White matter diffusion estimates in obsessive-compulsive disorder across 1,653 individuals: Machine learning findings from the ENIGMA OCD Working Group
Authors: Cha Jiook , Kim Bogyeom , Kim Gakyung , Thompson Paul , Bruin Willem , Wingen Guido van , Piras Federica , Piras Fabrizio , Stein Dan , Heuvel Odile van den , Simpson H. , Marsh Rachel , Abe Yoshinari , Alonso Pino , Ameis Stephanie , Anticevic Alan , Arnold Paul , Balachander Srinivas , Banaj Nerisa , Bargallo Nuria , Batistuzzo Marcelo , Benedetti Francesco , Triquell Sara Bertolin , Beucke Jan , Bollettini Irene , Brem Silvia , Brennan Brian , Buitelaar Jan , Calvo-Escalona Rosa , Cheng Yuqi , Chhatkuli Ritu , Coelho Ana , Couto Beatriz , Dallaspezia Sara , Ely Benjamin , Ferreira Sónia , Fontaine Martine , Fouche Jean-Paul , Grazioplene Rachael , Gruner Patricia , Hagen Kristen , Hansen Bjarne , Hirano Yoshiyuki , Hoexter Marcelo , Hough Morgan , Hu Hao , Huyser Chaim , Ikuta Toshikazu , James Anthony , Jaspers-Fayer Fern , Kasprzak Selina , Kathmann Norbert , Kaufmann Christian , Kim Minah , Koch Kathrin , Kvale Gerd , Kwon Jun Soo , Lázaro Luisa , Lee Junhee , Lochner Christine , Lu Jin , Rodriguez-Manrique Daniela , Martínez-Zalacaín Ignacio , Masuda Yoshitada , MATSUMOTO Koji , Menchón José , Moreira Pedro , Morgado Pedro , Narayanaswamy Janardhanan , Narumoto Jin , Ortiz Ana , Ota Junko , Pariente Jose , Perriello Chris , Picó-Pérez Maria , Pittenger Christopher , Poletti Sara , Real Eva , Reddy Yemmiganur , Rooij Daan van , Sakai Yuki , Segalas Cinto , Shen Zonglin , Shimiziu Eiji , Shivakumar Venkataram , Soriano-Mas Carles , Sousa Nuno , Sousa Mafalda , Spalletta Gianfranco , Stern Emily , Stewart S. Evelyn , Szeszko Philip , Vriend Chris , Walitza Susanne , Wang Zhen , Watanabe Anri , Wolters Lidewij , Xu Jian , Yamada Kei , Yun Je-Yeon , Zarei Mojtaba , Zhao Qing 
Published: June 2023

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 1,336 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.