Fiber connection pattern-guided structured sparse representation of whole-brain fMRI signals for functional network inference

A variety of studies in the brain mapping field have reported that the dictionary learning and sparse representation framework is efficient and effective in reconstructing concurrent functional brain networks based on the functional magnetic resonance imaging (fMRI) signals. However, previous approaches are pure data-driven and do not integrate brain science domain knowledge when reconstructing functional networks. The group-wise correspondence of the reconstructed functional networks across individual subjects is thus not well guaranteed. Moreover, the fiber connection pattern consistency of those functional networks across subjects is largely unknown. To tackle these challenges, in this paper, we propose a novel fiber connection pattern-guided structured sparse representation of whole-brain resting state fMRI (rsfMRI) signals to infer functional networks. In particular, the fiber connection patterns derived from diffusion tensor imaging (DTI) data are adopted as the connectional features to perform consistent cortical parcellation across subjects. Those consistent parcellated regions with similar fiber connection patterns are then employed as the group structured constraint to guide group-wise multi-task sparse representation of whole-brain rsfMRI signals to reconstruct functional networks. Using the recently publicly released high quality Human Connectome Project (HCP) rsfMRI and DTI data, our experimental results demonstrate that the identified functional networks via the proposed approach have both reasonable spatial pattern correspondence and fiber connection pattern consistency across individual subjects.