Graph analysis of nonlinear fMRI connectivity dynamics reveals distinct brain network configurations for integrative and segregated information processing

The human brain is organized into functional networks, whose spatial layout can be described with functional magnetic resonance imaging (fMRI). Interactions among these networks are highly dynamic and nonlinear, and evidence suggests that distinct functional network configurations interact on different levels of complexity. To gain new insights into topological properties of constellations interacting on different levels of complexity, we analyze a resting state fMRI dataset from the human connectome project. We first measure the complexity of correlational time series among resting state networks, obtained from sliding window analysis, by calculating their sample entropy. We then use graph analysis to create two functional representations of the network: A 'high complexity network' (HCN), whose inter-node interactions display irregular fast changes, and a 'low complexity network' (LCN), whose interactions are more self-similar and change more slowly in time. Graph analysis shows that the HCNs structure is significantly more globally efficient, compared to the LCNs, indicative of an architecture that allows for more integrative information processing. The LCNs layout displays significantly higher modularity than the HCNs, indicative of an architecture lending itself to segregated information processing. In the HCN, subcortical thalamic and basal ganglia networks display global hub properties, whereas cortical networks act as connector hubs in the LCN. These results can be replicated in a split sample dataset. Our findings show that investigating nonlinear properties of resting state dynamics offers new insights regarding the relative importance of specific brain regions to the two fundamental requirements for healthy brain functioning, that is, integration and segregation.