Individual variability in brain representations of pain

doi:10.1038/s41593-022-01081-x

Authors:

Kohoutová, Lada, Atlas, Lauren, Büchel, Christian, Buhle, Jason, Geuter, Stephan, Jepma, Marieke, Koban, Leonie, Krishnan, Anjali, Lee, Dong Hee, Lee, Sungwoo, Roy, Mathieu, Schafer, Scott, Schmidt, Liane, Wager, Tor, Woo, Choong-Wan + 10

Contributors:

Sungkyunkwan University Suwon (SKKU), Institute for Basic Science (IBS), Pusan National University (PNU), National Institutes of Health Bethesda, MD, USA (NIH), National Institute on Drug Abuse NIH, Bethesda, USA (NIDA), National Institute of Mental Health (NIMH), University Medical Centre Hamburg-Eppendorf Hamburg, Germany, University of Southern California (USC), Johns Hopkins University (JHU), University of Colorado Boulder, University of Amsterdam Amsterdam = Universiteit van Amsterdam (UvA), Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière AP-HP, Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Brooklyn College CUNY, City University of New York New York (CUNY), McGill University = Université McGill Montréal, Canada, Dartmouth College Hanover + 16

Published in:

ISSN: 1097-6256

Publisher:

HAL CCSD, Nature Publishing Group

Abstract

International audience ; Characterizing cerebral contributions to individual variability in pain processing is crucial for personalized pain medicine, but has yet to be done. In the present study, we address this problem by identifying brain regions with high versus low interindividual variability in their relationship with pain. We trained idiographic pain-predictive models with 13 single-trial functional MRI datasets (n = 404, discovery set) and quantified voxel-level importance for individualized pain prediction. With 21 regions identified as important pain predictors, we examined the interindividual variability of local pain-predictive weights in these regions. Higher-order transmodal regions, such as ventromedial and ventrolateral prefrontal cortices, showed larger individual variability, whereas unimodal regions, such as somatomotor cortices, showed more stable pain representations across individuals. We replicated this result in an independent dataset (n = 124). Overall, our study identifies cerebral sources of individual differences in pain processing, providing potential targets for personalized assessment and treatment of pain.