Dataset Information

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.

SUBMITTER: Huang L

PROVIDER: S-EPMC6075629 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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Two-way principal component analysis for matrix-variate data, with an application to functional magnetic resonance imaging data.

Huang Lei L Reiss Philip T PT Xiao Luo L Zipunnikov Vadim V Lindquist Martin A MA Crainiceanu Ciprian M CM

Biostatistics (Oxford, England) 20170401 2

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 mo ...[more]

PMID: 27578805

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Dataset Information

Two-way principal component analysis for matrix-variate data, with an application to functional magnetic resonance imaging data.

Publications

Two-way principal component analysis for matrix-variate data, with an application to functional magnetic resonance imaging data.

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