作者
Vladimir Belov, Tracy Erwin-Grabner, Moji Aghajani, Andre Aleman, Alyssa R Amod, Zeynep Basgoze, Francesco Benedetti, Bianca Besteher, Robin Bülow, Christopher RK Ching, Colm G Connolly, Kathryn Cullen, Christopher G Davey, Danai Dima, Annemiek Dols, Jennifer W Evans, Cynthia HY Fu, Ali Saffet Gonul, Ian H Gotlib, Hans J Grabe, Nynke Groenewold, J Paul Hamilton, Ben J Harrison, Tiffany C Ho, Benson Mwangi, Natalia Jaworska, Neda Jahanshad, Bonnie Klimes-Dougan, Sheri-Michelle Koopowitz, Thomas Lancaster, Meng Li, David EJ Linden, Frank P MacMaster, David MA Mehler, Elisa Melloni, Bryon A Mueller, Amar Ojha, Mardien L Oudega, Brenda WJH Penninx, Sara Poletti, Edith Pomarol-Clotet, Maria J Portella, Elena Pozzi, Liesbeth Reneman, Matthew D Sacchet, Philipp G Sämann, Anouk Schrantee, Kang Sim, Jair C Soares, Dan J Stein, Sophia I Thomopoulos, Aslihan Uyar-Demir, Nic JA van der Wee, Steven JA van der Werff, Henry Völzke, Sarah Whittle, Katharina Wittfeld, Margaret J Wright, Mon-Ju Wu, Tony T Yang, Carlos Zarate, Dick J Veltman, Lianne Schmaal, Paul M Thompson, Roberto Goya-Maldonado, ENIGMA Major Depressive Disorder working group https://enigma. ini. usc. edu/ongoing/enigma-mdd-working-group/
发表日期
2024/1/11
期刊
Scientific reports
卷号
14
期号
1
页码范围
1084
出版商
Nature Publishing Group UK
简介
Machine 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 …
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V Belov, T Erwin-Grabner, M Aghajani, A Aleman… - Scientific reports, 2024