Electroencephalography and magnetoencephalography in pain research-current state and future perspectives

An Innovative EEG-Based Pain Identification and Quantification: A Pilot Study

OBJECTIVE

The present pilot study aimed to propose an innovative scale-independent measure based on electroencephalographic (EEG) signals for the identification and quantification of the magnitude of chronic pain.

METHODS

EEG data were collected from three groups of participants at rest: seven healthy participants with pain, 15 healthy participants submitted to thermal pain, and 66 participants living with chronic pain. Every 30 s, the pain intensity score felt by the participant was also recorded. Electrodes positioned in the contralateral motor region were of interest. After EEG preprocessing, a complex analytical signal was obtained using Hilbert transform, and the upper envelope of the EEG signal was extracted. The average coefficient of variation of the upper envelope of the signal was then calculated for the beta (13-30 Hz) band and proposed as a new EEG-based indicator, namely Piqβ, to identify and quantify pain.

MAIN RESULTS

The main results are as follows: (1) A Piqβ threshold at 10%, that is, Piqβ ≥ 10%, indicates the presence of pain, and (2) the higher the Piqβ (%), the higher the extent of pain.

CONCLUSIONS

This finding indicates that Piqβ can objectively identify and quantify pain in a population living with chronic pain. This new EEG-based indicator can be used for objective pain assessment based on the neurophysiological body response to pain.

SIGNIFICANCE

Objective pain assessment is a valuable decision-making aid and an important contribution to pain management and monitoring.

Incorporation of "Artificial Intelligence" for Objective Pain Assessment: A Comprehensive Review

Pain is a significant health issue, and pain assessment is essential for proper diagnosis, follow-up, and effective management of pain. The conventional methods of pain assessment often suffer from subjectivity and variability. The main issue is to understand better how people experience pain. In recent years, artificial intelligence (AI) has been playing a growing role in improving clinical diagnosis and decision-making. The application of AI offers promising opportunities to improve the accuracy and efficiency of pain assessment. This review article provides an overview of the current state of AI in pain assessment and explores its potential for improving accuracy, efficiency, and personalized care. By examining the existing literature, research gaps, and future directions, this article aims to guide further advancements in the field of pain management. An online database search was conducted via multiple websites to identify the relevant articles. The inclusion criteria were English articles published between January 2014 and January 2024). Articles that were available as full text clinical trials, observational studies, review articles, systemic reviews, and meta-analyses were included in this review. The exclusion criteria were articles that were not in the English language, not available as free full text, those involving pediatric patients, case reports, and editorials. A total of (47) articles were included in this review. In conclusion, the application of AI in pain management could present promising solutions for pain assessment. AI can potentially increase the accuracy, precision, and efficiency of objective pain assessment.

Temporal summation of second pain is affected by cognitive load

This work attempted to clarify the interaction of cognition and pain sensitization during a paradigm of Temporal Summation of Second Pain (TSSP). We analyzed pain ratings and electroencephalographic (EEG) activity obtained from 21 healthy participants during the presentation of four experimental conditions that differed in the manipulation of attention to painful stimuli or working memory load (Attention to hand & TSSP; 0-back & TSSP (low cognitive load); 2-back & TSSP (high cognitive load); 2-back (without pain)). We found that the TSSP was reduced when the attention was diverted and the cognitive load increased, and this reduction was accompanied by higher midfrontal theta activity and lower posterior alpha and central beta activity. Although it is well established that TSSP is a phenomenon that occurs at the spinal level, here we show that it is also affected by supraspinal attentional mechanisms. Delivery of painful repeated stimuli did not affect the performance of the 2-back task but was associated with smaller amplitudes of attentional event-related potentials (ERPs) after standard stimuli (not the target). The study of brain activity during TSSP allowed to clarify the role of top-down attentional modulation in pain sensitization processes. Results contribute to a better understanding of cognitive dysfunction in pain conditions and reinforce the use of therapeutic strategies based on distracting attention away from pain.

A Scoping Review of the Effect of EEG Neurofeedback on Pain Complaints in Adults with Chronic Pain

Background and Aim: Non-pharmacological treatments such as electroencephalogram (EEG) neurofeedback have become more important in multidisciplinary approaches to treat chronic pain. The aim of this scoping review is to identify the literature on the effects of EEG neurofeedback in reducing pain complaints in adult chronic-pain patients and to elaborate on the neurophysiological rationale for using specific frequency bands as targets for EEG neurofeedback. Methods: A pre-registered scoping review was set up and reported following the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) extension for Scoping Reviews (PRISMA-ScR). The data were collected by searching for studies published between 1985 and January 2023 in PubMed, EMBASE, and PsycINFO. Results: Thirty-two studies on various types of chronic pain were included. The intervention was well-tolerated. Approximately half of the studies used a protocol that reinforced alpha or sensorimotor rhythms and suppressed theta or beta activity. However, the underlying neurophysiological rationale behind these specific frequency bands remains unclear. Conclusions: There are indications that neurofeedback in patients with chronic pain probably has short-term analgesic effects; however, the long-term effects are less clear. In order to draw more stable conclusions on the effectiveness of neurofeedback in chronic pain, additional research on the neurophysiological mechanisms of targeted frequency bands is definitely worthwhile. Several recommendations for setting up and evaluating the effect of neurofeedback protocols are suggested.

Self-regulation training for people with knee osteoarthritis: a protocol for a feasibility randomised control trial (MiNT trial)

Introduction

Knee osteoarthritis (OA) is a chronic secondary musculoskeletal pain condition resulting in disability, reduced quality of life, and high societal costs. Pain associated with knee OA is linked to increased sensitivity in sensory, cognitive, and emotional areas of the brain. Self-regulation training targeting brain functioning related to pain experience could reduce pain and its associated disability. Self-regulatory treatments such as mindfulness meditation (MM) and electroencephalography neurofeedback (EEG-NF) training improve clinical outcomes in people with knee OA. A feasibility clinical trial can address factors that could inform the design of the full trial investigating the effectiveness of self-regulation training programmes in people with knee OA. This clinical trial will evaluate the feasibility, safety, acceptability, experience and perceptions of the self-regulatory training programmes.

Methods

The proposed feasibility trial is based on a double-blind (outcome assessor and investigators), three-arm (MM usual care, EEG-NF + usual care and usual care control group) randomised controlled parallel clinical trial. Participants with knee OA will be recruited from the community and healthcare practices. A research assistant (RA) will administer both interventions (20-min sessions, four sessions each week, and 12 sessions over three successive weeks). Feasibility measures (participant recruitment rate, adherence to interventions, retention rate), safety, and acceptability of interventions will be recorded. An RA blinded to the group allocation will record secondary outcomes at baseline, immediately post-intervention (4th week), and 3 months post-intervention. The quantitative outcome measures will be descriptively summarised. The qualitative interviews will evaluate the participants' experiences and perceptions regarding various aspects of the trial, which includes identifying the barriers and facilitators in participating in the trial, evaluating their opinions on the research procedures, such as their preferences for the study site, and determining the level of acceptability of the interventions as potential clinical treatments for managing knee OA. Māori participant perceptions of how assessment and training practices could be acceptable to a Māori worldview will be explored. The interviews will be audio-recorded and analysed thematically.

