@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 {800,
	title = {Hierarchical Adaptive Regression Kernels for Regression with Functional Predictors.},
	journal = {J Comput Graph Stat},
	volume = {22},
	year = {2013},
	month = {2013},
	abstract = {<p>We propose a new method for regression using a parsimonious and scientifically interpretable representation of functional predictors. Our approach is designed for data that exhibit features such as spikes, dips, and plateaus whose frequency, location, size, and shape varies stochastically across subjects. We propose Bayesian inference of the joint functional and exposure models, and give a method for efficient computation. We contrast our approach with existing state-of-the-art methods for regression with functional predictors, and show that our method is more effective and efficient for data that include features occurring at varying locations. We apply our methodology to a large and complex dataset from the Sleep Heart Health Study, to quantify the association between sleep characteristics and health outcomes. Software and technical appendices are provided in online supplemental materials.</p>},
	issn = {1061-8600},
	doi = {10.1080/10618600.2012.694765},
	author = {Woodard, Dawn B and Crainiceanu, Ciprian and Ruppert, David}
}
