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Activity Number:
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576
- Brain Connectivity Studies
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Type:
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Contributed
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Date/Time:
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Wednesday, July 31, 2019 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistics in Imaging
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Abstract #305171
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Title:
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A Dynamic Stochastic Block Model for Change Detection in Community Structure of Brain Networks
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Author(s):
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Chee-Ming Ting* and Siti Balqis Samdin and Hernando Ombao
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Companies:
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KAUST and King Abdullah University of Science and Technology and King Abdullah University of Science and Technology (KAUST)
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Keywords:
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Stochastic block model;
fMRI;
imaging
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Abstract:
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Large-scale functional brain networks exhibit ongoing dynamic changes in spatiotemporal organization in response to tasks and during rest. We present a dynamic stochastic block model (SBM) to detect changes in modular community structure of the brain networks inferred from functional magnetic resonance imaging (fMRI) data. We propose a Markov- switching SBM (MS-SBM) that combines a time-varying SBM with a Markov process to capture temporal evolution in the community membership of nodes and the network connectivity. The time-varying node membership and the connectivity parameters within and between communities are estimated from temporal networks based on sliding-window approach. A hidden Markov model is then used to quantify switching in the community structure between a set of recurring, piece-wise stable regimes or states. Simulation shows accurate tracking of dynamic community regimes by the MS-SBM. Application to a task- evoked fMRI data reveals dynamic reconfiguration of the brain network modularity in language comprehension between alternating blocks of story and math tasks.
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Authors who are presenting talks have a * after their name.
