A novel multi-exposure-to-multi-mediator mediation model for imaging genetic study of brain disorders

2025-11-25

AI Summary

This study addresses the challenge of identifying mediating neurobiological mechanisms—specifically, how genetic risk influences psychiatric and neurological disorders via alterations in brain structure and function—under high-dimensional genomics–neuroimaging data with multiple exposures and mediators. We propose a novel mediation modeling framework that jointly performs dimension reduction and sparse loading estimation, integrating polygenic risk scores, multimodal neuroimaging (e.g., structural MRI, functional connectivity), and clinical phenotypes. An efficient optimization algorithm is developed based on the alternating direction method of multipliers (ADMM) and block-coordinate descent, accompanied by theoretical guarantees on convergence and asymptotic properties. Compared to existing approaches, our method demonstrates substantially improved statistical power and interpretability in both simulation studies and empirical analysis of UK Biobank data. It successfully identifies a key genetic pathway for nicotine dependence mediated by altered functional connectivity within the default mode network.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Social Networks and Social Media: Computational social science

Abstract

Common psychiatric and brain disorders are highly heritable and affected by a number of genetic risk factors, yet the mechanism by which these genetic factors contribute to the disorders through alterations in brain structure and function remain poorly understood. Contemporary imaging genetic studies integrate genetic and neuroimaging data to investigate how genetic variation contributes to brain disorders via intermediate neuroimaging endophenotypes. However, the large number of potential exposures (genes) and mediators (neuroimaging features) pose new challenges to the traditional mediation analysis. In this paper, we propose a novel multi-exposure-to-multi-mediator mediation model that integrates genetic, neuroimaging and phenotypic data to investigate the "geneneuroimaging-brain disorder" mediation pathway. Our method jointly reduces the dimensions of exposures and mediators into low-dimensional aggregators where the mediation effect is maximized. We further introduce sparsity into the loadings to improve the interpretability. To target the bi-convex optimization problem, we implement an efficient alternating direction method of multipliers algorithm with block coordinate updates. We provide theoretical guarantees for the convergence of our algorithm and establish the asymptotic properties of the resulting estimators. Through extensive simulations, we demonstrate that our method outperforms other competing methods in recovering true loadings and true mediation proportions across a wide range of signal strengths, noise levels, and correlation structures. We further illustrate the utility of the method through a mediation analysis that integrates genetic, brain functional connectivity and smoking behavior data from UK Biobank, and identifies critical genes that impact nicotine dependence via changing the functional connectivity in specific brain regions.

Problem

• Modeling genetic influences on brain disorders via neuroimaging mediators
• Addressing high-dimensional exposure and mediator challenges in mediation analysis
• Identifying gene-brain pathway mechanisms underlying psychiatric disorders

Innovation

• Multi-exposure-to-multi-mediator mediation model for brain disorders
• Joint dimension reduction with sparsity for interpretability
• Efficient alternating direction method of multipliers algorithm