3 ). The results of GO analysis showed that there were 10 molecular functions of differential genes. Differential genes were mainly localized in seven regions around the cells, and enriched in 10 biology processes. The results of the KEGG signaling pathway enrichment analysis showed that differential genes played an important role in seven pathways. The top 15 genes selected according to the Cytoscape software included Bgn and Mapkapk3 . Chemogenetic activation of GLP-1 in NTS induced a decrease in food intake and body mass, while chemogenetic inhibition induced the opposite effect. The gene and protein expression of GLP-1 were upregulated in NTS when activated by chemogenetics. In addition, the expression of Bgn was upregulated and that of Mapkapk3 was downregulated in the hypothalamus.
## Conclusion
Our data showed that GLP-1 could modulate the protein expression of Bgn and Mapkapk3. Our findings elucidated the regulatory network in GLP-1 to obesity and might provide a novel diagnostic and therapeutic target for obesity.
## Introduction
Over the past 40 years, the prevalence of obesity has increased substantially worldwide. The prevalence in children increased from less than 1% in 1975 to 6 to 8% in 2016, in adult males increased from 3 to 11%, and in adult females increased from 6 to 15% during the same period ( ). China has the highest number of obese people in the world, with about 46% of adults and 15% of children suffering from obesity or being overweight ( ). As one of the most important risk factors for diabetes, cardiovascular disease, cancer, osteoarthritis, movement disorders, and sleep apnea, obesity seriously affects human health ( ). One of the most effective solutions to obesity is to control appetite and reduce caloric intake ( ). Various therapies have shown efficacy to treat obesity, including pharmacotherapy ( ), operation therapy ( ), and even traditional Chinese medicine (TCM) ( ; ).
Appetite is regulated by a complex system of central and peripheral signals that interact to modulate an individual’s response to nutrition. Peripheral regulation includes satiety signaling and adiposity signaling, whereas central regulation is accomplished by a variety of factors, including the neuropeptidergic, monoaminergic, and endocannabinoid systems. Satiety signals mainly include cholecystokinin (CCK), GLP-1, and casein tyrosine ( ).
Glucagon-like peptide 1 is an intestinal hormone that is released in response to food intake. It can enhance the stimulation of insulin synthesis and secretion by glucose while inhibiting the secretion of glucagon and delaying gastric emptying ( ). GLP-1 exerts an inhibitory effect on food intake via its receptor GLP-1R, which is widely distributed in the brain, gastrointestinal tract, and pancreas ( ). In clinical practice, medication and surgery are widely used. The mechanisms of these interventions in treating obesity are all related to the regulation of GLP-1 ( ; ). Some non-pharmaceutical TCM therapies, such as acupuncture, have also shown the potential to affect metabolism by modulating GLP-1 ( ). GLP-1 is well-known to medical workers as an important hormone in appetite regulatory pathways, but little is known about how they act in the central nervous system and peripheral nervous system to produce enhanced satiety and inhibit appetite mechanisms. At present, high-throughput gene chip technology and bioinformatics analysis have been widely used in the pathogenesis of diseases, molecular diagnosis, and other aspects ( ). New scientific and technological methods represented by genomics approaches provide a large amount of research data for mechanistic studies of appetite regulation. Therefore, in this study, we used high-throughput sequencing data from public platforms to decipher the central regulatory mechanism of GLP-1R agonist (GLP1-RA) administration through a bioinformatics approach. Overall, this study advances our understanding of the central mechanisms that synergize the inhibitory effects of appetite and body weight to treat obesity and provides a basis for further research.
## Materials and methods
### Gene chip data acquisition
The high-throughput dataset GSE135862 related to the mechanisms of appetite regulation by GLP-1/CCK was retrieved and downloaded from GEO, a comprehensive database of gene expression. The chip contains a total of 48 samples. All of the studies were conducted in 9- to 10-week-old C57/Bl6 male mice, which were maintained on standard chow and fasted for 6 h before treatment administration. Multiple variables such as injection of drugs, injection sites, and group settings were included in the experimental conditions. Mice were randomized into treatment groups and received an IP injection of saline, AC710222, exenatide, AC3174, AC170222, or a combination of these drugs twice daily for 5 days. The injection sites were the dorsal vagal complex (DVC) and the medial basal hypothalamus (MBH), respectively. The experimental content is RIP-Seq (RNA co-immunoprecipitation combined with high-throughput sequencing). The purpose of this study was to investigate the pathway of GLP-1 in the brain. In this study, we selected the groups injected with GLP1-RA AC3174 and the blank control group for data comparison and analysis.
