1. The functional connectome in obsessive-compulsive disorder: resting-state mega-analysis and machine learning classification for the ENIGMA-OCD consortium.
- Author
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Bruin WB, Abe Y, Alonso P, Anticevic A, Backhausen LL, Balachander S, Bargallo N, Batistuzzo MC, Benedetti F, Bertolin Triquell S, Brem S, Calesella F, Couto B, Denys DAJP, Echevarria MAN, Eng GK, Ferreira S, Feusner JD, Grazioplene RG, Gruner P, Guo JY, Hagen K, Hansen B, Hirano Y, Hoexter MQ, Jahanshad N, Jaspers-Fayer F, Kasprzak S, Kim M, Koch K, Bin Kwak Y, Kwon JS, Lazaro L, Li CR, Lochner C, Marsh R, Martínez-Zalacaín I, Menchon JM, Moreira PS, Morgado P, Nakagawa A, Nakao T, Narayanaswamy JC, Nurmi EL, Zorrilla JCP, Piacentini J, Picó-Pérez M, Piras F, Piras F, Pittenger C, Reddy JYC, Rodriguez-Manrique D, Sakai Y, Shimizu E, Shivakumar V, Simpson BH, Soriano-Mas C, Sousa N, Spalletta G, Stern ER, Evelyn Stewart S, Szeszko PR, Tang J, Thomopoulos SI, Thorsen AL, Yoshida T, Tomiyama H, Vai B, Veer IM, Venkatasubramanian G, Vetter NC, Vriend C, Walitza S, Waller L, Wang Z, Watanabe A, Wolff N, Yun JY, Zhao Q, van Leeuwen WA, van Marle HJF, van de Mortel LA, van der Straten A, van der Werf YD, Thompson PM, Stein DJ, van den Heuvel OA, and van Wingen GA
- Subjects
- Humans, Brain Mapping methods, Magnetic Resonance Imaging methods, Brain, Biomarkers, Neural Pathways, Connectome methods, Obsessive-Compulsive Disorder
- Abstract
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., (© 2023. The Author(s).)
- Published
- 2023
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