Electroencephalographic signatures of migraine in small prospective and large retrospective cohorts

Migraine is one of the most common neurological disorders in the US. Currently, the diagnosis and management of migraine are based primarily on subjective self-reported measures, which compromises the reliability of clinical diagnosis and the ability to robustly discern candidacy for available therapies and track treatment response. In this study, we used a computational pipeline for the automated, rapid, high-throughput, and objective analysis of encephalography (EEG) data at Cleveland Clinic to identify signatures that correlate with migraine. We performed two independent analyses, a prospective analysis (n = 62 subjects) and a retrospective age-matched analysis on a larger cohort (n = 734) obtained from the sleep registry at Cleveland Clinic. In the prospective analysis, no significant difference between migraine and control groups was detected in the mean power spectral density (PSD) of an all-electrodes montage in the frequency range of 1-32 Hz, whereas a significant PSD increase in single occipital electrodes was found at 12 Hz in migraine patients. We then trained machine learning models on the binary classification of migraine versus control using EEG power features, resulting in high accuracies (82-83%) with occipital electrodes' power at 12 Hz ranking highest in the contribution to the model's performance. Further retrospective analysis also showed a consistent increase in power from occipital electrodes at 12 and 13 Hz in migraine patients. These results demonstrate distinct and localized changes in brain activity measured by EEG that can potentially serve as biomarkers in the diagnosis and personalized therapy for individuals with migraine.

Application of Artificial Intelligence in the Headache Field

PURPOSE OF REVIEW

Headache disorders are highly prevalent worldwide. Rapidly advancing capabilities in artificial intelligence (AI) have expanded headache-related research with the potential to solve unmet needs in the headache field. We provide an overview of AI in headache research in this article.

RECENT FINDINGS

We briefly introduce machine learning models and commonly used evaluation metrics. We then review studies that have utilized AI in the field to advance diagnostic accuracy and classification, predict treatment responses, gather insights from various data sources, and forecast migraine attacks. Furthermore, given the emergence of ChatGPT, a type of large language model (LLM), and the popularity it has gained, we also discuss how LLMs could be used to advance the field. Finally, we discuss the potential pitfalls, bias, and future directions of employing AI in headache medicine. Many recent studies on headache medicine incorporated machine learning, generative AI and LLMs. A comprehensive understanding of potential pitfalls and biases is crucial to using these novel techniques with minimum harm. When used appropriately, AI has the potential to revolutionize headache medicine.

Insights from triggers and prodromal symptoms on how migraine attacks start: The threshold hypothesis

BACKGROUND

The prodrome or premonitory phase is the initial phase of a migraine attack, and it is considered as a symptomatic phase in which prodromal symptoms may occur. There is evidence that attacks start 24-48 hours before the headache phase. Individuals with migraine also report several potential triggers for their attacks, which may be mistaken for premonitory symptoms and hinder migraine research.

METHODS

This review aims to summarize published studies that describe contributions to understanding the fine difference between prodromal/premonitory symptoms and triggers, give insights for research, and propose a way forward to study these phenomena. We finally aim to formulate a theory to unify migraine triggers and prodromal symptoms. For this purpose, a comprehensive narrative review of the published literature on clinical, neurophysiological and imaging evidence on migraine prodromal symptoms and triggers was conducted using the PubMed database.

RESULTS

Brain activity and network connectivity changes occur during the prodromal phase. These changes give rise to prodromal/premonitory symptoms in some individuals, which may be falsely interpreted as triggers at the same time as representing the early manifestation of the beginning of the attack. By contrast, certain migraine triggers, such as stress, hormone changes or sleep deprivation, acting as a catalyst in reducing the migraine threshold, might facilitate these changes and increase the chances of a migraine attack. Migraine triggers and prodromal/premonitory symptoms can be confused and have an intertwined relationship with the hypothalamus as the central hub for integrating external and internal body signals.

CONCLUSIONS

Differentiating migraine triggers and prodromal symptoms is crucial for shedding light on migraine pathophysiology and improve migraine management.

Resting-state electroencephalography and magnetoencephalography in migraine-a systematic review and meta-analysis

Magnetoencephalography/electroencephalography (M/EEG) can provide insights into migraine pathophysiology and help develop clinically valuable biomarkers. To integrate and summarize the existing evidence on changes in brain function in migraine, we performed a systematic review and meta-analysis (PROSPERO CRD42021272622) of resting-state M/EEG findings in migraine. We included 27 studies after searching MEDLINE, Web of Science Core Collection, and EMBASE. Risk of bias was assessed using a modified Newcastle-Ottawa Scale. Semi-quantitative analysis was conducted by vote counting, and meta-analyses of M/EEG differences between people with migraine and healthy participants were performed using random-effects models. In people with migraine during the interictal phase, meta-analysis revealed higher power of brain activity at theta frequencies (3-8 Hz) than in healthy participants. Furthermore, we found evidence for lower alpha and beta connectivity in people with migraine in the interictal phase. No associations between M/EEG features and disease severity were observed. Moreover, some evidence for higher delta and beta power in the premonitory compared to the interictal phase was found. Strongest risk of bias of included studies arose from a lack of controlling for comorbidities and non-automatized or non-blinded M/EEG assessments. These findings can guide future M/EEG studies on migraine pathophysiology and brain-based biomarkers, which should consider comorbidities and aim for standardized, collaborative approaches.

