Signal sampling for efficient sparse representation of resting state FMRI data

As brain imaging data such as fMRI is growing explosively, how to reduce its size but not to lose much information becomes a pressing problem. To address this problem, this work aims to represent resting state fMRI (rs-fMRI) signals of a whole brain via a statistical sampling based sparse representation. Specifically, we improve the online dictionary learning and sparse coding algorithm by adding a sampling step before the whole-brain sparse representation. Our comparison experiments demonstrated that this sampling-enabled sparse representation method can speedup by ten times without losing much information. In particular, our results showed that anatomical landmark-guided sampling is substantially better than statistical random sampling in reconstructing concurrent functional brain networks from the Human Connectome Project (HCP) rs-fMRI data.