Cerebral peak alpha frequency predicts individual differences in pain sensitivity

The independence and predictivity of resting pain-free slow alpha frequency as a biomarker of pain: A reply to Mazaheri et al

In response to Mazaheri et al.'s critique, we revisited our study (Valentini et al., 2022) on the relationship between peak alpha frequency (PAF) and pain. Their commentary prompted us to reassess our data to address the independence between slow and slowing alpha brain oscillations, as well as the predictivity of slow alpha oscillations in pain perception. Bayesian correlation analyses revealed mixed support for independence. Investigating predictivity, we found inconsistent associations between pre-PAF and unpleasantness ratings. We critically reflected on methodological and theoretical issues on the path to PAF validation as a pain biomarker. We emphasized the need for diversified methodology and analytical approaches as well as robust findings across research groups.

Closed-Loop Infraslow Brain-Computer Interface can Modulate Cortical Activity and Connectivity in Individuals With Chronic Painful Knee Osteoarthritis: A Secondary Analysis of a Randomized Placebo-Controlled Clinical Trial

Introduction. Chronic pain is a percept due to an imbalance in the activity between sensory-discriminative, motivational-affective, and descending pain-inhibitory brain regions. Evidence suggests that electroencephalography (EEG) infraslow fluctuation neurofeedback (ISF-NF) training can improve clinical outcomes. It is unknown whether such training can induce EEG activity and functional connectivity (FC) changes. A secondary data analysis of a feasibility clinical trial was conducted to determine whether EEG ISF-NF training can significantly alter EEG activity and FC between the targeted cortical regions in people with chronic painful knee osteoarthritis (OA). Methods. A parallel, two-arm, double-blind, randomized, sham-controlled clinical trial was conducted. People with chronic knee pain associated with OA were randomized to receive sham NF training or source-localized ratio ISF-NF training protocol to down-train ISF bands at the somatosensory (SSC), dorsal anterior cingulate (dACC), and uptrain pregenual anterior cingulate cortices (pgACC). Resting state EEG was recorded at baseline and immediate post-training. Results. The source localization mapping demonstrated a reduction (P = .04) in the ISF band activity at the left dorsolateral prefrontal cortex (LdlPFC) in the active NF group. Region of interest analysis yielded significant differences for ISF (P = .008), slow (P = .007), beta (P = .043), and gamma (P = .012) band activities at LdlPFC, dACC, and bilateral SSC. The FC between pgACC and left SSC in the delta band was negatively correlated with pain bothersomeness in the ISF-NF group. Conclusion. The EEG ISF-NF training can modulate EEG activity and connectivity in individuals with chronic painful knee osteoarthritis, and the observed EEG changes correlate with clinical pain measures.

A 5-day course of rTMS before pain onset ameliorates future pain and increases sensorimotor peak alpha frequency

Repetitive transcranial magnetic stimulation (rTMS) has shown promise as an intervention for pain. An unexplored research question is whether the delivery of rTMS prior to pain onset might protect against a future episode of prolonged pain. The present study aimed to determine i) whether 5 consecutive days of rTMS delivered prior to experimentally-induced prolonged jaw pain could reduce future pain intensity and ii) whether any effects of rTMS on pain were mediated by changes in corticomotor excitability (CME) and/or sensorimotor peak alpha frequency (PAF). On each day from Day 0-4, forty healthy individuals received a single session of active (n = 21) or sham (n = 19) rTMS over the left primary motor cortex. PAF and CME were assessed on Day 0 (before rTMS) and Day 4 (after rTMS). Prolonged pain was induced via intramuscular injection of nerve growth factor (NGF) in the right masseter muscle after the final rTMS session. From Days 5-25, participants completed twice-daily electronic dairies including pain on chewing and yawning (primary outcomes), as well as pain during other activities (e.g. talking), functional limitation in jaw function and muscle soreness (secondary outcomes). Compared to sham, individuals who received active rTMS subsequently experienced lower pain on chewing and yawning. Although active rTMS increased PAF, the effects of rTMS on pain were not mediated by changes in PAF or CME. This study is the first to show that rTMS delivered prior to pain onset can protect against future pain and associated functional impairment. Thus, rTMS may hold promise as a prophylactic intervention for persistent pain.

Neuropsychological mechanisms of observational learning in human placebo effects

Scientific evidence indicates that placebo effects are psychoneurobiological events involving the contribution of distinct central nervous systems and peripheral physiological mechanisms that influence pain perception and other symptoms. Placebo effects can occur without formal conditioning and direct prior experience because crucial information can be acquired through observational learning. Observation of benefits in another person results in placebo effects of a magnitude like those induced by directly experiencing an analgesic benefit. Understanding the psychological mechanisms of observationally induced placebo effects is a complex and multifaceted endeavor. While previous reviews have highlighted various frameworks and models to understand these phenomena, the underlying biological mechanisms have been overlooked. We summarize critically current understanding of its behavioral and neural mechanisms. Understanding the neural mechanisms of hypoalgesia driven by observation can serve as a foundation for future development of novel theoretical and methodological approaches and ultimately, applications.

Lower individual alpha frequency in individuals with chronic low back pain and fear of movement

ABSTRACT

Significant progress has been made in linking measures of individual alpha frequency (IAF) and pain. A lower IAF has been associated with chronic neuropathic pain and with an increased sensitivity to pain in healthy young adults. However, the translation of these findings to chronic low back pain (cLBP) are sparse and inconsistent. To address this limitation, we assessed IAFs in a cohort of 70 individuals with cLBP, implemented 3 different IAF calculations, and separated cLBP subjects based on psychological variables. We hypothesized that a higher fear movement in cLBP is associated with a lower IAF at rest. A total of 10 minutes of resting data were collected from 128 electroencephalography channels. Our results offer 3 novel contributions to the literature. First, the high fear group had a significantly lower peak alpha frequency. The high fear group also reported higher pain and higher disability. Second, we calculated individual alpha frequency using 3 different but established methods; the effect of fear on individual alpha frequency was robust across all methods. Third, fear of movement, pain intensity, and disability highly correlated with each other and together significantly predicted IAF. Our findings are the first to show that individuals with cLBP and high fear have a lower peak alpha frequency.

