Detecting state changes in community structure of functional brain networks using a markov-switching stochastic block model

S. Balqis Samdin, Chee Ming Ting, Hernando Ombao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Functional brain networks exhibit modular community structure with highly inter-connected nodes within a same module, but sparsely connected between different modules. Recent neuroimaging studies also suggest dynamic changes in brain connectivity over time. We propose a dynamic stochastic block model (SBM) to characterize changes in community structure of the brain networks inferred from neuroimaging data. We develop a Markov-switching SBM (MS-SBM) which is a non-stationary extension combining time-varying SBMs with a Markov process to allow for state-driven evolution of the network community structure. The time-varying connectivity parameters within and between communities are estimated from dynamic networks based on sliding-window approach, assuming a constant community membership of nodes recovered by using spectral clustering. We then partition the time-evolving community structure into recurring, piecewise constant regimes or states using a hidden Markov model. Simulation shows that the proposed MS-SBM gives accurate tracking of dynamic community regimes. Application to a task-evoked fMRI data reveals dynamic reconfiguration of the brain network modular structure in language processing between alternating blocks of story and math tasks.
Original languageEnglish (US)
Title of host publication2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)
PublisherIEEE
Pages1483-1487
Number of pages5
ISBN (Print)9781538636411
DOIs
StatePublished - Jul 11 2019

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