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Abstract #1872

Cortical thickness predicts pain sensitivity in a multi-centre cohort: a machine learning approach

Raviteja Kotikalapudi1,2, Balint Kincses1,2,3, Kevin Hoffschlag1, Matthias Zunhammer2, Tobias Schmidt-Wilcke4,5, Zsigmond T Kincses3, Ulrike Bingel2, and Tamas Spisak1,2
1Laboratory of Predictive NeuroImaging, ‚Äč‚ÄčInstitute for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany, 2The Bingel Laboratory, Translational Pain Research Unit, University Hospital Essen, Essen, Germany, 3Department of Neurology, University of Szeged, Szeged, Hungary, 4Institute for Clinical Neurosciences and Medical Psychology, Heinrich Heine University, Dusseldorf, Germany, 5Neurocentre, District Hospital Mainkofen, Mainkofen, Deggendorf, Germany


Individual sensitivity to pain is both a precursor and a symptom of many clinical pain conditions. A pain predictive model would have potential applications in objectively characterizing pain in acute and chronic pain individuals. Here, we developed a cortical thickness-based predictive model of pain sensitivity using a machine learning approach and multi-centre T1-weighted MRI and quantitative pain threshold data. We found that our model significantly predicts pain sensitivity, that was measured through heat, cold and mechanical stimuli. Furthermore, the predictions were exclusively driven by cortical thickness and not confounded by variables of demographic and psychological value.

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