Evoked oscillatory cortical activity during acute pain: Probing brain in pain by transcranial magnetic stimulation combined with electroencephalogram

Temporal dynamics of local cortical rhythms during acute pain remain largely unknown. The current study used a novel approach based on transcranial magnetic stimulation combined with electroencephalogram (TMS-EEG) to investigate evoked-oscillatory cortical activity during acute pain. Motor (M1) and dorsolateral prefrontal cortex (DLPFC) were probed by TMS, respectively, to record oscillatory power (event-related spectral perturbation and relative spectral power) and phase synchronization (inter-trial coherence) by 63 EEG channels during experimentally induced acute heat pain in 24 healthy participants. TMS-EEG was recorded before, during, and after noxious heat (acute pain condition) and non-noxious warm (Control condition), delivered in a randomized sequence. The main frequency bands (α, β1, and β2) of TMS-evoked potentials after M1 and DLPFC stimulation were recorded close to the TMS coil and remotely. Cold and heat pain thresholds were measured before TMS-EEG. Over M1, acute pain decreased α-band oscillatory power locally and α-band phase synchronization remotely in parietal-occipital clusters compared with non-noxious warm (all p < .05). The remote (parietal-occipital) decrease in α-band phase synchronization during acute pain correlated with the cold (p = .001) and heat pain thresholds (p = .023) and to local (M1) α-band oscillatory power decrease (p = .024). Over DLPFC, acute pain only decreased β1-band power locally compared with non-noxious warm (p = .015). Thus, evoked-oscillatory cortical activity to M1 stimulation is reduced by acute pain in central and parietal-occipital regions and correlated with pain sensitivity, in contrast to DLPFC, which had only local effects. This finding expands the significance of α and β band oscillations and may have relevance for pain therapies.

Brain network hypersensitivity underlies pain crises in sickle cell disease

Sickle cell disease (SCD) is a genetic disorder causing painful and unpredictable Vaso-occlusive crises (VOCs) through blood vessel blockages. In this study, we propose explosive synchronization (ES) as a novel approach to comprehend the hypersensitivity and occurrence of VOCs in the SCD brain network. We hypothesized that the accumulated disruptions in the brain network induced by SCD might lead to strengthened ES and hypersensitivity. We explored ES's relationship with patient reported outcome measures (PROMs) as well as VOCs by analyzing EEG data from 25 SCD patients and 18 matched controls. SCD patients exhibited lower alpha frequency than controls. SCD patients showed correlation between frequency disassortativity (FDA), an ES condition, and three important PROMs. Furthermore, stronger FDA was observed in SCD patients with a higher frequency of VOCs and EEG recording near VOC. We also conducted computational modeling on SCD brain network to study FDA's role in network sensitivity. Our model demonstrated that a stronger FDA could be linked to increased sensitivity and frequency of VOCs. This study establishes connections between SCD pain and the universal network mechanism, ES, offering a strong theoretical foundation. This understanding will aid predicting VOCs and refining pain management for SCD patients.

Sleep disturbance correlated with severity of neuropathic pain in sciatic nerve crush injury model

The association between sleep and pain has been investigated widely. However, inconsistent results from animal studies compared with human data show the need for a validated animal model in the sleep-pain association field. Our study aims to validate common neuropathic pain models as a tool for evaluating the sleep-pain association. Electrodes electroencephalogram (EEG) and electromyogram (EMG) were surgically implanted to measure sleep. The von Frey test was used to measure pain sensitivity. Following the baseline data acquisition, two pain-modelling procedures were performed: sciatic nerve crush injury (SCI) and common peroneal nerve ligation (CPL). Post-injury measurements were performed on days 1, 5, 10, and 15 post-surgery. The results presented decreased paw withdrawal thresholds and reduced NREM sleep duration in both models on the first post-surgery day. In the SCI model, NREM sleep duration was negatively correlated with paw withdrawal thresholds (p = 0.0466), but not in the CPL model. Wake alpha and theta EEG powers were also correlated with the pain threshold. The results confirm that the SCI model shows disturbed sleep patterns associated with increased pain sensitivity, suggesting it is a reliable tool for investigating sleep disturbances associated with neuropathic pain.

Sleep-EEG in comorbid pain and insomnia: implications for the treatment of pain disorders

Introduction

Patients with chronic pain experience a high prevalence of comorbid insomnia, which is associated with functional impairment. Recent advances in sleep electroencephalography (sleep-EEG) may clarify the mechanisms that link sleep and chronic pain. In this clinical update, we outline current advancements in sleep-EEG assessments for pain and provide research recommendations.

Results

Promising preliminary work suggests that sleep-EEG spectral bands, particularly beta, gamma, alpha, and delta power, may create candidate neurophysiological signatures of pain, and macro-architectural parameters (e.g., total sleep time, arousals, and sleep continuity) may facilitate EEG-derived sleep phenotyping and may enable future stratification in the treatment of pain.

Conclusion

Integration of measures obtained through sleep-EEG represent feasible and scalable approaches that could be adopted in the future. We provide research recommendations to progress the field towards a deeper understanding of their utility and potential future applications in clinical practice.

Transcranial Alternating Current Stimulation to Modulate Alpha Activity: A Systematic Review

BACKGROUND

Transcranial alternating current stimulation (tACS) has been one of numerous investigation methods used for their potential to modulate brain oscillations; however, such investigations have given contradictory results and a lack of standardization.

OBJECTIVES

In this systematic review, we aimed to assess the potential of tACS to modulate alpha spectral power. The secondary outcome was the identification of tACS methodologic key parameters, adverse effects, and sensations.

MATERIALS AND METHODS

Studies in healthy adults who were receiving active and sham tACS intervention or any differential condition were included. The main outcome assessed was the increase/decrease of alpha spectral power through either electroencephalography or magnetoencephalography. Secondary outcomes were methodologic parameters, sensation reporting, and adverse effects. Risks of bias and the study quality were assessed with the Cochrane assessment tool.

RESULTS

We obtained 1429 references, and 20 met the selection criteria. A statistically significant alpha-power increase was observed in nine studies using continuous tACS stimulation and two using intermittent tACS stimulation set at a frequency within the alpha range. A statistically significant alpha-power increase was observed in three more studies using a stimulation frequency outside the alpha range. Heterogeneity among stimulation parameters was recognized. Reported adverse effects were mild. The implementation of double blind was identified as challenging using tACS, in part owing to electrical artifacts generated by stimulation on the recorded signal.

CONCLUSIONS

Most assessed studies reported that tACS has the potential to modulate brain alpha power. The optimization of this noninvasive brain stimulation method is of interest mostly for its potential clinical applications with neurological conditions associated with perturbations in alpha brain activity. However, more research efforts are needed to standardize optimal parameters to achieve lasting modulation effects, develop methodologic alternatives to reduce experimental bias, and improve the quality of studies using tACS to modulate brain activity.

