Dynamics of brain function in patients with chronic pain assessed by microstate analysis of resting-state electroencephalography

The role of testosterone in modulating positive and negative empathy in social interactions

Empathy encompasses both negative (e.g., distress) and positive (e.g., shared joy) dimensions, yet the effects of testosterone on positive empathy and its modulation of intrinsic neural dynamics remain underexplored. This double-blind, placebo-controlled study examined how testosterone influences neural sensitivity to empathy within social inclusion and exclusion contexts, as well as its impact on resting-state EEG microstates-millisecond-scale transient patterns of brain activity. Healthy male participants received either testosterone or placebo before completing resting-state EEG recordings and an empathy task featuring social scenarios. While self-reported empathy ratings remained unchanged, testosterone amplified neurophysiological responses: it enhanced anterior N2 amplitude (250-310 ms), associated with negative empathy toward social exclusion, and increased posterior α-event-related desynchronization (8.28-10 Hz; 1226-1901 ms), linked to positive empathy during social inclusion. These findings suggest that testosterone enhances neural responsiveness to both threatening and affiliative social cues, reinforcing its role in adaptive social vigilance. Resting-state EEG microstate analysis further revealed that testosterone prolonged the temporal dominance of microstate E-a centro-parietal activity pattern associated with interoceptive awareness and emotional processing. Notably, these microstate E changes predicted increased emotional empathy across both positive and negative contexts. Together, our findings suggest that testosterone indirectly enhances empathy-related responsiveness by amplifying baseline interoceptive sensitivity to socially salient stimuli. These dual effects-enhanced intrinsic interoceptive processing and heightened neural reactivity to social cues-position testosterone as a key neuromodulator of context-adaptive social perception.

Individual differences in resting alpha band power and changes in theta band power during sustained pain are correlated with the pain-relieving efficacy of alpha HD-tACS on SM1

High-definition transcranial alternating current stimulation (HD-tACS) targeting alpha rhythms (8-13 Hz) shows promise as a pain-relieving intervention, but individual responses vary widely. Understanding the neurobiological mechanism behind this variability is crucial for optimizing HD-tACS parameters to enhance its efficacy in pain relief. In a double-blind, within-subject, sham-controlled experimental study, 34 healthy participants were recruited. We investigated how individual differences in brain oscillations during rest and capsaicin-induced sustained pain states influence the efficacy of alpha HD-tACS. Participants underwent EEG assessments at rest and during capsaicin-induced sustained pain. They then received either sham or active HD-tACS on the sensorimotor cortex (SM1) or dorsolateral prefrontal cortex (DLPFC). We found significant reductions in delta and theta band power at the C4 electrode during sustained pain correlated with individual pain intensity. Additionally, stimulating the SM1 and DLPFC significantly relieved sustained pain. Resting alpha band power and changes in theta band power during sustained pain (the difference in theta band power between sustained pain and rest) at the C4 electrode were both significantly correlated with the pain-relieving efficacy of alpha HD-tACS on SM1. Notably, changes in theta band power mediated the relationship between resting alpha band power and pain-relieving efficacy. These results were not found with alpha HD-tACS on DLPFC. Our results suggest that the variations in theta band power during sustained pain may be crucial for understanding the variability in the efficacy of alpha HD-tACS targeting SM1. The factors influencing the efficacy of alpha HD-tACS on the DLPFC might be multifaceted.

Multimodal hypersensitivity and somatic symptoms predict adolescent postmenarchal widespread pain

