0 Datasets
0 Files
Get instant academic access to this publication’s datasets.
Yes. After verification, you can browse and download datasets at no cost. Some premium assets may require author approval.
Files are stored on encrypted storage. Access is restricted to verified users and all downloads are logged.
Yes, message the author after sign-up to request supplementary files or replication code.
Join 50,000+ researchers worldwide. Get instant access to peer-reviewed datasets, advanced analytics, and global collaboration tools.
✓ Immediate verification • ✓ Free institutional access • ✓ Global collaborationJoin our academic network to download verified datasets and collaborate with researchers worldwide.
Get Free AccessMachine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects.
V. Belov, Tracy Erwin-Grabner, Moji Aghajani, André Alemán, Alyssa R. Amod, Zeynep Başgöze, Francesco Benedetti, Bianca Besteher, Robin Bülow, Christopher R. K. Ching, Colm G. Connolly, Kathryn R. Cullen, Christopher G. Davey, Danai Dima, Annemiek Dols, Jennifer W. Evans, Cynthia H.Y. Fu, Ali Saffet Gönül, Ian H. Gotlib, Hans J. Grabe, Nynke A. Groenewold, J. Paul Hamilton, Ben J. Harrison, Tiffany C. Ho, Benson Mwangi, Natalia Jaworska, Neda Jahanshad, Bonnie Klimes‐Dougan, Sheri‐Michelle Koopowitz, T. Lancaster, Meng Li, David E.J. Linden, Frank P. MacMaster, David M. A. Mehler, Elisa Melloni, Bryon A. Mueller, Amar Ojha, Mardien L. Oudega, Brenda W.J.H. Penninx, Sara Poletti, Edith Pomarol‐Clotet, Marı́a J. Portella, Elena Pozzi, Liesbeth Reneman, Matthew D. Sacchet, Philipp G. Sämann, Anouk Schrantee, Kang Sim, Jair C. Soares, Dan Joseph Stein, Sophia I. Thomopoulos, Aslihan Uyar-Demir, Nic J.A. van der Wee, Steven J.A. van der Werff, Henry Völzke, Sarah Whittle, Katharina Wittfeld, Margaret J. Wright, Mon-Ju Wu, Tony T. Yang, Carlos A. Zarate, Dick J. Veltman, Lianne Schmaal, Paul M. Thompson, Roberto Goya‐Maldonado (2024). Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures. , 14(1), DOI: https://doi.org/10.1038/s41598-023-47934-8.
Datasets shared by verified academics with rich metadata and previews.
Authors choose access levels; downloads are logged for transparency.
Students and faculty get instant access after verification.
Type
Article
Year
2024
Authors
65
Datasets
0
Total Files
0
Language
en
DOI
https://doi.org/10.1038/s41598-023-47934-8
Access datasets from 50,000+ researchers worldwide with institutional verification.
Get Free Access