Individual differences in information processing during sleep and wake predict sleep-based memory consolidation of complex rules

Memory is critical for many cognitive functions, from remembering facts, to learning complex environmental rules. While memory encoding occurs during wake, memory consolidation is associated with sleep-related neural activity. Further, research suggests that individual differences in alpha frequency during wake (∼7 - 13 Hz) modulate memory processes, with higher individual alpha frequency (IAF) associated with greater memory performance. However, the relationship between wake-related EEG individual differences, such as IAF, and sleep-related neural correlates of memory consolidation has been largely unexplored, particularly in a complex rule-based memory context. Here, we aimed to investigate whether wake-derived IAF and sleep neurophysiology interact to influence rule learning in a sample of 35 healthy adults (16 males; mean age = 25.4, range: 18 - 40). Participants learned rules of a modified miniature language prior to either 8hrs of sleep or wake, after which they were tested on their knowledge of the rules in a grammaticality judgement task. Results indicate that sleep neurophysiology and wake-derived IAF do not interact but modulate memory for complex linguistic rules separately. Phase-amplitude coupling between slow oscillations and spindles during non-rapid eye-movement (NREM) sleep also promoted memory for rules that were analogous to the canonical English word order. As an exploratory analysis, we found that rapid eye-movement (REM) sleep theta power at posterior regions interacts with IAF to predict rule learning and proportion of time in REM sleep predicts rule learning differentially depending on grammatical rule type. Taken together, the current study provides behavioural and electrophysiological evidence for a complex role of NREM and REM sleep neurophysiology and wake-derived IAF in the consolidation of rule-based information.

Brain network hypersensitivity underlies pain crises in sickle cell disease

Sickle cell disease (SCD) is a genetic disorder causing blood vessel blockages and painful Vaso-occlusive crises (VOCs). VOCs, characterized by severe pain due to blocked blood flow, are recurrent and unpredictable, posing challenges for preventive strategies. In this study we propose explosive synchronization (ES), a phenomenon characterized by abrupt brain network phase transitions, as a novel approach to address this challenge. We hypothesized that the accumulated disruptions in the brain network induced by SCD might lead to strengthened ES and hypersensitivity. We explored ES's relationship with patient reported outcome measures (PROMs) and VOCs by analyzing EEG data from 25 SCD patients and 18 matched controls. SCD patients exhibited significantly lower alpha wave frequency than controls. SCD patients under painful pressure stimulation showed correlation between frequency disassortativity (FDA), an ES condition, and three important PROMs. Furthermore, patients who had a higher frequency of VOCs in the preceding 12 months presented with stronger FDA. The timing of VOC occurrence relative to EEG recordings was significantly associated to FDA. We also conducted computational modeling on SCD brain network to study FDA's role in network sensitivity. Stronger FDA correlated with higher responsivity and complexity in our model. Simulation under noisy environment showed that higher FDA could be linked to increased occurrence frequency of crisis. This study establishes connections between SCD pain and the universal network mechanism, ES, offering a strong theoretical foundation. This understanding will aid predicting VOCs and refining pain management for SCD patients.

Pain-free default mode network connectivity contributes to tonic experimental pain intensity beyond the role of negative mood and other pain-related factors

BACKGROUND

Alterations in the default mode network (DMN) connectivity across pain stages suggest a possible DMN involvement in the transition to persistent pain.

AIM

This study examined whether pain-free DMN connectivity at lower alpha oscillations (8-10 Hz) accounts for a unique variation in experimental peak pain intensity beyond the contribution of factors known to influence pain intensity.

METHODS

Pain-free DMN connectivity was measured with electroencephalography prior to 1 h of capsaicin-evoked pain using a topical capsaicin patch on the right forearm. Pain intensity was assessed on a (0-10) numerical rating scale and the association between peak pain intensity and baseline measurements was examined using hierarchical multiple regression in 52 healthy volunteers (26 women). The baseline measurements consisted of catastrophizing (helplessness, rumination, magnification), vigilance, depression, negative and positive affect, sex, age, sleep, fatigue, thermal and mechanical pain thresholds and DMN connectivity (medial prefrontal cortex [mPFC]-posterior cingulate cortex [PCC], mPFC-right angular gyrus [rAG], mPFC-left Angular gyrus [lAG], rAG-mPFC and rAG-PCC).

RESULTS

Pain-free DMN connectivity increased the explained variance in peak pain intensity beyond the contribution of other factors (ΔR2  = 0.10, p = 0.003), with the final model explaining 66% of the variation (R2  = 0.66, ANOVA: p < 0.001). In this model, negative affect (β = 0.51, p < 0.001), helplessness (β = 0.49, p = 0.007), pain-free mPFC-lAG connectivity (β = 0.36, p = 0.003) and depression (β = -0.39, p = 0.009) correlated significantly with peak pain intensity. Interestingly, negative affect and depression, albeit both being negative mood indices, showed opposing relationships with peak pain intensity.

CONCLUSIONS

This work suggests that pain-free mPFC-lAG connectivity (at lower alpha) may contribute to individual variations in pain-related vulnerability.

SIGNIFICANCE

These findings could potentially lead the way for investigations in which DMN connectivity is used in identifying individuals more likely to develop chronic pain.

Validation of Cross-Individual Pain Assessment with Individual Recognition Model from Electroencephalogram

Cross-individual pain assessment models based on electroencephalography (EEG) allow pain assessment in individuals who cannot report pain (e.g., unresponsive patients). The main obstacle to the generalisation of pain assessment models is the individual variation of brain responses to pain. Hence, we took the individual variation into account in cross-individual model development. We developed two convolutional neural networks (CNN) sharing an encoder architecture. One CNN predicts pain, while the other predicts the identity of an individual. We performed a leave-one-out (LOO) test with the exclusion of each subject and applied evidence accumulation to it for validating the pain prediction model's performance, where the binary classifier involves the states of pain (Hot) and resting state (Eyes-open). The mean accuracy produced by the LOO tests was 57.81% (max: 73.33%), and the mean accuracy of evidence accumulation achieved 69.75% (max: 100.00%). The individual recognition model achieved an accuracy of 99.63%. Nevertheless, when we acquired the most similar subject to a novel subject using the individual recognition model, where the most similar subject was used to train a subject-wise pain prediction model. The accuracy of predicting the pain-related conditions of the novel subject by the subject-wise model was only 53.73% (max: 79.50%). Therefore, the approach to utilising the features related to individual variation extracted by the CNN model needs more investigation for improving cross-individual pain assessment.Clinical relevance- This model can be applied to assess pain from EEG signals at the bedside with future improvement, which can help caretakers of unresponsive patients.

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.

Stability, change, and reliable individual differences in electroencephalography measures: a lifespan perspective on progress and opportunities

Electroencephalographic (EEG) methods have great potential to serve both basic and clinical science approaches to understand individual differences in human neural function. Importantly, the psychometric properties of EEG data, such as internal consistency and test-retest reliability, constrain their ability to differentiate individuals successfully. Rapid and recent technological and computational advancements in EEG research make it timely to revisit the topic of psychometric reliability in the context of individual difference analyses. Moreover, pediatric and clinical samples provide some of the most salient and urgent opportunities to apply individual difference approaches, but the changes these populations experience over time also provide unique challenges from a psychometric perspective. Here we take a developmental neuroscience perspective to consider progress and new opportunities for parsing the reliability and stability of individual differences in EEG measurements across the lifespan. We first conceptually map the different profiles of measurement reliability expected for different types of individual difference analyses over the lifespan. Next, we summarize and evaluate the state of the field's empirical knowledge and need for testing measurement reliability, both internal consistency and test-retest reliability, across EEG measures of power, event-related potentials, nonlinearity, and functional connectivity across ages. Finally, we highlight how standardized pre-processing software for EEG denoising and empirical metrics of individual data quality may be used to further improve EEG-based individual differences research moving forward. We also include recommendations and resources throughout that individual researchers can implement to improve the utility and reproducibility of individual differences analyses with EEG across the lifespan.

