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Cited by 113Functional Brain Connectivity StudiesComplex Network Analysis TechniquesAdvanced Neuroimaging Techniques and Applications

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

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BETZEL, RICHARD F., GRIFFA, ALESSANDRA, AVENA-KOENIGSBERGER, ANDREA, GOÑI, JOAQUÍN, THIRAN, JEAN-PHILIPPE, HAGMANN, PATRIC, SPORNS, OLAF·2013·Network Science
DOI 10.1017/nws.2013.19
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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. Past studies have often used single-scale modularity measures in order to infer the connectome's community structure, possibly overlooking interesting structure at other organizational scales. 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 scales that were closely related to functional connectivity. This result suggests a mapping between communities in structural networks, models of influence-spreading and diffusion, and brain function. It further suggests that the spread of influence among brain regions may not be limited to a single characteristic scale.

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  • Modular structure constrains information flow through the network in a manner thought to promote functional segregation and specialized information processing.

    “This modular structure constrains the flow of information through the network in a manner thought to promote functional segregation and clusters of specialized information processing (Betzel et al., 2013).”

    From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Betzel et al., 2013

    The Introduction describes communities as preferentially interconnected nodes that can trap the flow of random walkers, a model the paper says can represent information diffusion. The Discussion links connection topology to functional coupling, but our copy does not say that modular structure promotes specialized information processing or functional segregation in those terms. No page numbers were provided for these passages. (LLM, conf=0.82)

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Cited by

Sorted by citations

Authors
BETZEL, R. F., GRIFFA, A., AVENA-KOENIGSBERGER, A., GOÑI, J., THIRAN, J.-P., HAGMANN, P., & SPORNS, O.
Affiliations
  • Indiana University Bloomington (US)
  • Centre Hospitalier Universitaire Vaudois (CH)
ORCID
Venue
Network Science
Publisher
Cambridge University Press (CUP)
Published
2013-12
Volume
1
Issue
3
Pages
353-373
ISSN
  • 2050-1242 (ISSN-L)
  • 2050-1250
DOI
10.1017/nws.2013.19
OpenAlex
W1964757651
Cited by
113
References
10
Citations by year
2026: 2 · 2025: 7 · 2024: 6 · 2023: 4 · 2022: 13 · 2021: 8 · 2020: 13 · 2019: 4 · 2018: 9 · 2017: 12 · 2016: 8 · 2015: 16 · 2014: 11
FWCI
6.40
Type
journal article
Language
en
Open access
Licence
Locations
  • Network Science (publishedVersion, cc-by)
  • SERVAL (Université de Lausanne) (publishedVersion, other-oa)
  • arXiv (Cornell University) (submittedVersion)
  • reroDoc Digital Library (submittedVersion)
  • IRIS (submittedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
  • Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
  • arXiv (Cornell University)
Funders
National Science Foundation
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
Keywords
resting-state functional connectivity, community structure, random walks, modularity, brain network organization, diffusion

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