Separating stimulus-induced and background components of dynamic functional connectivity in naturalistic fMRI

Chee-Ming Ting, Jeremy I. Skipper, Fuad Noman, Steven L. Small, Hernando Ombao

Research output: Contribution to journalArticleResearchpeer-review

4 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)1431-1442
Number of pages12
JournalIEEE Transactions on Medical Imaging
Volume41
Issue number6
DOIs
Publication statusPublished - 1 Jun 2022

Keywords

  • Correlation
  • dynamic functional connectivity
  • fMRI
  • Functional magnetic resonance imaging
  • inter-subject correlation
  • Low-rank plus sparse decomposition
  • Matrix decomposition
  • Motion pictures
  • Reliability
  • Sparse matrices
  • Task analysis

Cite this