Electrophysiological and Behavioral Effects of Alpha-Band Sensory Entrainment: Neural Mechanisms and Clinical Applications

Alpha-band (7-13 Hz) activity has been linked to visuo-attentional performance in healthy participants and to impaired functionality of the visual system in a variety of clinical populations including patients with acquired posterior brain lesion and neurodevelopmental and psychiatric disorders. Crucially, several studies suggested that short uni- and multi-sensory rhythmic stimulation (i.e., visual, auditory and audio-visual) administered in the alpha-band effectively induces transient changes in alpha oscillatory activity and improvements in visuo-attentional performance by synchronizing the intrinsic brain oscillations to the external stimulation (neural entrainment). The present review aims to address the current state of the art on the alpha-band sensory entrainment, outlining its potential functional effects and current limitations. Indeed, the results of the alpha-band entrainment studies are currently mixed, possibly due to the different stimulation modalities, task features and behavioral and physiological measures employed in the various paradigms. Furthermore, it is still unknown whether prolonged alpha-band sensory entrainment might lead to long-lasting effects at a neural and behavioral level. Overall, despite the limitations emerging from the current literature, alpha-band sensory entrainment may represent a promising and valuable tool, inducing functionally relevant changes in oscillatory activity, with potential rehabilitative applications in individuals characterized by impaired alpha activity.

Brain structure and cortical activity changes of new daily persistent headache: multimodal evidence from MEG/sMRI

BACKGROUND

New daily persistent headache (NDPH) is a rare but debilitating primary headache disorder that poses a significant burden on individuals and society. Despite its clinical importance, the underlying pathophysiological mechanisms of NDPH remain unclear. In this study, we aimed to investigate the brain structural changes and neural activity patterns in patients with NDPH using multimodal brain imaging analysis of structural magnetic resonance imaging (sMRI) combined with magnetoencephalography (MEG).

METHODS

Twenty-eight patients with NDPH and 37 healthy controls (HCs) were recruited for this study, and their structural and resting-state data were collected by 3.0 Tesla MRI and MEG. We analyzed the brain morphology using voxel-based morphometry and source-based morphometry. In each brain region, MEG sensor signals from 1 to 200 Hz were analyzed using an adapted version of Welch's method. MEG source localization was conducted using the dynamic statistical parametric mapping, and the difference of source distribution between patients with NDPH and HCs was examined.

RESULTS

Our results revealed significant differences in the regional grey matter volume, cortical thickness, and cortical surface area between the two groups. Specifically, compared with HCs, patients with NDPH showed a significant decrease in cortical thickness of the left rostral cortex in the middle frontal gyrus, decreased cortical surface area of the left fusiform gyrus, decreased grey matter volume of the left superior frontal gyrus and the left middle frontal gyrus, and increased grey matter volume of the left calcarine. Furthermore, the power of the whole brain, bilateral frontal lobes, and right temporal lobe in the NDPH group were higher than that in HCs in the ripple frequency band (80-200 Hz). Functional and structural analysis suggested that there were structural changes and abnormal high frequency cortical activity in both frontal and temporal lobes in patients with NDPH.

CONCLUSION

Our findings indicated that patients with NDPH have abnormalities in brain morphology, such as cortical area, cortical thickness, and grey matter volume, accompanied by abnormal cortical neural activity. Brain structural changes in the frontotemporal cortex and abnormalities in cortical ripple activity may be involved in the pathogenesis of NDPH.

Synchronized activity of sensory neurons initiates cortical synchrony in a model of neuropathic pain

Increased low frequency cortical oscillations are observed in people with neuropathic pain, but the cause of such elevated cortical oscillations and their impact on pain development remain unclear. By imaging neuronal activity in a spared nerve injury (SNI) mouse model of neuropathic pain, we show that neurons in dorsal root ganglia (DRG) and somatosensory cortex (S1) exhibit synchronized activity after peripheral nerve injury. Notably, synchronized activity of DRG neurons occurs within hours after injury and 1-2 days before increased cortical oscillations. This DRG synchrony is initiated by axotomized neurons and mediated by local purinergic signaling at the site of nerve injury. We further show that synchronized DRG activity after SNI is responsible for increasing low frequency cortical oscillations and synaptic remodeling in S1, as well as for inducing animals' pain-like behaviors. In naive mice, enhancing the synchrony, not the level, of DRG neuronal activity causes synaptic changes in S1 and pain-like behaviors similar to SNI mice. Taken together, these results reveal the critical role of synchronized DRG neuronal activity in increasing cortical plasticity and oscillations in a neuropathic pain model. These findings also suggest the potential importance of detection and suppression of elevated cortical oscillations in neuropathic pain states.

Repetitive transcranial magnetic stimulation treatment of major depressive disorder and comorbid chronic pain: response rates and neurophysiologic biomarkers

BACKGROUND

Major depressive disorder (MDD) and chronic pain are highly comorbid, and pain symptoms are associated with a poorer response to antidepressant medication treatment. It is unclear whether comorbid pain also is associated with a poorer response to treatment with repetitive transcranial magnetic stimulation (rTMS).

METHODS

162 MDD subjects received 30 sessions of 10 Hz rTMS treatment administered to the left dorsolateral prefrontal cortex (DLPFC) with depression and pain symptoms measured before and after treatment. For a subset of 96 patients, a resting-state electroencephalogram (EEG) was recorded at baseline. Clinical outcome was compared between subjects with and without comorbid pain, and the relationships among outcome, pain severity, individual peak alpha frequency (PAF), and PAF phase-coherence in the EEG were examined.

RESULTS

64.8% of all subjects reported pain, and both depressive and pain symptoms were significantly reduced after rTMS treatment, irrespective of age or gender. Patients with severe pain were 27% less likely to respond to MDD treatment than pain-free individuals. PAF was positively associated with pain severity. PAF phase-coherence in the somatosensory and default mode networks was significantly lower for MDD subjects with pain who failed to respond to MDD treatment.

CONCLUSIONS

Pain symptoms improved after rTMS to left DLPFC in MDD irrespective of age or gender, although the presence of chronic pain symptoms reduced the likelihood of treatment response. Individual PAF and baseline phase-coherence in the sensorimotor and midline regions may represent predictors of rTMS treatment outcome in comorbid pain and MDD.

