@article{999e8562770744759833105c40f40c13,
title = "Functional principal component model for high-dimensional brain imaging",
abstract = "We explore a connection between the singular value decomposition (SVD) and functional principal component analysis (FPCA) models in high-dimensional brain imaging applications. We formally link right singular vectors to principal scores of FPCA. This, combined with the fact that left singular vectors estimate principal components, allows us to deploy the numerical efficiency of SVD to fully estimate the components of FPCA, even for extremely high-dimensional functional objects, such as brain images. As an example, a FPCA model is fit to high-resolution morphometric (RAVENS) images. The main directions of morphometric variation in brain volumes are identified and discussed.",
keywords = "Brain imaging data, FPCA, MRI, SVD, Voxel-based morphometry (VBM)",
author = "Vadim Zipunnikov and Brian Caffo and Yousem, {David M.} and Christos Davatzikos and Schwartz, {Brian S.} and Ciprian Crainiceanu",
note = "Funding Information: The authors would like to thank the reviewers and the editor for their helpful comments and suggestions which led to an improved version of the manuscript. The research of Vadim Zipunnikov, Brian Caffo and Ciprian Crainiceanu was supported by award number R01NS060910 from the National Institute of Neurological Disorders and Stroke and by Award Number EB012547 from the NIH National Institute of Biomedical Imaging and Bioengineering (NIBIB). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Neurological Disorders and Stroke or the National Institute of Biomedical Imaging and Bioengineering or the National Institutes of Health.",
year = "2011",
month = oct,
day = "1",
doi = "10.1016/j.neuroimage.2011.05.085",
language = "English (US)",
volume = "58",
pages = "772--784",
journal = "NeuroImage",
issn = "1053-8119",
publisher = "Academic Press Inc.",
number = "3",
}