Improving functional connectivity detection in FMRI by combining sparse dictionary learning and canonical correlation analysis

In this paper a novel framework that combines data-driven methods is proposed for functional connectivity analysis of functional magnetic resonance imaging (fMRI) data. The basic idea is to overcome the shortcomings of compressed sensing based data-driven method by incorporating canonical correlation analysis (CCA) to extract a more meaningful temporal profile that is based solely on underlying brain hemodynamics, which can be further investigated to detect functional connectivity using regression analysis. We apply our method on synthetic and task-related fMRI data to show that the combined framework which better adapts to individual variations of distinct activity patterns in the brain is an effective approach to reveal functionally connected brain regions.