Individual Variation in Brain Network Topology and Social Cognitive Ability

Background: Social cognitive ability is a significant determinant of functional outcome and deficits in social cognition are a disabling symptom of psychotic disorders. The neurobiological underpinnings of social cognition are not well understood, hampering our ability to ameliorate these deficits. Objective: Using a data-driven approach, we sought to identify the brain network basis of social cognition using a combination of fMRI (functional magnetic resonance imaging) and a validated measure of social cognition. Methods: Study participants included 60 participants with a diagnosis of schizophrenia or schizoaffective disorder and 46 healthy comparison participants. All participants underwent a resting-state fMRI scan. Social cognition was measured using the MATRICS battery of cognitive tests. A data-driven, connectome-wide analysis of brain connectivity examined how each individual brain voxel’s connectivity correlated with social cognitive performance using multivariate distance matrix regression (MDMR). Results: We identified a region in the left superior parietal lobule (SPL) where individual network topology predicted social cognitive ability. Specifically, the association of this region with the Default Mode Network predicted higher social cognitive performance and association with the Dorsal Attention Network predicted poorer performance. This correlation was observed in both schizophrenia and healthy comparison participants. Conclusion: Previous studies have demonstrated individual variance in brain network topology but the cognitive or behavioral relevance of these differences was undetermined. We observe that the left SPL, a region of high individual variance at the cytoarchitectonic level, also demonstrates individual variance in its association with large scale brain networks and that network topology predicts complex cognitive ability.