ABSTRACT

Widespread pain in adolescence is linked with poor mental health, pain, and somatic symptoms in childhood. This prospective study in 207 premenarchal adolescents used quantitative sensory testing (QST) and multimodal hypersensitivity (MMH) measures to assess somatosensory system function and identify predictors for widespread pain (≥3/7 sites). We hypothesized that premenarchal pain, somatic symptoms, psychological factors, and somatosensory system function would predict postmenarchal widespread pain, which would be associated with greater menstrual pain intensity. At premenarchal and postmenarchal study visits, participants completed measures of somatic symptoms, a pain body map, psychosocial questionnaires, QST, and experimental MMH measures including auditory, visual, and visceral stimulation. Electroencephalography (EEG) was collected during auditory and visual tasks to identify neural correlates of MMH. Premenarchal widespread pain was reported by 25% of participants, whereas 29% developed new incident widespread pain postmenarche. Adolescents with postmenarchal widespread pain reported greater menstrual pain intensity (median [interquartile range] 47 [28-61]; 0-100 visual analog scale) than those without (24 [8-50], P = 0.001). Elevated somatic symptoms (P = 0.012), stress (P = 0.015), and sensitivity to visceral (bladder filling) (P = 0.046) and unpleasant visual stimuli (P = 0.043) were significant predictors of postmenarche widespread pain. A multivariable regression model found premenarchal body map score (OR = 1.75, 95% CI [1.20, 2.55]), somatic symptoms (OR = 1.47, 95% CI [1.03, 2.11]), and visual hypersensitivity (OR = 1.62, 95% CI [1.12, 2.33]) predicted postmenarchal widespread pain. No EEG differences in early cortical sensory processing were found. Our results suggest that increased sensitivity to multimodal unpleasant and painful stimuli represents a novel risk factor for postmenarche widespread pain.

Pain and the Brain: A Systematic Review of Methods, EEG Biomarkers, Limitations, and Future Directions

Background: Pain is prevalent in almost all populations and may often hinder visual, auditory, tactile, olfactory, and taste perception as it alters brain neural processing. The quantitative methods emerging to define pain and assess its effects on neural functions and perception are important. Identifying pain biomarkers is one of the initial stages in developing such models and interventions. The existing literature has explored chronic and experimentally induced pain, leveraging electroencephalograms (EEGs) to identify biomarkers and employing various qualitative and quantitative approaches to measure pain. Objectives: This systematic review examines the methods, participant characteristics, types of pain states, associated pain biomarkers of the brain's electrical activity, and limitations of current pain studies. The review identifies what experimental methods researchers implement to study human pain states compared to human control pain-free states, as well as the limitations in the current techniques of studying human pain states and future directions for research. Methods: The research questions were formed using the Population, Intervention, Comparison, Outcome (PICO) framework. A literature search was conducted using PubMed, PsycINFO, Embase, the Cochrane Library, IEEE Explore, Medline, Scopus, and Web of Science until December 2024, following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines to obtain relevant studies. The inclusion criteria included studies that focused on pain states and EEG data reporting. The exclusion criteria included studies that used only MEG or fMRI neuroimaging techniques and those that did not focus on the evaluation or assessment of neural markers. Bias risk was determined by the Newcastle-Ottawa Scale. Target data were compared between studies to organize the findings among the reported results. Results: The initial search resulted in 592 articles. After exclusions, 24 studies were included in the review, 6 of which focused on chronic pain populations. Experimentally induced pain methods were identified as techniques that centered on tactile perception: thermal, electrical, mechanical, and chemical. Across both chronic and stimulated pain studies, pain was associated with decreased or slowing peak alpha frequency (PAF). In the chronic pain studies, beta power increases were seen with pain intensity. The functional connectivity and pain networks of chronic pain patients differ from those of healthy controls; this includes the processing of experimental pain. Reportedly small sample sizes, participant comorbidities such as neuropsychiatric disorders and peripheral nerve damage, and uncontrolled studies were the common drawbacks of the studies. Standardizing methods and establishing collaborations to collect open-access comprehensive longitudinal data were identified as necessary future directions to generalize neuro markers of pain. Conclusions: This review presents a variety of experimental setups, participant populations, pain stimulation methods, lack of standardized data analysis methods, supporting and contradicting study findings, limitations, and future directions. Comprehensive studies are needed to understand the pain and brain relationship deeper in order to confirm or disregard the existing findings and to generalize biomarkers across chronic and experimentally induced pain studies. This requires the implementation of larger, diverse cohorts in longitudinal study designs, establishment of procedural standards, and creation of repositories. Additional techniques include the utilization of machine learning and analyzing data from long-term wearable EEG systems. The review protocol is registered on INPLASY (# 202520040).