A low-cost and open-hardware portable 3-electrode sleep monitoring device

To continue sleep research activities during the lockdown resulting from the COVID-19 pandemic, experiments that were previously conducted in laboratories were shifted to the homes of volunteers. Furthermore, for extensive data collection, it is necessary to use a large number of portable devices. Hence, to achieve these objectives, we developed a low-cost and open-source portable monitor (PM) device capable of acquiring electroencephalographic (EEG) signals using the popular ESP32 microcontroller. The device operates based on instrumentation amplifiers. It also has a connectivity microcontroller with Wi-Fi and Bluetooth that can be used to stream EEG signals. This portable single-channel 3-electrode EEG device allowed us to record short naps and score different sleep stages, such as wakefulness, non rapid eye movement sleep (NREM), stage 1 (S1), stage 2 (S2), stage 3 (S3) and stage 4 (S4). We validated the device by comparing the obtained signals to those generated by a research-grade counterpart. The results showed a high level of accurate similarity between both devices, demonstrating the feasibility of using this approach for extensive and low-cost data collection of EEG sleep recordings.

The Ethical Stewardship of Artificial Intelligence in Chronic Pain and Headache: A Narrative Review

PURPOSE OF REVIEW

As artificial intelligence (AI) and machine learning (ML) are becoming more pervasive in medicine, understanding their ethical considerations for chronic pain and headache management is crucial for optimizing their safety.

RECENT FINDINGS

We reviewed thirty-eight editorial and original research articles published between 2018 and 2023, focusing on the application of AI and ML to chronic pain or headache. The core medical principles of beneficence, non-maleficence, autonomy, and justice constituted the evaluation framework. The AI applications addressed topics such as pain intensity prediction, diagnostic aides, risk assessment for medication misuse, empowering patients to self-manage their conditions, and optimizing access to care. Virtually all AI applications aligned both positively and negatively with specific medical ethics principles. This review highlights the potential of AI to enhance patient outcomes and physicians' experiences in managing chronic pain and headache. We emphasize the importance of carefully considering the advantages, disadvantages, and unintended consequences of utilizing AI tools in chronic pain and headache, and propose the four core principles of medical ethics as an evaluation framework.

Changes in Resting-State Brain Activity After Cognitive Behavioral Therapy for Chronic Pain: A Magnetoencephalography Study

Cognitive behavioral therapy (CBT) is believed to be an effective treatment for chronic pain due to its association with cognitive and emotional factors. Nevertheless, there is a paucity of magnetoencephalography (MEG) investigations elucidating its underlying mechanisms. This study investigated the neurophysiological effects of CBT employing MEG and analytical techniques. We administered resting-state MEG scans to 30 patients with chronic pain and 31 age-matched healthy controls. Patients engaged in a 12-session group CBT program. We conducted pretreatment (T1) and post-treatment (T2) MEG and clinical assessments. MEG data were examined within predefined regions of interest, guided by the authors' and others' prior magnetic resonance imaging studies. Initially, we selected regions displaying significant changes in power spectral density and multiscale entropy between patients at T1 and healthy controls. Then, we examined the changes within these regions after conducting CBT. Furthermore, we applied support vector machine analysis to MEG data to assess the potential for classifying treatment effects. We observed normalization of power in the gamma2 band (61-90 Hz) within the right inferior frontal gyrus (IFG) and multiscale entropy within the right dorsolateral prefrontal cortex (DLPFC) of patients with chronic pain after CBT. Notably, changes in pain intensity before and after CBT positively correlated with the alterations of multiscale entropy. Importantly, responders predicted by the support vector machine classifier had significantly higher treatment improvement rates than nonresponders. These findings underscore the pivotal role of the right IFG and DLPFC in ameliorating pain intensity through CBT. Further accumulation of evidence is essential for future applications. PERSPECTIVE: We conducted MEG scans on 30 patients with chronic pain before and after a CBT program, comparing results with 31 healthy individuals. There were CBT-related changes in the right IFG and DLPFC. These results highlight the importance of specific brain regions in pain reduction through CBT.

Resting-state electroencephalography microstate dynamics altered in patients with migraine with and without aura-A pilot study

OBJECTIVE

To evaluate electroencephalography (EEG) microstate differences between patients with migraine with aura (MWA), patients with migraine without aura (MWoA), and healthy controls (HC).

BACKGROUND

Previous research employing microstate analysis found unique microstate alterations in patients with MWoA; however, it is uncertain how microstates appear in patients with MWA.

METHODS

This study was conducted at the Headache Clinic of the First Affiliated Hospital of Xi'an Jiaotong University. In total, 30 patients with MWA, 30 with MWoA, and 30 HC were enrolled in this cross-sectional study. An EEG was recorded for all participants under resting state. The microstate parameters of four widely recognized microstate classes A-D were calculated and compared across the three groups.

