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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 twostage 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 useddoi:10.1093/biostatistics/kxs055 pmid:23314416 pmcid:PMC3677736 fatcat:z43xopo6zvhw7lmyq5nply24ea