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

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.

Corrected small typographical mistakes, changed order of authors and funding information, and also chose a more efficient compression for figures

References in corpus (14)

• Fast unfolding of communities in large networks
• Modularity and community structure in networks
• Resolution limit in community detection
• Statistical Mechanics of Community Detection
• Weight-conserving characterization of complex functional brain networks
• Consensus clustering in complex networks
• Brain organization into resting state networks emerges at criticality on a model of the human connectome
• Robust Detection of Dynamic Community Structure in Networks
• Community detection in networks with positive and negative links
• Analysis of the structure of complex networks at different resolution levels
• Markov dynamics as a zooming lens for multiscale community detection: non clique-like communities and the field-of-view limit
• Task-Based Core-Periphery Organisation of Human Brain Dynamics
• Multi-scale Modularity in Complex Networks
• Protein multi-scale organization through graph partitioning and robustness analysis: Application to the myosin-myosin light chain interaction

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• Nonparametric weighted stochastic block models
• Frustrated hierarchical synchronization and emergent complexity in the human connectome network
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• Classification of Autism Spectrum Disorder Using Supervised Learning of Brain Connectivity Measures Extracted from Synchrostates
• Flow-based network analysis of the Caenorhabditis elegans connectome
• Fluctuations between high- and low-modularity topology in time-resolved functional connectivity
• Weight Thresholding on Complex Networks
• Does the brain behave like a (complex) network? I. Dynamics
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• A novel brain partition highlights the modular skeleton shared by structure and function
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• The specificity and robustness of long-distance connections in weighted, interareal connectomes
• Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: a review