### Screening of differentially expressed genes
Samples injected with drug as saline were set as the control group and samples injected with drug as GLP1-RA were set as the intervention group using P < 0.05, log value of gene fold change (logFC) <−1, or logFC > 1 as the criterion ( ). The GSE135862 dataset was analyzed by the R language DESeq2 package to screen differentially expressed genes between different groups of the dataset and find out differentially expressed genes that may be involved in the regulation of appetite by GLP-1R.
### Gene ontology and Kyoto encyclopedia of genes and genomes pathway enrichment analysis of differentially expressed genes
Gene ontology is a commonly used analytical method whose main function is to annotate genes or their products and identify the characteristic biological characteristics of high-throughput genomic or transcriptome data. GO annotates and classifies genes according to Biological Process (BP), Molecular Function (MF), and Cellular Component (CC). The KEGG database is available for querying pathway information and signaling pathway retrieval, and so on. The main goal of the KEGG database is to endow genes and genomes with functional significance at the molecular level and higher levels, which establish links between genes in the genome and advanced functions of cells and organisms ( , ). This study used the DAVID (version: 6.8) database for integrative analysis. Using Fisher Exact or EASE Score statistical methods, GO items were screened with P < 0.05, false discovery rate (FDR) <1, and KEGG items with P < 0.05 ( ).
### Analysis of differentially expressed gene protein interaction network
STRING (version: 11.5) is an online analysis tool that can be used to present and evaluate interactions between proteins ( ). The magnitude of the likelihood of a protein is evaluated by scoring its mutual-action network. In this study, the analyzed differential genes were input into the STRING analysis tool to find potential links between them. The screening condition for constructing the interaction network of differentially gene-encoded proteins was the threshold of interaction likelihood. The STRING database export results were then imported into Cytoscape 3.9.1 software for visual analysis. The cytoHubba plugin in the software was used to find out the top 15 hub genes scored according to the Maximal Clique Centrality (MCC) and Degree scoring methods, respectively ( ).
### Preparation of recombinant adeno-associated virus
Promoter sequences of GLP-1 protein were determined by searching the gene database and published relevant literature ( ; ). Customized recombinant adeno-associated virus (rAAV), which included GLP-1 promoter and cyclization recombination enzyme (cre) applied in this experiment, was provided by Wuhan BrainVTA Technology Co., Ltd. The sequences of the other three rAAVs are as follows: rAAV-Efla-DIO-EGFP-WPRE-pA (rAAV-GFP), rAAV-hSyn-DIO-hM3D (Gq) -mCherry-WPRE-pA (rAAV-HM3D), and rAAV-hSyn-DIO-hM4D (Gi) -mCherry-WPRE-pA (rAAV-HM4D), which were purchased from commercial companies (BrainVTA Co., Ltd, Wuhan, China).
### Animals and intervention
We acquired 15 male Sprague–Dawley rats, aged 6 weeks, and weighing 180 to 200 g from Beijing Vital River Laboratory Animal Technology Co., Ltd. (Beijing, China, No. 11400700298971). All rats were placed in a controlled environment at 22 ± 2°C and 50 ± 10% relative humidity with a 12-h light/12-h dark cycle in the Experimental Animal Center, Zhongnan Hospital of Wuhan University, with standard food and water provided ad libitum for the duration of the study. The study protocol was authorized by the Institutional Animal Care and Use Committee of Wuhan University, Wuhan City, Hubei Province, China (AUP Number: WP2020-08085).
