Neural oscillations and connectivity characterizing the state of tonic experimental pain in humans

Pain is a complex phenomenon that is served by neural oscillations and connectivity involving different brain areas and frequencies. Here, we aimed to systematically and comprehensively assess the pattern of neural oscillations and connectivity characterizing the state of tonic experimental pain in humans. To this end, we applied 10-min heat pain stimuli consecutively to the right and left hand of 39 healthy participants and recorded electroencephalography. We systematically analyzed global and local measures of oscillatory brain activity, connectivity, and graph theory-based network measures during tonic pain and compared them to a nonpainful control condition. Local measures showed suppressions of oscillatory activity at alpha frequencies together with stronger connectivity at alpha and beta frequencies in sensorimotor areas during tonic pain. Furthermore, sensorimotor areas contralateral to stimulation showed significantly increased connectivity to a common area in the medial prefrontal cortex at alpha frequencies. Together, these observations indicate that the state of tonic experimental pain is associated with a sensorimotor-prefrontal network connected at alpha frequencies. These findings represent a step further toward understanding the brain mechanisms underlying long-lasting pain states in health and disease

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

Modulating Brain Rhythms of Pain using Transcranial Alternating Current Stimulation (tACS)?

Pain protects the body. However, pain can also occur for longer periods without serving protective functions. Such chronic pain conditions are difficult to treat. Thus, a better understanding of the underlying neural mechanisms and new approaches for the treatment of pain are urgently needed. Here, we investigated a causal role of oscillatory brain activity for pain and explored the potential of transcranial alternating current stimulation (tACS) as a new treatment approach for pain. To this end, we investigated whether tACS can modulate pain and pain-related autonomic activity in 29 healthy human participants using a tonic heat pain paradigm as an experimental model of chronic pain. In 6 recording sessions, participants received tACS over prefrontal or somatosensory cortices at alpha or gamma frequencies or sham tACS. During tACS, pain ratings and autonomic responses were collected. TACS did not modulate pain intensity, the stability of pain ratings or the translation of the noxious stimulus into pain. Likewise, tACS did not change autonomic responses. Bayesian statistics further indicated a lack of tACS effects in most conditions. The only exception was alpha tACS over somatosensory cortex where evidence for tACS effects was inconclusive. Taken together, the present study did not find significant tACS effects on tonic experimental pain in healthy human participants. However, considering the conceptual plausibility of using tACS to modulate pain and the urgent need for novel pain treatments, further tACS studies are warranted. Based on the present findings, such studies might apply refined stimulation protocols targeting alpha oscillations in somatosensory cortices

Brain dysfunction in chronic pain patients assessed by resting-state electroencephalography

Chronic pain is a common and severely disabling disease whose treatment is often unsatisfactory. Insights into the brain mechanisms of chronic pain promise to advance the understanding of the underlying pathophysiology and might help to develop disease markers and novel treatments. Here, we systematically exploited the potential of electroencephalography to determine abnormalities of brain function during the resting state in chronic pain. To this end, we performed state-of-the-art analyses of oscillatory brain activity, brain connectivity, and brain networks in 101 patients of either sex suffering from chronic pain. The results show that global and local measures of brain activity did not differ between chronic pain patients and a healthy control group. However, we observed significantly increased connectivity at theta (4-8 Hz) and gamma (>60 Hz) frequencies in frontal brain areas as well as global network reorganization at gamma frequencies in chronic pain patients. Furthermore, a machine learning algorithm could differentiate between patients and healthy controls with an above-chance accuracy of 57%, mostly based on frontal connectivity. These results suggest that increased theta and gamma synchrony in frontal brain areas are involved in the pathophysiology of chronic pain. Although substantial challenges concerning the reproducibility of the findings and the accuracy, specificity, and validity of potential electroencephalography-based disease markers remain to be overcome, our study indicates that abnormal frontal synchrony at theta and gamma frequencies might be promising targets for noninvasive brain stimulation and/or neurofeedback approaches

Insomnia Severity in Adults with Autism Spectrum Disorder is Associated with sensory Hyper-Reactivity and Social Skill Impairment

Insomnia is a common source of distress in adults with autism spectrum disorder (ASD). Two characteristics of ASD could be relevant to insomnia complaints by hampering the entrainment of a circadian sleep-wake rhythm. First, sensory hyper-reactivity could lead to bright light avoidance and thus affect photoperiodic input to the circadian system. Second, impaired social skills complicate the establishment of a social interactions and thus affect scheduled social-behavioral input to the circadian system. We investigated the association of insomnia severity with sensory reactivity and social skills in 631 adults (18-65 years) with ASD. Results revealed positive associations of insomnia severity with general and visual sensory hyper-reactivity and with impairment of social skills. The findings warrant further studies which (1) directly assess whether a suboptimal functioning of the biological clock underlies these associations and (2) identify other factors that could contribute to observed sleep problems

DISCOVER-EEG: an open, fully automated EEG pipeline for biomarker discovery in clinical neuroscience

Abstract Biomarker discovery in neurological and psychiatric disorders critically depends on reproducible and transparent methods applied to large-scale datasets. Electroencephalography (EEG) is a promising tool for identifying biomarkers. However, recording, preprocessing, and analysis of EEG data is time-consuming and researcher-dependent. Therefore, we developed DISCOVER-EEG, an open and fully automated pipeline that enables easy and fast preprocessing, analysis, and visualization of resting state EEG data. Data in the Brain Imaging Data Structure (BIDS) standard are automatically preprocessed, and physiologically meaningful features of brain function (including oscillatory power, connectivity, and network characteristics) are extracted and visualized using two open-source and widely used Matlab toolboxes (EEGLAB and FieldTrip). We tested the pipeline in two large, openly available datasets containing EEG recordings of healthy participants and patients with a psychiatric condition. Additionally, we performed an exploratory analysis that could inspire the development of biomarkers for healthy aging. Thus, the DISCOVER-EEG pipeline facilitates the aggregation, reuse, and analysis of large EEG datasets, promoting open and reproducible research on brain function