ArticleBiostatistics (Oxford, England)2017
Two-way principal component analysis for matrix-variate data, with an application to functional magnetic resonance imaging data.
Article in Biostatistics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- A novel two-way functional linear model with applications in human mortality data analysis.Journal of applied statistics · 2024Article
- Metabolomic Profiles in Childhood and Adolescence Are Associated with Fetal Overnutrition.Metabolites · 2022Article
- A functional mixed model for scalar on function regression with application to a functional MRI study.Biostatistics (Oxford, England) · 2021Article
- Extracting Voxel-Based Cartilage Relaxometry Features in Hip Osteoarthritis Subjects Using Principal Component Analysis.Journal of magnetic resonance imaging : JMRI · 2020Article
- Hybrid principal components analysis for region-referenced longitudinal functional EEG data.Biostatistics (Oxford, England) · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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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Registered trials
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