A causal modelling of the brain as a network of networks: a comparison between autism and controls

Motivation: While functional-connectivity-based approaches to finding differences between autism spectrum disorder (ASD) and controls are common, approaches based on causality add directionality aiding interpretation. Goal(s): To obtain causal modellings of the brain as a network of networks and to test if these modellings show a difference between ASD and controls. Approach: Resting-state networks are identified with group independent component analysis. Effective connections between these networks are obtained using dynamic causal modelling. Group comparisons are performed to find group differences using the identified effective connections. Results: Group effective connections indicate typical effective architectures. ASD shows two deviations from these typical architectures, but with limited generalization. Impact: The causal modelling of resting-state networks gives insight into the brain's effective architecture as a network of networks. Where autists deviate from this typical architecture can relate to autism's pathophysiology and motivates research, e.g., into diagnostic or prognostic relevance.