Random Effects Models for Understanding Variability and Association between Brain Functional and Structural Connectivity

TL;DR

This paper introduces random effects models to analyze variability in brain functional and structural connectivity, revealing different sources of correlation at network and edge levels, and providing new insights into brain network relationships.

Contribution

The study presents the first statistical framework to decompose and compare sources of variability in brain connectivity measures at network and edge levels.

Findings

Network and edge FC-SC correlations are driven by different effects.

The models quantify contributions of subject, edge, and interaction effects.

New insights into the variability sources of brain connectivity.

Abstract

The human brain is organized as a complex network, where connections between regions are characterized by both functional connectivity (FC) and structural connectivity (SC). While previous studies have primarily focused on network-level FC-SC correlations (i.e., the correlation between FC and SC across all edges within a predefined network), edge-level correlations (i.e., the correlation between FC and SC across subjects at each edge) has received comparatively little attention. In this study, we systematically analyze both network-level and edge-level FC-SC correlations, demonstrating that they lead to divergent conclusions about the strength of brain function-structure association. To explain these discrepancies, we introduce new random effects models that decompose FC and SC variability into different sources: subject effects, edge effects, and their interactions. Our results reveal that network-level and edge-level FC-SC correlations are influenced by different effects, each contributing differently to the total variability in FC and SC. This modeling framework provides the first statistical approach for disentangling and quantitatively assessing different sources of FC and SC variability and yields new insights into the relationship between functional and structural brain networks.