IVA-Based Spatio-Temporal Dynamic Connectivity Analysis in Large-Scale FMRI Data

Abstract

Recently, much attention has been devoted to examining time-varying changes in functional connectivity to understand the network structure in the human brain. Most studies, however, analyze the time-varying functional connectivity but ignore the time-varying spatial information. In this paper, we propose a method based on independent vector analysis (IVA) to study dynamic functional network connectivity (dFNC) as well as dynamic spatial functional network connectivity (dsFNC) in fMRI data. Though IVA allows one to effectively capture both, its performance degrades with the increase in the number of datasets. Hence, we propose an effective scheme to bypass this limitation followed by graph theoretical analysis to study both inter-network dynamics and intra-network stationarity. We observe higher dFNC fluctuations for patients with schizophrenia in the default-mode (DM)-salience network and cerebellum with associated connections. dsFNC analysis indicates higher inter-network fluctuation in patients while DM, anterior DM and frontal networks demonstrate significant intra-network fluctuation in controls.