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

Higher-order features of brain organisation 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 properties of the cortex. 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 – synaesthesia, autism, early psychosis, and anxiety or depression – and compared global network properties with those of age- and sex-matched controls. SCNs for synaesthesia 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 to controls. The results for autism and depression were replicated across two datasets each. These findings support the notion that synaesthesia and autism share neurodevelopmental mechanisms, while psychosis may involve a diverging process. This study is also an important proof of principle for analysing diverse populations under one methodological framework.