title: White matter diffusion estimates in obsessive-compulsive disorder across 1653 individuals: machine learning findings from the ENIGMA OCD Working Group
Creators: name: Kim, Bo-Gyeom
name: Kim, Gakyung
name: Abe, Yoshinari
name: Alonso, Pino
name: Ameis, Stephanie
name: Anticevic, Alan
name: Arnold, Paul D.
name: Balachander, Srinivas
name: Banaj, Nerisa
name: Bargalló, Nuria
name: Batistuzzo, Marcelo C.
name: Benedetti, Francesco
name: Bertolín, Sara
name: Beucke, Jan Carl
name: Bollettini, Irene
name: Brem, Silvia
name: Brennan, Brian P.
name: Buitelaar, Jan K.
name: Calvo, Rosa
name: Castelo-Branco, Miguel
name: Cheng, Yuqi
name: Chhatkuli, Ritu Bhusal
name: Ciullo, Valentina
name: Coelho, Ana
name: Couto, Beatriz
name: Dallaspezia, Sara
name: Ely, Benjamin A.
name: Ferreira, Sónia
name: Fontaine, Martine
name: Fouche, Jean-Paul
name: Grazioplene, Rachael
name: Gruner, Patricia
name: Hagen, Kristen
name: Hansen, Bjarne
name: Hanna, Gregory L.
name: Hirano, Yoshiyuki
name: Höxter, Marcelo Q.
name: Hough, Morgan
name: Hu, Hao
name: Huyser, Chaim
name: Ikuta, Toshikazu
name: Jahanshad, Neda
name: James, Anthony
name: Jaspers-Fayer, Fern
name: Kasprzak, Selina
name: Kathmann, Norbert
name: Kaufmann, Christian
name: Kim, Minah
name: Koch, Kathrin
name: Kvale, Gerd
name: Kwon, Jun Soo
name: Lazaro, Luisa
name: Lee, Junhee
name: Lochner, Christine
name: Lu, Jin
name: Manrique, Daniela Rodriguez
name: Martínez-Zalacaín, Ignacio
name: Masuda, Yoshitada
name: Matsumoto, Koji
name: Maziero, Maria Paula
name: Menchón, Jose M.
name: Minuzzi, Luciano
name: Moreira, Pedro Silva
name: Morgado, Pedro
name: Narayanaswamy, Janardhanan C.
name: Narumoto, Jin
name: Ortiz, Ana E.
name: Ota, Junko
name: Pariente, Jose C.
name: Perriello, Chris
name: Picó-Pérez, Maria
name: Pittenger, Christopher
name: Poletti, Sara
name: Real, Eva
name: Reddy, Y. C. Janardhan
name: Van Rooij, Daan
name: Sakai, Yuki
name: Sato, João Ricardo
name: Segalas, Cinto
name: Shavitt, Roseli G.
name: Shen, Zonglin
name: Shimizu, Eiji
name: Shivakumar, Venkataram
name: Soreni, Noam
name: Soriano-Mas, Carles
name: Sousa, Nuno
name: Sousa, Mafalda Machado
name: Spalletta, Gianfranco
name: Stern, Emily R.
name: Stewart, S. Evelyn
name: Szeszko, Philip R.
name: Thomas, Rajat
name: Thomopoulos, Sophia I.
name: Vecchio, Daniela
name: Venkatasubramanian, Ganesan
name: Vriend, Chris
name: Walitza, Susanne
name: Wang, Zhen
name: Watanabe, Anri
name: Wolters, Lidewij
name: Xu, Jian
name: Yamada, Kei
name: Yun, Je-Yeon
name: Zarei, Mojtaba
name: Zhao, Qing
name: Zhu, Xi
name: ,
name: Thompson, Paul M.
name: Bruin, Willem B.
name: Van Wingen, Guido A.
name: Piras, Federica
name: Piras, Fabrizio
name: Stein, Dan J.
name: Van Den Heuvel, Odile A.
name: Simpson, Helen Blair
name: Marsh, Rachel
name: Cha, Jiook
Publisher: name: Humboldt-Universität zu Berlin
Year: 2024
Subjects: subject: Diagnostic markers
subject: Neuroscience
subject: 610 Medizin und Gesundheit

description: White matter pathways, typically studied with diffusion tensor imaging (DTI), have been implicated in the neurobiology of obsessive-compulsive disorder (OCD). However, due to limited sample sizes and the predominance of single-site studies, the generalizability of OCD classification based on diffusion white matter estimates remains unclear. Here, we tested classification accuracy using the largest OCD DTI dataset to date, involving 1336 adult participants (690 OCD patients and 646 healthy controls) and 317 pediatric participants (175 OCD patients and 142 healthy controls) from 18 international sites within the ENIGMA OCD Working Group. We used an automatic machine learning pipeline (with feature engineering and selection, and model optimization) and examined the cross-site generalizability of the OCD classification models using leave-one-site-out cross-validation. Our models showed low-to-moderate accuracy in classifying (1) “OCD vs. healthy controls” (Adults, receiver operator characteristic-area under the curve = 57.19 ± 3.47 in the replication set; Children, 59.8 ± 7.39), (2) “unmedicated OCD vs. healthy controls” (Adults, 62.67 ± 3.84; Children, 48.51 ± 10.14), and (3) “medicated OCD vs. unmedicated OCD” (Adults, 76.72 ± 3.97; Children, 72.45 ± 8.87). There was significant site variability in model performance (cross-validated ROC AUC ranges 51.6–79.1 in adults; 35.9–63.2 in children). Machine learning interpretation showed that diffusivity measures of the corpus callosum, internal capsule, and posterior thalamic radiation contributed to the classification of OCD from HC. The classification performance appeared greater than the model trained on grey matter morphometry in the prior ENIGMA OCD study (our study includes subsamples from the morphometry study). Taken together, this study points to the meaningful multivariate patterns of white matter features relevant to the neurobiology of OCD, but with low-to-moderate classification accuracy. The OCD classification performance may be constrained by site variability and medication effects on the white matter integrity, indicating room for improvement for future research.