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

TL;DR

This paper introduces a novel matrix-response generalized linear mixed model designed for longitudinal brain network data, enabling the detection of covariate effects on brain connectivity over time with efficient estimation methods.

Contribution

The paper presents a new statistical model for longitudinal brain networks and an efficient estimation algorithm, addressing a gap in analyzing high-dimensional neuroimaging data over time.

Findings

Effective identification of covariate-related network components

Accurate parameter estimation demonstrated in simulations

Successful application to DTI and fMRI datasets

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.