Spontaneous pain dynamics characterized by stochasticity in neural recordings of awake humans with chronic pain

ABSTRACT

Chronic pain is characterized by spontaneous fluctuations in pain intensity, a phenomenon that remains poorly understood. The aim of this study is to elucidate the neural mechanisms underlying pain fluctuations in patients with chronic pain undergoing deep brain stimulation surgery. We recorded local field potentials (LFPs) from pain-processing hub structures, including the ventral posteromedial nucleus of the thalamus, subgenual cingulate cortex, and periventricular and periaqueductal gray, while patients continuously reported their pain levels. Using novel auto-mutual information metrics to analyze LFP stochastic patterns, we found that pain intensity correlated with both increased regularity of spike-like events and greater past-dependency of neural oscillations in the 4- to 15-Hz frequency band. In addition, during periods of higher pain states, we observed enhanced functional connectivity between the examined hub structures and the prefrontal cortex, suggesting a more focused flow of pain-related information within the pain circuit. By characterizing the dynamic nature of pain fluctuations, this study bridges the gap in understanding moment-to-moment pain variations and their underlying neural mechanisms, paving the way for improved chronic pain management strategies.

Peak alpha frequency differs between chronic back pain and chronic widespread pain

BACKGROUND

Low peak alpha frequency (PAF) is an electroencephalography (EEG) outcome associated reliably with high acute pain sensitivity. However, existing research suggests that the relationship between PAF and chronic pain is more variable. This variability could be attributable to chronic pain groups typically being examined as homogenous populations, without consideration being given to potential diagnosis-specific differences. Indeed, while emerging work has compared individuals with chronic pain to healthy controls, no previous studies have examined differences in PAF between diagnoses or across chronic pain subtypes.

METHODS

To address this gap, we reanalysed a dataset of resting state EEG previously used to demonstrate a lack of difference in PAF between individuals with chronic pain and healthy controls. In this new analysis, we separated patients by diagnosis before comparing PAF across three subgroups: chronic widespread pain (n = 30), chronic back pain (n = 38), and healthy controls (n = 87).

RESULTS

We replicate the original finding of no significant difference between chronic pain groups and controls, but also find that individuals with widespread pain had significantly higher global average PAF values than those of people with chronic back pain [p = 0.028, β = 0.25 Hz] after controlling for age, sex, and depression.

CONCLUSIONS

These novel findings reveal PAF values in individuals with chronic pain may be diagnosis-specific and not uniformly shifted from the values of healthy controls. Future studies should account for diagnosis and be cautious with exploring homogenous 'chronic pain' classifications during investigations of PAF.

SIGNIFICANCE

Our work suggests that, contrary to previous hypotheses, inter-individual differences in PAF reflect diagnosis-specific mechanisms rather than the general presence of chronic pain, and therefore may have important implications for future work regarding individually-tailored pain management strategies.

Similarities and differences between chronic primary pain and depression in brain activities: Evidence from resting-state microstates and auditory Oddball task

BACKGROUND

In 2019, the International Association for the Study of Pain introduced the concept of 'chronic primary pain (CPP)', characterized by persistent non-organic pain with emotional and functional abnormalities. Underdiagnosed and linked to depression, CPP has poorly understood neural characteristics. Electroencephalogram (EEG) microstates enable detailed examination of brain network dynamics at the millisecond level. Incorporating task-related EEG features offers a comprehensive neurophysiological signature of brain dysfunction, facilitating exploration of potential neural mechanisms.

METHODS

This study employed resting-state and task-related auditory Oddball EEG paradigm to evaluate 20 healthy controls, 20 patients with depression, and 20 patients with CPP. An 8-minute recording of resting-state EEG was conducted to identify four typical microstates (A-D). Additionally, power spectral density (PSD) features were examined during an auditory Oddball paradigm.

