Detecting state changes in community structure of functional brain networks using a markov-switching stochastic block model
Type
Conference PaperKAUST Department
Statistics Program, CEMSE, King Abdullah University of Science and Technology, Saudi ArabiaStatistics Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Date
2019-07-11Online Publication Date
2019-07-11Print Publication Date
2019-04Permanent link to this record
http://hdl.handle.net/10754/660604
Metadata
Show full item recordAbstract
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.2019.8759405Conference/Event name
16th IEEE International Symposium on Biomedical Imaging, ISBI 2019Additional Links
https://ieeexplore.ieee.org/document/8759405/ae974a485f413a2113503eed53cd6c53
10.1109/ISBI.2019.8759405