A dynamic Bayesian network analysis of functional connectivity during a language listening comprehension task

Abstract

We aim to characterize functional connectivity during a listening comprehension task in terms of fit to common network topology models. The functional connectivity is expressed as a network structure which is reconstructed from cerebral blood volume measurements. The cerebral blood volume in the frontal lobe is measured using functional near-infrared spectroscopy (NIRS). Based on the reconstructed functional network structure, we discuss whether the functional connectivity has a scale-free or random graph structure. The feasibility of the reconstructed network is evaluated based on the distribution of the number of edges at nodes. In order to validate our proposed model, two language listening comprehension tasks were presented to subjects and the feasibility of the model structure is discussed. The experimental results suggest that the reconstructed functional connectivity network is more likely to be a scale-free network with an “ultra-small” world than a random network.