Statistical parametric mapping of fMRI data using sparse dictionary learning

Abstract

Statistical parametric mapping (SPM) of functional magnetic resonance imaging (fMRI) uses a canonical hemodynamic response function (HRF) to construct the design matrix within the general linear model (GLM) framework. Recently, there has been many research on data-driven method on fMRI data, such as the independence component analysis (ICA). The main weakness of ICA for fMRI is its restrictive assumption, especially independence. Furthermore, recent study demonstrated that sparsity is more important than independency in ICA analysis for fMRI. Hence, we propose sparse learning algorithm, such as K-SVD, as an alternative, that decomposes the dictionary-atoms using sparsity rather than independence of the components. For the fMRI finger tapping task data, we employed the K-SVD algorithm to extract the time-course signal atoms of brain activation. The activation maps using trained dictionary as a design matrix showed tightly localized signals in a small set of brain areas.