Bayesian joint modelling for high-dimensional network mediation analysis

Music City Center 

Causal mediation analysis provides critical insights into how exposures influence outcomes through intermediate variables, or mediators. In this study, we examine mediation effects in complex-structured data, focusing on brain connectivity networks derived from fMRI. Capturing these mediation pathways is essential for understanding neurobiological mechanisms, yet the high dimensionality of brain connectivity data presents challenges for traditional mediation methods. To address this, we apply manifold learning techniques to project high-dimensional connectivity matrices onto lower-dimensional latent spaces, preserving node-level characteristics and facilitating the identification of key mediating brain regions. Additionally, we leverage a joint sampling strategy within a Bayesian framework to retain mediator-specific features while effectively handling sparsity and complexity in the data. These methodological advancements enhance causal inference by improving mediation effect estimation and providing deeper insights into the pathways linking exposures to outcomes. This work contributes to advancing mediation analysis for complex neuroimaging data.

Bayesian Modelling

Causal Mediation Analysis

Brain connectivity network

Dimension reduction