Biology subjects

Heitmann, H.

Publications and source records attributed to Heitmann, H..

7 recordsLinked to original sources

bioRxiv2024

EEG-based analysis of intrinsic brain network function in chronic pain: Insights from a comprehensive multi-data set study

Chronic pain is associated with alterations in brain function. A better understanding of these alterations might help to develop new approaches for the diagnosis, prediction, and treatment of chronic pain. Here, we analyzed associations between chronic pain and alterations of intrinsic brain network function using resting-state electroencephalography. We included data from 537 people with chronic pain obtained from various research groups worldwide. We found strong evidence for associations between pain intensity and intrinsic brain network connectivity, but the replicability of these associations in independent data was mostly low. However, a mega-analysis revealed associations of chronic pain with salience-somatomotor network connectivity at theta frequencies and with more complex patterns of intrinsic brain network connectivity. These findings provide novel insights into brain network function in chronic pain. Moreover, they highlight the need for collaborative multi-center studies, which can be guided by the present approach to promote replicability and consistency of findings.

neuroscience↗

bioRxiv2024

Resting-State Electroencephalography and Magnetoencephalography in Migraine - A Systematic Review and Meta-Analysis

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

neuroscience↗

bioRxiv2024

Assessing the balance between excitation and inhibition in chronic pain through the aperiodic component of EEG

Chronic pain is a prevalent and debilitating condition whose neural mechanisms are incompletely understood. An imbalance of cerebral excitation and inhibition (E/I), particularly in the medial prefrontal cortex (mPFC), is believed to represent a crucial mechanism in the development and maintenance of chronic pain. Thus, identifying a non-invasive, scalable marker of E/I could provide valuable insights into the neural mechanisms of chronic pain and aid in developing clinically useful biomarkers. Recently, the aperiodic component of the electroencephalography (EEG) power spectrum has been proposed to represent a non-invasive proxy for E/I. We, therefore, assessed the aperiodic component in the mPFC of resting-state EEG recordings in 149 people with chronic pain and 115 healthy participants. We found robust evidence against differences in the aperiodic component in the mPFC between people with chronic pain and healthy participants, and no correlation between the aperiodic component and pain intensity. These findings were consistent across different subtypes of chronic pain and were similarly found in a whole-brain analysis. Their robustness was supported by preregistration and multiverse analyses across many different methodological choices. Together, our results suggest that the EEG aperiodic component does not differentiate between people with chronic pain and healthy individuals. These findings and the rigorous methodological approach can guide future studies investigating non-invasive, scalable markers of cerebral dysfunction in people with chronic pain and beyond.

neuroscience↗

bioRxiv2024

Effects of Centrally Acting Analgesics on Resting-State Electroencephalography Biomarker Candidates of Chronic Pain

Resting-state electroencephalography (rsEEG) holds promise as a biomarker of chronic pain. However, the impact of centrally acting analgesics like opioids, antiepileptics, and antidepressants on rsEEG remains unclear. This confounds and limits the interpretability of previous studies and questions the validity of rsEEG biomarker candidates of chronic pain. We, therefore, aimed to elucidate the effects of opioids, antiepileptics, and antidepressants on common rsEEG biomarker candidates of chronic pain. To this end, we analyzed two large, independent rsEEG datasets, including 220 people with chronic pain. We performed preregistered multivariate Bayesian analyses and controlled for the potential confounds of age, pain intensity, and depression. The results predominantly provided evidence against effects of centrally acting analgesics on peak alpha frequency, oscillatory power in different frequency bands, and connectivity-based network measures. Although these findings do not rule out any effects of analgesics on rsEEG, they argue against medium to large effects of centrally acting analgesics on rsEEG. Thus, the findings strengthen the validity of rsEEG biomarker candidates of chronic pain and might thereby help to develop clinically valuable biomarkers of chronic pain.

neuroscience↗

bioRxiv2020

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

Chronic pain is a highly prevalent and severely disabling disease, which is associated with substantial changes of brain function. Such changes have mostly been observed when analyzing static measures of brain activity during the resting-state. 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 (EEG) data of 101 patients suffering from chronic pain and 88 age- and gender-matched healthy controls. Microstate analysis describes EEG activity as a sequence of a limited number of topographies termed microstates, which remain stable for tens of milliseconds. Our results revealed that sequences of 5 microstates, labelled with the letters A to E, described resting-state brain activity in both groups and conditions. Bayesian analysis of the temporal characteristics of microstates revealed that microstate D has a less predominant role in patients than in healthy participants. This difference was consistently found in eyes-open and eyes-closed EEG recordings. No evidence for differences in other microstates was found. As microstate D has been previously related to attentional networks and functions, abnormalities of microstate D might relate to dysfunctional attentional processes in chronic pain. These findings add to the understanding of the pathophysiology of chronic pain and might eventually contribute to the development of an EEG-based biomarker of chronic pain.

neuroscience↗

bioRxiv2020

Modulating Brain Rhythms of Pain using Transcranial Alternating Current Stimulation (tACS)? A Sham-controlled Study in Healthy Human Participants

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.

neuroscience↗

bioRxiv2019

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 and comprehensively exploited the potential of electroencephalography (EEG) 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 implicate increased theta and gamma synchrony in frontal brain areas in the pathophysiology of chronic pain. While substantial challenges concerning accuracy, specificity and validity of potential EEG-based disease markers remain to be overcome, our study identifies abnormal frontal synchrony at theta and gamma frequencies as promising targets for non-invasive brain stimulation and/or neurofeedback approaches.

neuroscience↗