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dc.contributor.authorSamdin, S. Balqis
dc.contributor.authorTing, Chee Ming
dc.contributor.authorOmbao, Hernando
dc.date.accessioned2019-12-16T10:40:34Z
dc.date.available2019-12-16T10:40:34Z
dc.date.issued2019-07-11
dc.identifier.citationSamdin, S. B., Ting, C.-M., & Ombao, H. (2019). Detecting State Changes in Community Structure of Functional Brain Networks Using a Markov-Switching Stochastic Block Model. 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). doi:10.1109/isbi.2019.8759405
dc.identifier.doi10.1109/ISBI.2019.8759405
dc.identifier.urihttp://hdl.handle.net/10754/660604
dc.description.abstractFunctional 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.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttps://ieeexplore.ieee.org/document/8759405/
dc.rightsArchived with thanks to IEEE
dc.titleDetecting state changes in community structure of functional brain networks using a markov-switching stochastic block model
dc.typeConference Paper
dc.contributor.departmentStatistics Program, CEMSE, King Abdullah University of Science and Technology, Saudi Arabia
dc.contributor.departmentStatistics Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.conference.date2019-04-08 to 2019-04-11
dc.conference.name16th IEEE International Symposium on Biomedical Imaging, ISBI 2019
dc.conference.locationVenice, ITA
dc.eprint.versionPost-print
dc.contributor.institutionSchool of Biomedical Engineering and Health Sciences, Universiti Teknologi Malaysia, Malaysia
kaust.personSamdin, S. Balqis
kaust.personTing, Chee Ming
kaust.personOmbao, Hernando
dc.date.published-online2019-07-11
dc.date.published-print2019-04


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