• Raviteja Kotikalapudi, Balint Kincses, Matthias Zunhammer, Frederik Schlitt, Livia Asan, Tobias Schmidt-Wilcke, Zsigmond T Kincses, Ulrike Bingel, and Tamas Spisak.
• Institute for Diagnostic and Interventional Radiology and Neuroradiology, University Medicine Essen, Essen, Germany.
• Pain. 2023 Nov 1; 164 (11): 251625272516-2527.

AbstractSensitivity to pain shows a remarkable interindividual variance that has been reported to both forecast and accompany various clinical pain conditions. Although pain thresholds have been reported to be associated to brain morphology, it is still unclear how well these findings replicate in independent data and whether they are powerful enough to provide reliable pain sensitivity predictions on the individual level. In this study, we constructed a predictive model of pain sensitivity (as measured with pain thresholds) using structural magnetic resonance imaging-based cortical thickness data from a multicentre data set (3 centres and 131 healthy participants). Cross-validated estimates revealed a statistically significant and clinically relevant predictive performance (Pearson r = 0.36, P < 0.0002, R2 = 0.13). The predictions were found to be specific to physical pain thresholds and not biased towards potential confounding effects (eg, anxiety, stress, depression, centre effects, and pain self-evaluation). Analysis of model coefficients suggests that the most robust cortical thickness predictors of pain sensitivity are the right rostral anterior cingulate gyrus, left parahippocampal gyrus, and left temporal pole. Cortical thickness in these regions was negatively correlated to pain sensitivity. Our results can be considered as a proof-of-concept for the capacity of brain morphology to predict pain sensitivity, paving the way towards future multimodal brain-based biomarkers of pain.Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the International Association for the Study of Pain.

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