Analysis of fMRI data based on sparsity of source components in signal dictionary

Blind Source Separation (BSS) methods, like Independent Component Analysis (ICA), show good performance in the analysis of fMRI data. However, the independence assumption used in ICA, may be violated in practice. Hence, it is important to develop algorithm which can fully exploit the characteristics of fMRI data and use more reliable assumptions. In this paper, we propose an fMRI data analysis method which exploits the sparsity of source components in a signal dictionary. The proposed method, derived as a two-stage method, is established by reformulating the blind separation problem as a sparse approximation problem. First, a priori selection of a particular dictionary, in which the source components are assumed to be sparsely representable. By choosing a particular dictionary (like wavelet dictionary), the source components, which can be well sparsified in the selected dictionary, are estimated more accurately. Second, the source components are extracted by exploiting their sparse representability. The extracted signal components are applied to find consistent task related (CTR) component, activation voxels of CTR, and performance of neural decoding. Numerical results show that compared to ICA based method, the proposed method can extract more useful information from fMRI data, and higher performance on voxel selection and neural decoding can be achieved by using the separated sources. (C) 2015 Elsevier B.V. All rights reserved.