Independent component analysis (ICA) is a widely used technique for blind source separation, used heavily in several scientific research areas including acoustics, electrophysiology and functional neuroimaging. We propose a scalable two-stage iterative true group ICA methodology for analyzing population level fMRI data where the number of subjects is very large. The method is based on likelihood estimators of the underlying source densities and the mixing matrix. As opposed to many commonly used group ICA algorithms the proposed method does not require significant data reduction by a twofold singular value decomposition. In addition, the method can be applied to a large group of subjects since the memory requirements are not restrictive. The performance of our approach is compared with commonly used group ICA algorithms is shown by using simulation studies. Furthermore, the proposed method is applied to a large collection of resting state fMRI datasets. The results show that the postulated brain networks are recovered by the proposed algorithm.
Eloyan, Ani; Crainiceanu, Ciprian M.; and Caffo, Brian S., "LIKELIHOOD BASED POPULATION INDEPENDENT COMPONENT ANALYSIS" (November 2011). Johns Hopkins University, Dept. of Biostatistics Working Papers. Working Paper 232.