Separating Stimulus-Induced and Background Components of Dynamic Functional Connectivity in Naturalistic fMRI
This work addresses the challenge of extracting meaningful neural signals from noisy fMRI data for researchers in neuroscience and brain imaging, though it is incremental as it builds on existing decomposition techniques.
The authors tackled the problem of isolating stimulus-driven neural dynamics from background noise in naturalistic fMRI by proposing a low-rank plus sparse decomposition method, which improved detection of group-level homogeneity and captured inter-subject variability, revealing FC changes time-locked to auditory processing during movie watching and providing better mapping to auditory content than inter-subject correlations.
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 rows 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.