@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 = {<p>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.</p>},
	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 {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 = {<p>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.</p>},
	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}
}
