Double soft-thresholded model for multi-group scalar on vector-valued image regression

TL;DR

This paper introduces a new Bayesian spatial variable selection method for multi-group scalar on vector-valued image regression, effectively identifying important spatial regions in complex imaging data.

Contribution

It develops a novel soft-thresholding prior for sparse, piecewise-smooth vector-valued regression coefficients, with efficient inference and theoretical guarantees.

Findings

Demonstrates superior performance in simulations

Achieves consistent variable selection and estimation

Successfully applied to ADNI DTI imaging data

Abstract

In this paper, we develop a novel spatial variable selection method for scalar on vector-valued image regression in a multi-group setting. Here, 'vector-valued image' refers to the imaging datasets that contain vector-valued information at each pixel/voxel location, such as in RGB color images, multimodal medical images, DTI imaging, etc. The focus of this work is to identify the spatial locations in the image having an important effect on the scalar outcome measure. Specifically, the overall effect of each voxel is of interest. We thus develop a novel shrinkage prior by soft-thresholding the \ell_2 norm of a latent multivariate Gaussian process. It will allow us to estimate sparse and piecewise-smooth spatially varying vector-valued regression coefficient functions. For posterior inference, an efficient MCMC algorithm is developed. We establish the posterior contraction rate for parameter estimation and consistency for variable selection of the proposed Bayesian model, assuming that the true regression coefficients are Holder smooth. Finally, we demonstrate the advantages of the proposed method in simulation studies and further illustrate in an ADNI dataset for modeling MMSE scores based on DTI-based vector-valued imaging markers.