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 specificity of rsEEG biomarker candidates of chronic pain, especially for potential diagnostic biomarkers. 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 217 people with chronic pain. We performed preregistered multivariate Bayesian analyses to allow for the quantification and interpretation of evidence in favor of as well as against medication effects on EEG. We specifically evaluated the effects of different centrally acting analgesics on rsEEG features and controlled for the potential confounds of age, pain intensity, and depression. 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. These results help to interpret previous and future rsEEG findings in people with chronic pain and strengthen the validity of rsEEG biomarker candidates of chronic pain. Thus, the present findings can help to develop clinically valuable biomarkers of chronic pain. Perspective: This study investigated the effects of centrally acting analgesics on brain-based biomarker candidates of chronic pain, as assessed by electroencephalography. The results predominantly provided evidence against effects on peak alpha frequency, oscillatory power, and connectivity-based network measures. This might help to develop clinically useful biomarkers of chronic pain. Data availability: Datasets are available at https://osf.io/uzgxw/files/osfstorage.