# Multi-scale detection of hierarchical community architecture in structural and functional brain networks

## TL;DR

This paper introduces a multi-scale community detection method for brain networks that captures hierarchical structures across different topological levels, improving understanding of structural and functional brain organization.

## Contribution

The authors develop a multi-scale extension of modularity maximization for hierarchical community detection in brain graphs, applicable to synthetic and real neuroimaging data.

## Key findings

- Structural brain networks exhibit more topological scales than functional networks.
- The method identifies conserved community organization across hierarchical levels.
- Multimodal analysis reveals scales where structural and functional communities align or differ.

## Abstract

Community detection algorithms have been widely used to study the organization of complex systems like the brain. A principal appeal of these techniques is their ability to identify a partition of brain regions (or nodes) into communities, where nodes within a community are densely interconnected. In their simplest application, community detection algorithms are agnostic to the presence of community hierarchies, but a common characteristic of many neural systems is a nested hierarchy. To address this limitation, we exercise a multi-scale extension of a community detection technique known as modularity maximization, and we apply the tool to both synthetic graphs and graphs derived from human structural and functional imaging data. Our multi-scale community detection algorithm links a graph to copies of itself across neighboring topological scales, thereby becoming sensitive to conserved community organization across neighboring levels of the hierarchy. We demonstrate that this method allows for a better characterization of topological inhomogeneities of the graph's hierarchy by providing a local (node) measure of community stability and inter-scale reliability across topological scales. We compare the brain's structural and functional network architectures and demonstrate that structural graphs display a wider range of topological scales than functional graphs. Finally, we build a multimodal multiplex graph that combines structural and functional connectivity in a single model, and we identify the topological scales where resting state functional connectivity and underlying structural connectivity show similar versus unique hierarchical community architecture. Together, our results showcase the advantages of the multi-scale community detection algorithm in studying hierarchical community structure in brain graphs, and they illustrate its utility in modeling multimodal neuroimaging data.