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

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    Type
    Conference Paper
    Authors
    Samdin, S. Balqis
    Ting, Chee Ming
    Ombao, Hernando cc
    KAUST Department
    Statistics Program, CEMSE, King Abdullah University of Science and Technology, Saudi Arabia
    Statistics Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2019-07-11
    Online Publication Date
    2019-07-11
    Print Publication Date
    2019-04
    Permanent link to this record
    http://hdl.handle.net/10754/660604
    
    Metadata
    Show full item record
    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.
    Citation
    Samdin, 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
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Conference/Event name
    16th IEEE International Symposium on Biomedical Imaging, ISBI 2019
    DOI
    10.1109/ISBI.2019.8759405
    Additional Links
    https://ieeexplore.ieee.org/document/8759405/
    ae974a485f413a2113503eed53cd6c53
    10.1109/ISBI.2019.8759405
    Scopus Count
    Collections
    Conference Papers; Statistics Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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