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dc.contributor.authorTing, Chee-Ming
dc.contributor.authorSkipper, Jeremy I.
dc.contributor.authorNoman, Fuad
dc.contributor.authorSmall, Steven L.
dc.contributor.authorOmbao, Hernando
dc.identifier.citationTing, C.-M., Skipper, J. I., Noman, F., Small, S. L., & Ombao, H. (2021). Separating Stimulus-Induced and Background Components of Dynamic Functional Connectivity in Naturalistic fMRI. IEEE Transactions on Medical Imaging, 1–1. doi:10.1109/tmi.2021.3139428
dc.description.abstractWe consider the challenges in extracting stimulus-related neural dynamics from other intrinsic processes and noise in naturalistic functional magnetic resonance imaging (fMRI). Most studies rely on inter-subject correlations (ISC) of low-level regional activity and neglect varying responses in individuals. We propose a novel, data-driven approach based on low-rank plus sparse (L+S) decomposition to isolate stimulus-driven dynamic changes in brain functional connectivity (FC) from the background noise, by exploiting shared network structure among subjects receiving the same naturalistic stimuli. The time-resolved multi-subject FC matrices are modeled as a sum of a low-rank component of correlated FC patterns across subjects, and a sparse component of subject-specific, idiosyncratic background activities. To recover the shared low-rank subspace, we introduce a fused version of principal component pursuit (PCP) by adding a fusion-type penalty on the differences between the columns of the low-rank matrix. The method improves the detection of stimulus-induced group-level homogeneity in the FC profile while capturing inter-subject variability. We develop an efficient algorithm via a linearized alternating direction method of multipliers to solve the fused-PCP. Simulations show accurate recovery by the fused-PCP even when a large fraction of FC edges are severely corrupted. When applied to natural fMRI data, our method reveals FC changes that were time-locked to auditory processing during movie watching, with dynamic engagement of sensorimotor systems for speech-in-noise. It also provides a better mapping to auditory content in the movie than ISC.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.rights(c) 2021 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.subjectLow-rank plus sparse decomposition
dc.subjectdynamic functional connectivity
dc.subjectinter-subject correlation
dc.titleSeparating Stimulus-Induced and Background Components of Dynamic Functional Connectivity in Naturalistic fMRI
dc.contributor.departmentBiostatistics Group, King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia
dc.contributor.departmentStatistics Program
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
dc.identifier.journalIEEE Transactions on Medical Imaging
dc.contributor.institutionInformation Technology, Monash University Malaysia, Subang Jaya 47500, Malaysia
dc.contributor.institutionDivision of Psychology and Language Sciences, University College London, UK
dc.contributor.institutionDepartment of Experimental Psychology, University College London, London WC1H, UK
dc.contributor.institutionSchool of Behavioral and Brain Sciences, University of Texas at Dallas, TX 75080, USA
kaust.personTing, Chee-Ming
kaust.personOmbao, Hernando

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