Discussion

This trial will provide evidence on the feasibility, safety, and acceptability of the MM and EEG-NF training in people with knee OA, thus informing the design of a full randomised clinical control trial.

Combined transcranial magnetic stimulation and electroencephalography reveals alterations in cortical excitability during pain

Transcranial magnetic stimulation (TMS) has been used to examine inhibitory and facilitatory circuits during experimental pain and in chronic pain populations. However, current applications of TMS to pain have been restricted to measurements of motor evoked potentials (MEPs) from peripheral muscles. Here, TMS was combined with electroencephalography (EEG) to determine whether experimental pain could induce alterations in cortical inhibitory/facilitatory activity observed in TMS-evoked potentials (TEPs). In Experiment 1 (n=29), multiple sustained thermal stimuli were administered to the forearm, with the first, second, and third block of thermal stimuli consisting of warm but non-painful (pre-pain block), painful (pain block) and warm but non-painful (post-pain block) temperatures, respectively. During each stimulus, TMS pulses were delivered while EEG (64 channels) was simultaneously recorded. Verbal pain ratings were collected between TMS pulses. Relative to pre-pain warm stimuli, painful stimuli led to an increase in the amplitude of the frontocentral negative peak ~45 ms post-TMS (N45), with a larger increase associated with higher pain ratings. Experiments 2 and 3 (n=10 in each) showed that the increase in the N45 in response to pain was not due to changes in sensory potentials associated with TMS, or a result of stronger reafferent muscle feedback during pain. This is the first study to use combined TMS-EEG to examine alterations in cortical excitability in response to pain. These results suggest that the N45 TEP peak, which indexes GABAergic neurotransmission, is implicated in pain perception and is a potential marker of individual differences in pain sensitivity.

Changes in alpha, theta, and gamma oscillations in distinct cortical areas are associated with altered acute pain responses in chronic low back pain patients

Introduction

Chronic pain negatively impacts a range of sensory and affective behaviors. Previous studies have shown that the presence of chronic pain not only causes hypersensitivity at the site of injury but may also be associated with pain-aversive experiences at anatomically unrelated sites. While animal studies have indicated that the cingulate and prefrontal cortices are involved in this generalized hyperalgesia, the mechanisms distinguishing increased sensitivity at the site of injury from a generalized site-nonspecific enhancement in the aversive response to nociceptive inputs are not well known.

Methods

We compared measured pain responses to peripheral mechanical stimuli applied to a site of chronic pain and at a pain-free site in participants suffering from chronic lower back pain (n = 15) versus pain-free control participants (n = 15) by analyzing behavioral and electroencephalographic (EEG) data.

Results

As expected, participants with chronic pain endorsed enhanced pain with mechanical stimuli in both back and hand. We further analyzed electroencephalographic (EEG) recordings during these evoked pain episodes. Brain oscillations in theta and alpha bands in the medial orbitofrontal cortex (mOFC) were associated with localized hypersensitivity, while increased gamma oscillations in the anterior cingulate cortex (ACC) and increased theta oscillations in the dorsolateral prefrontal cortex (dlPFC) were associated with generalized hyperalgesia.

Discussion

These findings indicate that chronic pain may disrupt multiple cortical circuits to impact nociceptive processing.

Personalized treatment of neuropathic pain: Where are we now?

BACKGROUND

The treatment of neuropathic pain remains a major unmet need that the development of personalized and refined treatment strategies may contribute to address.

DATABASE

In this narrative review, we summarize the various approaches based on objective biomarkers or clinical markers that could be used.

RESULTS

In principle, the validation of objective biomarkers would be the most robust approach. However, although promising results have been reported demonstrating a potential value of genomics, anatomical or functional markers, the clinical validation of these markers has only just begun. Thus, most of the strategies documented to date have been based on the development of clinical markers. In particular, many studies have suggested that the identification of specific subgroups of patients presenting with specific combinations of symptoms and signs would be a relevant approach. Two main approaches have been used to identify relevant sensory profiles: quantitative sensory testing and specific patients reported outcomes based on description of pain qualities.

CONCLUSION

We discuss here the advantages and limitations of these approaches, which are not mutually exclusive.

SIGNIFICANCE

Recent data indicate that various new treatment strategies based on predictive biological and/or clinical markers could be helpful to better personalized and therefore improve the management of neuropathic pain.

Current Evidence for Biological Biomarkers and Mechanisms Underlying Acute to Chronic Pain Transition across the Pediatric Age Spectrum

Chronic pain is highly prevalent in the pediatric population. Many factors are involved in the transition from acute to chronic pain. Currently, there are conceptual models proposed, but they lack a mechanistically sound integrated theory considering the stages of child development. Objective biomarkers are critically needed for the diagnosis, risk stratification, and prognosis of the pathological stages of pain chronification. In this article, we summarize the current evidence on mechanisms and biomarkers of acute to chronic pain transitions in infants and children through the developmental lens. The goal is to identify gaps and outline future directions for basic and clinical research toward a developmentally informed theory of pain chronification in the pediatric population. At the outset, the importance of objective biomarkers for chronification of pain in children is outlined, followed by a summary of the current evidence on the mechanisms of acute to chronic pain transition in adults, in order to contrast with the developmental mechanisms of pain chronification in the pediatric population. Evidence is presented to show that chronic pain may have its origin from insults early in life, which prime the child for the development of chronic pain in later life. Furthermore, available genetic, epigenetic, psychophysical, electrophysiological, neuroimaging, neuroimmune, and sex mechanisms are described in infants and older children. In conclusion, future directions are discussed with a focus on research gaps, translational and clinical implications. Utilization of developmental mechanisms framework to inform clinical decision-making and strategies for prevention and management of acute to chronic pain transitions in children, is highlighted.

