Journal
NEUROIMAGE
Volume 58, Issue 3, Pages 772-784Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2011.05.085
Keywords
Voxel-based morphometry (VBM); MRI; FPCA; SVD; Brain imaging data
Funding
- National Institute of Neurological Disorders and Stroke [R01NS060910]
- NIH National Institute of Biomedical Imaging and Bioengineering (NIBIB) [EB012547]
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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. (C) 2011 Elsevier Inc. All rights reserved.
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