After 1 week of adaptive feeding, all rats were numbered according to body weight, and 15 rats were divided into the GLP-1 group ( n = 5), HM3D group ( n = 5), and HM4D group ( n = 5) by stratified randomization. Stereotactic injection of NTS was performed according to validated parameters (AP: Lambda-3.2 mm, ML: ± 0.5–0.7 mm, DV: 9.6 mm, and oblique angle backward 24°) for injection ( ). Rats in each group were injected with rAAV as follows: rats in the GLP-1 group were injected with rAAV-GLP-1 and rAAV-GFP into NTS with 260 nl of each rAAV; rats in the HM3D group were injected with rAAV-GLP-1 and rAAV-3D into NTS, 260 nl of each rAAV; and rats in HM4D group were injected with rAAV-GLP-1 and rAAV-4D, 260 nl of each rAAV. The chronological order of injection was based on the body weight of the rats, and the rats whose body weight first reached about 400 g were injected first, and finally, the NTS virus injection was completed in all rats at about 5 days. All rats were observed for 1 week after the completion of the injection, with the supplement of food and water ad libitum during the observation period. Specific rAAV gene sequence and combinatorial strategy were shown in . Three weeks after rAAV injection in all rats, the exogenous gene carried by rAAV was expressed in target neurons. We collected the baseline data of all rats (body mass, lee’s index, and 24 h food intake) 3 weeks after the injection of the last rat, and assigned this day as day 0. Rats in all three groups received four times intraperitoneal injections of Clozapine-N-oxide (CNO) at a dose of 1 mg/kg on the 1st, 3rd, 6th, and 9th day of the experiment ( ).
Gene sequence and combinatorial strategy of rAAVs.
### Behavior test
After collecting the baseline data on day 0 of the experiment, we intraperitoneally injected CNO into all rats on the 1st day and recorded the cumulative food intake at 0.5, 1, 2, and 24 h after injection to evaluate the short-term feeding behavior of rats after regulating neuronal excitability. Subsequently, we recorded the cumulative 24 h food intake after the rats received CNO injection and body weight on the 3rd, 6th, and 9th days of the experiment, which aims to observe the long-term feeding behavior and body weight changes of rats after multiple regulations of neuronal excitability.
### Western blotting
All rats were anesthetized and sacrificed by cervical dislocation after 9 days of treatment. Thirty minutes before sacrifice, rats in HM3D and HM4D groups were in intraperitoneal injection with CNO to activate or inhibit GLP-1 neurons. We froze the tissue of NTS and hypothalamus in liquid nitrogen after stripping them from the rat brains and stored them at −80°C. NTS tissue was detected with GLP-1 antibody, while hypothalamus tissue was detected with Bgn antibody and Mapkapk3 antibody applying a standard WB protocol. The primary antibody concentrations were as follows: GLP-1 (1:1000, AF0166, Affinity, Japan), Bgn (1:1000, 16409-1-AP, Proteintech, China), and Mapkapk3 (1:1000, 15424-1-AP, China). We used the housekeeping protein β-actin (1:1000; Proteintech, China) for normalization. We performed WB in triplicate for all target proteins. Protein expression was calculated based on the target protein and β-actin ratios of optical density, which we analyzed using the BandScan software.
### Reverse transcription-quantitative polymerase chain reaction
We isolated total RNA of rat NTS and hypothalamus stored at −80°C with an AZfresh total RNA extraction kit (15596-026, Ambion, United States) and determined RNA concentrations at an absorbance ratio of 260/280 nm. We then reverse transcribed an aliquot (1 μg) of extracted RNA into first-strain complementary DNA (cDNA) using a ReverTra Ace qPCR RT kit (R223-01, VAZYME). We quantified gene expression of GLP-1 of NTS, Bgn, and Mapkapk3 of the hypothalamus by using an SYBR Green real-time PCR Master Mix Plus (Q111-02; VAZYME) and standard protocol. Measurements were conducted in triplicate under standard reaction conditions, and normalization was ensured to β-actin. We obtained primaries from the Biofavor Technology Company (Wuhan, China). All temperature circulation and gene amplification were processed in a CFX96 Touch real-time PCR detection system (Bio-Rad). All RT-PCR assays and primer sequences are shown in .
Real-time PCR primer sequences.
