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Structural neuroimaging biomarkers for obsessive-compulsive disorder in the ENIGMA-OCD consortium

Research group Willuhn
Publication year 2020
Published in Translational Psychiatry
Authors R.M. Thomas, D. Denys, Willem B Bruin, Luke Taylor, Jonathan P Shock, Paul Zhutovsky, Yoshinari Abe, Pino Alonso, Stephanie H Ameis, Alan Anticevic, Paul D Arnold, Francesca Assogna, Francesco Benedetti, Jan C Beucke, Premika S W Boedhoe, Irene Bollettini, Anushree Bose, Silvia Brem, Jan K Buitelaar, Brian P Brennan, Rosa Calvo, Yuqi Cheng, Kang Ik K Cho, Sara Dallaspezia, Benjamin A Ely, Jamie D Feusner, Kate D Fitzgerald, Jean-Paul Fouche, Egill A Fridgeirsson, Patricia Gruner, Deniz A Gürsel, Tobias U Hauser, Yoshiyuki Hirano, Marcelo Q Hoexter, Hao Hu, Chaim Huyser, Iliyan Ivanov, Anthony James, Fern Jaspers-Fayer, Norbert Kathmann, Christian Kaufmann, Kathrin Koch, Masaru Kuno, Gerd Kvale, Jun Soo Kwon, Yanni Liu, Christine Lochner, Luisa Lázaro, Paulo Marques, Rachel Marsh, P.M. Thompson, O.A. Van den Heuvel, D.J. Stein, Guido A van Wingen,

No diagnostic biomarkers are available for obsessive-compulsive disorder (OCD). Here, we aimed to identify magnetic resonance imaging (MRI) biomarkers for OCD, using 46 data sets with 2304 OCD patients and 2068 healthy controls from the ENIGMA consortium. We performed machine learning analysis of regional measures of cortical thickness, surface area and subcortical volume and tested classification performance using cross-validation. Classification performance for OCD vs. controls using the complete sample with different classifiers and cross-validation strategies was poor. When models were validated on data from other sites, model performance did not exceed chance-level. In contrast, fair classification performance was achieved when patients were grouped according to their medication status. These results indicate that medication use is associated with substantial differences in brain anatomy that are widely distributed, and indicate that clinical heterogeneity contributes to the poor performance of structural MRI as a disease marker.

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