Exploring electroencephalographic chronic pain biomarkers: a mega-analysis
Felix S. Bott, Paul Theo Zebhauser, Vanessa D. Hohn, Özgün Turgut, Elisabeth S. May, Laura Tiemann, Cristina Gil Ávila, Henrik Heitmann, Moritz M. Nickel, Melissa A. Day, Divya Bharatkumar Adhia, Yoni K. Ashar, Tor D. Wager, Yelena Granovsky, David Yarnitsky, Mark P. Jensen, Joachim Groß, Markus Ploner
Abstract
BACKGROUND: Chronic pain is associated with alterations in brain function, offering promising avenues for advancing diagnostic and therapeutic strategies. In particular, these alterations may serve as brain-based biomarkers to support diagnosis, guide treatment decisions and monitor clinical courses of chronic pain. METHODS: Motivated by this potential, this study analysed associations between chronic pain and changes of large-scale brain network function using resting-state electroencephalography (EEG) from 614 individuals with chronic pain, collected by research groups from Australia, Germany, Israel, New Zealand, and the US. FINDINGS: Employing a discovery-replication approach, we found limited replicability of associations between pain intensity and brain network connectivity. However, a mega-analysis combining all datasets revealed robust associations between pain intensity and large-scale brain network connectivity at theta frequencies and including the limbic network. Additionally, multivariate analyses identified connectivity patterns spanning theta, alpha, and beta frequencies with strong evidence for associations with pain intensity. Variations and ablations of model features yielded deeper insights into the relative importance of distinct electrophysiological brain features in assessing chronic pain. INTERPRETATION: Our findings highlight challenges and provide guidance for developing EEG-based, scalable, and affordable biomarkers of chronic pain. FUNDING: This project was funded by the Deutsche Forschungsgemeinschaft and the Technical University of Munich.