### Immunofluorescence staining
Rats were anesthetized with isoflurane and received transcardial perfusion with saline and 5% paraformaldehyde. Rat brains were immersion in 5% paraformaldehyde for 72 h for perfusion fixation followed by dehydration in 25% sucrose solution. The coronal plane where the NTS and hypothalamus are located was selected for the frozen section. The frozen sections in the NTS area with a thickness of 10 μm were selected. After thawing by natural gradient, 10% donkey serum or goat serum was used for blocking. After repairing with sodium citrate antigen retrieval solution, which leads the virus fluorescence to be destroyed, the three groups of brain slices were incubated with c-fos primary antibody. In GLP-1 group, GLP-1 neurons were labeled with anti-GFP (1:2000, ab5450, Abcam, United States) + green fluorescent secondary antibody (1:500, Alexa Fluor 488 Donkey Anti-Goat, ab150129, Abcam, United States), and activated neurons were labeled with anti-cfos (#2250S, Cell Signaling Technology, United States) + red fluorescent secondary antibody (1:500, SA00013-8, CoraLite594 Donkey Anti-Rabbit, Proteintech, China). In HM3D and HM4D groups, GLP-1 neurons were labeled with anti-DsRed (632496, TAKARA, Japan) + red fluorescent secondary antibody (1:800, SA00013-4, CoraLite594 Goat Anti-Rabbit, Proteintech, China) and activated neurons were labeled with anti-cfos (1:2000, ab208942, Abcam, United States) + green fluorescent secondary antibody (1:200, SA00013-1, CoraLite488 Goat Anti-Mouse, Proteintech, China). The co-expression of GLP-1 neurons and cfos in the neuron cells indicated that GLP-1 neurons were activated. The activated GLP-1 neurons in the NTS area of each group were counted and statistically analyzed. The protein expression of differential genes in the hypothalamus of rats in each group was demonstrated by immunofluorescence single labeling. The tissue of the hypothalamus was prepared into frozen slices with a thickness of 20 μm, cubed with anti-Bgn (1:100, 16409-1-AP, Proteintech, China) or anti-Mapkapk3 (1:200, 15424-1-AP, Proteintech, China), and labeled with a green fluorescent (1:500, SA00013-2, CoraLite488 Goat Anti-Rabbit, Proteintech, China). Nuclei were stained with DAPI and observed under a fluorescence microscope. The expression of Bgn and Mapkapk3 in the hypothalamus was analyzed by the imagepro plus 6.0 software with integrated optical density (IOD) for fluorescence intensity analysis.
### Statistical analysis
SPSS 25.0 software package was used for statistical analysis. The results were expressed as mean ± standard deviation (x ± s). One-way analysis of variance was used for comparison between groups within the same time period. If there was an overall difference, multiple comparisons with the Tukey’s method were used. Repeated measures analysis of variance was used to compare different time points within the same group, and if there were differences, multiple comparisons with the Tukey’s method were used. P < 0.05 was considered significant.
## Results
### Differentially expressed gene analysis results
Based on the screening criteria of P < 0.05, |logFC |> 1, 49 related differentially expressed genes were analyzed in the GSE135862 dataset. Among them, there were 11 upregulated differentially expressed genes and 38 downregulated differentially expressed genes. According to the expression fold of the differential genes, the related genes with the largest differential fold were selected, which then upregulated Bgn and down-regulated Mapkapk3 , respectively. A specific heatmap can be seen in .
Heatmap of DEGs. Red areas represent highly expressed genes and green areas represent lowly expressed genes. DEGs, differentially expressed genes.
### Gene ontology and Kyoto encyclopedia of genes and genomes pathway enrichment analysis results of differentially expressed genes
The results of GO analysis showed that the molecular functions of differential genes mainly included pseudouridine synthase activity, BMP receptor binding, xylosyltransferase activity, proton antiporter activity (sodium, monovalent cation, potassium), calcium-dependent protein kinase activity, low-density lipoprotein particle receptor activity, transferase activity, and transferring glycosyl and pentosyl groups; differential genes were mainly localized in seven regions around the cells, which were catenin complex, organelle membrane contact site, nuclear outer membrane, varicosity, mitochondria-associated ER membrane, nuclear lamina, and transport vesicle and extracellular matrix; and the biological pathways in which differential genes were involved included apoptotic process involved in development, extracellular structure organization, activation of MAPKK activity, apoptotic process involved in morphogenesis, positive regulation of coagulation, positive regulation of hemostasis, positive regulation of blood coagulation, regulation of apoptotic process involved in development, cell junction maintenance, and regulation of apoptotic process involved in morphogenesis. The results of KEGG signaling pathway enrichment analysis showed that differential genes played an important role in platelet activation pathway, complement and coagulation cascades pathway, other glycan degradation pathway, other type of O-glycan biosynthesis pathway, cellular senescence pathway, cytokine-cytokine receptor interaction pathway, and apoptosis pathway. Specific results can be seen in .
Gene ontology and KEGG enrichment analysis. (A) GO enrichment result of DEGs; (B) the results of KEGG signaling pathway enrichment analysis; The x -axis label represents P -value and the number of genes and the y -axis label represents GO and KEGG terms, different colors represent different classification. DEGs, differentially expressed genes; GO, gene ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes pathway.
