Bayesian Dynamic Tensor Factor Model for High-dimensional Multi-group Longitudinal Neuroimaging Data

Oregon Convention Center 

Longitudinal neuroimaging data are often collected for studying temporal changes in the brain leading, e.g., to cognitive decline with age or neurodegenerative diseases. The
analyses of such data present daunting structural complexities, dimensionality issues, and modeling and computational challenges. To overcome these hurdles, we introduce
a novel individualized longitudinal image regression model that combines several popular low-rank frameworks, namely basis function representation, latent factor models,
and tensor factor models. Specifically, through the combined use of a basis mixture representation of the stacked images followed by a Tucker tensor factorization of the associated basis coefficients, we accommodate smooth variations in both space and time, account for differences between groups, and capture subject heterogeneity within those
groups, while also obtaining a massive multifold reduction in model dimensions.