Using T1‐weighted MRI derived measures of brain morphometry (bilaterally‐averaged values of 33 cortical surface areas, 33 cortical thickness values, and six subcortical volumes) from OCD patients (n = 1,616) and healthy controls (n = 1,463), we calculated intra‐individual brain structural covariance networks, in which edge weights were proportional to the similarity between two brain morphological features in terms of deviation from controls (z‐score transformed; Yun et al., 2020). We focused on measures of network segregation (clustering and modularity), network integration (global efficiency), their balance (small‐worldness), and community membership. We also studied hub profiling of regional brain areas using measures of betweenness, closeness, and eigenvector centrality. Individually calculated network measures were integrated across the 37 ENIGMA‐OCD datasets using a meta‐analytic approach. At the global level, compared to healthy controls, OCD patients showed lower clustering (p < .0001), modularity (p < .0001), small‐worldness (p = .017), and community membership, suggesting lower network segregation. At the regional level, compared to healthy controls, OCD patients showed lower (rank‐transformed) centrality values for caudate and thalamus volume, and surface area of paracentral cortex, suggesting an altered
