
==== Front
medRxiv
MEDRXIV
medRxiv
Cold Spring Harbor Laboratory

10.1101/2024.09.02.24312960
preprint
3
Article
Distinct cognitive and functional connectivity features from healthy cohorts can identify clinical obsessive-compulsive disorder
Hearne Luke J. http://orcid.org/0000-0002-2014-1148

Yeo B.T. Thomas
Webb Lachlan
Zalesky Andrew
Fitzgerald Paul B.
Murphy Oscar W.
Tian Ye
Breakspear Michael
Hall Caitlin V.
Choi Sunah
Kim Minah
Kwon Jun Soo
Cocchi Luca
06 9 2024
2024.09.02.24312960https://creativecommons.org/licenses/by-nc-nd/4.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.
http://medrxiv.org/lookup/doi/10.1101/2024.09.02.24312960
nihpp-2024.09.02.24312960.pdf
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 clinical 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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