The reliability of two prospective cortical biomarkers for pain: EEG peak alpha frequency and TMS corticomotor excitability

BACKGROUND

Many pain biomarkers fail to move from discovery to clinical application, attributed to poor reliability and an inability to accurately classify at-risk individuals. Preliminary evidence has shown that high pain sensitivity is associated with slow peak alpha frequency (PAF), and depression of corticomotor excitability (CME), potentially due to impairments in ascending sensory and descending motor pathway signalling respectively NEW METHOD: The present study evaluated the reliability of PAF and CME responses during sustained pain. Specifically, we determined whether, over several days of pain, a) PAF remains stable and b) individuals show two stable and distinct CME responses: facilitation and depression. Participants were given an injection of nerve growth factor (NGF) into the right masseter muscle on Day 0 and Day 2, inducing sustained pain. Electroencephalography (EEG) to assess PAF and transcranial magnetic stimulation (TMS) to assess CME were recorded on Day 0, Day 2 and Day 5.

RESULTS

Using a weighted peak estimate, PAF reliability (n = 75) was in the excellent range even without standard pre-processing and ∼2 min recording length. Using a single peak estimate, PAF reliability was in the moderate-good range. For CME (n = 74), 80% of participants showed facilitation or depression of CME beyond an optimal cut-off point, with the stability of these changes in the good range.

COMPARISON WITH EXISTING METHODS

No study has assessed the reliability of PAF or feasibility of classifying individuals as facilitators/depressors, in response to sustained pain. PAF was reliable even in the presence of pain. The use of a weighted peak estimate for PAF is recommended, as excellent test-retest reliability can be obtained even when using minimal pre-processing and ∼2 min recording. We also showed that 80% of individuals exhibit either facilitation or depression of CME, with these changes being stable across sessions.

CONCLUSIONS

Our study provides support for the reliability of PAF and CME as prospective cortical biomarkers. As such, our paper adds important methodological advances to the rapidly growing field of pain biomarkers.

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.

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.

Peak alpha frequency as a candidate biomarker of pain sensitivity: the importance of distinguishing slow from slowing

The study by Valentini et al. (2022) observed that the peak alpha frequency (PAF) of participants became slower after they were exposed to painful, as well as non-painful but unpleasant stimuli. The authors interpreted this as a challenge to our previous studies which propose that the speed of resting PAF, independently of pain-induced changes to PAF, can be a reliable biomarker marker for gaging individual pain sensitivity. While investigations into the role that PAF plays in pain perception are timely, we have some concerns about the assumptions and methodology employed by Valentini et al. Moreover, we believe the authors here have also misrepresented some of our previous work. In the current commentary, we detail the critical differences between our respective studies, with the ultimate aim of guiding future investigations.

Source localized infraslow neurofeedback training in people with chronic painful knee osteoarthritis: A randomized, double-blind, sham-controlled feasibility clinical trial

Persistent pain is a key symptom in people living with knee osteoarthritis (KOA). Infra-slow Neurofeedback (ISF-NF) training is a recent development focusing on modulating cortical slow-wave activity to improve pain outcomes. A parallel, two-armed double-blinded, randomized sham-controlled, feasibility clinical trial aimed to determine the feasibility and safety of a novel electroencephalography-based infraslow fluctuation neurofeedback (EEG ISF-NF) training in people with KOA and determine the variability of clinical outcomes and EEG changes following NF training. Eligible participants attended nine 30-min ISF-NF training sessions involving three cortical regions linked to pain. Feasibility measures were monitored during the trial period. Pain and functional outcomes were measured at baseline, post-intervention, and follow-up after 2 weeks. Resting-state EEG was recorded at baseline and immediate post-intervention. Participants were middle-aged (61.7 ± 7.6 years), New Zealand European (90.5%), and mostly females (62%) with an average knee pain duration of 4 ± 3.4 years. The study achieved a retention rate of 91%, with 20/22 participants completing all the sessions. Participants rated high levels of acceptance and “moderate to high levels of perceived effectiveness of the training.” No serious adverse events were reported during the trial. Mean difference (95% CI) for clinical pain and function measures are as follows for pain severity [active: 0.89 ± 1.7 (−0.27 to 2.0); sham: 0.98 ± 1.1 (0.22–1.7)], pain interference [active: 0.75 ± 2.3 (−0.82 to 2.3); Sham: 0.89 ± 2.1 (−0.60 to 2.4)], pain unpleasantness [active: 2.6 ± 3.7 (0.17–5.1); sham: 2.8 ± 3 (0.62–5.0)] and physical function [active: 6.2 ± 13 (−2.6 to 15); sham: 1.6 ± 12 (−6.8 to 10)]. EEG sources demonstrated frequency-specific neuronal activity, functional connectivity, and ISF ratio changes following NF training. The findings of the study indicated that the ISF-NF training is a feasible, safe, and acceptable intervention for pain management in people with KOA, with high levels of perceived effectiveness. The study also reports the variability in clinical, brain activity, and connectivity changes following training.

Alpha power during task performance predicts individual language comprehension

Alpha power attenuation during cognitive task performing has been suggested to reflect a process of release of inhibition, increase of excitability, and thereby benefit the improvement of performance. Here, we hypothesized that changes in individual alpha power during the execution of a complex language comprehension task may correlate with the individual performance in that task. We tested this using magnetoencephalography (MEG) recorded during comprehension of German sentences of different syntactic complexity. Results showed that neither the frequency nor the power of the spontaneous oscillatory activity at rest were associated with the individual performance. However, during the execution of a sentences processing task, the individual alpha power attenuation did correlate with individual language comprehension performance. Source reconstruction localized these effects in left temporal-parietal brain regions known to be associated with language processing and their right-hemisphere homologues. Our results support the notion that in-task attenuation of individual alpha power is related to the essential mechanisms of the underlying cognitive processes, rather than merely to general phenomena like attention or vigilance.

Exploring sex differences in alpha brain activity as a potential neuromarker associated with neuropathic pain

ABSTRACT

Alpha oscillatory activity (8-13 Hz) is the dominant rhythm in the awake brain and is known to play an important role in pain states. Previous studies have identified alpha band slowing and increased power in the dynamic pain connectome (DPC) of people with chronic neuropathic pain. However, a link between alpha-band abnormalities and sex differences in brain organization in healthy individuals and those with chronic pain is not known. Here, we used resting-state magnetoencephalography to test the hypothesis that peak alpha frequency (PAF) abnormalities are general features across chronic central and peripheral conditions causing neuropathic pain but exhibit sex-specific differences in networks of the DPC (ascending nociceptive pathway [ANP], default mode network, salience network [SN], and subgenual anterior cingulate cortex). We found that neuropathic pain (N = 25 men and 25 women) was associated with increased PAF power in the DPC compared with 50 age- and sex-matched healthy controls, whereas slower PAF in nodes of the SN (temporoparietal junction) and the ANP (posterior insula) was associated with higher trait pain intensity. In the neuropathic pain group, women exhibited lower PAF power in the subgenual anterior cingulate cortex and faster PAF in the ANP and SN than men. The within-sex analyses indicated that women had neuropathic pain-related increased PAF power in the ANP, SN, and default mode network, whereas men with neuropathic pain had increased PAF power restricted to the ANP. These findings highlight neuropathic pain-related and sex-specific abnormalities in alpha oscillations across the DPC that could underlie aberrant neuronal communication in nociceptive processing and modulation.

