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UID:6181@i2m.univ-amu.fr
DTSTART;TZID=Europe/Paris:20220124T140000
DTEND;TZID=Europe/Paris:20220124T150000
DTSTAMP:20241120T200929Z
URL:https://www.i2m.univ-amu.fr/evenements/low-rank-and-sparse-decompositi
 on-for-brain-functional-connectivity-in-naturalistic-fmri-data/
SUMMARY:Ting Chee Ming (Monash University Malaysia): Low-rank and sparse de
 composition for brain functional connectivity in naturalistic fMRI data
DESCRIPTION:Ting Chee Ming: Low-rank and sparse decomposition for brain fun
 ctional connectivity in naturalistic fMRI data\n\n&nbsp\;\n\nChee-Ming Tin
 g*\, Monash University Malaysia\, Malaysia\n\nJeremy I. Skipper\, Universi
 ty College London\, UK\n\nFuad Noman\, Monash University Malaysia\, Malays
 ia\n\nSteven L. Small\, University of Texas at Dallas\, USA\n\nHernando Om
 bao\, King Abdullah University of Science and Technology\, Saudi Arabia\n\
 nAbstract\nWe consider the challenges in extracting stimulus-related neura
 l dynamics from other intrinsic processes and noise in naturalistic functi
 onal magnetic resonance imaging (fMRI). Most studies rely on inter-subject
  correlations (ISC) of low-level regional activity and neglect varying res
 ponses in individuals. We propose a novel\, data-driven approach based on 
 low-rank plus sparse (L+S) decomposition to isolate stimulus-driven dynami
 c 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 mod
 eled as a sum of a low-rank component of correlated FC patterns across sub
 jects\, and a sparse component of subject-specific\, idiosyncratic backgro
 und activities. To recover the shared low-rank subspace\, we introduce a f
 used version of principal component pursuit (PCP) by adding a fusion-type 
 penalty on the differences between the rows of the low-rank matrix. The me
 thod improves the detection of stimulus-induced group-level homogeneity in
  the FC profile while capturing inter-subject variability. We develop an e
 fficient algorithm via a linearized alternating direction method of multip
 liers to solve the fused-PCP. Simulations show accurate recovery by the fu
 sed-PCP even when a large fraction of FC edges are severely corrupted. Whe
 n applied to natural fMRI data\, our method reveals FC changes that were t
 ime-locked to auditory processing during movie watching\, with dynamic eng
 agement of sensorimotor systems for speech-in-noise. It also provides a be
 tter mapping to auditory content in the movie than ISC.
CATEGORIES:Séminaire,Statistique
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