Bayesian Image Mediation Analysis
Abstract
Mediation analysis aims to separate the indirect effect through mediators from the direct effect of the exposure on the outcome. It is challenging to perform mediation analysis with neuroimaging data which involves high dimensionality, complex spatial correlations, sparse activation patterns and relatively low signal-to-noise ratio. To address these issues, we develop a new spatially varying coefficient structural equation model for Bayesian Image Mediation Analysis (BIMA). We define spatially varying mediation effects within the potential outcomes framework, employing a soft-thresholded Gaussian process prior for functional parameters. We establish posterior consistency for spatially varying mediation effects along with selection consistency on important regions that contribute to the mediation effects. We develop an efficient posterior computation algorithm scalable to analysis of large-scale imaging data. Through extensive simulations, we show that BIMA can improve the estimation accuracy and computational efficiency for high-dimensional mediation analysis over existing methods. We apply BIMA to analyze behavioral and fMRI data in the Adolescent Brain Cognitive Development (ABCD) study with a focus on inferring the mediation effects of the parental education level on the children's general cognitive ability that are mediated through the working memory brain activity.
Cite
@article{arxiv.2310.16284,
title = {Bayesian Image Mediation Analysis},
author = {Yuliang Xu and Timothy D Johnson and Mary Heitzeg and Jian Kang},
journal= {arXiv preprint arXiv:2310.16284},
year = {2025}
}