title: Likelihood-based population independent component analysis.
abstract: 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 functional magnetic resonance imaging (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 2-fold 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 a commonly used group ICA algorithm via simulation studies. Furthermore, the proposed method is applied to a large collection of resting state fMRI datasets. The results show that established brain networks are well recovered by the proposed algorithm.
authors: Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA. aeloyan@jhsph.edu
authors: Ani
authors: A
authors: Eloyan
authors: Ciprian M
authors: CM
authors: Crainiceanu
authors: Brian S
authors: BS
authors: Caffo
mesh_terms: D000328:Adult; D000465:Algorithms; D001289:Attention Deficit Disorder with Hyperactivity / Q000473:pathology / Q000503:physiopathology; D056808:Biostatistics; D001921:Brain / Q000473:pathology / Q000503:physiopathology; D002648:Child; D003198:Computer Simulation; D016208:Databases, Factual / Q000706:statistics & numerical data; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000706:statistics & numerical data; D016013:Likelihood Functions; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data*; D025341:Principal Component Analysis; D018709:Statistics, Nonparametric
keywords: Functional MRI; Signal processing; Source separation