RESULTS

The occurrence of microstate B (MsB) in the MWoA group was significantly higher than in the HC (p = 0.006, Cohen's d = 0.72) and MWA (p = 0.016, Cohen's d = 0.57) groups, while the contribution of MsB was significantly increased in the MWoA group compared to the HC group (p = 0.016, Cohen's d = 0.64). Microstate A (MsA) displayed a longer duration in the MWA group compared to the MWoA group (p = 0.007, Cohen's d = 0.69). Furthermore, the transition probability between MsB and microstate D was significantly increased in the MWoA group compared to the HC group (p = 0.009, Cohen's d = 0.68 for B to D; p = 0.007, Cohen's d = 0.71 for D to B). Finally, the occurrence and contribution of MsB were positively related to headache characteristics in the MWoA group but negatively in the MWA group, whereas the duration of MsA was positively related to the visual analog scale in the MWA group (all p < 0.05).

CONCLUSIONS

Patients with MWA and MWoA have altered microstate dynamics, indicating that resting-state brain network disorders may play a role in migraine pathogenesis. Microstate parameters may have the potential to aid clinical management, which needs to be investigated further.

Electrophysiological findings in migraine may reflect abnormal synaptic plasticity mechanisms: A narrative review

BACKGROUND

The cyclical brain disorder of sensory processing accompanying migraine phases lacks an explanatory unified theory.

METHODS

We searched Pubmed for non-invasive neurophysiological studies on migraine and related conditions using transcranial magnetic stimulation, electroencephalography, visual and somatosensory evoked potentials. We summarized the literature, reviewed methods, and proposed a unified theory for the pathophysiology of electrophysiological abnormalities underlying migraine recurrence.

RESULTS

All electrophysiological modalities have determined specific changes in brain dynamics across the different phases of the migraine cycle. Transcranial magnetic stimulation studies show unbalanced recruitment of inhibitory and excitatory circuits, more consistently in aura, which ultimately results in a substantially distorted response to neuromodulation protocols. Electroencephalography investigations highlight a steady pattern of reduced alpha and increased slow rhythms, largely located in posterior brain regions, which tends to normalize closer to the attacks. Finally, non-painful evoked potentials suggest dysfunctions in habituation mechanisms of sensory cortices that revert during ictal phases.

CONCLUSION

Electrophysiology shows dynamic and recurrent functional alterations within the brainstem-thalamus-cortex loop varies continuously and recurrently in migraineurs. Given the central role of these structures in the selection, elaboration, and learning of sensory information, these functional alterations suggest chronic, probably genetically determined dysfunctions of the synaptic short- and long-term learning mechanisms.

In search of a composite biomarker for chronic pain by way of EEG and machine learning: where do we currently stand?

Machine learning is becoming an increasingly common component of routine data analyses in clinical research. The past decade in pain research has witnessed great advances in human neuroimaging and machine learning. With each finding, the pain research community takes one step closer to uncovering fundamental mechanisms underlying chronic pain and at the same time proposing neurophysiological biomarkers. However, it remains challenging to fully understand chronic pain due to its multidimensional representations within the brain. By utilizing cost-effective and non-invasive imaging techniques such as electroencephalography (EEG) and analyzing the resulting data with advanced analytic methods, we have the opportunity to better understand and identify specific neural mechanisms associated with the processing and perception of chronic pain. This narrative literature review summarizes studies from the last decade describing the utility of EEG as a potential biomarker for chronic pain by synergizing clinical and computational perspectives.

Application of EEG in migraine

Migraine is a common disease of the nervous system that seriously affects the quality of life of patients and constitutes a growing global health crisis. However, many limitations and challenges exist in migraine research, including the unclear etiology and the lack of specific biomarkers for diagnosis and treatment. Electroencephalography (EEG) is a neurophysiological technique for measuring brain activity. With the updating of data processing and analysis methods in recent years, EEG offers the possibility to explore altered brain functional patterns and brain network characteristics of migraines in depth. In this paper, we provide an overview of the methodology that can be applied to EEG data processing and analysis and a narrative review of EEG-based migraine-related research. To better understand the neural changes of migraine or to provide a new idea for the clinical diagnosis and treatment of migraine in the future, we discussed the study of EEG and evoked potential in migraine, compared the relevant research methods, and put forwards suggestions for future migraine EEG studies.

The relationships between dynamic resting-state networks and social behavior in autism spectrum disorder revealed by fuzzy entropy-based temporal variability analysis of large-scale network

Autism spectrum disorder (ASD) is a common neurodevelopmental disorder characterized by a core deficit in social processes. However, it is still unclear whether the core clinical symptoms of the disorder can be reflected by the temporal variability of resting-state network functional connectivity (FC). In this article, we examined the large-scale network FC temporal variability at the local region, within-network, and between-network levels using the fuzzy entropy technique. Then, we correlated the network FC temporal variability to social-related scores. We found that the social behavior correlated with the FC temporal variability of the precuneus, parietal, occipital, temporal, and precentral. Our results also showed that social behavior was significantly negatively correlated with the temporal variability of FC within the default mode network, between the frontoparietal network and cingulo-opercular task control network, and the dorsal attention network. In contrast, social behavior correlated significantly positively with the temporal variability of FC within the subcortical network. Finally, using temporal variability as a feature, we construct a model to predict the social score of ASD. These findings suggest that the network FC temporal variability has a close relationship with social behavioral inflexibility in ASD and may serve as a potential biomarker for predicting ASD symptom severity.