Alterations in cortical excitability during pain: A combined TMS-EEG Study

Transcranial magnetic stimulation (TMS) has been used to examine inhibitory and facilitatory circuits during experimental pain and in chronic pain populations. However, current applications of TMS to pain have been restricted to measurements of motor evoked potentials (MEPs) from peripheral muscles. Here, TMS was combined with electroencephalography (EEG) to determine whether experimental pain could induce alterations in cortical inhibitory/facilitatory activity observed in TMS-evoked potentials (TEPs). In Experiment 1 (n = 29), multiple sustained thermal stimuli were administered to the forearm, with the first, second and third block of thermal stimuli consisting of warm but non-painful (pre-pain block), painful (pain block) and warm but non-painful (post-pain block) temperatures respectively. During each stimulus, TMS pulses were delivered while EEG (64 channels) was simultaneously recorded. Verbal pain ratings were collected between TMS pulses. Relative to pre-pain warm stimuli, painful stimuli led to an increase in the amplitude of the frontocentral negative peak ~45ms post-TMS (N45), with a larger increase associated with higher pain ratings. Experiments 2 and 3 (n = 10 in each) showed that the increase in the N45 in response to pain was not due to changes in sensory potentials associated with TMS, or a result of stronger reafferent muscle feedback during pain. This is the first study to use combined TMS-EEG to examine alterations in cortical excitability in response to pain. These results suggest that the N45 TEP peak, which indexes GABAergic neurotransmission, is implicated in pain perception and is a potential marker of individual differences in pain sensitivity.

Pain Management in the Most Vulnerable Intellectual Disability: A Review

This review is made up of two parts; the first part discussing intellectual disability (ID) in general, while the second part covers the pain associated with intellectual disability and the challenges and practical tips for the management of pain associated with (ID). Intellectual disability is characterized by deficits in general mental abilities, such as reasoning, problem solving, planning, abstract thinking, judgment, academic learning, and learning from experience. ID is a disorder with no definite cause but has multiple risk factors, including genetic, medical, and acquired. Vulnerable populations such as individuals with intellectual disability may experience more pain than the general population due to additional comorbidities and secondary conditions, or at least the same frequency of pain as in the general population. Pain in patients with ID remains largely unrecognized and untreated due to barriers to verbal and non-verbal communication. It is important to identify patients at risk to promptly prevent or minimize those risk factors. As pain is multifactorial, thus, a multimodal approach using both pharmacotherapy and non-pharmacological management is often the most beneficial. Parents and caregivers should be oriented to this disorder, given adequate training and education, and be actively involved with the treatment program. Significant work to create new pain assessment tools to improve pain practices for individuals with ID has taken place, including neuroimaging and electrophysiological studies. Recent advances in technology-based interventions such as virtual reality and artificial intelligence are rapidly growing to help give patients with ID promising results to develop pain coping skills with effective reduction of pain and anxiety. Therefore, this narrative review highlights the different aspects regarding the current status of the pain associated with intellectual disability, with more emphasis on the recent pieces of evidence for the assessment and management of pain among populations with intellectual disability.

Interhemispheric neural characteristics of noxious mechano-nociceptive stimulation in the anterior cingulate cortex

Background

Pain is an unpleasant sensory and emotional experience. One of the most critical regions of the brain for pain processing is the anterior cingulate cortex (ACC). Several studies have examined the role of this region in thermal nociceptive pain. However, studies on mechanical nociceptive pain have been very limited to date. Although several studies have investigated pain, the interactions between the two hemispheres are still not clear. This study aimed to investigate nociceptive mechanical pain in the ACC bilaterally.

Methods

Local field potential (LFP) signals were recorded from seven male Wistar rats' ACC regions of both hemispheres. Mechanical stimulations with two intensities, high-intensity noxious (HN) and non-noxious (NN) were applied to the left hind paw. At the same time, the LFP signals were recorded bilaterally from awake and freely moving rats. The recorded signals were analyzed from different perspectives, including spectral analysis, intensity classification, evoked potential (EP) analysis, and synchrony and similarity of two hemispheres.

Results

By using spectro-temporal features and support vector machine (SVM) classifier, HN vs. no-stimulation (NS), NN vs. NS, and HN vs. NN were classified with accuracies of 89.6, 71.1, and 84.7%, respectively. Analyses of the signals from the two hemispheres showed that the EPs in the two hemispheres were very similar and occurred simultaneously; however, the correlation and phase locking value (PLV) between the two hemispheres changed significantly after HN stimulation. These variations persisted for up to 4 s after the stimulation. In contrast, variations in the PLV and correlation for NN stimulation were not significant.

Conclusions

This study showed that the ACC area was able to distinguish the intensity of mechanical stimulation based on the power activities of neural responses. In addition, our results suggest that the ACC region is activated bilaterally due to nociceptive mechanical pain. Additionally, stimulations above the pain threshold (HN) significantly affect the synchronicity and correlation between the two hemispheres compared to non-noxious stimuli.

External validation of binary machine learning models for pain intensity perception classification from EEG in healthy individuals

Discrimination of pain intensity using machine learning (ML) and electroencephalography (EEG) has significant potential for clinical applications, especially in scenarios where self-report is unsuitable. However, existing research is limited due to a lack of external validation (assessing performance using novel data). We aimed for the first external validation study for pain intensity classification with EEG. Pneumatic pressure stimuli were delivered to the fingernail bed at high and low pain intensities during two independent EEG experiments with healthy participants. Study one (n = 25) was utilised for training and cross-validation. Study two (n = 15) was used for external validation one (identical stimulation parameters to study one) and external validation two (new stimulation parameters). Time-frequency features of peri-stimulus EEG were computed on a single-trial basis for all electrodes. ML training and analysis were performed on a subset of features, identified through feature selection, which were distributed across scalp electrodes and included frontal, central, and parietal regions. Results demonstrated that ML models outperformed chance. The Random Forest (RF) achieved the greatest accuracies of 73.18, 68.32 and 60.42% for cross-validation, external validation one and two, respectively. Importantly, this research is the first to externally validate ML and EEG for the classification of intensity during experimental pain, demonstrating promising performance which generalises to novel samples and paradigms. These findings offer the most rigorous estimates of ML's clinical potential for pain classification.

EEG assessment of brain dysfunction for patients with chronic primary pain and depression under auditory oddball task

In 2019, the International Classification of Diseases 11th Revision International Classification of Diseases (ICD-11) put forward a new concept of "chronic primary pain" (CPP), a kind of chronic pain characterized by severe functional disability and emotional distress, which is a medical problem that deserves great attention. Although CPP is closely related to depressive disorder, its potential neural characteristics are still unclear. This paper collected EEG data from 67 subjects (23 healthy subjects, 22 patients with depression, and 22 patients with CPP) under the auditory oddball paradigm, systematically analyzed the brain network connection matrix and graph theory characteristic indicators, and classified the EEG and PLI matrices of three groups of people by frequency band based on deep learning. The results showed significant differences in brain network connectivity between CPP patients and depressive patients. Specifically, the connectivity within the frontoparietal network of the Theta band in CPP patients is significantly enhanced. The CNN classification model of EEG is better than that of PLI, with the highest accuracy of 85.01% in Gamma band in former and 79.64% in Theta band in later. We propose hyperexcitability in attentional control in CPP patients and provide a novel method for objective assessment of chronic primary pain.

