A Two-Part Mixed-Effects Modeling Framework For Analyzing Whole-Brain Network Data

TL;DR

This paper introduces a two-part mixed-effects modeling framework for analyzing whole-brain network data, enabling detailed statistical analysis of connectivity patterns and their relationship with outcomes like disease status.

Contribution

The proposed framework uniquely models both the presence and strength of connections, integrating multivariate statistics with network science to improve group comparisons and predictions.

Findings

Allows modeling both connection probability and strength

Reduces spurious correlations with covariates

Enables network simulation and thresholding

Abstract

Whole-brain network analyses remain the vanguard in neuroimaging research, coming to prominence within the last decade. Network science approaches have facilitated these analyses and allowed examining the brain as an integrated system. However, statistical methods for modeling and comparing groups of networks have lagged behind. Fusing multivariate statistical approaches with network science presents the best path to develop these methods. Toward this end, we propose a two-part mixed-effects modeling framework that allows modeling both the probability of a connection (presence/absence of an edge) and the strength of a connection if it exists. Models within this framework enable quantifying the relationship between an outcome (e.g., disease status) and connectivity patterns in the brain while reducing spurious correlations through inclusion of confounding covariates. They also enable prediction about an outcome based on connectivity structure and vice versa, simulating networks to gain a better understanding of normal ranges of topological variability, and thresholding networks leveraging group information. Thus, they provide a comprehensive approach to studying system level brain properties to further our understanding of normal and abnormal brain function.