Show simple item record

dc.contributor.authorTing, Chee-Ming
dc.contributor.authorOmbao, Hernando
dc.contributor.authorSamdin, S. Balqis
dc.contributor.authorSalleh, Sh-Hussain
dc.date.accessioned2017-12-21T13:57:04Z
dc.date.available2017-12-21T13:57:04Z
dc.date.issued2017-12-06
dc.identifier.citationTing C-M, Ombao H, Samdin SB, Salleh S-H (2017) Estimating Dynamic Connectivity States in fMRI Using Regime-Switching Factor Models. IEEE Transactions on Medical Imaging: 1–1. Available: http://dx.doi.org/10.1109/TMI.2017.2780185.
dc.identifier.issn0278-0062
dc.identifier.issn1558-254X
dc.identifier.doi10.1109/TMI.2017.2780185
dc.identifier.urihttp://hdl.handle.net/10754/626407
dc.description.abstractWe consider the challenges in estimating state-related changes in brain connectivity networks with a large number of nodes. Existing studies use sliding-window analysis or time-varying coefficient models which are unable to capture both smooth and abrupt changes simultaneously, and rely on ad-hoc approaches to the high-dimensional estimation. To overcome these limitations, we propose a Markov-switching dynamic factor model which allows the dynamic connectivity states in functional magnetic resonance imaging (fMRI) data to be driven by lower-dimensional latent factors. We specify a regime-switching vector autoregressive (SVAR) factor process to quantity the time-varying directed connectivity. The model enables a reliable, data-adaptive estimation of change-points of connectivity regimes and the massive dependencies associated with each regime. We develop a three-step estimation procedure: 1) extracting the factors using principal component analysis, 2) identifying connectivity regimes in a low-dimensional subspace based on the factor-based SVAR model, 3) constructing high-dimensional state connectivity metrics based on the subspace estimates. Simulation results show that our estimator outperforms K-means clustering of time-windowed coefficients, providing more accurate estimate of time-evolving connectivity. It achieves percentage of reduction in mean squared error by 60% when the network dimension is comparable to the sample size. When applied to resting-state fMRI data, our method successfully identifies modular organization in resting-state networks in consistency with other studies. It further reveals changes in brain states with variations across subjects and distinct large-scale directed connectivity patterns across states.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.rights(c) 2017 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.subjectBrain modeling
dc.subjectCovariance matrices
dc.subjectdynamic brain connectivity
dc.subjectEstimation
dc.subjectfactor analysis
dc.subjectfMRI
dc.subjectHidden Markov models
dc.subjectlarge VAR models
dc.subjectLoad modeling
dc.subjectReactive power
dc.subjectRegime-switching models
dc.titleEstimating Dynamic Connectivity States in fMRI Using Regime-Switching Factor Models
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalIEEE Transactions on Medical Imaging
dc.eprint.versionPost-print
dc.contributor.institutionCenter for Biomedical Engineering (CBE), Universiti Teknologi Malaysia (UTM), 81310 Skudai, Johor, Malaysia.
kaust.personTing, Chee-Ming
kaust.personOmbao, Hernando
refterms.dateFOA2018-06-13T19:09:04Z


Files in this item

Thumbnail
Name:
08166781.pdf
Size:
8.146Mb
Format:
PDF
Description:
Accepted Manuscript

This item appears in the following Collection(s)

Show simple item record