Electroencephalography functional connectivity-A biomarker for painful polyneuropathy

BACKGROUND AND PURPOSE

Advanced analysis of electroencephalography (EEG) data has become an essential tool in brain research. Based solely on resting state EEG signals, a data-driven, predictive and explanatory approach is presented to discriminate painful from non-painful diabetic polyneuropathy (DPN) patients.

METHODS

Three minutes long, 64 electrode resting-state recordings were obtained from 180 DPN patients. The analysis consisted of a mixture of traditional, explanatory and machine learning analyses. First, the 10 functional bivariate connections best differentiating between painful and non-painful patients in each EEG band were identified and the relevant receiver operating characteristic was calculated. Later, those connections were correlated with selected clinical parameters.

RESULTS

Predictive analysis indicated that theta and beta bands contain most of the information required for discrimination between painful and non-painful polyneuropathy patients, with area under the receiver operating characteristic curve values of 0.93 for theta and 0.89 for beta bands. Assessing statistical differences between the average magnitude of functional connectivity values and clinical pain parameters revealed that painful DPN patients had significantly higher cortical functional connectivity than non-painful ones (p = 0.008 for theta and p = 0.001 for alpha bands). Moreover, intra-band analysis of individual significant functional connections revealed a positive correlation with average reported pain in the previous 3 months in all frequency bands.

CONCLUSIONS

Resting state EEG functional connectivity can serve as a highly accurate biomarker for the presence or absence of pain in DPN patients. This highlights the importance of the brain, in addition to the peripheral lesions, in generating the clinical pain picture. This tool can probably be extended to other pain syndromes.

Why pain hurts

We have made great strides in understanding how the human brain constructs the multidimensional experience of pain - both acute and chronic - over the past few decades. Pain wears many guises, but at its core, it hurts. How is this core component of pain represented in the brain, and how can we target it for relief?

A scoping review of electroencephalographic (EEG) markers for tracking neurophysiological changes and predicting outcomes in substance use disorder treatment

Substance use disorders (SUDs) constitute a growing global health crisis, yet many limitations and challenges exist in SUD treatment research, including the lack of objective brain-based markers for tracking treatment outcomes. Electroencephalography (EEG) is a neurophysiological technique for measuring brain activity, and although much is known about EEG activity in acute and chronic substance use, knowledge regarding EEG in relation to abstinence and treatment outcomes is sparse. We performed a scoping review of longitudinal and pre-post treatment EEG studies that explored putative changes in brain function associated with abstinence and/or treatment in individuals with SUD. Following PRISMA guidelines, we identified studies published between January 2000 and March 2022 from online databases. Search keywords included EEG, addictive substances (e.g., alcohol, cocaine, methamphetamine), and treatment related terms (e.g., abstinence, relapse). Selected studies used EEG at least at one time point as a predictor of abstinence or other treatment-related outcomes; or examined pre- vs. post-SUD intervention (brain stimulation, pharmacological, behavioral) EEG effects. Studies were also rated on the risk of bias and quality using validated instruments. Forty-four studies met the inclusion criteria. More consistent findings included lower oddball P3 and higher resting beta at baseline predicting negative outcomes, and abstinence-mediated longitudinal decrease in cue-elicited P3 amplitude and resting beta power. Other findings included abstinence or treatment-related changes in late positive potential (LPP) and N2 amplitudes, as well as in delta and theta power. Existing studies were heterogeneous and limited in terms of specific substances of interest, brief times for follow-ups, and inconsistent or sparse results. Encouragingly, in this limited but maturing literature, many studies demonstrated partial associations of EEG markers with abstinence, treatment outcomes, or pre-post treatment-effects. Studies were generally of good quality in terms of risk of bias. More EEG studies are warranted to better understand abstinence- or treatment-mediated neural changes or to predict SUD treatment outcomes. Future research can benefit from prospective large-sample cohorts and the use of standardized methods such as task batteries. EEG markers elucidating the temporal dynamics of changes in brain function related to abstinence and/or treatment may enable evidence-based planning for more effective and targeted treatments, potentially pre-empting relapse or minimizing negative lifespan effects of SUD.

Longitudinal resting-state electroencephalography in patients with chronic pain undergoing interdisciplinary multimodal pain therapy

ABSTRACT

Chronic pain is a major healthcare issue posing a large burden on individuals and society. Converging lines of evidence indicate that chronic pain is associated with substantial changes of brain structure and function. However, it remains unclear which neuronal measures relate to changes of clinical parameters over time and could thus monitor chronic pain and treatment responses. We therefore performed a longitudinal study in which we assessed clinical characteristics and resting-state electroencephalography data of 41 patients with chronic pain before and 6 months after interdisciplinary multimodal pain therapy. We specifically assessed electroencephalography measures that have previously been shown to differ between patients with chronic pain and healthy people. These included the dominant peak frequency; the amplitudes of neuronal oscillations at theta, alpha, beta, and gamma frequencies; as well as graph theory-based measures of brain network organization. The results show that pain intensity, pain-related disability, and depression were significantly improved after interdisciplinary multimodal pain therapy. Bayesian hypothesis testing indicated that these clinical changes were not related to changes of the dominant peak frequency or amplitudes of oscillations at any frequency band. Clinical changes were, however, associated with an increase in global network efficiency at theta frequencies. Thus, changes in chronic pain might be reflected by global network changes in the theta band. These longitudinal insights further the understanding of the brain mechanisms of chronic pain. Beyond, they might help to identify biomarkers for the monitoring of chronic pain.

Towards the Objective Identification of the Presence of Pain Based on Electroencephalography Signals' Analysis: A Proof-of-Concept

This proof-of-concept study explores the potential of developing objective pain identification based on the analysis of electroencephalography (EEG) signals. Data were collected from participants living with chronic fibromyalgia pain (n = 4) and from healthy volunteers (n = 7) submitted to experimental pain by the application of capsaicin cream (1%) on the right upper trapezius. This data collection was conducted in two parts: (1) baseline measures including pain intensity and EEG signals, with the participant at rest; (2) active measures collected under the execution of a visuo-motor task, including EEG signals and the task performance index. The main measure for the objective identification of the presence of pain was the coefficient of variation of the upper envelope (CVUE) of the EEG signal from left fronto-central (FC5) and left temporal (T7) electrodes, in alpha (8-12 Hz), beta (12-30 Hz) and gamma (30-43 Hz) frequency bands. The task performance index was also calculated. CVUE (%) was compared between groups: those with chronic fibromyalgia pain, healthy volunteers with "No pain" and healthy volunteers with experimentally-induced pain. The identification of the presence of pain was determined by an increased CVUE in beta (CVUE_β) from the EEG signals captured at the left FC5 electrode. More specifically, CVUE_β increased up to 20% in the pain condition at rest. In addition, no correlation was found between CVUE_β and pain intensity or the task performance index. These results support the objective identification of the presence of pain based on the quantification of the coefficient of variation of the upper envelope of the EEG signal.

