SACICA: a sparse approximation coefficient-based ICA model for functional magnetic resonance imaging data analysis.

Independent component analysis (ICA) has been widely used in functional magnetic resonance imaging (fMRI) data to evaluate the functional connectivity, which assumes that the sources of functional networks are statistically independent. Recently, many researchers have demonstrated that sparsity is an effective assumption for fMRI signal separation. In this research, we present a sparse approximation coefficient-based ICA (SACICA) model to analyse fMRI data, which is a promising combination model of sparse features and an ICA technique. The SACICA method consists of three procedures. The wavelet packet decomposition procedure, which decomposes the fMRI data into wavelet tree nodes with different degrees of sparsity, is first. Then, the sparse approximation coefficients set formation procedure, in which an effective Lp norm is proposed to measure the sparse degree of the distinct wavelet tree nodes, is second. The ICA decomposition and reconstruction procedure, which utilises the sparse approximation coefficients set of the fMRI data, is last. The hybrid data experimental results demonstrated that the SACICA method exhibited the stronger spatial source reconstruction ability with respect to the unsmoothed fMRI data and better detection sensitivity of the functional signal on the smoothed fMRI data than the FastICA method. Furthermore, task-related experiments also revealed that SACICA was not only effective in discovering the functional networks but also exhibited a better detection sensitivity of the visual-related functional signal. In addition, the SACICA combined with Fast-FENICA proposed by Wang et al. (2012) was demonstrated to conduct the group analysis effectively on the resting-state data set.