Matrix-Response Generalized Linear Mixed Model with Applications to Longitudinal Brain Images

2026-01-22

AI Summary

This study addresses the challenge of effectively modeling high-dimensional longitudinal brain network data and identifying connectivity edges influenced by external covariates. To this end, it introduces, for the first time, a matrix-response generalized linear mixed model into longitudinal neuroimaging analysis, enabling flexible modeling and rigorous statistical inference of covariate effects on time-varying brain networks. An efficient Monte Carlo Expectation–Maximization (MCEM) algorithm is employed for parameter estimation. Experiments on both simulated and real DTI/fMRI datasets demonstrate that the proposed method accurately detects covariate-associated brain connectivity patterns with high parameter estimation accuracy, substantially enhancing the analytical capability for longitudinal brain networks.

Technology Category

• Machine Learning: Matrix & Tensor Methods
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

Longitudinal brain imaging data facilitate the monitoring of structural and functional alterations in individual brains across time, offering essential understanding of dynamic neurobiological mechanisms. Such data improve sensitivity for detecting early biomarkers of disease progression and enhance the evaluation of intervention effects. While recent matrix-response regression models can relate static brain networks to external predictors, there remain few statistical methods for longitudinal brain networks, especially those derived from high-dimensional imaging data. We introduce a matrix-response generalized linear mixed model that accommodates longitudinal brain networks and identifies edges whose connectivity is influenced by external predictors. An efficient Monte Carlo Expectation-Maximization algorithm is developed for parameter estimation. Extensive simulations demonstrate effective identification of covariate-related network components and accurate parameter estimation. We further demonstrate the usage of the proposed method through applications to diffusion tensor imaging (DTI) and functional MRI (fMRI) datasets.

Problem

• longitudinal brain networks
• matrix-response regression
• high-dimensional imaging data
• connectivity analysis
• neuroimaging

Innovation

• matrix-response regression
• longitudinal brain networks
• generalized linear mixed model
• Monte Carlo EM algorithm
• neuroimaging