Multi-scale community organization of the human structural connectome and its relationship with resting-state functional connectivity

TL;DR

This study uses a Markov process-based framework to identify multi-scale community structures in the human connectome, revealing their close relationship with resting-state functional connectivity and suggesting scale-selective communication in the brain.

Contribution

It introduces a novel multi-scale community detection method based on partition stability, addressing limitations of previous modularity-based approaches.

Findings

Communities across multiple scales closely relate to functional connectivity

Brain communication occurs over multiple characteristic scales

Proposes a heuristic for scale-selective cortical communication

Abstract

The human connectome has been widely studied over the past decade. A principal finding is that it can be decomposed into communities of densely interconnected brain regions. This result, however, may be limited methodologically. Past studies have often used a flawed modularity measure in order to infer the connectome's community structure. Also, these studies relied on the intuition that community structure is best defined in terms of a network's static topology as opposed to a more dynamical definition. In this report we used the partition stability framework, which defines communities in terms of a Markov process (random walk), to infer the connectome's multi-scale community structure. Comparing the community structure to observed resting-state functional connectivity revealed communities across a broad range of dynamical scales that were closely related to functional connectivity. This result suggests a mapping between communities in structural networks, models of communication processes, and brain function. It further suggests that communication in the brain is not limited to a single characteristic scale, leading us to posit a heuristic for scale-selective communication in the cerebral cortex.