@article {822,
	title = {An evaluation of independent component analyses with an application to resting-state fMRI.},
	journal = {Biometrics},
	volume = {70},
	year = {2014},
	month = {2014 Mar},
	pages = {224-36},
	abstract = {<p>We examine differences between independent component analyses (ICAs) arising from different assumptions, measures of dependence, and starting points of the algorithms. ICA is a popular method with diverse applications including artifact removal in electrophysiology data, feature extraction in microarray data, and identifying brain networks in functional magnetic resonance imaging (fMRI). ICA can be viewed as a generalization of principal component analysis (PCA) that takes into account higher-order cross-correlations. Whereas the PCA solution is unique, there are many ICA methods-whose solutions may differ. Infomax, FastICA, and JADE are commonly applied to fMRI studies, with FastICA being arguably the most popular. Hastie and Tibshirani (2003) demonstrated that ProDenICA outperformed FastICA in simulations with two components. We introduce the application of ProDenICA to simulations with more components and to fMRI data. ProDenICA was more accurate in simulations, and we identified differences between biologically meaningful ICs from ProDenICA versus other methods in the fMRI analysis. ICA methods require nonconvex optimization, yet current practices do not recognize the importance of, nor adequately address sensitivity to, initial values. We found that local optima led to dramatically different estimates in both simulations and group ICA of fMRI, and we provide evidence that the global optimum from ProDenICA is the best estimate. We applied a modification of the Hungarian (Kuhn-Munkres) algorithm to match ICs from multiple estimates, thereby gaining novel insights into how brain networks vary in their sensitivity to initial values and ICA method.</p>},
	keywords = {Adolescent, Algorithms, Attention Deficit Disorder with Hyperactivity, brain mapping, Child, Child, Preschool, Computer Simulation, Humans, Magnetic Resonance Imaging, Models, Statistical, Principal Component Analysis},
	issn = {1541-0420},
	doi = {10.1111/biom.12111},
	author = {Risk, Benjamin B and Matteson, David S and Ruppert, David and Eloyan, Ani and Caffo, Brian S}
}
@article {812,
	title = {Likelihood-based population independent component analysis.},
	journal = {Biostatistics},
	volume = {14},
	year = {2013},
	month = {2013 Jul},
	pages = {514-27},
	abstract = {<p>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.</p>},
	keywords = {Adult, Algorithms, Attention Deficit Disorder with Hyperactivity, Biostatistics, Brain, Child, Computer Simulation, Databases, Factual, Humans, Imaging, Three-Dimensional, Likelihood Functions, Magnetic Resonance Imaging, Principal Component Analysis, Statistics, Nonparametric},
	issn = {1468-4357},
	doi = {10.1093/biostatistics/kxs055},
	author = {Eloyan, Ani and Crainiceanu, Ciprian M and Caffo, Brian S}
}