Resting-state electroencephalography (EEG) biomarkers of chronic neuropathic pain. A systematic review

Diagnosis and management of chronic neuropathic pain are challenging, leading to current efforts to characterize 'objective' biomarkers of pain using imaging or neurophysiological techniques, such as electroencephalography (EEG). A systematic literature review was conducted in PubMed-Medline and Web-of-Science until October 2021 to identify EEG biomarkers of chronic neuropathic pain in humans. The risk of bias was assessed by the Newcastle-Ottawa-Scale. Experimental, provoked, or chronic non-neuropathic pain studies were excluded. We identified 14 studies, in which resting-state EEG spectral analysis was compared between patients with pain related to a neurological disease and patients with the same disease but without pain or healthy controls. From these heterogeneous exploratory studies, some conclusions can be drawn, even if they must be weighted by the fact that confounding factors, such as medication and association with anxio-depressive disorders, are generally not taken into account. Overall, EEG signal power was increased in the θ band (4-7Hz) and possibly in the high-β band (20-30Hz), but decreased in the high-α-low-β band (10-20Hz) in the presence of ongoing neuropathic pain, while increased γ band oscillations were not evidenced, unlike in experimental pain. Consequently, the dominant peak frequency was decreased in the θ-α band and increased in the whole-β band in neuropathic pain patients. Disappointingly, pain intensity correlated with various EEG changes across studies, with no consistent trend. This review also discusses the location of regional pain-related EEG changes in the pain connectome, as the perspectives offered by advanced techniques of EEG signal analysis (source location, connectivity, or classification methods based on artificial intelligence). The biomarkers provided by resting-state EEG are of particular interest for optimizing the treatment of chronic neuropathic pain by neuromodulation techniques, such as transcranial alternating current stimulation or neurofeedback procedures.

Neural processes responsible for the translation of sustained nociceptive inputs into subjective pain experience

Tracking and predicting the temporal structure of nociceptive inputs is crucial to promote survival, as proper and immediate reactions are necessary to avoid actual or potential bodily injury. Neural activities elicited by nociceptive stimuli with different temporal structures have been described, but the neural processes responsible for translating nociception into pain perception are not fully elucidated. To tap into this issue, we recorded electroencephalographic signals from 48 healthy participants receiving thermo-nociceptive stimuli with 3 different durations and 2 different intensities. We observed that pain perception and several brain responses are modulated by stimulus duration and intensity. Crucially, we identified 2 sustained brain responses that were related to the emergence of painful percepts: a low-frequency component (LFC, < 1 Hz) originated from the insula and anterior cingulate cortex, and an alpha-band event-related desynchronization (α-ERD, 8-13 Hz) generated from the sensorimotor cortex. These 2 sustained brain responses were highly coupled, with the α-oscillation amplitude that fluctuated with the LFC phase. Furthermore, the translation of stimulus duration into pain perception was serially mediated by α-ERD and LFC. The present study reveals how brain responses elicited by nociceptive stimulation reflect the complex processes occurring during the translation of nociceptive information into pain perception.

Late responses in the anterior insula reflect the cognitive component of pain: evidence of nonpain processing

Supplemental Digital Content is Available in the Text.

Distinguishing sensory and cognitive aspects of pain-related insular activity and the temporal profile of anterior insula activity suggested a key role of cognitive modulation.

Introduction:

Pain is a complex experience influenced by sensory and psychological factors. The insula is considered to be a core part of the pain network in the brain. Previous studies have suggested a relationship between the posterior insula (PI) and sensory processing, and between the anterior insula (AI) and cognitive–affective factors.

Objectives:

Our aim was to distinguish sensory and cognitive responses in pain-related insular activities.

Methods:

We recorded spatiotemporal insular activation patterns of healthy participants (n = 20) during pain or tactile processing with painful or nonpainful movie stimuli, using a magnetoencephalography. We compared the peak latency between PI and AI activities in each stimulus condition, and between pain and tactile processing in each response. The peak latency and amplitude between different movies were then examined to explore the effects of cognitive influence. A visual analogue scale was used to assess subjective perception.

Results:

The results revealed one clear PI activity and 2 AI activities (early and late) in insular responses induced by pain/tactile stimulation. The early response transmitted from the PI to AI was observed during sensory-associated brain activity, whereas the late AI response was observed during cognitive-associated activity. In addition, we found that painful movie stimuli had a significant influence on both late AI activity and subjective perception, caused by nonpainful actual stimulation.

Conclusions:

The current findings suggested that late AI activation reflects the processing of cognitive pain information, whereas the PI and early AI responses reflect sensory processing.

Clinical use of Electroencephalography in the Assessment of Acute Thermal Pain: A Narrative Review Based on Articles From 2009 to 2019

Nowadays, no practical system has successfully been able to decode and predict pain in clinical settings. The inability of some patients to verbally express their pain creates the need for a tool that could objectively assess pain in these individuals. Neuroimaging techniques combined with machine learning are seen as possible candidates for the identification of pain biomarkers. This review aimed to address the potential use of electroencephalographic features as predictors of acute experimental pain. Twenty-six studies using only thermal stimulations were identified using a PubMed and Scopus search. Combinations of the following terms were used: "EEG," "Electroencephalography," "Acute," "Pain," "Tonic," "Noxious," "Thermal," "Stimulation," "Brain," "Activity," "Cold," "Subjective," and "Perception." Results revealed that contact-heat-evoked potentials have been widely recorded over central areas during noxious heat stimulations. Furthermore, a decrease in alpha power over central regions was revealed, as well as increased theta and gamma powers over frontal areas. Gamma and theta rhythms were associated with connectivity between sensory and affective regions involved in pain processing. A machine learning analysis revealed that the gamma band is a predominant predictor of acute thermal pain. This review also addressed the need of supplementing current spectral features with techniques that allow the investigation of network dynamics.

Temporal–spectral signaling of sensory information and expectations in the cerebral processing of pain

Pain is not only shaped by sensory information but also by an individual’s expectations. Here, we investigated how commonly analyzed electroencephalography (EEG) responses to pain signal sensory information, expectations, and discrepancies thereof (prediction errors) in the processing of pain. Bayesian analysis confirmed that pain perception was shaped by objective sensory information and expectations. In contrast, EEG responses at different latencies (including the N1, N2, and P2 components) and frequencies (including alpha, beta, and gamma oscillations) were shaped by sensory information but not by expectations. Thus, EEG responses to pain are more involved in signaling sensory information than in signaling expectations or prediction errors. Expectation effects are obviously mediated by other brain mechanisms than the effects of sensory information on pain.

