Abstract
BACKGROUND: Cognitive abilities including neurocognition and social cognition are tightly related to an efficient network organization of the human connectome. Deficits are important factors in poor functional outcome in schizophrenia spectrum disorders (SSDs) which have long been considered disorders of dysconnectivity in the human brain. Here we test how aberrant individual network architecture contributes to cognitive deficits in the general and the social domain. METHODS: We used cortical network mapping based on the similarity of inter-regional morphometric parameters measured by multimodal MRI (diffusion weighted imaging and structural MRI) to compute similarity networks in each of 180 patients with schizophrenia spectrum disorder and 122 healthy controls. Using graph theory, we derived metrics describing network organization and efficiency as well as variation in network nodes and entered them in a partial least squares regression to predict variation in general and social cognition. RESULTS: In a preliminary analysis we found that deficits in social cognition were predicted by increased network segregation. In addition, the relationship between nodal degree (the number of edges connecting a node to the rest of the network) and verbal intelligence was significantly weaker in SSDs compared to healthy controls. Finally, patients with SSDs showed increased clustering and decreased network integration compared to controls. CONCLUSIONS: Individual modeling of brain network characteristics provides the means to elucidate the association between cognition and brain organization across diagnostic groups. Specifically, these results suggest that distinct features of aberrant cortical network architecture contribute to deficits in social as well as general cognition across the schizophrenia spectrum.