Functional principal component model for high-dimensional brain imaging

Vadim Zipunnikov; Brian Caffo; David M Yousem; Christos Davatzikos; Brian S Schwartz; Ciprian Crainiceanu

doi:10.1016/j.neuroimage.2011.05.085

Functional principal component model for high-dimensional brain imaging

Neuroimage. 2011 Oct 1;58(3):772-84. doi: 10.1016/j.neuroimage.2011.05.085. Epub 2011 Jun 21.

Authors

Vadim Zipunnikov¹, Brian Caffo, David M Yousem, Christos Davatzikos, Brian S Schwartz, Ciprian Crainiceanu

Affiliation

¹ Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21205, USA. vzipunni@jhsph.edu

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.

Publication types

Research Support, N.I.H., Extramural

MeSH terms

Brain Mapping / methods*
Humans
Image Interpretation, Computer-Assisted / methods*
Magnetic Resonance Imaging / methods*
Models, Neurological*
Principal Component Analysis / methods*

Abstract

Publication types

MeSH terms

Grants and funding