Choong-Wan Woo1,2,†, Liane Schmidt3,4,*, Anjali Krishnan5,*, Marieke Jepma6,7, Mathieu Roy8, Martin A. Lindquist9, Lauren Y. Atlas10,11 & Tor D. Wager1,2
1 Department of Psychology and Neuroscience, University of Colorado, Boulder, Colorado 80309, USA. 2 Institute of Cognitive Science, University of Colorado, Boulder, Colorado 80309, USA. 3 INSEAD, Fontainebleau 77300, France. 4 Cognitive Neuroscience Laboratory, INSERM U960, Department of Cognitive Sciences, Ecole Normale Supe´rieure, Paris 75005, France. 5 Department of Psychology, Brooklyn College of the City University of New York, Brooklyn, New York 11210, USA. 6 Cognitive Psychology Unit, Institute of Psychology, Leiden University, Leiden 2300, The Netherlands. 7 Leiden Institute for Brain and Cognition, Leiden University, Leiden 2300, The Netherlands. 8 Department of Psychology, McGill University, Montre´al, Quebec H3A 0G4, Canada. 9 Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland 21211, USA. 10 National Center for Complementary and Integrative Health, National Institutes of Health, Bethesda, Maryland 20892, USA. 11 National Institute on Drug Abuse, National Institutes of Health, Rockville, Maryland 20852, USA.
† Present addresses: Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon 16419, Republic of Korea; Department of Biomedical Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea.
* These authors contributed equally to this work.
Correspondence to Tor D. Wager.
Cerebral processes contribute to pain beyond the level of nociceptive input and mediate psychological and behavioural influences. However, cerebral contributions beyond nociception are not yet well characterized, leading to a predominant focus on nociception when studying pain and developing interventions. Here we use functional magnetic resonance imaging combined with machine learning to develop a multivariate pattern signature-termed the stimulus intensity independent pain signature-1 (SIIPS1) that predicts pain above and beyond nociceptive input in four training data sets (Studies 1-4, N=137). The SIIPS1 includes patterns of activity in nucleus accumbens, lateral prefrontal and parahippocampal cortices, and other regions. In cross-validated analyses of Studies 1-4 and in two independent test data sets (Studies 5-6, N=46), SIIPS1 responses explain variation in trial-by-trial pain ratings not captured by a previous fMRI-based marker for nociceptive pain. In addition, SIIPS1 responses mediate the pain-modulating effects of three psychological manipulations of expectations and perceived control. The SIIPS1 provides an extensible characterization of cerebral contributions to pain and specific brain targets for interventions.