Two-way principal component analysis for matrix-variate data, with an application to functional magnetic resonance imaging data.
Biostatistics
; 18(2): 214-229, 2017 04 01.
Article
em En
| MEDLINE
| ID: mdl-27578805
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.
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MEDLINE
Assunto principal:
Mapeamento Encefálico
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Imageamento por Ressonância Magnética
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Análise de Componente Principal
Idioma:
En
Ano de publicação:
2017
Tipo de documento:
Article