Directed interactivity of large-scale brain networks: Introducing a new method for estimating resting-state effective connectivity MRI

Abstract

Resting-state functional MRI (rs fMRI) is widely used to non-invasively study human brain networks. Network functional connectivity is estimated by calculating the standard correlation between blood-oxygen-level dependent (BOLD) signals in specific regions of interests (ROIs). However, standard correlation fails to characterize the causality and the direction of information flow between regions, which are important factors in characterizing a network. Here, we use causal linear time-invariant models, with the impulse response duration estimated by Information Criteria, to describe the effective connectivity between ROIs. To do so, we replace the standard correlation between BOLD signals with a correlation between a BOLD signal and a prediction via the model of that BOLD signal. Prediction correlation is then used in a network analysis similar to that used with standard correlation. Our results include the causality information, the direction of information flow, and the possibility of delays in information flow. This approach replicates the local and distributed network architecture of the human brain previously observed with standard correlations, as well as providing novel insight into the directed interactivity of regions comprising these networks.