The perception of pain is shaped by somatosensory information about threat. However, pain is also influenced by an individual’s expectations. Such expectations can result in clinically relevant modulations and abnormalities of pain. In the brain, sensory information, expectations (predictions), and discrepancies thereof (prediction errors) are signaled by an extended network of brain areas which generate evoked potentials and oscillatory responses at different latencies and frequencies. However, a comprehensive picture of how evoked and oscillatory brain responses signal sensory information, predictions, and prediction errors in the processing of pain is lacking so far. Here, we therefore applied brief painful stimuli to 48 healthy human participants and independently modulated sensory information (stimulus intensity) and expectations of pain intensity while measuring brain activity using electroencephalography (EEG). Pain ratings confirmed that pain intensity was shaped by both sensory information and expectations. In contrast, Bayesian analyses revealed that stimulus-induced EEG responses at different latencies (the N1, N2, and P2 components) and frequencies (alpha, beta, and gamma oscillations) were shaped by sensory information but not by expectations. Expectations, however, shaped alpha and beta oscillations before the painful stimuli. These findings indicate that commonly analyzed EEG responses to painful stimuli are more involved in signaling sensory information than in signaling expectations or mismatches of sensory information and expectations. Moreover, they indicate that the effects of expectations on pain are served by brain mechanisms which differ from those conveying effects of sensory information on pain.

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

ABSTRACT

Chronic pain is a highly prevalent and severely disabling disease that is associated with substantial changes of brain function. Such changes have mostly been observed when analyzing static measures of resting-state brain activity. However, brain activity varies over time, and it is increasingly recognized that the temporal dynamics of brain activity provide behaviorally relevant information in different neuropsychiatric disorders. Here, we therefore investigated whether the temporal dynamics of brain function are altered in chronic pain. To this end, we applied microstate analysis to eyes-open and eyes-closed resting-state electroencephalography data of 101 patients suffering from chronic pain and 88 age- and sex-matched healthy controls. Microstate analysis describes electroencephalography activity as a sequence of a limited number of topographies termed microstates that remain stable for tens of milliseconds. Our results revealed that sequences of 5 microstates, labelled with the letters A to E, consistently described resting-state brain activity in both groups in the eyes-closed condition. Bayesian analysis of the temporal characteristics of microstates revealed that microstate D has a less predominant role in patients than in controls. As microstate D has previously been related to attentional networks and functions, these abnormalities might relate to dysfunctional attentional processes in chronic pain. Subgroup analyses replicated microstate D changes in patients with chronic back pain, while patients with chronic widespread pain did not show microstates alterations. Together, these findings add to the understanding of the pathophysiology of chronic pain and point to changes of brain dynamics specific to certain types of chronic pain.

Common Spatial Pattern EEG decomposition for Phantom Limb Pain detection

Phantom Limb Pain (PLP) is a chronic condition frequent among individuals with acquired amputation. PLP has been often investigated with the use of functional MRI focusing on the changes that take place in the sensorimotor cortex after amputation. In the present study, we investigated whether a different type of data, namely electroencephalographic (EEG) recordings, can be used to study the condition. We acquired resting state EEG data from people with and without PLP and then used machine learning for a binary classification task that differentiates the two. Common Spatial Pattern (CSP) decomposition was used as the feature extraction method and two validation schemes were followed for the classification task. Six classifiers (LDA, Log, QDA, LinearSVC, SVC and RF) were optimized through grid search and their performance compared. Two validation approaches, namely all-subjects validation and leave-one-out cross-validation (LOOCV), resulted in high classification accuracy. Most notably, the 93.7% accuracy achieved with SVC in LOOCV holds promise for good diagnostic capabilities using EEG biomarkers. In conclusion, our findings indicate that EEG data is a promising target for future research aiming at elucidating the neural mechanisms underlying PLP and its diagnosis.

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.

The raw and processed electroencephalogram in modern anesthesia practice: a brief primer on select clinical applications

The evidence supporting the intraoperative use of processed electroencephalography (pEEG) monitoring to guide anesthetic delivery is growing rapidly. This article reviews the key features of electroencephalography (EEG) waveforms and their clinical implications in select patient populations and anesthetic techniques. The first patient topic reviewed is the vulnerable brain. This term has emerged as a description of patients who may exhibit increased sensitivity to anesthetics and/or may develop adverse neurocognitive effects following anesthesia. pEEG monitoring of patients who are known to have or are suspected of having vulnerable brains, with focused attention on the suppression ratio, alpha band power, and pEEG indices, may prove useful. Second, pEEG monitoring along with vigilant attention to anesthetic delivery may minimize the risk of intraoperative awareness when administering a total intravenous anesthesia in combination with a neuromuscular blockade. Third, we suggest that processed EEG monitoring may play a role in anesthetic and resuscitative management when adverse changes in blood pressure occur. Fourth, pEEG monitoring can be used to better identify anesthesia requirements and guide anesthetic titration in patients with known or suspected substance use.

Peripheral Electrical Stimulation Modulates Cortical Beta-Band Activity

Low-frequency peripheral electrical stimulation using a matrix electrode (PEMS) modulates spinal nociceptive pathways. However, the effects of this intervention on cortical oscillatory activity have not been assessed yet. The aim of this study was to investigate the effects of low-frequency PEMS (4 Hz) on cortical oscillatory activity in different brain states in healthy pain-free participants. In experiment 1, PEMS was compared to sham stimulation. In experiment 2, motor imagery (MI) was used to modulate the sensorimotor brain state. PEMS was applied either during MI-induced oscillatory desynchronization (concurrent PEMS) or after MI (delayed PEMS) in a cross-over design. For both experiments, PEMS was applied on the left forearm and resting-state electroencephalography (EEG) was recording before and after each stimulation condition. Experiment 1 showed a significant decrease of global resting-state beta power after PEMS compared to sham (p = 0.016), with a median change from baseline of −16% for PEMS and −0.54% for sham. A cluster-based permutation test showed a significant difference in resting-state beta power comparing pre- and post-PEMS (p = 0.018) that was most pronounced over bilateral central and left frontal sensors. Experiment 2 did not identify a significant difference in the change from baseline of global EEG power for concurrent PEMS compared to delayed PEMS. Two cluster-based permutation tests suggested that frontal beta power may be increased following both concurrent and delayed PEMS. This study provides novel evidence for supraspinal effects of low-frequency PEMS and an initial indication that the presence of a cognitive task such as MI may influence the effects of PEMS on beta activity. Chronic pain has been associated with changes in beta activity, in particular an increase of beta power in frontal regions. Thus, brain state-dependent PEMS may offer a novel approach to the treatment of chronic pain. However, further studies are warranted to investigate optimal stimulation conditions to achieve a reduction of pain.