RESULTS

Both CPP and Major Depressive Disorder (MDD) patients exhibited reduced occurrence rate and transition probabilities of other microstates to microstate C during resting-state EEG. Furthermore, more pronounced increase in Gamma PSD was observed in the occipital region of CPP during the Oddball task. In CPP, both resting-state microstate C and task-related Gamma PSD correlated with pain and emotional indicators. Notably, microstate C occurrence positively correlated with occipital Gamma PSD in MDD.

CONCLUSION

Conclusively, both CPP and MDD display dynamic abnormalities within the salient network, closely associated with pain and depressive symptoms in CPP. Unlike MDD, CPPs' dynamic network changes appear unrelated to perceptual integration function, indicating differing microstate functional impacts. Combining resting-state microstates and Oddball tasks may offer a promising avenue for identifying potential biomarkers in objectively assessing chronic primary pain.

Current source density and functional connectivity extracted from resting-state electroencephalography as biomarkers for chronic low back pain

Introduction

Chronic low back pain (CLBP) is a global health issue, and its nonspecific causes make treatment challenging. Understanding the neural mechanisms of CLBP should contribute to developing effective therapies.

Objectives

To compare current source density (CSD) and functional connectivity (FC) extracted from resting electroencephalography (EEG) between patients with CLBP and healthy controls and to examine the correlations between EEG indices and symptoms.

Methods

Thirty-four patients with CLBP and 34 healthy controls in an open data set were analyzed. Five-minute resting-state closed-eye EEG was acquired using the international 10-20 system. Current source density across frequency bands was calculated using exact low-resolution electromagnetic tomography. Functional connectivity was assessed between 24 cortical regions using lagged linear connectivity. Correlations between pain symptoms and CSD distribution and FC were examined in patients with CLBP.

Results

Current source density analysis showed no significant differences between the groups. The CLBP group exhibited significantly reduced FC in the β3 band between the left middle temporal gyrus and the posterior cingulate cortex, and between the ventral medial prefrontal cortex and the left inferior parietal lobule. Prefrontal θ and δ activity positively correlated with pain symptoms. Increased β1 band FC between the right dorsolateral prefrontal cortex and right auditory cortex correlated with greater pain intensity.

Conclusions

We found altered neural activity and connectivity in patients with CLBP, particularly in prefrontal and temporal regions. These results suggest potential targets for pain modulation through brain pathways and highlight the value of EEG biomarkers in understanding pain mechanisms and assessing treatment efficacy.

Spatiotemporal Microstate Dynamics of Spike-Free Scalp EEG Offer a Potential Biomarker for Refractory Temporal Lobe Epilepsy

Refractory temporal lobe epilepsy (TLE) is one of the most frequently observed subtypes of epilepsy and endangers more than 50 million people world-wide. Although electroencephalogram (EEG) had been widely recognized as a classic tool to screen and diagnose epilepsy, for many years it heavily relied on identifying epileptic discharges and epileptogenic zone localization, which however, limits the understanding of refractory epilepsy due to the network nature of this disease. This work hypothesizes that the microstate dynamics based on resting-state scalp EEG can offer an additional network depiction of the disease and provide potential complementary evaluation tool for the TLE even without detectable epileptic discharges on EEG. We propose a novel framework for EEG microstate spatial-temporal dynamics (EEG-MiSTD) analysis based on machine learning to comprehensively model millisecond-changing whole-brain network dynamics. With only 100 seconds of resting-state EEG even without epileptic discharges, this approach successfully distinguishes TLE patients from healthy controls and is related to the lateralization of epileptic focus. Besides, microstate temporal and spatial features are found to be widely related to clinical parameters, which further demonstrate that TLE is a network disease. A preliminary exploration suggests that the spatial topography is sensitive to the following surgical outcomes. From such a new perspective, our results suggest that spatiotemporal microstate dynamics is potentially a biomarker of the disease. The developed EEG-MiSTD framework can probably be considered as a general tool to examine dynamical brain network disruption in a user-friendly way for other types of epilepsy.