Modulation Of Experimental Prolonged Pain and Sensitization Using High-Definition Transcranial Direct Current Stimulation: A Double-Blind, Sham-Controlled Study

High definition transcranial direct current stimulation (HD-tDCS) targeting brain areas involved in pain processing has shown analgesic effects in some chronic pain conditions, but less modulatory effect on mechanical and thermal pain thresholds in asymptomatic subjects. This double-blinded study assessed the HD-tDCS effects on experimental pain and hyperalgesia maintained for several days in healthy participants. Hyperalgesia and pain were assessed during three consecutive days following provocation of experimental pain (nerve growth factor injected into the right-hand muscle) and daily HD-tDCS sessions (20-minutes). Forty subjects were randomly assigned to Active-tDCS targeting primary motor cortex and dorsolateral prefrontal cortex simultaneously or Sham-tDCS. Tactile and pressure pain sensitivity were assessed before and after each HD-tDCS session, as well as the experimentally-induced pain intensity scored on a numerical rating scale (NRS). Subjects were effectively blinded to the type of HD-tDCS protocol. The Active-tDCS did not significantly reduce the NGF-induced NRS pain score (3.5±2.4) compared to Sham-tDCS (3.9±2.0, P > .05) on day 3 and both groups showed similarly NGF-decreased pressure pain threshold in the right hand (P < .001). Comparing Active-tDCS with Sham-tDCS, the manifestation of pressure hyperalgesia was delayed on day 1, and an immediate (pre-HD-tDCS to post-HD-tDCS) reduction in pressure hyperalgesia was found across all days (P < .05). PERSPECTIVE: The non-significant differences between Active-tDCS and Sham-tDCS on experimental prolonged pain and hyperalgesia suggest that HD-tDCS has no effect on moderate persistent experimental pain. The intervention may still have a positive effect in more severe pain conditions, with increased intensity, more widespread distribution, or increased duration and/or involving stronger affective components.

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.

Assessing the specificity of the relationship between brain alpha oscillations and tonic pain

Recent research proposed that the slowing of individual alpha frequency (IAF) could be an objective marker of pain. However, it is unclear whether this research can fully address the requirements of specificity and sensitivity of IAF to the pain experience. Here, we sought to develop a robust methodology for assessing the specificity of the relationship between alpha oscillations and acute tonic pain in healthy individuals. We recorded electroencephalography (EEG) of 36 volunteers during consecutive 5-minute sessions of painful hot water immersion, innocuous warm water immersion and aversive, non-painful auditory stimulus, matched by unpleasantness to the painful condition. Participants rated stimulus unpleasantness throughout each condition. We isolated two regions of the scalp displaying peak alpha activity across participants: centro-parietal (CP) and parieto-occipital (PO) ROI. In line with previous research our findings revealed decreased IAF during hot compared with warm stimulation, however the effect was not specific for pain as we found no difference between hot and sound in the CP ROI (compared to baseline). In contrast, the PO ROI reported the same pattern of differences, but their direction was opposite to the CP in that this ROI revealed faster frequency during hot condition than controls. Finally, we show that IAF in both ROIs did not mediate the relationship between the experimental manipulation and the affective experience. Altogether, these findings emphasize the importance of a robust methodological and analytical design to disclose the functional role of alpha oscillations during affective processing. Likewise, they suggest the absence of a causal role of IAF in the generation of acute pain experience in healthy individuals.

No indications for altered EEG oscillatory activity in patients with chronic post-burn itch compared to healthy controls

A large proportion of patients with burn injuries develop chronic itch, which impacts quality of life. The underlying pathophysiological mechanisms are poorly understood. This cross-sectional pilot study investigates whether altered cortical oscillatory processes are involved in chronic post-burn itch. Continuous electroencephalography (EEG) data were recorded during rest and stimulation of non-injured skin, inducing itch (histamine and electrical) and cold-pressor task pain for 15 adults with chronic post-burn itch and 15 matched healthy controls. Quantitative metrics comprised oscillatory power and peak frequencies in theta, alpha, and beta bands. No statistical differences between patients and healthy controls were found in oscillatory activity during rest or stimulation, with Bayesian analysis suggesting equivocal evidence. However, post-traumatic stress symptoms and duration of chronic itch may be associated with changes in oscillatory activity. A lack of differences in cortical oscillatory processing and itch levels at non-injured sites, suggests that itch symptoms have a localised character in this sample of patients with post-burn itch. For future studies, a biopsychological approach with integration of peripheral and central nervous system techniques, linear and non-linear EEG analysis, injured and non-injured stimulation sites, and incorporation of individual characteristics is recommended. Insight into pathophysiological mechanisms underlying chronic post-burn itch could improve diagnostics and treatments.

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.

Neurofeedback Therapy for Sensory Over-Responsiveness-A Feasibility Study

Background: Difficulty in modulating multisensory input, specifically the sensory over-responsive (SOR) type, is linked to pain hypersensitivity and anxiety, impacting daily function and quality of life in children and adults. Reduced cortical activity recorded under resting state has been reported, suggestive of neuromodulation as a potential therapeutic modality. This feasibility study aimed to explore neurofeedback intervention in SOR. Methods: Healthy women with SOR (n = 10) underwent an experimental feasibility study comprising four measurement time points (T1—baseline; T2—preintervention; T3—postintervention; T4—follow-up). Outcome measures included resting-state EEG recording, in addition to behavioral assessments of life satisfaction, attaining functional goals, pain sensitivity, and anxiety. Intervention targeted the upregulation of alpha oscillatory power over ten sessions. Results: No changes were detected in all measures between T1 and T2. Exploring the changes in brain activity between T2 and T4 revealed power enhancement in delta, theta, beta, and gamma oscillatory bands, detected in the frontal region (p = 0.03−<0.001; Cohen’s d = 0.637−1.126) but not in alpha oscillations. Furthermore, a large effect was found in enhancing life satisfaction and goal attainment (Cohen’s d = 1.18; 1.04, respectively), and reduced pain sensitivity and anxiety trait (Cohen’s d = 0.70). Conclusion: This is the first study demonstrating the feasibility of neurofeedback intervention in SOR.

