Decoding obsessive-compulsive disorder: Regional Vulnerability Index and its Association with Clinical Symptoms.

Kathrin Koch, Daniela Rodriguez Manrique, Sandra Gigl, Hanyang Ruan, Deniz A Gürsel, Georgiana Rus-Oswald, Tim Reess, Götz Berberich
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Abstract

Background: Patients with obsessive-compulsive disorder (OCD) exhibit notable alterations in brain structure, which are likely to be of clinical relevance. Recently, in schizophrenia, the regional vulnerability index (RVI) was introduced to translate findings from Enhancing Neuro Imaging Genetics Meta-Analysis (ENIGMA) studies to the individual level. Building on this framework, the present study sought to investigate whether the RVI might also serve as a vulnerability index for OCD.

Methods: To this aim, we asssessed subcortical volume and cortical thickness in a sample of 250 participants (140 patients with OCD, 110 healthy volunteers) and calculated the RVI by leveraging ENIGMA-derived deficits as the "ground truth" for expected regional brain alterations.

Results: Subcortical volume and cortical thickness RVI values were significantly different in patients compared to healthy controls. In addition, RVI values based on subcortical volume were significantly correlated with the severity of clinical symptoms. Moreover, RVI values for both subcortical volume and cortical thickness were significantly different in medicated subgroups while there was no significant difference in unmedicated patients.

Conclusions: The present results suggest that RVI may represent an individual characteristic reflecting the degree of correspondence between individual patterns of structural alterations and disease-characteristic patterns of structural alterations. However, our findings also indicate that relatively large effect sizes in the meta-analytic "ground truth" are a prerequisite for obtaining a meaningful RVI parameter that can also be related to clinical severity. Hence, present findings require further validation through additional research to confirm the RVI's robustness and to determine its predictive value.

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