Mechanical Punctate Pain Thresholds in Patients With Migraine Across Different Migraine Phases: A Narrative Review

Purpose of the Review

We reviewed the studies of mechanical punctate pain thresholds (MPTs) in patients with migraine and summarized their findings focusing on the differences in MPT measurement and MPTs in different phases of migraine.

Methods

We searched the English-written articles that investigate the MPTs in the migraine population published in peer-reviewed journals with full-text using the PubMed, Web of Science, and Google Scholar databases. Moreover, we manually searched the references from the articles for possibly related studies.

Main Findings

We collected 276 articles and finally included twelve studies in this review. Most of the studies that included MPTs were measured with traditional von Frey filaments. The cephalic areas were always included in the assessment. Most studies compared the inter-ictal MPT in patients with migraine to controls. Among them, the majority found no significant differences; however, there were studies found either higher or lower levels of MPTs in migraine. Even though the studies provided the criteria to define the inter-ictal phase, not all of them followed up with the subjects regarding the next migraine attack. In studies that compared MPT between phases, lower MPTs were found during peri-ictal phases.

Summary

Changes to MPT in migraine patients were inconclusive. The selection of measurement methods as well as properly defined migraine phases should be considered for future studies.

Markers of Central Neuropathic Pain in Higuchi Fractal Analysis of EEG Signals From People With Spinal Cord Injury

Central neuropathic pain (CNP) negatively impacts the quality of life in a large proportion of people with spinal cord injury (SCI). With no cure at present, it is crucial to improve our understanding of how CNP manifests, to develop diagnostic biomarkers for drug development, and to explore prognostic biomarkers for personalised therapy. Previous work has found early evidence of diagnostic and prognostic markers analysing Electroencephalogram (EEG) oscillatory features. In this paper, we explore whether non-linear non-oscillatory EEG features, specifically Higuchi Fractal Dimension (HFD), can be used as prognostic biomarkers to increase the repertoire of available analyses on the EEG of people with subacute SCI, where having both linear and non-linear features for classifying pain may ultimately lead to higher classification accuracy and an intrinsically transferable classifier. We focus on EEG recorded during imagined movement because of the known relation between the motor cortex over-activity and CNP. Analyses were performed on two existing datasets. The first dataset consists of EEG recordings from able-bodied participants (N = 10), participants with chronic SCI and chronic CNP (N = 10), and participants with chronic SCI and no CNP (N = 10). We tested for statistically significant differences in HFD across all pairs of groups using bootstrapping, and found significant differences between all pairs of groups at multiple electrode locations. The second dataset consists of EEG recordings from participants with subacute SCI and no CNP (N = 20). They were followed-up 6 months post recording to test for CNP, at which point (N = 10) participants had developed CNP and (N = 10) participants had not developed CNP. We tested for statistically significant differences in HFD between these two groups using bootstrapping and, encouragingly, also found significant differences at multiple electrode locations. Transferable machine learning classifiers achieved over 80% accuracy discriminating between groups of participants with chronic SCI based on only a single EEG channel as input. The most significant finding is that future and chronic CNP share common features and as a result, the same classifier can be used for both. This sheds new light on pain chronification by showing that frontal areas, involved in the affective aspects of pain and believed to be influenced by long-standing pain, are affected in a much earlier phase of pain development.

EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications

Brain-Computer interfaces (BCIs) enhance the capability of human brain activities to interact with the environment. Recent advancements in technology and machine learning algorithms have increased interest in electroencephalographic (EEG)-based BCI applications. EEG-based intelligent BCI systems can facilitate continuous monitoring of fluctuations in human cognitive states under monotonous tasks, which is both beneficial for people in need of healthcare support and general researchers in different domain areas. In this review, we survey the recent literature on EEG signal sensing technologies and computational intelligence approaches in BCI applications, compensating for the gaps in the systematic summary of the past five years. Specifically, we first review the current status of BCI and signal sensing technologies for collecting reliable EEG signals. Then, we demonstrate state-of-the-art computational intelligence techniques, including fuzzy models and transfer learning in machine learning and deep learning algorithms, to detect, monitor, and maintain human cognitive states and task performance in prevalent applications. Finally, we present a couple of innovative BCI-inspired healthcare applications and discuss future research directions in EEG-based BCI research.