Variability and effect sizes of intracranial current source density estimations during pain: Systematic review, experimental findings, and future perspectives

Pain arises from the integration of sensory and cognitive processes in the brain, resulting in specific patterns of neural oscillations that can be characterized by measuring electrical brain activity. Current source density (CSD) estimation from low-resolution brain electromagnetic tomography (LORETA) and its standardized (sLORETA) and exact (eLORETA) variants, is a common approach to identify the spatiotemporal dynamics of the brain sources in physiological and pathological pain-related conditions. However, there is no consensus on the magnitude and variability of clinically or experimentally relevant effects for CSD estimations. Here, we systematically examined reports of sample size calculations and effect size estimations in all studies that included the keywords pain, and LORETA, sLORETA, or eLORETA in Scopus and PubMed. We also assessed the reliability of LORETA CSD estimations during non-painful and painful conditions to estimate hypothetical sample sizes for future experiments using CSD estimations. We found that none of the studies included in the systematic review reported sample size calculations, and less than 20% reported measures of central tendency and dispersion, which are necessary to estimate effect sizes. Based on these data and our experimental results, we determined that sample sizes commonly used in pain studies using CSD estimations are suitable to detect medium and large effect sizes in crossover designs and only large effects in parallel designs. These results provide a comprehensive summary of the effect sizes observed using LORETA in pain research, and this information can be used by clinicians and researchers to improve settings and designs of future pain studies.

Demystifying signal processing techniques to extract resting-state EEG features for psychologists

Electroencephalography (EEG) is a powerful tool for investigating the brain bases of human psychological processes non‐invasively. Some important mental functions could be encoded by resting‐state EEG activity; that is, the intrinsic neural activity not elicited by a specific task or stimulus. The extraction of informative features from resting‐state EEG requires complex signal processing techniques. This review aims to demystify the widely used resting‐state EEG signal processing techniques. To this end, we first offer a preprocessing pipeline and discuss how to apply it to resting‐state EEG preprocessing. We then examine in detail spectral, connectivity, and microstate analysis, covering the oft‐used EEG measures, practical issues involved, and data visualization. Finally, we briefly touch upon advanced techniques like nonlinear neural dynamics, complex networks, and machine learning.

Application of Referencing Techniques in EEG-Based Recordings of Contact Heat Evoked Potentials (CHEPS)

Evoked potentials in the amplitude-time spectrum of the electroencephalogram are commonly used to assess the extent of brain responses to stimulation with noxious contact heat. The magnitude of the N- and P-waves are used as a semi-objective measure of the response to the painful stimulus: the higher the magnitude, the more painful the stimulus has been perceived. The strength of the N-P-wave response is also largely dependent on the chosen reference electrode site. The goal of this study was to examine which reference technique excels both in practical and theoretical terms when analyzing noxious contact heat evoked potentials (CHEPS) in the amplitude-time spectrum. We recruited 21 subjects (10 male, 11 female, mean age of 55.79 years). We applied seven noxious contact heat stimuli using two temperatures, 51°C, and 54°C, to each subject. During EEG analysis, we aimed to identify the referencing technique which produces the highest N-wave and P-wave amplitudes with as little artifactual influence as possible. For this purpose, we applied the following six referencing techniques: mathematically linked A1/A2 (earlobes), average reference, REST, AFz, Pz, and mathematically linked PO7/PO8. We evaluated how these techniques impact the N-P amplitudes of CHEPS based on our data from healthy subjects. Considering all factors, we found that mathematically linked earlobes to be the ideal referencing site to use when displaying and evaluating CHEPS in the amplitude-time spectrum.

Neural Oscillations: Understanding a Neural Code of Pain

Neural oscillations play an important role in the integration and segregation of brain regions that are important for brain functions, including pain. Disturbances in oscillatory activity are associated with several disease states, including chronic pain. Studies of neural oscillations related to pain have identified several functional bands, especially alpha, beta, and gamma bands, implicated in nociceptive processing. In this review, we introduce several properties of neural oscillations that are important to understand the role of brain oscillations in nociceptive processing. We also discuss the role of neural oscillations in the maintenance of efficient communication in the brain. Finally, we discuss the role of neural oscillations in healthy and chronic pain nociceptive processing. These data and concepts illustrate the key role of regional and interregional neural oscillations in nociceptive processing underlying acute and chronic pains.

Discovery and validation of biomarkers to aid the development of safe and effective pain therapeutics: challenges and opportunities

Pain medication plays an important role in the treatment of acute and chronic pain conditions, but some drugs, opioids in particular, have been overprescribed or prescribed without adequate safeguards, leading to an alarming rise in medication-related overdose deaths. The NIH Helping to End Addiction Long-term (HEAL) Initiative is a trans-agency effort to provide scientific solutions to stem the opioid crisis. One component of the initiative is to support biomarker discovery and rigorous validation in collaboration with industry leaders to accelerate high-quality clinical research into neurotherapeutics and pain. The use of objective biomarkers and clinical trial end points throughout the drug discovery and development process is crucial to help define pathophysiological subsets of pain, evaluate target engagement of new drugs and predict the analgesic efficacy of new drugs. In 2018, the NIH-led Discovery and Validation of Biomarkers to Develop Non-Addictive Therapeutics for Pain workshop convened scientific leaders from academia, industry, government and patient advocacy groups to discuss progress, challenges, gaps and ideas to facilitate the development of biomarkers and end points for pain. The outcomes of this workshop are outlined in this Consensus Statement.

Ultralow-frequency neural entrainment to pain

Nervous systems exploit regularities in the sensory environment to predict sensory input, adjust behavior, and thereby maximize fitness. Entrainment of neural oscillations allows retaining temporal regularities of sensory information, a prerequisite for prediction. Entrainment has been extensively described at the frequencies of periodic inputs most commonly present in visual and auditory landscapes (e.g., >0.5 Hz). An open question is whether neural entrainment also occurs for regularities at much longer timescales. Here, we exploited the fact that the temporal dynamics of thermal stimuli in natural environment can unfold very slowly. We show that ultralow-frequency neural oscillations preserved a long-lasting trace of sensory information through neural entrainment to periodic thermo-nociceptive input as low as 0.1 Hz. Importantly, revealing the functional significance of this phenomenon, both power and phase of the entrainment predicted individual pain sensitivity. In contrast, periodic auditory input at the same ultralow frequency did not entrain ultralow-frequency oscillations. These results demonstrate that a functionally significant neural entrainment can occur at temporal scales far longer than those commonly explored. The non-supramodal nature of our results suggests that ultralow-frequency entrainment might be tuned to the temporal scale of the statistical regularities characteristic of different sensory modalities.

