Resting-state fMRI Analysis of Alzheimer's Disease Progress Using Sparse Dictionary Learning

Abstract

A novel data-driven resting state fMRI analysis based on sparse dictionary learning is presented. Although ICA has been a popular data-driven method for resting state fMRI data, the assumption that sources are independent often leads to a paradox in analyzing closely interconnected brain networks. Rather than using independency, the proposed approach starts from an assumption that a temporal dynamics at each voxel position is a sparse combination of global brain dynamics and then proposes a novel sparse dictionary learning method for analyzing the resting state fMRI analysis. Moreover, using a mixed model, we provide a statistically rigorous group analysis. Using extensive data set obtained from normal, Mild Cognitive Impairment (MCI), Clinical Dementia Rating scale (CDR) 0.5, CDR 1.0, and CDR 2.0 patients groups, we demonstrated that the changes of default mode network extracted by the proposed method is more closely correlated with the progress of Alzheimer disease.