Systematic Review of the Effectiveness of Machine Learning Algorithms for Classifying Pain Intensity, Phenotype or Treatment Outcomes Using Electroencephalogram Data

Recent attempts to utilize machine learning (ML) to predict pain-related outcomes from Electroencephalogram (EEG) data demonstrate promising results. The primary aim of this review was to evaluate the effectiveness of ML algorithms for predicting pain intensity, phenotypes or treatment response from EEG. Electronic databases MEDLINE, EMBASE, Web of Science, PsycINFO and The Cochrane Library were searched. A total of 44 eligible studies were identified, with 22 presenting attempts to predict pain intensity, 15 investigating the prediction of pain phenotypes and seven assessing the prediction of treatment response. A meta-analysis was not considered appropriate for this review due to heterogenos methods and reporting. Consequently, data were narratively synthesized. The results demonstrate that the best performing model of the individual studies allows for the prediction of pain intensity, phenotypes and treatment response with accuracies ranging between 62 to 100%, 57 to 99% and 65 to 95.24%, respectively. The results suggest that ML has the potential to effectively predict pain outcomes, which may eventually be used to assist clinical care. However, inadequate reporting and potential bias reduce confidence in the results. Future research should improve reporting standards and externally validate models to decrease bias, which would increase the feasibility of clinical translation. PERSPECTIVE: This systematic review explores the state-of-the-art machine learning methods for predicting pain intensity, phenotype or treatmentresponse from EEG data. Results suggest that machine learning may demonstrate clinical utility, pending further research and development. Areas for improvement, including standardized processing, reporting and the need for better methodological assessment tools, are discussed.

Brain variability in dynamic resting-state networks identified by fuzzy entropy: a scalp EEG study

Objective.Exploring the temporal variability in spatial topology during the resting state attracts growing interest and becomes increasingly useful to tackle the cognitive process of brain networks. In particular, the temporal brain dynamics during the resting state may be delineated and quantified aligning with cognitive performance, but few studies investigated the temporal variability in the electroencephalogram (EEG) network as well as its relationship with cognitive performance.Approach.In this study, we proposed an EEG-based protocol to measure the nonlinear complexity of the dynamic resting-state network by applying the fuzzy entropy. To further validate its applicability, the fuzzy entropy was applied into simulated and two independent datasets (i.e. decision-making and P300).Main results.The simulation study first proved that compared to the existing methods, this approach could not only exactly capture the pattern dynamics in time series but also overcame the magnitude effect of time series. Concerning the two EEG datasets, the flexible and robust network architectures of the brain cortex at rest were identified and distributed at the bilateral temporal lobe and frontal/occipital lobe, respectively, whose variability metrics were found to accurately classify different groups. Moreover, the temporal variability of resting-state network property was also either positively or negatively related to individual cognitive performance.Significance.This outcome suggested the potential of fuzzy entropy for evaluating the temporal variability of the dynamic resting-state brain networks, and the fuzzy entropy is also helpful for uncovering the fluctuating network variability that accounts for the individual decision differences.

Residual and compensatory changes of resting‐state EEG in successful recovery after moderate TBI

Purpose:

Even in years after recovery from moderate traumatic brain injury (moderate TBI), patients complain about residual cognitive impairment and fatigue. We hypothesized that non‐linear and linear resting‐state electroencephalography (rsEEG) features might also reflect neural underpinnings of these deficits.

Methods:

We analyzed a 10‐minute rsEEG in 77 moderate TBI‐survivors and 151 healthy volunteers after cognitive and psychological assessment. The rsEEG analysis included linear measures, such as power spectral density and peak alpha frequency, and non‐linear parameters such as Higuchi fractal dimension, envelope frequency, and Hjorth complexity.

Results:

The patients with moderate TBI had higher scores for fatigue and sleepiness and lower scores for mood and life satisfaction than controls. The behavioral test for directed attention showed a smaller and non‐significant between‐group difference. In rsEEG patterns, moderate TBI‐group had significantly higher deltaand theta‐rhythm power, which correlated with higher sleepiness and fatigue scores. The higher beta and lower alpha power were associated with a higher attention level in moderate TBI patients. Non‐linear rsEEG features were significantly higher in moderate TBI patients than in healthy controls but correlated with sleepiness and fatigue scores in both controls and patients.

Conclusion:

The rsEEG patterns may reflect compensatory processes supporting directed attention and residual effect of moderate TBI causing subjective fatigue in patients even after full physiological recovery.

How does the brain change in chronic migraine? Developing disease biomarkers

BACKGROUND

Validated chronic migraine biomarkers could improve diagnostic, prognostic, and predictive abilities for clinicians and researchers, as well as increase knowledge on migraine pathophysiology.

OBJECTIVE

The objective of this narrative review is to summarise and interpret the published literature regarding the current state of development of chronic migraine biomarkers.

FINDINGS

Data from functional and structural imaging, neurophysiological, and biochemical studies have been utilised towards the development of chronic migraine biomarkers. These biomarkers could contribute to chronic migraine classification/diagnosis, prognosticating patient outcomes, predicting response to treatment, and measuring treatment responses early after initiation. Results show promise for using measures of brain structure and function, evoked potentials, and sensory neuropeptide concentrations for the development of chronic migraine biomarkers, yet further optimisation and validation are still required.

