Journal
IEEE TRANSACTIONS ON MEDICAL IMAGING
Volume 41, Issue 6, Pages 1431-1442Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMI.2021.3139428
Keywords
Low-rank plus sparse decomposition; dynamic functional connectivity; inter-subject correlation; fMRI
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Funding
- Monash University Malaysia [SED-000044]
- King Abdullah University of Science and Technology Research Fund
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We propose a novel method to extract stimulus-related neural dynamics from naturalistic functional magnetic resonance imaging (fMRI) by isolating dynamic changes in brain functional connectivity (FC) using a low-rank plus sparse decomposition approach. Our method improves the detection of stimulus-induced group-level homogeneity in FC and captures inter-subject variability by exploiting shared network structure among subjects receiving the same naturalistic stimuli.
We 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.
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