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 estimationprocedures.Themethodismotivatedbyandappliedtoafunctionalmagneticresonanceimaging study designed to analyze the relationship between pain and brain activity.
|Number of pages||16|
|State||Published - 1 Apr 2017|
Bibliographical noteFunding Information:
This work is supported in part by National Institues of Health Grant 1R01MH095836.
© 2016 The Author.
- Latent process modeling
- Principal component analysis
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty