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.
authors.affiliation: Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21205, USA. vzipunni@jhsph.edu
authors.forename: Vadim
authors.initials: V
authors.lastname: Zipunnikov
authors.forename: Brian
authors.initials: B
authors.lastname: Caffo
authors.forename: David M
authors.initials: DM
authors.lastname: Yousem
authors.forename: Christos
authors.initials: C
authors.lastname: Davatzikos
authors.forename: Brian S
authors.initials: BS
authors.lastname: Schwartz
authors.forename: Ciprian
authors.initials: C
authors.lastname: Crainiceanu
mesh_terms: D001931:Brain Mapping / Q000379:methods*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D025341:Principal Component Analysis / Q000379:methods*