Nature Communications · 2017 · 233 citations · 54 references
Life SciencesNeurobiologyNeuroscienceBehavioral NeurosciencePain MedicinePain ScienceFunctional NeuroimagingPain CharacterizationPain ModulationPain NeurosciencePain PhysiologyPain ProcessingCentral Pain MechanismsFunctional Magnetic Resonance ImagingFunctional Magnetic Resonance Imaging CorrelatesFunctional Magnetic Resonance Imaging MarkersPain-related Cortical RepresentationParahippocampal GyrusPrefrontal Cortex FunctionPrefrontal Cortex Involvement
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.
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