A variational Bayesian approach to identifying whole-brain directed networks with fMRI data

Abstract

The brain is a high-dimensional directed network system, as it consists of many regions as network nodes that exert influence on each other. The directed influence exerted by one region on another is referred to as directed connectivity. We aim to reveal whole-brain directed networks based on resting-state functional magnetic resonance imaging (fMRI) data of many subjects. However, it is both statistically and computationally challenging to produce scientifically meaningful estimates of whole-brain directed networks. To address the statistical modeling challenge, we assume modular brain networks which reflect functional specialization and functional integration of the brain. We address the computational challenge by developing a variational Bayesian method to estimate the new model. We apply our method to resting-state fMRI data of many subjects and identify modules and directed connections in whole-brain directed networks. The identified modules are accordant with functional brain systems specialized for different functions. We also detect directed connections between functionally specialized modules, which is not attainable by existing network methods, based on functional connectivity. In summary, this paper presents a new computationally efficient and flexible method for directed network studies of the brain as well as new scientific findings regarding the functional organization of the human brain.