Analytic consistency and neural correlates of peak alpha frequency in the study of pain

BACKGROUND

Several studies have found evidence of reduced resting-state peak alpha frequency (PAF) in populations with pain. However, the stability of PAF from different analytic pipelines used to study pain has not been determined and underlying neural correlates of PAF have not been validated in humans.

NEW METHOD

For the first time we compare analytic pipelines and the relationship of PAF to activity in the whole brain and thalamus, a hypothesized generator of PAF. We collected resting-state functional magnetic resonance imaging (rs-fMRI) data and subsequently 64 channel resting-state electroencephalographic (EEG) from 47 healthy men, controls from an ongoing study of chronic prostatitis (a pain condition affecting men). We identified important variations in EEG processing for PAF from a review of 17 papers investigating the relationship between pain and PAF. We tested three progressively complex pre-processing pipelines and varied four postprocessing variables (epoch length, alpha band, calculation method, and region-of-interest [ROI]) that were inconsistent across the literature.

RESULTS

We found a single principal component, well-represented by the average PAF across all electrodes (grand-average PAF), explained > 95% of the variance across participants. We also found the grand-average PAF was highly correlated among the pre-processing pipelines and primarily impacted by calculation method and ROI. Across methods, interindividual differences in PAF were correlated with rs-fMRI-estimated activity in the thalamus, insula, cingulate, and sensory cortices.

CONCLUSIONS

These results suggest PAF is a relatively stable marker with respect to common pre and post-processing methods used in pain research and reflects interindividual differences in thalamic and salience network function.

Power spectrum slope confounds estimation of instantaneous oscillatory frequency

Oscillatory neural dynamics are highly non-stationary and require methods capable of quantifying time-resolved changes in oscillatory activity in order to understand neural function. Recently, a method termed 'frequency sliding' was introduced to estimate the instantaneous frequency of oscillatory activity, providing a means of tracking temporal changes in the dominant frequency within a sub-band of field potential recordings. Here, the ability of frequency sliding to recover ground-truth oscillatory frequency in simulated data is tested while the exponent (slope) of the 1/f^x component of the signal power spectrum is systematically varied, mimicking real electrophysiological data. The results show that 1) in the presence of 1/f activity, frequency sliding systematically underestimates the true frequency of the signal, 2) the magnitude of underestimation is correlated with the steepness of the slope, suggesting that, if unaccounted for, slope changes could be misinterpreted as frequency changes, 3) the impact of slope on frequency estimates interacts with oscillation amplitude, indicating that changes in oscillation amplitude alone may also influence instantaneous frequency estimates in the presence of strong 1/f activity; and 4) analysis parameters such as filter bandwidth and location also mediate the influence of slope on estimated frequency, indicating that these settings should be considered when interpreting estimates obtained via frequency sliding. The origin of these biases resides in the output of the filtering step of frequency sliding, whose energy is biased towards lower frequencies precisely because of the 1/f structure of the data. We discuss several strategies to mitigate these biases and provide a proof-of-principle for a 1/f normalization strategy.

Breaking Down a Rhythm: Dissecting the Mechanisms Underlying Task-Related Neural Oscillations

A century worth of research has linked multiple cognitive, perceptual and behavioral states to various brain oscillations. However, the mechanistic roles and circuit underpinnings of these oscillations remain an area of active study. In this review, we argue that the advent of optogenetic and related systems neuroscience techniques has shifted the field from correlational to causal observations regarding the role of oscillations in brain function. As a result, studying brain rhythms associated with behavior can provide insight at different levels, such as decoding task-relevant information, mapping relevant circuits or determining key proteins involved in rhythmicity. We summarize recent advances in this field, highlighting the methods that are being used for this purpose, and discussing their relative strengths and limitations. We conclude with promising future approaches that will help unravel the functional role of brain rhythms in orchestrating the repertoire of complex behavior.

time-frequency analysis of scalp EEG with Hilbert-Huang transform and deep learning

Electroencephalography (EEG) is a brain imaging approach that has been widely used in neuroscience and clinical settings. The conventional EEG analyses usually require pre-defined frequency bands when characterizing neural oscillations and extracting features for classifying EEG signals. However, neural responses are naturally heterogeneous by showing variations in frequency bands of brainwaves and peak frequencies of oscillatory modes across individuals. Fail to account for such variations might result in information loss and classifiers with low accuracy but high variation across individuals. To address these issues, we present a systematic time-frequency analysis approach for analyzing scalp EEG signals. In particular, we propose a data-driven method to compute the subject-specific frequency bands for brain oscillations via Hilbert-Huang Transform, lifting the restriction of using fixed frequency bands for all subjects. Then, we propose two novel metrics to quantify the power and frequency aspects of brainwaves represented by sub-signals decomposed from the EEG signals. The effectiveness of the proposed metrics are tested on two scalp EEG datasets and compared with four commonly used features sets extracted from wavelet and Hilbert-Huang Transform. The validation results show that the proposed metrics are more discriminatory than other features leading to accuracies in the range of 94.93% to 99.84%. Besides classification, the proposed metrics show great potential in quantification of neural oscillations and serving as biomarkers in the neuroscience research.

The Effects of Transcranial Direct Current Stimulation Combined with Aerobic Exercise on Pain Thresholds and Electroencephalography in Healthy Adults

OBJECTIVES

We examined whether transcranial direct current stimulation (tDCS) combined with aerobic exercise (AE) modulated the pressure pain threshold (PPT) and peak alpha frequency (PAF) measured via resting electroencephalography.

DESIGN

Single-blind experimental study with a cross-over design.

SETTING

Neuro Rehabilitation Research Center, Kio University.

SUBJECTS

Ten healthy controls participated in this study.

METHODS

Three types of sessions--(i) tDCS, (ii) Sham tDCS/AE, and (iii) tDCS/AE--were tested in this investigation. Anodal stimulation (2 mA, 20 minutes) was applied over the left primary motor cortex. Each session was 20 minutes long. We used the PPT and short-form Profile of Mood States-Brief, as well as PAF measured via resting-electroencephalography, to investigate the effects of tDCS and AE. Heart rate and scores on the Borg scale were used to confirm exercise intensity. PAF was calculated in four regions of interest: frontal, central, parietal, and occipital areas.

RESULTS

The change ratio of PPT increased during each session. The maximum change ratio of PPT were tDCS: 40.7%, Sham tDCS/AE: 51.5%, and tDCS/AE: 83.4%. change ratio of PPT was earlier and higher in the tDCS/AE trials compared with the other sessions. Negative mood was improved after session completion. Significant differences in PAF were found in the occipital area in the Sham tDCS/AE and tDCS/AE sessions.

CONCLUSIONS

The combined tDCS and AE intervention induced significant changes in PPT in a single session, with a PAF that was earlier and higher than those produced during the Sham tDCS/AE and tDCS sessions.