### Protein-protein interaction networks and important genes of differentially expressed genes
The PPI network of DEGs and its modules are shown in . With the STRING database, 46 of 49 DEGs were mapped onto a PPI network. There are 46 nodes and 66 edges ( ). After the PPI network is imported into Cytoscape, the key nodes and subnetworks in the PPI network are predicted and explored by several topological algorithms using the cytoHubba plugin. The top 15 genes selected according to the MCC and degree method were F12 , Gadd45b , Cdh12 , Nyx , Mapkapk3 , Bgn , Tnf , Lmnb1 , Tnfrsf1b , F2r , Bmpr1a , Acvr1 , Bmp7 , Nog , and F2 ( ).
The PPI networks of DEGs and modules. (A) PPI network analyzed by STRING database; (B,C) the top 15 genes selected according to the MCC and degree method. PPI, protein-protein interaction; DEGs, differentially expressed genes; MCC, maximal clique centrality.
### Different glucagon-like peptide 1 neuronal excitability modulates appetite-related behavior
As shown in , there was no significant difference in body weight, Lee’s index, and 24-h food intake among the three groups before intervention ( P > 0.05). After the first time of CNO injection, the feeding behavior of the three groups of rats showed significant differences ( ). Compared with the GLP-1 group, rats in the HM3D group showed a significant reduction in food intake from 1 h after CNO injection ( P < 0.05), and the cumulative food intake was significantly lower than that in the GLP-1 group at 1 h, 2 h, and 24 h after CNO injection ( P < 0.05 or P < 0.01). Compared with the GLP-1 group, the HM4D group showed a significant increase in food intake only at 1 h after CNO injection ( P < 0.01); compared with the HM3D group, the food intake of the HM4D group increased significantly at 1, 2, and 24 h after injection ( P < 0.05 or P < 0.01). There was no statistical difference in the 24-h food intake before CNO injection among the groups of rats, which showed the highest food intake in the HM4D group and the lowest food intake in the HM3D group on days 3, 6, and 9 of injection ( ). The body weights of rats in the three groups showed different trends during the experiment. Rats in the GLP-1 group did not show significant variation in body weights after CNO injection. The body weights of rats in the HM3D group were lower than the baseline since the 6th day after CNO injection ( P < 0.05), and the body weights of rats in the HM4D group increased since the 3rd day after CNO injection. However, we did not observe significant differences in body weight among the three groups of rats at each time point ( ).
Appetite-related behavioral changes in each group. (A–C) Baseline of body mass, lee’s index, and 24 h food intake of rats in each group, which were collected at day 0; (D) cumulative food intake of rats in each group at different time points after the first CNO injection; (E) changes in 24 h food intake of rats in each group after CNO injection during the intervention period; (F) changes in body mass of rats in each group during the intervention period; vs. GLP-1 group, * P < 0.05, ** P < 0.01; vs. HM3D group, P < 0.05, P < 0.01; vs. day 0, P < 0.05, P < 0.01. CNO, Clozapine-N-oxide.
### Chemical genetics technology can activate or inhibit glucagon-like peptide 1 neurons in the nucleus tractus solitarius
As shown in , both the protein and gene expression of GLP-1 in NTS were upregulated in the HM3D group, while in the HM4D group were downregulated. As shown in , the GLP-1 neurons in the NTS region of the three groups of rats were successfully labeled with GFP or DsRed primary antibody, and the GLP-1 neurons expressed different amounts of c-fos. The number of activated GLP-1 neurons in the HM3D group was significantly higher than that in the GLP-1 group and the HM4D group ( P < 0.05); the number of activated neurons in the GLP-1 group was higher than that in the HM4D group ( P < 0.05) ( ). The results indicated that CNO injection could effectively activate or inhibit GLP-1 neurons in NTS.
Comparison of protein and gene expression of GLP-1 in NTS. (A,B) WB and protein expression of GLP-1in NTS; (C) gene expression of GLP-1 in NTS; (D) the slice range and localization pattern of the coronal plane where the NTS is located; (E) comparison of activation numbers of GLP-1 neurons in NTS; (F) representative figures of activated GLP-1 neurons in the NTS of rats in each group; * P < 0.05, ** P < 0.01. WB, Western blotting; GLP-1, glucagon-like peptide 1; cc, central canal; NTS, nucleus of the solitary tract.
