A multistudy analysis reveals that evoked pain intensity representation is distributed across brain systems

• Published in: PLoS biology Vol. 20; no. 5; p. e3001620
• Main Authors: Petre, Bogdan, Kragel, Philip, Atlas, Lauren Y, Geuter, Stephan, Jepma, Marieke, Koban, Leonie, Krishnan, Anjali, Lopez-Sola, Marina, Losin, Elizabeth A Reynolds, Roy, Mathieu, Woo, Choong-Wan, Wager, Tor D
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 02.05.2022; Public Library of Science (PLoS)
• Subjects: Biology and Life Sciences; Brain; Brain - physiology; Brain Mapping - methods; Computer and Information Sciences; Humans; Investigations; Life Sciences; Localization; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medicine and Health Sciences; Model accuracy; Neurons and Cognition; Pain; Pain Measurement; Pain perception; Prediction models; Representations; Research and Analysis Methods; Social Sciences
• ISSN: 1545-7885; 1544-9173; 1545-7885
• DOI: 10.1371/journal.pbio.3001620

Information is coded in the brain at multiple anatomical scales: locally, distributed across regions and networks, and globally. For pain, the scale of representation has not been formally tested, and quantitative comparisons of pain representations across regions and networks are lacking. In this multistudy analysis of 376 participants across 11 studies, we compared multivariate predictive models to investigate the spatial scale and location of evoked heat pain intensity representation. We compared models based on (a) a single most pain-predictive region or resting-state network; (b) pain-associated cortical-subcortical systems developed from prior literature ("multisystem models"); and (c) a model spanning the full brain. We estimated model accuracy using leave-one-study-out cross-validation (CV; 7 studies) and subsequently validated in 4 independent holdout studies. All spatial scales conveyed information about pain intensity, but distributed, multisystem models predicted pain 20% more accurately than any individual region or network and were more generalizable to multimodal pain (thermal, visceral, and mechanical) and specific to pain. Full brain models showed no predictive advantage over multisystem models. These findings show that multiple cortical and subcortical systems are needed to decode pain intensity, especially heat pain, and that representation of pain experience may not be circumscribed by any elementary region or canonical network. Finally, the learner generalization methods we employ provide a blueprint for evaluating the spatial scale of information in other domains.