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

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

Targeting neural oscillations with transcranial alternating current stimulation

Neural oscillations at the network level synchronize activity between regions and temporal scales. Transcranial alternating current stimulation (tACS), the delivery of low-amplitude electric current to the scalp, provides a tool for investigating the causal role of neural oscillations in cognition. The parameter space for tACS is vast and optimization is required in terms of temporal and spatial targeting. We review emerging techniques and suggest novel approaches that capitalize on the non-sinusoidal and transient nature of neural oscillations and leverage the flexibility provided by a customizable electrode montage and electrical waveform. The customizability and safety profile of tACS make it a promising tool for precision intervention in psychiatric illnesses.

Calibrating rhythmic stimulation parameters to individual EEG markers: the consistency of individual alpha frequency in practical lab settings

Rhythmic stimulation can be applied to modulate neuronal oscillations. Such 'entrainment' is optimized when stimulation frequency is individually-calibrated based on magneto/encephalography markers. It remains unknown how consistent such individual markers are across days/sessions, within a session, or across cognitive states, hemispheres, and estimation methods, especially in a realistic, practical, lab setting. We here estimated individual alpha frequency (IAF) repeatedly from short EEG measurements at rest or during an attention task (cognitive state), using single parieto-occipital electrodes in 24 participants on four days (between-sessions), with multiple measurements over an hour on one day (within-session). First, we introduce an algorithm to automatically reject power spectra without a sufficiently clear peak to ensure unbiased IAF estimations. Then we estimated IAF via the traditional 'maximum' method and a 'Gaussian fit' method. IAF was reliable within- and between-sessions for both cognitive states and hemispheres, though task-IAF estimates tended to be more variable. Overall, the 'Gaussian fit' method was more reliable than the 'maximum' method. Furthermore, we evaluated how far from an approximated 'true' task-related IAF the selected 'stimulation frequency' was, when calibrating this frequency based on a short rest-EEG, a short task-EEG, or simply selecting 10Hertz for all participants. For the 'maximum' method, rest-EEG calibration was best, followed by task-EEG, and then 10 Hertz. For the 'Gaussian fit' method, rest-EEG and task-EEG-based calibration were similarly accurate, and better than 10 Hertz. These results lead to concrete recommendations about valid, and automated, estimation of individual oscillation markers in experimental and clinical settings.

Slowing in Peak-Alpha Frequency Recorded After Experimentally-Induced Muscle Pain is not Significantly Different Between High and Low Pain-Sensitive Subjects

Peak alpha frequency (PAF) reduces during cutaneous pain, but no studies have investigated PAF during movement-related muscle pain. Whether high-pain sensitive (HPS) individuals exhibit a more pronounced PAF response to pain than low-pain sensitive (LPS) individuals is unclear. As a pain model, twenty-four participants received nerve growth factor injections into a wrist extensor muscle at Day 0, Day 2, and Day 4. At Day 4, a subgroup of twelve participants also undertook eccentric wrist exercise to induce additional pain. Pain numerical rating scale (NRS) scores and electroencephalography were recorded at Day 0 (before injection), Day 4, and Day 6 for 3 minutes (eyes closed) with wrist at rest (Resting-state) and extension (Contraction-state). The average pain NRS scores in contraction-state across Days were used to divide participants into HPS (NRS-scores≥2) and LPS groups. PAF was calculated by frequency decomposition of electroencephalographic recordings. Compared with Day 0, contraction NRS-scores only increased in HPS-group at Day 4 and Day 6 (P < .001). PAF in Contraction-state decreased in both groups at Day 6 compared with Day 0 (P = .011). Across days, HPS-group showed faster PAF than LPS-group during Resting-state and Contraction-state (P < .04). Average pain NRS-scores across days during Contraction-states correlated with PAF at Day 0 (P = .012). Pain NRS-scores were associated with PAF during Contraction-state at Day 4 and Day 6 (P < .05). PERSPECTIVE: PAF was slowed during long-lasting movement-related pain in both groups, suggesting a widespread change in cortical excitability independent of the pain sensitivity. Moreover, HPS individuals showed faster PAF than LPS individuals during muscle pain, which may reflect a different cognitive, emotional, or attentional response to muscle pain among individuals.

The breathing brain: The potential of neural oscillations for the understanding of respiratory perception in health and disease

Dyspnea or breathlessness is a symptom occurring in multiple acute and chronic illnesses, however, the understanding of the neural mechanisms underlying its subjective experience is limited. In this topical review, we propose neural oscillatory dynamics and cross-frequency coupling as viable candidates for a neural mechanism underlying respiratory perception, and a technique warranting more attention in respiration research. With the evidence for the potential of neural oscillations in the study of normal and disordered breathing coming from disparate research fields with a limited history of interdisciplinary collaboration, the main objective of the review was to converge the existing research and suggest future directions. The existing findings show that distinct limbic and cortical activations, as measured by hemodynamic responses, underlie dyspnea, however, the time-scale of these activations is not well understood. The recent findings of oscillatory neural activity coupled with the respiratory rhythm could provide the solution to this problem, however, more research with a focus on dyspnea is needed. We also touch on the findings of distinct spectral patterns underlying the changes in breathing due to experimental manipulations, meditation and disease. Subsequently, we suggest general research directions and specific research designs to supplement the current knowledge using neural oscillation techniques. We argue for the benefits of interdisciplinary collaboration and the converging of neuroimaging and behavioral methods to best explain the emergence of the subjective and aversive individual experience of dyspnea.

Pain in Axial Spondyloarthritis: Insights from Immunology and Brain Imaging

Inflammatory back pain is characteristic of spondyloarthritis (SpA); however, this pain may not respond to treatment with NSAIDs or biologics. Pain is multifactorial and a combination of mechanical and inflammatory factors. A growing body of literature examines the impact of emotions on pain in SpA; many patients with this condition suffer from depression and fibromyalgia. Advanced imaging techniques can investigate the interplay of various brain networks in pain perception. Animal models have helped understand the interplay between the immune and nervous systems in pain generation and have highlighted differences in pain perception between the sexes.

Magnetoencephalography: physics, techniques, and applications in the basic and clinical neurosciences

Magnetoencephalography (MEG) is a technique used to measure the magnetic fields generated from neuronal activity in the brain. MEG has a high temporal resolution on the order of milliseconds and provides a more direct measure of brain activity when compared with hemodynamic-based neuroimaging methods such as magnetic resonance imaging and positron emission tomography. The current review focuses on basic features of MEG such as the instrumentation and the physics that are integral to the signals that can be measured, and the principles of source localization techniques, particularly the physics of beamforming and the techniques that are used to localize the signal of interest. In addition, we review several metrics that can be used to assess functional coupling in MEG and describe the advantages and disadvantages of each approach. Lastly, we discuss the current and future applications of MEG.