### Different excitatory properties of glucagon-like peptide 1 neurons regulate the protein and gene expression of biglycan and mitogen-activated protein kinase activated protein kinase 3 in the hypothalamus
As shown in , the activation of GLP-1 neurons upregulated the gene and protein expression of Bgn in the hypothalamus ( P < 0.01); while suppressing it induced the opposite effect ( P < 0.01). On the contrary, activated GLP-1 neurons can downregulate the gene and protein expression of Mapkapk3 in the hypothalamus ( P < 0.01), while suppressing GLP-1 can activate Mapkapk3 ( P < 0.01) ( ). Observing the fluorescence images of the hypothalamus, it was found that both Bgn and Mapkapk3 proteins were widely expressed in the hypothalamus without obvious cell specificity ( ). By analyzing the fluorescence intensity, we found that the expression of Bgn increased after activation of GLP-1, while Mapkapk3 decreased ( ), which was consistent with the results of WB and qPCR.
Comparison of protein and gene expression of Bgn and Mapkapk3 in the hypothalamus. (A) WB and protein expression of Bgn in hypothalamus; (B) gene expression of Bgn in hypothalamus; (C) WB and protein expression of Mapkapk3 in hypothalamus; (D) gene expression of Mapkapk3 in hypothalamus; (E) representative figures of Bgn and Mapkapk3 in hypothalamus of rats in each group; (F) comparison of IOD of Bgn and Mapkapk3 in hypothalamus; * P < 0.05, ** P < 0.01. 3V, the 3rd ventricle. WB, Western blotting; Bgn, biglycan; Mapkapk3, mitogen-activated protein kinase activated protein kinase 3; IOD, integrated optical density.
## Discussion
Glucagon-like peptide 1, a peptide hormone from the intestinal tract, plays a central role in the coordination of postprandial glucose homeostasis ( ). Intake and digestion of carbohydrates, fats, proteins, and bile acids are the main physiological stimuli that promote GLP-1 secretion ( ). GLP-1 biosynthesis and release are performed by enteroendocrine cells of the intestine, L cells ( ). L cells are more distributed in the jejunum, ileum, and colon ( ). Therefore, the stimulation of L cells occurs downstream of food digestion, which is closely related to the local rate of nutrient absorption of the intestine and can control gastric emptying and intestinal tract creeping through feedback regulation ( ). Therefore, its main functions include not only stimulating insulin secretion and inhibiting glucagon secretion but also regulating gastrointestinal motility, and working together in many ways to maintain blood glucose homeostasis. In addition to being secreted by the intestine, GLP-1 also exists in neurons of NTS, which release GLP-1 in the central nervous system, including the project to the feeding centers in the hypothalamus ( ). GLP-1-mediated neural circuits are the core mechanism by which GLP-1 neurons in NTS exert their appetite-suppressing effects ( ). Therefore, it is also a physiological regulator of appetite and food intake ( ).
Glucagon-like peptide 1 exerts its physiological function by binding to a dedicated G-protein-coupled receptor, GLP-1R, expressed in a variety of cell types ( ). Numerous attempts have been made to identify alternative GLP-1R or subtypes, but at present only a single GLP-1R has been identified, whether expressed in the brain, the stomach, or the pancreas ( ). In addition to being expressed in the gastrointestinal tract, pancreas, and brain, studies have shown that GLP-1R can be also detected in the vagal afferent nerves, scattered atrial cardiomyocytes, vascular smooth muscle, lung, and some immune cells ( ; ; ). The lateral hypothalamic GLP-1R has been identified as an indispensable element of normal food reinforcement, food intake, and body weight regulation ( ). As mentioned earlier, GLP-1R-expressing vagal afferent cells have been found to form fibers within the intestinal villi adjacent to enteroendocrine cells. Therefore, it is generally believed that GLP-1R is a chemosensory neuron of the vagal afferents nerve, which helps the vagal afferents nerve receive nutrient detection information in a paracrine manner by binding to GLP-1 released locally by enteroendocrine cells ( ; ). Meanwhile, the activity of GLP-1R in vagal afferents also encodes the feeling of distention in the gastrointestinal tract through the inner ganglia of myenteric plexus intramuscular array detection in gastric and intestinal smooth muscle layers ( ; ). Thus, the vagal afferents of the intestine receive gastrointestinal stimulation through both chemoreceptors and mechanoreceptors to generate neural signals toward the corresponding nuclei of the brainstem, such as those in the NTS and posterior area (AP), then to the hypothalamus, which releases GLP-1 and activates the receptor. However, the current link of this endogenous peripheral GLP-1 indirectly interacting with the central GLP-1 system ( via the vagal and/or endocrine pathways) remains controversial, and the necessary intestine-brain circuit has not been empirically confirmed ( ). Therefore, further research in this field should be conducted in the future.
