Integrative Learning for Population of Dynamic Networks with Covariates

TL;DR

This paper introduces a Bayesian approach for estimating populations of dynamic networks that leverage covariate information to improve accuracy and interpretability, demonstrated through simulations and fMRI data analysis.

Contribution

It develops a novel Bayesian product mixture model with covariate-guided clustering for dynamic network estimation across heterogeneous samples.

Findings

Enhanced recovery of true dynamic networks compared to existing methods.

Identification of sub-groups with similar connectivity patterns in fMRI data.

Detection of intervention-related network changes in brain regions.

Abstract

Although there is a rapidly growing literature on dynamic connectivity methods, the primary focus has been on separate network estimation for each individual, which fails to leverage common patterns of information. We propose novel graph-theoretic approaches for estimating a population of dynamic networks that are able to borrow information across multiple heterogeneous samples in an unsupervised manner and guided by covariate information. Specifically, we develop a Bayesian product mixture model that imposes independent mixture priors at each time scan and uses covariates to model the mixture weights, which results in time-varying clusters of samples designed to pool information. The computation is carried out using an efficient Expectation-Maximization algorithm. Extensive simulation studies illustrate sharp gains in recovering the true dynamic network over existing dynamic connectivity methods. An analysis of fMRI block task data with behavioral interventions reveal sub-groups of individuals having similar dynamic connectivity, and identifies intervention-related dynamic network changes that are concentrated in biologically interpretable brain regions. In contrast, existing dynamic connectivity approaches are able to detect minimal or no changes in connectivity over time, which seems biologically unrealistic and highlights the challenges resulting from the inability to systematically borrow information across samples.