Quantifying cerebral contributions to pain beyond nociception

Choong-Wan Woo,Lauren Y Atlas,Marieke Jepma,Anjali Krishnan,Martin A Lindquist,Liane Schmidt,Mathieu Roy,Tor D Wager

doi:10.1038/ncomms14211

Abstract

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.

Highlights

Cerebral processes contribute to pain beyond the level of nociceptive input and mediate psychological and behavioural influences
The stimulus intensity independent pain signature-1 (SIIPS1) was predictive of trial-by-trial pain ratings above and beyond variations in noxious stimulus intensity, suggesting that SIIPS1 reflects endogenous cerebral contributions to pain independent of nociceptive input to the brain
It includes negative weights (‘anti-pain’ effects) in several regions related in previous studies to motivational value[14,56], context and memory[57], and cognitive context[58]

Summary

Introduction

Cerebral processes contribute to pain beyond the level of nociceptive input and mediate psychological and behavioural influences. 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 1⁄4 137). Similar to other pain-predictive patterns, the NPS was developed to predict pain experience driven largely, not entirely, by noxious stimuli based on fMRI activity mostly within noxious stimulus intensity-encoding regions It reflects only a subset of the various brain processes that contribute to pain and does not explain much of the variation in pain experience that is found even when the stimulus intensity is held constant (for an example case, see Fig. 1a). Recent studies have shown that the NPS does not explain the pain-modulating effects of several psychological interventions, including placebo treatment[32], cognitive self-regulation[17] and perceived control[34]

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