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

arXiv: https://arxiv.org/abs/1304.0485 · HTML: https://mlsanity.com/paper/1304.0485/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2013.

## Built on by (most cited first, 1 of 1)

- [Hierarchical Multiscale Structure-Function Coupling for Brain Connectome Integration](https://mlsanity.com/paper/2603.20680/) (2026)

## Abstract (arXiv)

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.