Predictive modelling and center effects: towards a robust functional connectivity-based neuromarker of pain sensitivity
Tamas Spisak1, Balint Kincses2,3, Raviteja Kotikalapudi1, Matthias Zunhammer2, Frederik Schlitt2, Tobias Schmidt-Wilcke4,5, Zsigmond Tamas Kincses3, and Ulrike Bingel2
1Laboratory of Predictive Neuroimaging, Institute for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany, 2Klinik für Neurologie, University Hospital Essen, Essen, Germany, 3Department of Neurology, University of Szeged, Szeged, Hungary, 4Institut für Klinische Neurowissenschaften und Medizinische Psychologie, Heinrich Heine Universität, Düsseldorf, Germany, 5Neurozentrum, Bezirksklinikum Mainkofen, Deggendorf, Germany
Center effects significantly limit the generalizability of brain imaging-based biomarker candidates. Although our previously published resting state functional connectivity-based predictive signature for pain sensitivity (the RPN-signature) showed remarkable out-of-center generalizability, it remained unclear which connectivity features are the most generalizable across study centers.
Here, we re-trained the RPN-signature on multi-center data and found that it outperforms the single-center model in all three centers (explained variance: 26-38% vs. 16%-19%). Our results highlight that neurobiological interpretation of feature importance in predictive modelling is constrained both by center-specific artifacts and by certain characteristics (e.g. regularization) of the applied machine learning algorithm.
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