@article {819, title = {Analytic programming with FMRI data: a quick-start guide for statisticians using R.}, journal = {PLoS One}, volume = {9}, year = {2014}, month = {2014}, pages = {e89470}, abstract = {

Functional magnetic resonance imaging (fMRI) is a thriving field that plays an important role in medical imaging analysis, biological and neuroscience research and practice. This manuscript gives a didactic introduction to the statistical analysis of fMRI data using the R project, along with the relevant R code. The goal is to give statisticians who would like to pursue research in this area a quick tutorial for programming with fMRI data. References of relevant packages and papers are provided for those interested in more advanced analysis.

}, keywords = {Brain, brain mapping, Humans, Magnetic Resonance Imaging, Programming Languages}, issn = {1932-6203}, doi = {10.1371/journal.pone.0089470}, author = {Eloyan, Ani and Li, Shanshan and Muschelli, John and Pekar, Jim J and Mostofsky, Stewart H and Caffo, Brian S} } @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 = {

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.

}, 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 {827, title = {Analysis of group ICA-based connectivity measures from fMRI: application to Alzheimer{\textquoteright}s disease.}, journal = {PLoS One}, volume = {7}, year = {2012}, month = {2012}, pages = {e49340}, abstract = {

Functional magnetic resonance imaging (fMRI) is a powerful tool for the in vivo study of the pathophysiology of brain disorders and disease. In this manuscript, we propose an analysis stream for fMRI functional connectivity data and apply it to a novel study of Alzheimer{\textquoteright}s disease. In the first stage, spatial independent component analysis is applied to group fMRI data to obtain common brain networks (spatial maps) and subject-specific mixing matrices (time courses). In the second stage, functional principal component analysis is utilized to decompose the mixing matrices into population-level eigenvectors and subject-specific loadings. Inference is performed using permutation-based exact logistic regression for matched pairs data. The method is applied to a novel fMRI study of Alzheimer{\textquoteright}s disease risk under a verbal paired associates task. We found empirical evidence of alternative ICA-based metrics of connectivity when comparing subjects evidencing mild cognitive impairment relative to carefully matched controls.

}, keywords = {Aged, Algorithms, Alzheimer Disease, Brain, brain mapping, Case-Control Studies, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Middle Aged, Mild Cognitive Impairment, Nerve Net, Principal Component Analysis, Task Performance and Analysis, Time Factors}, issn = {1932-6203}, doi = {10.1371/journal.pone.0049340}, author = {Li, Shanshan and Eloyan, Ani and Joel, Suresh and Mostofsky, Stewart and Pekar, James and Bassett, Susan Spear and Caffo, Brian} }