The functional connectome in obsessive-compulsive disorder: resting-state mega-analysis and machine learning classification for the ENIGMA-OCD consortium
Authors: Bruin Willem B. , Abe Yoshinari , Alonso Pino , Anticevic Alan , Backhausen Lea L. , Balachander Srinivas , Bargallo Nuria , Batistuzzo Marcelo C. , Benedetti Francesco , Bertolin Triquell Sara , Brem Silvia , Calesella Federico , Couto Beatriz , Denys Damiaan A. J. P. , Echevarria Marco A. N. , Eng Goi Khia , Ferreira Sónia , Feusner Jamie D. , Grazioplene Rachael G. , Gruner Patricia , Guo Joyce Y. , Hagen Kristen , Hansen Bjarne , Hirano Yoshiyuki , Hoexter Marcelo Q. , Jahanshad Neda , Jaspers-Fayer Fern , Kasprzak Selina , Kim Minah , Koch Kathrin , Bin Kwak Yoo , Kwon Jun Soo , Lazaro Luisa , Li Chiang-Shan R. , Lochner Christine , Marsh Rachel , Martínez-Zalacaín Ignacio , Menchon Jose M. , Moreira Pedro S. , Morgado Pedro , Nakagawa Akiko , Nakao Tomohiro , Narayanaswamy Janardhanan C. , Nurmi Erika L. , Zorrilla Jose C. Pariente , Piacentini John , Picó-Pérez Maria , Piras Fabrizio , Piras Federica , Pittenger Christopher , Reddy Janardhan Y. C. , Rodriguez-Manrique Daniela , Sakai Yuki , Shimizu Eiji , Shivakumar Venkataram , Simpson Blair H. , Soriano-Mas Carles , Sousa Nuno , Spalletta Gianfranco , Stern Emily R. , Evelyn Stewart S. , Szeszko Philip R. , Tang Jinsong , Thomopoulos Sophia I. , Thorsen Anders L. , Yoshida Tokiko , Tomiyama Hirofumi , Vai Benedetta , Veer Ilya M. , Venkatasubramanian Ganesan , Vetter Nora C. , Vriend Chris , Walitza Susanne , Waller Lea , Wang Zhen , Watanabe Anri , Wolff Nicole , Yun Je-Yeon , Zhao Qing , van Leeuwen Wieke A. , van Marle Hein J. F. , van de Mortel Laurens A. , van der Straten Anouk , van der Werf Ysbrand D. ,   ENIGMA-OCD Working Group, Arai Honami , Bollettini Irene , Escalona Rosa Calvo , Coelho Ana , Colombo Federica , Darwich Leila , Fontaine Martine , Ikuta Toshikazu , Ipser Jonathan C. , Juaneda-Seguí Asier , Kitagawa Hitomi , Kvale Gerd , Machado-Sousa Mafalda , Morer Astrid , Nakamae Takashi , Narumoto Jin , O’Neill Joseph , Okawa Sho , Real Eva , Roessner Veit , Sato Joao R. , Segalàs Cinto , Shavitt Roseli G. , Veltman Dick J. , Yamada Kei , van Leeuwen Wieke A. , van Marle Hein J. F. , van de Mortel Laurens A. , van der Straten Anouk , van der Werf Ysbrand D. , van den Heuvel Odile A. , van Wingen Guido A. , Thompson Paul M. , Stein Dan J. , van den Heuvel Odile A. , van Wingen Guido A. 
Published: May 2023

Current knowledge about functional connectivity in obsessive-compulsive disorder (OCD) is based on small-scale studies, limiting the generalizability of results. Moreover, the majority of studies have focused only on predefined regions or functional networks rather than connectivity throughout the entire brain. Here, we investigated differences in resting-state functional connectivity between OCD patients and healthy controls (HC) using mega-analysis of data from 1024 OCD patients and 1028 HC from 28 independent samples of the ENIGMA-OCD consortium. We assessed group differences in whole-brain functional connectivity at both the regional and network level, and investigated whether functional connectivity could serve as biomarker to identify patient status at the individual level using machine learning analysis. The mega-analyses revealed widespread abnormalities in functional connectivity in OCD, with global hypo-connectivity (Cohen’s d: -0.27 to -0.13) and few hyper-connections, mainly with the thalamus (Cohen’s d: 0.19 to 0.22). Most hypo-connections were located within the sensorimotor network and no fronto-striatal abnormalities were found. Overall, classification performances were poor, with area-under-the-receiver-operating-characteristic curve (AUC) scores ranging between 0.567 and 0.673, with better classification for medicated (AUC = 0.702) than unmedicated (AUC = 0.608) patients versus healthy controls. These findings provide partial support for existing pathophysiological models of OCD and highlight the important role of the sensorimotor network in OCD. However, resting-state connectivity does not so far provide an accurate biomarker for identifying patients at the individual level.