Dataset Information

The functional connectome in obsessive-compulsive disorder: resting-state mega-analysis and machine learning classification for the ENIGMA-OCD consortium.

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.

SUBMITTER: Bruin WB

PROVIDER: S-EPMC10827654 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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The functional connectome in obsessive-compulsive disorder: resting-state mega-analysis and machine learning classification for the ENIGMA-OCD consortium.

Bruin Willem B WB Abe Yoshinari Y Alonso Pino P Anticevic Alan A Backhausen Lea L LL Balachander Srinivas S Bargallo Nuria N Batistuzzo Marcelo C MC Benedetti Francesco F Bertolin Triquell Sara S Brem Silvia S Calesella Federico F Couto Beatriz B Denys Damiaan A J P DAJP Echevarria Marco A N MAN Eng Goi Khia GK Ferreira Sónia S Feusner Jamie D JD Grazioplene Rachael G RG Gruner Patricia P Guo Joyce Y JY Hagen Kristen K Hansen Bjarne B Hirano Yoshiyuki Y Hoexter Marcelo Q MQ Jahanshad Neda N Jaspers-Fayer Fern F Kasprzak Selina S Kim Minah M Koch Kathrin K Bin Kwak Yoo Y Kwon Jun Soo JS Lazaro Luisa L Li Chiang-Shan R CR Lochner Christine C Marsh Rachel R Martínez-Zalacaín Ignacio I Menchon Jose M JM Moreira Pedro S PS Morgado Pedro P Nakagawa Akiko A Nakao Tomohiro T Narayanaswamy Janardhanan C JC Nurmi Erika L EL Zorrilla Jose C Pariente JCP Piacentini John J Picó-Pérez Maria M Piras Fabrizio F Piras Federica F Pittenger Christopher C Reddy Janardhan Y C JYC Rodriguez-Manrique Daniela D Sakai Yuki Y Shimizu Eiji E Shivakumar Venkataram V Simpson Blair H BH Soriano-Mas Carles C Sousa Nuno N Spalletta Gianfranco G Stern Emily R ER Evelyn Stewart S S Szeszko Philip R PR Tang Jinsong J Thomopoulos Sophia I SI Thorsen Anders L AL Yoshida Tokiko T Tomiyama Hirofumi H Vai Benedetta B Veer Ilya M IM Venkatasubramanian Ganesan G Vetter Nora C NC Vriend Chris C Walitza Susanne S Waller Lea L Wang Zhen Z Watanabe Anri A Wolff Nicole N Yun Je-Yeon JY Zhao Qing Q van Leeuwen Wieke A WA van Marle Hein J F HJF van de Mortel Laurens A LA van der Straten Anouk A van der Werf Ysbrand D YD Thompson Paul M PM Stein Dan J DJ van den Heuvel Odile A OA van Wingen Guido A GA

Molecular psychiatry 20230502 10

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 fr ...[more]

PMID: 37131072

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Project description:Brain structural covariance networks reflect covariation in morphology of different brain areas and are thought to reflect common trajectories in brain development and maturation. Large-scale investigation of structural covariance networks in obsessive-compulsive disorder (OCD) may provide clues to the pathophysiology of this neurodevelopmental disorder. Using T1-weighted MRI scans acquired from 1616 individuals with OCD and 1463 healthy controls across 37 datasets participating in the ENIGMA-OCD Working Group, we calculated intra-individual brain structural covariance networks (using the bilaterally-averaged values of 33 cortical surface areas, 33 cortical thickness values, and six subcortical volumes), in which edge weights were proportional to the similarity between two brain morphological features in terms of deviation from healthy controls (i.e. z-score transformed). Global networks were characterized using measures of network segregation (clustering and modularity), network integration (global efficiency), and their balance (small-worldness), and their community membership was assessed. Hub profiling of regional networks was undertaken using measures of betweenness, closeness, and eigenvector centrality. Individually calculated network measures were integrated across the 37 datasets using a meta-analytical approach. These network measures were summated across the network density range of K = 0.10-0.25 per participant, and were integrated across the 37 datasets using a meta-analytical approach. Compared with healthy controls, at a global level, the structural covariance networks of OCD showed lower clustering (P < 0.0001), lower modularity (P < 0.0001), and lower small-worldness (P = 0.017). Detection of community membership emphasized lower network segregation in OCD compared to healthy controls. At the regional level, there were lower (rank-transformed) centrality values in OCD for volume of caudate nucleus and thalamus, and surface area of paracentral cortex, indicative of altered distribution of brain hubs. Centrality of cingulate and orbito-frontal as well as other brain areas was associated with OCD illness duration, suggesting greater involvement of these brain areas with illness chronicity. In summary, the findings of this study, the largest brain structural covariance study of OCD to date, point to a less segregated organization of structural covariance networks in OCD, and reorganization of brain hubs. The segregation findings suggest a possible signature of altered brain morphometry in OCD, while the hub findings point to OCD-related alterations in trajectories of brain development and maturation, particularly in cingulate and orbitofrontal regions.

Dataset Information

The functional connectome in obsessive-compulsive disorder: resting-state mega-analysis and machine learning classification for the ENIGMA-OCD consortium.

Publications

The functional connectome in obsessive-compulsive disorder: resting-state mega-analysis and machine learning classification for the ENIGMA-OCD consortium.

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