Abstract
Objective Functional interconnections between brain regions define the ‘connectome’ which is of central interest for understanding human brain function, and is increasingly recognized in the pathophysiology of mental disorders. Previous resting-state functional magnetic resonance (rsfMRI) work has revealed changes in static connectivity related to age, sex, cognitive abilities and psychiatric symptoms, yet little is known how these factors may alter the information flow. The commonly used approach infers functional brain connectivity using stationary coefficients yielding static estimates of the undirected connection strength between two brain regions. Dynamic graphical models (DGMs) are a multivariate model with dynamic coefficients reflecting directed temporal associations between network nodes, and can yield novel insight into directed functional brain connectivity. Here, we aimed to validate the DGM method and determine information flow across the brain connectome and its relationship to age, sex, intellectual abilities and mental health.
Methods We applied DGM to investigate patterns of information flow in data from 984 individuals from the Human Connectome Project (HCP) and 10,249 individuals from the UK Biobank.
Results Our analysis replicated previously reported patterns of directed connectivity in independent HCP and UK Biobank data, including that the cerebellum consistently receives information from other networks. We show robust associations between information flow and both age and sex for several connections, with strongest effects of age observed in the sensorimotor network. No significant effects where found for intellectual abilities or mental health.
Discussion Our findings support the use of DGM as a measure of directed connectivity in rsfMRI data and provide new insight into the shaping of the connectome during aging.