Group sparse dictionary learning and inference for resting-state fMRI analysis of Alzheimer's disease

Abstract

A novel group analysis tool for data-driven resting state fMRI analysis using group sparse dictionary learning and mixed model is presented along with the promising indications of Alzheimer's disease progression. Instead of using independency assumption as in popular ICA approaches, the proposed approach is based on the sparse graph assumption such that a temporal dynamics at each voxel position is a sparse combination of global brain dynamics. In estimating the unknown global dynamics and local network structures, we perform sparse dictionary learning for the concatenated temporal data across the subjects by constraining that the network structures within a group are similar. Under the homoscedasticity variance assumption across subjects and groups, we show that the mixed model group inference can be easily performed using second level GLM with summary statistics. Using extensive resting fMRI data set obtained from normal, Mild Cognitive Impairment (MCI), Clinical Dementia Rating scale (CDR) 0.5, CDR 1.0, and CDR 2.0 of Alzheimer's disease patients groups, we demonstrated that the changes of default mode network extracted by the proposed method is more closely correlated with the progression of Alzheimer's disease.