CONCLUSIONS

Imaging, neurophysiological, and biochemical changes that occur with the progression from episodic to chronic migraine could be utilised for developing chronic migraine biomarkers that might assist with diagnosis, prognosticating individual patient outcomes, and predicting responses to migraine therapies. Ultimately, validated biomarkers could move us closer to being able to practice precision medicine in the field and thus improve patient care.

The visual system as target of non-invasive brain stimulation for migraine treatment: Current insights and future challenges

The visual network is crucially implicated in the pathophysiology of migraine. Several lines of evidence indicate that migraine is characterized by an altered visual cortex excitability both during and between attacks. Visual symptoms, the most common clinical manifestation of migraine aura, are likely the result of cortical spreading depression originating from the extrastriate area V3A. Photophobia, a clinical hallmark of migraine, is linked to an abnormal sensory processing of the thalamus which is converged with the non-image forming visual pathway. Finally, visual snow is an increasingly recognized persistent visual phenomenon in migraine, possibly caused by increased perception of subthreshold visual stimuli. Emerging research in non-invasive brain stimulation (NIBS) has vastly developed into a diversity of areas with promising potential. One of its clinical applications is the single-pulse transcranial magnetic stimulation (sTMS) applied over the occipital cortex which has been approved for treating migraine with aura, albeit limited evidence. Studies have also investigated other NIBS techniques, such as repetitive TMS (rTMS) and transcranial direct current stimulation (tDCS), for migraine prophylaxis but with conflicting results. As a dynamic brain disorder with widespread pathophysiology, targeting migraine with NIBS is challenging. Furthermore, unlike the motor cortex, evidence suggests that the visual cortex may be less plastic. Controversy exists as to whether the same fundamental principles of NIBS, based mainly on findings in the motor cortex, can be applied to the visual cortex. This review aims to explore existing literature surrounding NIBS studies on the visual system of migraine. We will first provide an overview highlighting the direct implication of the visual network in migraine. Next, we will focus on the rationale behind using NIBS for migraine treatment, including its effects on the visual cortex, and the shortcomings of currently available evidence. Finally, we propose a broader perspective of how novel approaches, the concept of brain networks and the integration of multimodal imaging with computational modeling, can help refine current NIBS methods, with the ultimate goal of optimizing a more individualized treatment for migraine.

Mechanical punctate pain threshold is associated with headache frequency and phase in patients with migraine

Objective

Previous studies regarding the quantitative sensory testing are inconsistent in migraine. We hypothesized that the quantitative sensory testing results were influenced by headache frequency or migraine phase.

Methods

This study recruited chronic and episodic migraine patients as well as healthy controls. Participants underwent quantitative sensory testing, including heat, cold, and mechanical punctate pain thresholds at the supraorbital area (V1 dermatome) and the forearm (T1 dermatome). Prospective headache diaries were used for headache frequency and migraine phase when quantitative sensory testing was performed.

Results

Twenty-eight chronic migraine, 64 episodic migraine and 32 healthy controls completed the study. Significant higher mechanical punctate pain thresholds were found in episodic migraine but not chronic migraine when compared with healthy controls. The mechanical punctate pain thresholds decreased as headache frequency increased then nadired. In episodic migraine, mechanical punctate pain thresholds were highest ( p < 0.05) in those in the interictal phase and declined when approaching the ictal phase in both V1 and T1 dermatomes. Linear regression analyses showed that in those with episodic migraine, headache frequency and phase were independently associated with mechanical punctate pain thresholds and accounted for 29.7% and 38.9% of the variance in V1 ( p = 0.003) and T1 ( p < 0.001) respectively. Of note, unlike mechanical punctate pain thresholds, our study did not demonstrate similar findings for heat pain thresholds and cold pain thresholds in migraine.

Conclusion

Our study provides new insights into the dynamic changes of quantitative sensory testing, especially mechanical punctate pain thresholds in patients with migraine. Mechanical punctate pain thresholds vary depending on headache frequency and migraine phase, providing an explanation for the inconsistency across studies.

Redefining migraine phases - a suggestion based on clinical, physiological, and functional imaging evidence

Migraine is defined by attacks of headache with a specific length and associated symptoms such as photophobia, phonophobia and nausea. It is long recognized that migraine is more than just the attacks and that migraine should be understood as a cycling brain disorder with at least 4 phases: interictal, preictal, ictal and postictal. However, unlike the pain phase, the other phases are less well defined, which renders studies focusing on these phases susceptible to bias. We herewith review the available clinical, electrophysiological, and neuroimaging data and propose that the preictal phase should be defined as up to 48 hours before the headache attack and the postictal phase as up to 24 hours following the ictal phase. This would allow future studies to specifically investigate these migraine phases and to make study results more comparable.