EEG microstates in epilepsy with and without cognitive dysfunction: Alteration in intrinsic brain activity

OBJECTIVE

This study aims to investigate the difference between epilepsy comorbid with and without cognitive dysfunction.

METHOD

Participants were classified into patients with epilepsy comorbid cognitive dysfunction (PCCD) and patients with epilepsy without comorbid cognitive dysfunction (nPCCD). Microstate analysis was applied based on 20-channel electroencephalography (EEG) to detect the dynamic changes in the whole brain. The coverage, occurrence per second, duration, and transition probability were calculated.

RESULT

The occurrence per second and the coverage of microstate B in the PCCD group were higher than that of the nPCCD group. Coverage in microstate D was lower in the PCCD group than in the nPCCD group. In addition, the PCCD group has a higher probability of A to B and B to A transitions and a lower probability of A to D and D to A transitions.

CONCLUSION

Our research scrutinizes the disparities observed within EEG microstates among epilepsy patients both with and without comorbid cognitive dysfunction.

SIGNIFICANCE

EEG microstate analysis offers a novel metric for assessing neuropsychiatric disorders and supplies evidence for investigating the mechanisms and the dynamic change of epilepsy comorbid cognitive dysfunction.

Magnetoencephalography-derived oscillatory microstate patterns across lifespan: the Cambridge centre for ageing and neuroscience cohort

The aging brain represents the primary risk factor for many neurodegenerative disorders. Whole-brain oscillations may contribute novel early biomarkers of aging. Here, we investigated the dynamic oscillatory neural activities across lifespan (from 18 to 88 years) using resting Magnetoencephalography (MEG) in a large cohort of 624 individuals. Our aim was to examine the patterns of oscillation microstates during the aging process. By using a machine-learning algorithm, we identify four typical clusters of microstate patterns across different age groups and different frequency bands: left-to-right topographic MS1, right-to-left topographic MS2, anterior-posterior MS3 and fronto-central MS4. We observed a decreased alpha duration and an increased alpha occurrence for sensory-related microstate patterns (MS1 & MS2). Accordingly, theta and beta changes from MS1 & MS2 may be related to motor decline that increased with age. Furthermore, voluntary 'top-down' saliency/attention networks may be reflected by the increased MS3 & MS4 alpha occurrence and complementary beta activities. The findings of this study advance our knowledge of how the aging brain shows dysfunctions in neural state transitions. By leveraging the identified microstate patterns, this study provides new insights into predicting healthy aging and the potential neuropsychiatric cognitive decline.

Resting-state Electroencephalography Microstates Correlate with Pain Intensity in Patients with Complex Regional Pain Syndrome

Objective: Severe pain and other symptoms in complex regional pain syndrome (CRPS), such as allodynia and hyperalgesia, are associated with abnormal resting-state brain network activity. No studies to date have examined resting-state brain networks in CRPS patients using electroencephalography (EEG), which can clarify the temporal dynamics of brain networks. Methods: We conducted microstate analysis using resting-state EEG signals to prospectively reveal direct correlations with pain intensity in CRPS patients (n = 17). Five microstate topographies were fitted back to individual CRPS patients' EEG data, and temporal microstate measures were subsequently calculated. Results: Our results revealed five distinct microstates, termed microstates A to E, from resting EEG data in patients with CRPS. Microstates C, D and E were significantly correlated with pain intensity before pain treatment. Particularly, microstates D and E were significantly improved together with pain alleviation after pain treatment. As microstates D and E in the present study have previously been related to attentional networks and the default mode network, improvement in these networks might be related to pain relief in CRPS patients. Conclusions: The functional alterations of these brain networks affected the pain intensity of CRPS patients. Therefore, EEG microstate analyses may be used to identify surrogate markers for pain intensity.

