Classification of major depressive disorder using vertex-wise brain sulcal depth, curvature, and thickness with a deep and a shallow learning model

作者：Roberto Goya‐Maldonado, Tracy Erwin-Grabner, Ling‐Li Zeng, Christopher R. K. Ching, André Alemán, Alyssa R. Amod, Zeynep Başgöze, Francesco Benedetti, Bianca Besteher, Katharina Brosch, Robin Bülow, Romain Colle, Colm G. Connolly, Emmanuelle Corruble, Baptiste Couvy‐Duchesne, Kathryn R. Cullen, Udo Dannlowski, Christopher G. Davey, Annemiek Dols, Jan Ernsting, Jennifer W. Evans, L. Fisch, Paola Fuentes‐Claramonte, Ali Saffet Gönül, Ian H. Gotlib, Hans J. Grabe, Nynke A. Groenewold, Dominik Grotegerd, Tim Hahn, J. Paul Hamilton, Laura K. M. Han, Ben J. Harrison, Tiffany C. Ho, Neda Jahanshad, Alec J. Jamieson, Andriana Karuk, Tilo Kircher, Bonnie Klimes‐Dougan, Sheri‐Michelle Koopowitz, T. Lancaster, Ramona Leenings, Meng Li, David E.J. Linden, Frank P. MacMaster, David M. A. Mehler, Susanne Meinert, Elisa Melloni, Bryon A. Mueller, Benson Mwangi, Igor Nenadić, Amar Ojha, Yasumasa Okamoto, Mardien L. Oudega, Brenda W.J.H. Penninx, Sara Poletti, Edith Pomarol‐Clotet, Marı́a J. Portella, Joaquim Raduà, Elena Rodríguez‐Cano, Matthew D. Sacchet, Raymond Salvador, Anouk Schrantee, Kang Sim, Jair C. Soares, Aleix Solanes, Dan J. Stein, Frederike Stein, Aleks Stolicyn, Sophia I. Thomopoulos, Yara J. Toenders, Aslihan Uyar-Demir, Eduard Vieta, Yolanda Vives‐Gilabert, Henry Völzke, Martin Walter, Heather C. Whalley, Sarah Whittle, Nils R. Winter, Katharina Wittfeld, Margaret J. Wright, Mon-Ju Wu, Tony T. Yang, Carlos A. Zarate, Dick J. Veltman, Lianne Schmaal, Paul M. Thompson, on behalf of the ENIGMA Major Depressive Disorder working group, Jan Ernsting · 发表于：Molecular Psychiatry · 年份：2025 · DOI：10.1038/s41380-025-03273-w · 被引用次数：4 · 研究领域：Functional Brain Connectivity Studies、Advanced Neuroimaging Techniques and Applications、Advanced MRI Techniques and Applications

Major depressive disorder (MDD) is a complex psychiatric disorder that affects the lives of hundreds of millions of individuals around the globe. Even today, researchers debate if morphological alterations in the brain are linked to MDD, likely due to the heterogeneity of this disorder. The application of deep learning tools to neuroimaging data, capable of capturing complex non-linear patterns, has the potential to provide diagnostic and predictive biomarkers for MDD. However, previous attempts to demarcate MDD patients and healthy controls (HC) based on segmented cortical features via linear machine learning approaches have reported low accuracies. In this study, we used globally representative data from the ENIGMA-MDD working group containing 7,012 participants from 30 sites (N=2,772 MDD and N=4,240 HC), which allows a comprehensive analysis with generalizable results. Based on the hypothesis that integration of vertex-wise cortical features can improve classification performance, we evaluated the classification of a DenseNet and a Support Vector Machine (SVM), with the expectation that the former would outperform the latter. As we analyzed a multi-site sample, we additionally applied the ComBat harmonization tool to remove potential nuisance effects of site. We found that both classifiers exhibited close to chance performance (balanced accuracy DenseNet: 51%; SVM: 53%), when estimated on unseen sites. Slightly higher classification performance (balanced accuracy DenseNet:...