Sensory Modulation Disorder (SMD) and Pain: A New Perspective

Sensory modulation disorder (SMD) affects sensory processing across single or multiple sensory systems. The sensory over-responsivity (SOR) subtype of SMD is manifested clinically as a condition in which non-painful stimuli are perceived as abnormally irritating, unpleasant, or even painful. Moreover, SOR interferes with participation in daily routines and activities (Dunn, 2007; Bar-Shalita et al., 2008; Chien et al., 2016), co-occurs with daily pain hyper-sensitivity, and reduces quality of life due to bodily pain. Laboratory behavioral studies have confirmed abnormal pain perception, as demonstrated by hyperalgesia and an enhanced lingering painful sensation, in children and adults with SMD. Advanced quantitative sensory testing (QST) has revealed the mechanisms of altered pain processing in SOR whereby despite the existence of normal peripheral sensory processing, there is enhanced facilitation of pain-transmitting pathways along with preserved but delayed inhibitory pain modulation. These findings point to central nervous system (CNS) involvement as the underlying mechanism of pain hypersensitivity in SOR. Based on the mutual central processing of both non-painful and painful sensory stimuli, we suggest shared mechanisms such as cortical hyper-excitation, an excitatory-inhibitory neuronal imbalance, and sensory modulation alterations. This is supported by novel findings indicating that SOR is a risk factor and comorbidity of chronic non-neuropathic pain disorders. This is the first review to summarize current empirical knowledge investigating SMD and pain, a sensory modality not yet part of the official SMD realm. We propose a neurophysiological mechanism-based model for the interrelation between pain and SMD. Embracing the pain domain could significantly contribute to the understanding of this condition’s pathogenesis and how it manifests in daily life, as well as suggesting the basis for future potential mechanism-based therapies.

An Improved Multi-Source Data Fusion Method Based on the Belief Entropy and Divergence Measure

Dempster–Shafer (DS) evidence theory is widely applied in multi-source data fusion technology. However, classical DS combination rule fails to deal with the situation when evidence is highly in conflict. To address this problem, a novel multi-source data fusion method is proposed in this paper. The main steps of the proposed method are presented as follows. Firstly, the credibility weight of each piece of evidence is obtained after transforming the belief Jenson–Shannon divergence into belief similarities. Next, the belief entropy of each piece of evidence is calculated and the information volume weights of evidence are generated. Then, both credibility weights and information volume weights of evidence are unified to generate the final weight of each piece of evidence before the weighted average evidence is calculated. Then, the classical DS combination rule is used multiple times on the modified evidence to generate the fusing results. A numerical example compares the fusing result of the proposed method with that of other existing combination rules. Further, a practical application of fault diagnosis is presented to illustrate the plausibility and efficiency of the proposed method. The experimental result shows that the targeted type of fault is recognized most accurately by the proposed method in comparing with other combination rules.

Migraine classification using somatosensory evoked potentials

Objective

The automatic detection of migraine states using electrophysiological recordings may play a key role in migraine diagnosis and early treatment. Migraineurs are characterized by a deficit of habituation in cortical information processing, causing abnormal changes of somatosensory evoked potentials. Here, we propose a machine learning approach to utilize somatosensory evoked potential-based biomarkers for migraine classification in a noninvasive setting.

Methods

Forty-two migraine patients, including 29 interictal and 13 ictal, were recruited and compared with 15 healthy volunteers of similar age and gender distribution. The right median nerve somatosensory evoked potentials were collected from all subjects. State-of-the-art machine learning algorithms including random forest, extreme gradient-boosting trees, support vector machines, K-nearest neighbors, multilayer perceptron, linear discriminant analysis, and logistic regression were used for classification and were built upon somatosensory evoked potential features in time and frequency domains. A feature selection method was employed to assess the contribution of features and compare it with previous clinical findings, and to build an optimal feature set by removing redundant features.

Results

Using a set of relevant features and different machine learning models, accuracies ranging from 51.2% to 72.4% were achieved for the healthy volunteers-ictal-interictal classification task. Following model and feature selection, we successfully separated the three groups of subjects with an accuracy of 89.7% for the healthy volunteers-ictal, 88.7% for healthy volunteers-interictal, 80.2% for ictal-interictal, and 73.3% for healthy volunteers-ictal-interictal classification tasks, respectively.

Conclusion

Our proposed model suggests the potential use of somatosensory evoked potentials as a prominent and reliable signal in migraine classification. This non-invasive somatosensory evoked potential-based classification system offers the potential to reliably separate migraine patients in ictal and interictal states from healthy controls.

An Intuitionistic Evidential Method for Weight Determination in FMEA Based on Belief Entropy

Failure Mode and Effects Analysis (FMEA) has been regarded as an effective analysis approach to identify and rank the potential failure modes in many applications. However, how to determine the weights of team members appropriately, with the impact factor of domain experts’ uncertainty in decision-making of FMEA, is still an open issue. In this paper, a new method to determine the weights of team members, which combines evidence theory, intuitionistic fuzzy sets (IFSs) and belief entropy, is proposed to analyze the failure modes. One of the advantages of the presented model is that the uncertainty of experts in the decision-making process is taken into consideration. The proposed method is data driven with objective and reasonable properties, which considers the risk of weights more completely. A numerical example is shown to illustrate the feasibility and availability of the proposed method.