EEG spectral and microstate analysis originating residual inhibition of tinnitus induced by tailor-made notched music training

Tailor-made notched music training (TMNMT) is a promising therapy for tinnitus. Residual inhibition (RI) is one of the few interventions that can temporarily inhibit tinnitus, which is a useful technique that can be applied to tinnitus research and explore tinnitus mechanisms. In this study, RI effect of TMNMT in tinnitus was investigated mainly using behavioral tests, EEG spectral and microstate analysis. To our knowledge, this study is the first to investigate RI effect of TMNMT. A total of 44 participants with tinnitus were divided into TMNMT group (22 participants; ECnm, NMnm, RInm represent that EEG recordings with eyes closed stimuli-pre, stimuli-ing, stimuli-post by TMNMT music, respectively) and Placebo control group (22 participants; ECpb, PBpb, RIpb represent that EEG recordings with eyes closed stimuli-pre, stimuli-ing, stimuli-post by Placebo music, respectively) in a single-blind manner. Behavioral tests, EEG spectral analysis (covering delta, theta, alpha, beta, gamma frequency bands) and microstate analysis (involving four microstate classes, A to D) were employed to evaluate RI effect of TMNMT. The results of the study showed that TMNMT had a stronger inhibition ability and longer inhibition time according to the behavioral tests compared to Placebo. Spectral analysis showed that RI effect of TMNMT increased significantly the power spectral density (PSD) of delta, theta bands and decreased significantly the PSD of alpha2 band, and microstate analysis showed that RI effect of TMNMT had shorter duration (microstate B, microstate C), higher Occurrence (microstate A, microstate C, microstate D), Coverage (microstate A) and transition probabilities (microstate A to microstate B, microstate A to microstate D and microstate D to microstate A). Meanwhile, RI effect of Placebo decreased significantly the PSD of alpha2 band, and microstate analysis showed that RI effect of Placebo had shorter duration (microstate C, microstate D), higher occurrence (microstate B, microstate C), lower coverage (microstate C, microstate D), higher transition probabilities (microstate A to microstate B, microstate B to microstate A). It was also found that the intensity of tinnitus symptoms was significant positively correlated with the duration of microstate B in five subgroups (ECnm, NMnm, RInm, ECpb, PBpb). Our study provided valuable experimental evidence and practical applications for the effectiveness of TMNMT as a novel music therapy for tinnitus. The observed stronger residual inhibition (RI) ability of TMNMT supported its potential applications in tinnitus treatment. Furthermore, the temporal dynamics of EEG microstates serve as novel functional and trait markers of synchronous brain activity that contribute to a deep understanding of the neural mechanism underlying TMNMT treatment for tinnitus.

Preoperative resting-state microstate as a marker for chronic pain after breast cancer surgery

INTRODUCTION

Chronic postoperative pain poses challenges, emphasizing the importance of accurately predicting pain in advance. Generally, pain perception is associated with the temporal dynamics of the brain, which can be represented by microstates. Specifically, microstates are transient and patterned brain topographies formed by temporally overlapping and spatially synchronized oscillatory activities. Consequently, by characterizing brain activity, microstates offer valuable insights into pain perception.

METHODS

In this prospective study, 66 female patients undergoing breast cancer surgery were included. Their preoperative resting-state electroencephalography (EEG) was recorded. Preoperative resting-state EEG was recorded and four specific brain microstates (labeled as A, B, C, and D) were extracted. Temporal characteristics were then analyzed from these microstates. Patients were classified into two groups based on their Numerical Rating Scale (NRS) scores at three months postoperatively. Those with NRS scores ranging from 4 to 10 were classified as the high pain group, while patients with NRS ranging from 0 to 3 were classified as the lowpain group. Statistical analyses were performed to compare the microstate characteristics between these two groups.

RESULTS

Twenty-one patients (32%) were classified as the high pain group and forty-five (68%) as the low-pain group. The occurrence and coverage of microstate C were significantly higher in the high pain group. Additionally, there were significant differences in the microstates transitions between the two groups. Furthermore, the study revealed a positive correlation between the coverage of microstate C and the NRS.

