Multple-demand system identification and characterization via sparse representations of fMRI data

Identification of concurrent spatially overlapping functional networks and understanding of their mechanisms of jointly realizing the total brain function have been important yet challenging problems. In this work, we have applied a datadriven sparse representation framework to learn a dictionary consisting of multiple network components and their associated weight coefficients from a given fMRI dataset. Then we analyzed the network component composition at the voxel level by correlating component weights to the characteristics of regions with strong involvements in multiple components, which are defined as functionally highly heterogeneous regions (HHR). Consequently, the spatial overlap of HHRs obtained across multiple tasks of a given subject is defined as the multiple-demand (MD) system. By applying the proposed framework on the recently publicly released Human Connectome Project (HCP) task fMRI dataset, we have obtained reproducible HHR and MD systems that concentrated on the frontal and parietal cortex. Interestingly, the spatial distribution of those MD regions has been found to be highly correlated with the cortical folding and structural connectivities, revealing closely related brain structural and functional architectures.