Mixed-Effect Time-Varying Network Model and Application in Brain Connectivity Analysis

2018/06/11 by Jingfei Zhang, Zhang, Jingfei, Will Wei Sun +3 · 1 citation
Neuroscience · Physics and Astronomy · Psychology · #Complex Network Analysis Techniques #FOS: Computer and information sciences #Functional Brain Connectivity Studies #Mental Health Research Topics #Methodology (stat.ME)

paper · pdf · doi:10.48550/arxiv.1806.03829

openalex publication_date 2018/06/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Time-varying networks are fast emerging in a wide range of scientific and business applications. Most existing dynamic network models are limited to a single-subject and discrete-time setting. In this article, we propose a mixed-effect network model that characterizes the continuous time-varying behavior of the network at the population level, meanwhile taking into account both the individual subject variability as well as the prior module information. We develop a multistep optimization procedure for a constrained likelihood estimation and derive the associated asymptotic properties. We demonstrate the effectiveness of our method through both simulations and an application to a study of brain development in youth. Supplementary materials for this article are available online.

Citations

• Convergence of a Block Coordinate Descent Method for Nondifferentiable Minimization
• Truncated Power Method for Sparse Eigenvalue Problems

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• Self-Supervised Dynamical System Representations for Physiological Time-Series

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