CONCLUSIONS

Preoperative resting-state microstate features have shown correlations with postoperative pain. This study presents a novel and advanced perspective on the potential of microstates as a marker for postoperative pain.

Predicting Motor Imagery BCI Performance Based on EEG Microstate Analysis

Motor imagery (MI) electroencephalography (EEG) is natural and comfortable for controllers, and has become a research hotspot in the field of the brain-computer interface (BCI). Exploring the inter-subject MI-BCI performance variation is one of the fundamental problems in MI-BCI application. EEG microstates with high spatiotemporal resolution and multichannel information can represent brain cognitive function. In this paper, four EEG microstates (MS1, MS2, MS3, MS4) were used in the analysis of the differences in the subjects' MI-BCI performance, and the four microstate feature parameters (the mean duration, the occurrences per second, the time coverage ratio, and the transition probability) were calculated. The correlation between the resting-state EEG microstate feature parameters and the subjects' MI-BCI performance was measured. Based on the negative correlation of the occurrence of MS1 and the positive correlation of the mean duration of MS3, a resting-state microstate predictor was proposed. Twenty-eight subjects were recruited to participate in our MI experiments to assess the performance of our resting-state microstate predictor. The experimental results show that the average area under curve (AUC) value of our resting-state microstate predictor was 0.83, and increased by 17.9% compared with the spectral entropy predictor, representing that the microstate feature parameters can better fit the subjects' MI-BCI performance than spectral entropy predictor. Moreover, the AUC of microstate predictor is higher than that of spectral entropy predictor at both the single-session level and average level. Overall, our resting-state microstate predictor can help MI-BCI researchers better select subjects, save time, and promote MI-BCI development.

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.

Resting-state electroencephalography and magnetoencephalography as biomarkers of chronic pain: a systematic review

ABSTRACT

Reliable and objective biomarkers promise to improve the assessment and treatment of chronic pain. Resting-state electroencephalography (EEG) is broadly available, easy to use, and cost efficient and, therefore, appealing as a potential biomarker of chronic pain. However, results of EEG studies are heterogeneous. Therefore, we conducted a systematic review (PROSPERO CRD42021272622) of quantitative resting-state EEG and magnetoencephalography (MEG) studies in adult patients with different types of chronic pain. We excluded populations with severe psychiatric or neurologic comorbidity. Risk of bias was assessed using a modified Newcastle-Ottawa Scale. Semiquantitative data synthesis was conducted using modified albatross plots. We included 76 studies after searching MEDLINE, Web of Science Core Collection, Cochrane Central Register of Controlled Trials, and EMBASE. For cross-sectional studies that can serve to develop diagnostic biomarkers, we found higher theta and beta power in patients with chronic pain than in healthy participants. For longitudinal studies, which can yield monitoring and/or predictive biomarkers, we found no clear associations of pain relief with M/EEG measures. Similarly, descriptive studies that can yield diagnostic or monitoring biomarkers showed no clear correlations of pain intensity with M/EEG measures. Risk of bias was high in many studies and domains. Together, this systematic review synthesizes evidence on how resting-state M/EEG might serve as a diagnostic biomarker of chronic pain. Beyond, this review might help to guide future M/EEG studies on the development of pain biomarkers.

A developmental shift in habituation to pain in human neonates

Habituation to recurrent non-threatening or unavoidable noxious stimuli is an important aspect of adaptation to pain. Neonates, especially if preterm, are exposed to repeated noxious procedures during their clinical care. They can mount strong behavioral, autonomic, spinal, and cortical responses to a single noxious stimulus; however, it is not known whether the developing nervous system can adapt to the recurrence of these inputs. Here, we used electroencephalography to investigate changes in cortical microstates (representing the complex sequential processing of noxious inputs) following two consecutive clinically required heel lances in term and preterm infants. We show that stimulus repetition dampens the engagement of initial microstates and associated behavioral and autonomic responses in term infants, while preterm infants do not show signs of habituation. Nevertheless, both groups engage different longer-latency cortical microstates to each lance, which is likely to reflect changes in higher-level stimulus processing with repeated stimulation. These data suggest that while both age groups are capable of encoding contextual differences in pain, the preterm brain does not regulate the initial cortical, behavioral, and autonomic responses to repeated noxious stimuli. Habituation mechanisms to pain are already in place at term age but mature over the equivalent of the last trimester of gestation and are not fully functional in preterm neonates.

