0 Datasets
0 Files
Get instant academic access to this publication’s datasets.
Yes. After verification, you can browse and download datasets at no cost. Some premium assets may require author approval.
Files are stored on encrypted storage. Access is restricted to verified users and all downloads are logged.
Yes, message the author after sign-up to request supplementary files or replication code.
Join 50,000+ researchers worldwide. Get instant access to peer-reviewed datasets, advanced analytics, and global collaboration tools.
✓ Immediate verification • ✓ Free institutional access • ✓ Global collaborationJoin our academic network to download verified datasets and collaborate with researchers worldwide.
Get Free AccessCurrent 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 1,024 OCD patients and 1,028 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.
Willem B. Bruin, Yoshinari Abe, Pino Alonso, Alan Anticevic, Srinivas Balachander, Núria Bargalló, Marcelo C. Batistuzzo, Francesco Benedetti, Sara Bertolín, Silvia Brem, Federico Calesella, beatriz couto, Damiaan Denys, Marco A.N. Echevarria, Goi Khia Eng, Sónia Ferreira, Jamie D. Feusner, Rachael Grazioplene, Patricia Gruner, Joyce Guo, Kristen Hagen, Bjarne Hansen, Yoshiyuki Hirano, Marcelo Q. Hoexter, Neda Jahanshad, Fern Jaspers‐Fayer, Selina Kasprzak, Minah Kim, Kathrin Koch, Yoo Bin Kwak, Jun Soo Kwon, Luisa Lázaro, Chiang‐Shan R. Li, Christine Löchner, Rachel Marsh, Ignacio Martínez‐Zalacaín, José M. Menchón, Pedro Silva Moreira, Pedro Morgado, Akiko Nakagawa, Tomohiro Nakao, Janardhanan C. Narayanaswamy, Erika L. Nurmi, Jose C. Pariente Zorrilla, John Piacentini, Maria Picó‐Pérez, Fabrizio Piras, Fabrizio Piras, Christopher Pittenger, Janardhan Y. C. Reddy, Daniela Rodriguez-Manrique, Yuki Sakai, Eiji Shimizu, Venkataram Shivakumar, Blair H. Simpson, Carles Soriano‐Mas, Nuno Sousa, Gianfranco Spalletta, Emily Stern, S. Evelyn Stewart, Philip R. Szeszko, Jinsong Tang, Sophia I. Thomopoulos, Anders Lillevik Thorsen, Yoshida Tokiko, Hirofumi Tomiyama, Benedetta Vai, Ilya M. Veer, Ganesan Venkatasubramanian, Nora C. Vetter, Chris Vriend, Susanne Walitza, Lea Waller, Zhen Wang, Anri Watanabe, Nicole Wolff, Je‐Yeon Yun, Qing Zhao, Wieke A. van Leeuwen, Hein J. F. van Marle, Laurens A. van de Mortel, Anouk van der Straten, Ysbrand D. van der Werf, Paul M. Thompson, Dan Joseph Stein, Odile A. van den Heuvel, Guido van Wingen (2022). The functional connectome in obsessive-compulsive disorder: resting-state mega-analysis and machine learning classification for the ENIGMA-OCD consortium. , DOI: https://doi.org/10.31234/osf.io/yjxe8.
Datasets shared by verified academics with rich metadata and previews.
Authors choose access levels; downloads are logged for transparency.
Students and faculty get instant access after verification.
Type
Preprint
Year
2022
Authors
87
Datasets
0
Total Files
0
Language
en
DOI
https://doi.org/10.31234/osf.io/yjxe8
Access datasets from 50,000+ researchers worldwide with institutional verification.
Get Free Access