Abnormal Entropy Modulation of the EEG Signal in Patients With Schizophrenia During the Auditory Paired-Stimulus Paradigm

The complexity change in brain activity in schizophrenia is an interesting topic clinically. Schizophrenia patients exhibit abnormal task-related modulation of complexity, following entropy of electroencephalogram (EEG) analysis. However, complexity modulation in schizophrenia patients during the sensory gating (SG) task, remains unknown. In this study, the classical auditory paired-stimulus paradigm was introduced to investigate SG, and EEG data were recorded from 55 normal controls and 61 schizophrenia patients. Fuzzy entropy (FuzzyEn) was used to explore the complexity of brain activity under the conditions of baseline (BL) and the auditory paired-stimulus paradigm (S1 and S2). Generally, schizophrenia patients showed significantly higher FuzzyEn values in the frontal and occipital regions of interest (ROIs). Relative to the BL condition, the normalized values of FuzzyEn of normal controls were decreased greatly in condition S1 and showed less variance in condition S2. Schizophrenia patients showed a smaller decrease in the normalized values in condition S1. Moreover, schizophrenia patients showed significant diminution in the suppression ratios of FuzzyEn, attributed to the higher FuzzyEn values in condition S1. These results suggested that entropy modulation during the process of sensory information and SG was obvious in normal controls and significantly deficient in schizophrenia patients. Additionally, the FuzzyEn values measured in the frontal ROI were positively correlated with positive scores of Positive and Negative Syndrome Scale (PANSS), indicating that frontal entropy was a potential indicator in evaluating the clinical symptoms. However, negative associations were found between the FuzzyEn values of occipital ROIs and general and total scores of PANSS, likely reflecting the compensation effect in visual processing. Thus, our findings provided a deeper understanding of the deficits in sensory information processing and SG, which contribute to cognitive deficits and symptoms in patients with schizophrenia.

How Physical Activities Affect Mental Fatigue Based on EEG Energy, Connectivity, and Complexity

Many studies have verified that there is an interaction between physical activities and mental fatigue. However, few studies are focused on the effect of physical activities on mental fatigue. This study was to analyze the states of mental fatigue based on electroencephalography (EEG) and investigate how physical activities affect mental fatigue. Fourteen healthy participants participated in an experiment including a 2-back mental task (the control) and the same mental task with cycling simultaneously (physical-mental task). Each experiment consisted of three 20 min fatigue-inducing sessions repeatedly (mental fatigue for mental tasks or mental fatigue plus physical activities for physical-mental tasks). During the evaluation sessions (before and after the fatigue-inducing sessions), the states of the participants were assessed by EEG parameters. Wavelet Packet Energy (WPE), Spectral Coherence Value (SCV), and Lempel-Ziv Complexity (LZC) were used to indicate mental fatigue from the perspectives of activation, functional connectivity, and complexity of the brain. The indices are the beta band energy E_β, the energy ratio E_α/β, inter-hemispheric SCV of beta band SCV_β and LZC. The statistical analysis shows that mental fatigue was detected by E_β, E_α/β, SCV_β, and LZC in physical-mental task. The slopes of the linear fit on these indices verified that the mental fatigue increased more fast during physical-mental task. It is concluded form the result that physical activities can enhance the mental fatigue and speed up the fatigue process based on brain activation, functional connection, and complexity. This result differs from the traditional opinion that physical activities have no influence on mental fatigue, and finds that physical activities can increase mental fatigue. This finding helps fatigue management through exercise instruction.

A Novel Interpretation of Sample Entropy in Surface Electromyographic Examination of Complex Neuromuscular Alternations in Subacute and Chronic Stroke

The objective of this paper was to develop sample entropy (SampEn) as a novel surface electromyogram (EMG) biomarker to quantitatively examine post-stroke neuromuscular alternations. The SampEn method was performed on surface EMG interference patterns recorded from biceps brachii muscles of nine healthy control subjects, fourteen subjects with subacute stroke, and eleven subjects with chronic stroke, respectively. Measurements were collected during isometric contractions of elbow flexion at different constant force levels. By producing diagnostic decisions for individual muscles, two categories of abnormalities in some paretic muscles were discriminated in terms of abnormally increased and decreased SampEn. The efficiency of the SampEn was demonstrated by its comparable performance with a previously reported clustering index (CI) method. Mixed SampEn (or CI) patterns were observed in paretic muscles of subjects with stroke indicating complex neuromuscular changes at work as a result of a hemispheric brain lesion. Although both categories of SampEn (or CI) abnormalities were observed in both subacute and chronic stages of stroke, the underlying processes contributing to the SampEn abnormalities might vary a lot in stroke stage. The SampEn abnormalities were also found in contralateral muscles of subjects with chronic stroke indicating the necessity of applying interventions to contralateral muscles during stroke rehabilitation. Our work not only presents a novel method for quantitative examination of neuromuscular changes, but also explains the neuropathological mechanisms of motor impairments and offers guidelines for a better design of effective rehabilitation protocols toward improved motor recovery.