Global organization of structural covariance networks derived from parcellated cortical surface area in atypical populations.

Higher-order features of brain organization are powerful measures for understanding the relationship between brain and experience. In particular, the global arrangement of structural features of the cortex provides insight into neurodevelopmental processes that underlie individual differences in perception and cognition. Structural covariance networks (SCNs), which capture regional coordination of brain morphometry, are an efficient method to derive global cortical properties. However, their interpretation relies on an array of methodological choices that are often inconsistent between studies. Using a hierarchically clustered version of the Human Connectome Project (HCP) atlas, we constructed SCNs of regional cortical surface area for groups with four different conditions-synesthesia, autism, early psychosis, and anxiety or depression-and compared global network properties with those of age- and sex-matched controls. SCNs for synesthesia and autism showed globally stronger connectivity, with specific increases at moderate cortical distances, as well as lower network complexity. Conversely, the SCN for early psychosis showed a globally lower connectivity and a greater complexity, while depression and anxiety showed few differences compared with controls. The results for autism and depression were replicated across two datasets each. These findings support the notion that synesthesia and autism share neurodevelopmental mechanisms, while psychosis may involve a diverging process.