Resting-brain functional connectivity predicted by analytic measures of network communication

TLDR

Patterns of distributed brain activity are thought to underlie virtually all aspects of cognition and behavior. The study investigates whether functional connectivity can be predicted from the network of anatomical connections linking brain regions. Using three neuroimaging datasets of anatomical and functional connections, the authors applied analytically derived network communication measures to model functional connectivity. The network measures predict functional connectivity better than other models and highlight the role of anatomical networks in shaping functional activity.

Abstract

Significance Patterns of distributed brain activity are thought to underlie virtually all aspects of cognition and behavior. In this paper, we explore the degree to which it is possible to predict such functional patterns from the network of anatomical connections that link brain regions. To this end, we use three separately acquired neuroimaging datasets recording anatomical and functional connections in the human brain. We apply several measures of network communication that are derived analytically from the brain’s anatomical network. Our principal finding is that such network measures can predict empirically measured functional connectivity at levels that exceed other modeling approaches. Our study sheds light on the important role of anatomical networks and communication processes in shaping the brain’s functional activity.