Ontology highlight
ABSTRACT:
SUBMITTER: Belov V
PROVIDER: S-EPMC10784593 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
Belov Vladimir V Erwin-Grabner Tracy T Aghajani Moji M Aleman Andre A Amod Alyssa R AR Basgoze Zeynep Z Benedetti Francesco F Besteher Bianca B Bülow Robin R Ching Christopher R K CRK Connolly Colm G CG Cullen Kathryn K Davey Christopher G CG Dima Danai D Dols Annemiek A Evans Jennifer W JW Fu Cynthia H Y CHY Gonul Ali Saffet AS Gotlib Ian H IH Grabe Hans J HJ Groenewold Nynke N Hamilton J Paul JP Harrison Ben J BJ Ho Tiffany C TC Mwangi Benson B Jaworska Natalia N Jahanshad Neda N Klimes-Dougan Bonnie B Koopowitz Sheri-Michelle SM Lancaster Thomas T Li Meng M Linden David E J DEJ MacMaster Frank P FP Mehler David M A DMA Melloni Elisa E Mueller Bryon A BA Ojha Amar A Oudega Mardien L ML Penninx Brenda W J H BWJH Poletti Sara S Pomarol-Clotet Edith E Portella Maria J MJ Pozzi Elena E Reneman Liesbeth L Sacchet Matthew D MD Sämann Philipp G PG Schrantee Anouk A Sim Kang K Soares Jair C JC Stein Dan J DJ Thomopoulos Sophia I SI Uyar-Demir Aslihan A van der Wee Nic J A NJA van der Werff Steven J A SJA Völzke Henry H Whittle Sarah S Wittfeld Katharina K Wright Margaret J MJ Wu Mon-Ju MJ Yang Tony T TT Zarate Carlos C Veltman Dick J DJ Schmaal Lianne L Thompson Paul M PM Goya-Maldonado Roberto R
Scientific reports 20240111 1
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 (M ...[more]