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Distinct cognitive and functional connectivity features from healthy cohorts can identify clinical obsessive-compulsive disorder

Molecular Psychiatry (2025)Cite this article

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Abstract

Improving diagnostic accuracy of obsessive-compulsive disorder (OCD) using models of brain imaging data is a key goal of the field, but this objective is challenging due to the limited size and phenotypic depth of clinical datasets. Leveraging the phenotypic diversity in large non-clinical datasets such as the UK Biobank (UKBB), offers a potential solution to this problem. Nevertheless, it remains unclear whether classification models trained on non-clinical populations will generalise to individuals with clinical OCD. This question is also relevant for the conceptualisation of OCD; specifically, whether the symptomology of OCD exists on a continuum from normal to pathological. Here, we examined a recently published “meta-matching” model trained on functional connectivity data from five large normative datasets (N = 45,507) to predict cognitive, health and demographic variables. Specifically, we tested whether this model could classify OCD status in three independent datasets (N = 345). We found that the model could identify out-of-sample OCD individuals. Notably, the most predictive functional connectivity features mapped onto known cortico-striatal abnormalities in OCD and correlated with genetic brain expression maps previously implicated in the disorder. Further, the meta-matching model relied upon estimates of cognitive functions, such as cognitive flexibility and inhibition, to successfully predict OCD. These findings suggest that variability in non-clinical brain and behavioural features can discriminate clinical OCD status. These results support a dimensional and transdiagnostic conceptualisation of the brain and behavioural basis of OCD, with implications for research approaches and treatment targets.

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Fig. 1: Meta-matching model, prediction performance and brain features.
Fig. 2: Predictive weights within cortico-striatal circuits.
Fig. 3: Regional gene expression associated with meta-matching predictive feature weights.

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Data availability

Code used to generate the results are available on GitHub (https://github.com/ljhearne/CBN_MetaMatch_public). The version of the meta-matching model used in the current work is available online (V2.0; https://github.com/ThomasYeoLab/Meta_matching_models). De-identified participant data for research purposes are available on request for data collected at the Brisbane [18] and Melbourne sites. De-identified participant data for research purposes are available on request for data collected at the Seoul site from the original authors [19].

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Acknowledgements

This work was supported by the Australian NHMRC (GN2001283 and GNT2027597, L.C). A.Z. and L.J.H were supported by research fellowships from the NHMRC (APP1118153, APP1194070, respectively). PBF is supported by a National Health and Medical Research Council of Australia Investigator grant (1193596). BTTY is supported by the NUS Yong Loo Lin School of Medicine (NUHSRO/2020/124/TMR/LOA), the Singapore National Medical Research Council (NMRC) LCG (OFLCG19May-0035), NMRC CTG-IIT (CTGIIT23jan-0001), NMRC OF-IRG (OFIRG24jan-0006; OFIRG24jul-0049), NMRC STaR (STaR20nov-0003), Singapore Ministry of Health (MOH) Centre Grant (CG21APR1009), the United States National Institutes of Health (R01MH133334 & 2R01MH120080) and the Singapore National Research Foundation (NRF) Investigatorship (NRFI10-2024-0014).

Author information

Authors and Affiliations

  1. QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia

    Luke J. Hearne, Lachlan Webb, Caitlin V. Hall & Luca Cocchi

  2. Faculty of Medicine, School of Biomedical Sciences, University of Queensland, Brisbane, QLD, Australia

    Luke J. Hearne & Luca Cocchi

  3. Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, Singapore, National University of Singapore, Singapore, Singapore

    B. T. Thomas Yeo

  4. Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore

    B. T. Thomas Yeo

  5. N.1 Institute for Health, National University of Singapore, Singapore, Singapore

    B. T. Thomas Yeo

  6. Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore

    B. T. Thomas Yeo

  7. Department of Medicine, Human Potential Translational Research Programme & Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

    B. T. Thomas Yeo

  8. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, USA

    B. T. Thomas Yeo

  9. Department of Psychiatry, The University of Melbourne, Melbourne, VIC, Australia

    Andrew Zalesky & Ye Ella Tian

  10. School of Medicine and Psychology, Australian National University, Canberra, Australia

    Paul B. Fitzgerald

  11. Bionics Institute, East Melbourne, VIC, Australia

    Oscar W. Murphy

  12. School of Psychological Sciences, College of Engineering Science and Environment, University of Newcastle, Callaghan, NSW, Australia

    Michael Breakspear

  13. School of Medicine and Public Health, College of Health and Medicine, University of Newcastle, Callaghan, NSW, Australia

    Michael Breakspear

  14. Program of Neuromodulation, Hunter Medical Research Institute, New Lambton, NSW, Australia

    Michael Breakspear

  15. Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea

    Sunah Choi

  16. Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea

    Minah Kim

  17. Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea

    Minah Kim

  18. Institute of Human Behavioral Medicine, SNU-MRC, Seoul, Republic of Korea

    Minah Kim & Jun Soo Kwon

  19. Department of Psychiatry, Hanyang University Hospital, Seoul, South Korea

    Jun Soo Kwon

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Contributions

LJH and LC conceptualised the research. LW, PBF, OWM, YET, MB, CVH, MK, JSK, & LC contributed to data collection and/or data curation. BTTY developed methodology used in the analysis. LJH performed the analysis and wrote the first draft. All authors reviewed and edited the manuscript.

Corresponding author

Correspondence to Luke J. Hearne.

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Competing interests

L.C., L.J.H, and A.Z. are involved in a clinical neuromodulation centre (Queensland Neurostimulation Centre [QNC] as trading for Australian Brain Foundation) that offers neuroimaging-guided neurotherapeutics. LJH, AZ, and LC are not paid by QNC. This centre had no role in this study. L.C. serves as a co-inventor on a patent application by the National University of Singapore that covers neuroimaging-based personalised TMS. LC, LJH, and AZ are also involved in the development of imaging-based personalized TMS for depression with ANT Neuro. The provisional patent and ANT Neuro products are not directly related to this work. In the last 3 years PBF has received equipment for research from Neurosoft and Nexstim. He has served on a scientific advisory board for Magstim and received speaker fees from Otsuka. He has also acted as a founder and board member for TMS Clinics Australia and Resonance Therapeutics. BTTY holds shares and is a co-founder of B1neuro. The content in this manuscript is unrelated to the activities of B1neuro.

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Hearne, L.J., Yeo, B.T.T., Webb, L. et al. Distinct cognitive and functional connectivity features from healthy cohorts can identify clinical obsessive-compulsive disorder. Mol Psychiatry (2025). https://doi.org/10.1038/s41380-025-03416-z

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