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dc.contributor.authorTing, Chee-Ming
dc.contributor.authorSamdin, S. Balqis
dc.contributor.authorTang, Meini
dc.contributor.authorOmbao, Hernando
dc.identifier.citationTing, C.-M., Samdin, S. B., Tang, M., & Ombao, H. (2020). Detecting Dynamic Community Structure in Functional Brain Networks Across Individuals: A Multilayer Approach. IEEE Transactions on Medical Imaging, 1–1. doi:10.1109/tmi.2020.3030047
dc.description.abstractObjective: We present a unified statistical framework for characterizing community structure of brain functional networks that captures variation across individuals and evolution over time. Existing methods for community detection focus only on single-subject analysis of dynamic networks; while recent extensions to multiple-subjects analysis are limited to static networks. Method: To overcome these limitations, we propose a multi-subject, Markov-switching stochastic block model (MSS-SBM) to identify state-related changes in brain community organization over a group of individuals. We first formulate a multilayer extension of SBM to describe the time-dependent, multi-subject brain networks. We develop a novel procedure for fitting the multilayer SBM that builds on multislice modularity maximization which can uncover a common community partition of all layers (subjects) simultaneously. By augmenting with a dynamic Markov switching process, our proposed method is able to capture a set of distinct, recurring temporal states with respect to inter-community interactions over subjects and the change points between them. Results: Simulation shows accurate community recovery and tracking of dynamic community regimes over multilayer networks by the MSS-SBM. Application to task fMRI reveals meaningful non-assortative brain community motifs, e.g., core-periphery structure at the group level, that are associated with language comprehension and motor functions suggesting their putative role in complex information integration. Our approach detected dynamic reconfiguration of modular connectivity elicited by varying task demands and identified unique profiles of intra and inter-community connectivity across different task conditions. Conclusion: The proposed multilayer network representation provides a principled way of detecting synchronous, dynamic modularity in brain networks across subjects.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.rights(c) 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
dc.subjectDynamic functional connectivity
dc.subjectcommunity detection
dc.subjectstochastic blockmodel
dc.subjectMarkov-switching model
dc.titleDetecting Dynamic Community Structure in Functional Brain Networks Across Individuals: A Multilayer Approach
dc.contributor.departmentBiostatistics Group
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics Program
dc.identifier.journalIEEE Transactions on Medical Imaging
dc.contributor.institutionSchool of Information Technology, Monash University Malaysia, 47500 Subang Jaya, Malaysia,
dc.contributor.institutionSchool of Electrical and Computer Engineering, Xiamen University Malaysia, 43900 Sepang, Malaysia
kaust.personTing, Chee-Ming
kaust.personSamdin, S. Balqis
kaust.personTang, Meini
kaust.personOmbao, Hernando

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