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

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.

SUBMITTER: Zhang J 

PROVIDER: S-EPMC8314561 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature