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.
Resting-state electroencephalography reveals increased synchrony at theta and gamma frequencies in frontal brain areas and global network reorganization at gamma frequencies in chronic pain patients.
aDepartment of Neurology, School of Medicine, Technical University of Munich, Munich, Germany
bTUM-Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
cDepartment of Anesthesiology, School of Medicine, Technical University of Munich, Munich, Germany
dDepartment of Psychology, Ludwig-Maximilians-Universität München, Munich, Germany
eInstitute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany
fCentre for Cognitive Neuroimaging, University of Glasgow, Glasgow, United Kingdom
*Corresponding author. Address: Department of Neurology, School of Medicine, Technical University of Munich, Ismaninger Str 22, 81675 Munich, Germany. Tel.: +49-89-4140-4608; fax: +49-89-4140-4867. E-mail address: firstname.lastname@example.org (M. Ploner).
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