Deconstructing biomarkers for chronic pain: context- and hypothesis-dependent biomarker types in relation to chronic pain

This review expounds on types and properties of biomarkers for chronic pain, given a mechanistic model of processes underlying development of chronic pain. It covers advances in the field of developing biomarkers for chronic pain, while outlining the general principles of categorizing types of biomarkers driven by specific hypotheses regarding underlying mechanisms. Within this theoretical construct, example biomarkers are described and their properties expounded. We conclude that the field is advancing in important directions and the developed biomarkers have the potential of impacting both the science and the clinical practice regarding chronic pain.

From correlation towards causality: modulating brain rhythms of pain using transcranial alternating current stimulation

Introduction

Accumulating evidence suggests that neural oscillations at different frequencies and their synchrony between brain regions play a crucial role in the processing of nociceptive input and the emergence of pain. Most findings are limited by their correlative nature, however, which impedes causal inferences.

Objective

To move from correlative towards causal evidence, methods that allow to experimentally manipulate oscillatory brain activity are needed.

Results

Transcranial alternating current stimulation (tACS) is a noninvasive brain stimulation technique designed to modulate neural oscillations in a frequency specific manner and as such a suitable method to investigate the contribution of oscillatory brain activity to pain. Despite its appeal, tACS has been barely applied in the field of pain research. In the present review, we address this issue and discuss how tACS can be used to gather mechanistic evidence for the relationship between pain and neural oscillations in humans.

Conclusions

Transcranial alternating current stimulation holds great potential for the investigation of the neural mechanisms underlying pain and the development of new treatment approaches for chronic pain if necessary methodological precautions are taken.

Neuroimaging-based biomarkers for pain: state of the field and current directions

Chronic pain is an endemic problem involving both peripheral and brain pathophysiology. Although biomarkers have revolutionized many areas of medicine, biomarkers for pain have remained controversial and relatively underdeveloped. With the realization that biomarkers can reveal pain-causing mechanisms of disease in brain circuits and in the periphery, this situation is poised to change. In particular, brain pathophysiology may be diagnosable with human brain imaging, particularly when imaging is combined with machine learning techniques designed to identify predictive measures embedded in complex data sets. In this review, we explicate the need for brain-based biomarkers for pain, some of their potential uses, and some of the most popular machine learning approaches that have been brought to bear. Then, we evaluate the current state of pain biomarkers developed with several commonly used methods, including structural magnetic resonance imaging, functional magnetic resonance imaging and electroencephalography. The field is in the early stages of biomarker development, but these complementary methodologies have already produced some encouraging predictive models that must be tested more extensively across laboratories and clinical populations.

Effective Connectivity of Beta Oscillations in Endometriosis-Related Chronic Pain During rest and Pain-Related Mental Imagery

Using the EEG recordings of patients with endometriosis-related chronic pelvic pain, we have examined the effective connectivity within the cortical pain-related network during rest and during pain-related imagery. During rest, an altered connectivity was hypothesized between cortical somatosensory pain areas and regions involved in emotional and cognitive modulation of pain. During pain-related imagery, alterations in prefrontal-temporal connectivity were expected. The effective connectivity was estimated using the Directed Transfer Function method. Differences between endometriosis patients and controls were found in the beta band (14-25 Hz). During rest, endometriosis was associated with an increased connectivity from the left dorsolateral prefrontal cortex to the left somatosensory cortex and also from the left somatosensory cortex to the orbitofrontal cortex and the right temporal cortex. These results might be related to sustained activation of the somatosensory pain system caused by the ongoing pain. During pain-related imagery, endometriosis patients showed an increased connectivity from the left dorsolateral prefrontal cortex to the right temporal cortex. This finding might point to impaired emotional regulation when processing pain-related stimuli, or it might be related to altered memorization of pain experiences. Results of this study open up new directions in chronic pain research aimed at exploring the beta band connectivity alterations. PERSPECTIVE: This study examined the pain system's dynamics in endometriosis patients with chronic pelvic pain during resting-state and pain-related mental imagery. The results could contribute to the development of new therapies using guided mental imagery.

Cortical Pain Processing in the Rat Anterior Cingulate Cortex and Primary Somatosensory Cortex

Pain is a complex multidimensional experience encompassing sensory-discriminative, affective-motivational and cognitive-emotional components mediated by different neural mechanisms. Investigations of neurophysiological signals from simultaneous recordings of two or more cortical circuits may reveal important circuit mechanisms on cortical pain processing. The anterior cingulate cortex (ACC) and primary somatosensory cortex (S1) represent two most important cortical circuits related to sensory and affective processing of pain. Here, we recorded in vivo extracellular activity of the ACC and S1 simultaneously from male adult Sprague-Dale rats (n = 5), while repetitive noxious laser stimulations were delivered to animalÕs hindpaw during pain experiments. We identified spontaneous pain-like events based on stereotyped pain behaviors in rats. We further conducted systematic analyses of spike and local field potential (LFP) recordings from both ACC and S1 during evoked and spontaneous pain episodes. From LFP recordings, we found stronger phase-amplitude coupling (theta phase vs. gamma amplitude) in the S1 than the ACC (n = 10 sessions), in both evoked (p = 0.058) and spontaneous pain-like behaviors (p = 0.017, paired signed rank test). In addition, pain-modulated ACC and S1 neuronal firing correlated with the amplitude of stimulus-induced event-related potentials (ERPs) during evoked pain episodes. We further designed statistical and machine learning methods to detect pain signals by integrating ACC and S1 ensemble spikes and LFPs. Together, these results reveal differential coding roles between the ACC and S1 in cortical pain processing, as well as point to distinct neural mechanisms between evoked and putative spontaneous pain at both LFP and cellular levels.

Distinct patterns of brain activity mediate perceptual and motor and autonomic responses to noxious stimuli

Pain is a complex phenomenon involving perceptual, motor, and autonomic responses, but how the brain translates noxious stimuli into these different dimensions of pain is unclear. Here, we assessed perceptual, motor, and autonomic responses to brief noxious heat stimuli and recorded brain activity using electroencephalography (EEG) in humans. Multilevel mediation analysis reveals that each pain dimension is subserved by a distinct pattern of EEG responses and, conversely, that each EEG response differentially contributes to the different dimensions of pain. In particular, the translation of noxious stimuli into autonomic and motor responses involved the earliest N1 wave, whereas pain perception was mediated by later N2 and P2 waves. Gamma oscillations mediated motor responses rather than pain perception. These findings represent progress towards a mechanistic understanding of the brain processes translating noxious stimuli into pain and suggest that perceptual, motor, and autonomic dimensions of pain are partially independent rather than serial processes.

Pain is a complex phenomenon involving not just the perception of pain, but also autonomic and motor responses. Here, the authors show that these different dimensions of pain are associated with distinct patterns of neural responses to noxious stimuli as measured using EEG.