title: Two-way principal component analysis for matrix-variate data, with an application to functional magnetic resonance imaging data.
abstract: Many modern neuroimaging studies acquire large spatial images of the brain observed sequentially over time. Such data are often stored in the forms of matrices. To model these matrix-variate data we introduce a class of separable processes using explicit latent process modeling. To account for the size and two-way structure of the data, we extend principal component analysis to achieve dimensionality reduction at the individual level. We introduce necessary identifiability conditions for each model and develop scalable estimation procedures. The method is motivated by and applied to a functional magnetic resonance imaging study designed to analyze the relationship between pain and brain activity.
authors: Lei
authors: L
authors: Huang
authors: Philip T
authors: PT
authors: Reiss
authors: Luo
authors: L
authors: Xiao
authors: Vadim
authors: V
authors: Zipunnikov
authors: Martin A
authors: MA
authors: Lindquist
authors: Ciprian M
authors: CM
authors: Crainiceanu
mesh_terms: D001931:Brain Mapping / Q000379:methods*; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D025341:Principal Component Analysis
keywords: Latent process modeling; Matrix-variate; Principal component analysis; Separability; fMRI