title: A descriptive model of resting-state networks using Markov chains.
abstract: Resting-state functional connectivity (RSFC) studies considering pairwise linear correlations have attracted great interests while the underlying functional network structure still remains poorly understood. To further our understanding of RSFC, this paper presents an analysis of the resting-state networks (RSNs) based on the steady-state distributions and provides a novel angle to investigate the RSFC of multiple functional nodes. This paper evaluates the consistency of two networks based on the Hellinger distance between the steady-state distributions of the inferred Markov chain models. The results show that generated steady-state distributions of default mode network have higher consistency across subjects than random nodes from various RSNs.
authors.forename: H
authors.initials: H
authors.lastname: Xie
authors.forename: R
authors.initials: R
authors.lastname: Pal
authors.forename: S
authors.initials: S
authors.lastname: Mitra
mesh_terms: D000293:Adolescent; D000328:Adult; D001931:Brain Mapping / Q000379:methods; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008390:Markov Chains; D008959:Models, Neurological; D011897:Random Allocation; D015203:Reproducibility of Results; D012146:Rest / Q000502:physiology