The pro-inflammatory factors contribute to the EEG microstate abnormalities in patients with major depressive disorder

Pro-inflammatory factors may be associated with abnormalities in functional brain networks, which may be a mechanism in the pathogenesis of major depressive disorder (MDD). Electroencephalogram (EEG) microstates reflect the functioning of brain networks. However, the relationship between pro-inflammatory factors and the microstate abnormalities in patients with MDD is poorly understood. 24 MDD patients and 24 age-and sex-matched healthy controls (HC) were recruited. Montgomery-Asberg Depression Rating Scale(MADRS) were assessed. Serum (interleukin- 2(IL- 2), tumor necrosis factor-α (TNF-α) and hs-C-reactive protein (CRP)and EEG data were collected. K-means clustering was performed to characterize different microstates. For each microstate, duration, occurrence and coverage were estimated. Four microstates (e.g. A, B, C, D) were characterized, MDD group showed lower duration, occurrence and coverage of microstate B and microstate D, while higher duration of microstate A and microstate C and levels of IL-2, TNF-α, hs-CRP than HC group. The duration, occurrence and coverage of microstate D were negatively correlated with levels of pro-inflammatory factors (IL-2, TNF- α and hs- CRP) (all P < 0.05). Serum pro-inflammatory induced the abnormalities of microstate D. Together, these findings add to the understanding of the pathophysiology of MDD and point to pro-inflammatory factors contribute to EEG microstate abnormalities in patients with MDD.

A Hidden Markov Model reveals magnetoencephalography spectral frequency-specific abnormalities of brain state power and phase-coupling in neuropathic pain

Neuronal populations in the brain are engaged in a temporally coordinated manner at rest. Here we show that spontaneous transitions between large-scale resting-state networks are altered in chronic neuropathic pain. We applied an approach based on the Hidden Markov Model to magnetoencephalography data to describe how the brain moves from one activity state to another. This identified 12 fast transient (~80 ms) brain states including the sensorimotor, ascending nociceptive pathway, salience, visual, and default mode networks. Compared to healthy controls, we found that people with neuropathic pain exhibited abnormal alpha power in the right ascending nociceptive pathway state, but higher power and coherence in the sensorimotor network state in the beta band, and shorter time intervals between visits of the sensorimotor network, indicating more active time in this state. Conversely, the neuropathic pain group showed lower coherence and spent less time in the frontal attentional state. Therefore, this study reveals a temporal imbalance and dysregulation of spectral frequency-specific brain microstates in patients with neuropathic pain. These findings can potentially impact the development of a mechanism-based therapeutic approach by identifying brain targets to stimulate using neuromodulation to modify abnormal activity and to restore effective neuronal synchrony between brain states.

Decoding pain from brain activity

Pain is a dynamic, complex and multidimensional experience. The identification of pain from brain activity as neural readout may effectively provide a neural code for pain, and further provide useful information for pain diagnosis and treatment. Advances in neuroimaging and large-scale electrophysiology have enabled us to examine neural activity with improved spatial and temporal resolution, providing opportunities to decode pain in humans and freely behaving animals. This topical review provides a systematical overview of state-of-the-art methods for decoding pain from brain signals, with special emphasis on electrophysiological and neuroimaging modalities. We show how pain decoding analyses can help pain diagnosis and discovery of neurobiomarkers for chronic pain. Finally, we discuss the challenges in the research field and point to several important future research directions.