Glucagon-like peptide 1 forms the basis for a variety of current drugs for the treatment of type 2 diabetes and obesity, as well as new agents currently being developed. GLP1-RAs are used clinically to treat T2DM and promote weight loss in people with obesity ( ). Although the identity of the GLP-1R-expressing target cells underlying appetite suppression remains under discussion, powerful evidence indicates that GLP1-RAs exert their effects by targeting GLP-1R in the brainstem and/or hypothalamus ( ). The mechanism of action of GLP-1 and its receptor in the brain is still unclear, which has caused some difficulties in the optimization of drug development and obesity treatment. Therefore, this study aimed to investigate the differential genes in the brain after GLP1-RAs treatment. After bioinformatics analysis, we found two significantly different genes, Bgn of upregulated genes and Mapkapk3 of downregulated genes.
Biglycan, a class I small leucine-rich proteoglycans, is a key component of the extracellular matrix (ECM) that participates in scaffolding the collagen fibrils and mediates cell signaling. Dysregulation of Bgn expression can result in a wide range of clinical conditions such as metabolic disorder, inflammatory disorder, musculoskeletal defects, and malignancies ( ). ECM disorganization is a pivotal step in metabolic disorders like obesity which involves accommodating the expanding adipose tissue mass ( ). The ECM in these states releases non-fibrillar proteoglycans such as Bgn, which take part in receptor-mediated interactions to regulate inflammation and organ-specific metabolic disturbances. The ECM plays a role in regulating neurite outgrowth, and neural function and plasticity as well as metabolic diseases ( ). Also, ECM remodeling is crucial for regulating the morphogenesis of the intestine ( ). In this study, GO analysis also found differential genes enriched in ECM. KEGG analysis showed that differential genes play an important role in other types of glycans degradation and biosynthesis pathway. So, the upregulation of Bgn expression in this study may indicate increased ECM remodeling, and as previously mentioned, changes in intestinal mechanical signaling can regulate gastrointestinal motility and food intake.
Mitogen-activated protein kinase activated protein kinase 3 is a member of the Mapk signaling pathway family ( ). The Mapk family is significant in various cellular processes such as apoptosis, oxidative stress, differentiation, inflammatory response, and proliferation, and includes three members: extracellular signal-regulated kinase (Erk1/2), p38, and c-Jun N-terminal kinase (JNK) ( ). p38 Mapk is the best-characterized kinase upstream of Mapkapk3 ( ). Studies have shown that p38Mapk can promote β-cell exhaustion in the pancreas, and β-cells can express GLP-1R ( ). At the same time, p38 can also regulate the secretion of insulin, and the insulin level in p38-deficient mice is increased. Then, in this study, Mapkapk3 is a downstream factor of p38, and the downregulation of Mapkapk3 may inhibit the p38 Mapk pathway, thereby promoting insulin secretion and increasing the expression of GLP-1R. Studies have shown that the silencing of Bgn gene can upregulate p38Mapk ( ). In the verification of bioinformatics analysis results, we found that the expression of Bgn mRNA was upregulated and the expression of Mapkapk3 mRNA was downregulated when the GLP-1 neurons were activated, which implied the inhibitory effect of BGN in regulating the p38 pathway from another perspective.
## Conclusion
In summary, we screened out the key differential genes that affect feeding behavior after GLP-1R activation through bioinformatics analysis. Bgn and Mapkapk3 were found to be more associated with changes in feeding behavior. Changes in Bgn and Mapkapk3 in the hypothalamus were also detected after activating or inhibiting GLP-1 neurons in the NTS by chemogenetic treatment, which were consistent with the results of bioinformatics analysis. It shows that Bgn and Mapkapk3 may be important downstream cytokines of GLP-1 in regulating appetite, which were worthy of further research in the future.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The animal study was reviewed and approved by the Institutional Animal Care and Use Committee of Wuhan University.
## Author contributions
QS, JT, and YY designed the study protocol and obtained the funding. YS and YH were responsible for the bioinformatics analysis part, animal experimentation, and basic experiment parts. QS was responsible for data analysis. QS, JT, and YS drafted this manuscript. All authors have read and approved the final manuscript, adhere to the trial authorship guidelines, and agreed to publication.
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.