Bayesian Tensor Modeling for Dimension Reduction and Variable Selection in Neuroimaging Data

Music City Center 

The rapid growth of high-dimensional neuroimaging data demands advanced statistical models that extract meaningful features while handling sparsity and structural dependencies. In this talk, we introduce a Bayesian tensor regression framework for matrix- and tensor-variate neuroimaging models with mixed-type responses, such as disease status and clinical measures. Our approach employs global-local shrinkage priors to enforce sparsity and low-rank structure, efficiently capturing dependencies among imaging predictors. Using a data augmentation strategy, we enable computationally efficient posterior inference via Gibbs sampling. Applied to Alzheimer's Disease MRI data, our model identifies key imaging biomarkers linked to disease progression. We establish posterior consistency in high-dimensional settings and validate robustness through simulations. Extending our framework with hierarchical priors enhances interpretability and scalability. This method offers a flexible solution for structured low-rank regression with applications in neurodegenerative disease research, functional connectivity analysis, and precision medicine.

Bayesian Tensor Regression

Neuroimaging Data Analysis

Dimension Reduction

Variable Selection

Global-Local Shrinkage Priors

Low-Rank Structure 

Section on Statistics in Imaging