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Cited by 1,156Functional Brain Connectivity StudiesComplex Network Analysis TechniquesAdvanced Neuroimaging Techniques and Applications

Modular and hierarchically modular organization of brain networks

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Meunier, David, Lambiotte, Renaud, Bullmore, Edward T.·2010·Frontiers in Neuroscience
DOI 10.3389/fnins.2010.00200PMC PMC3000003
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Abstract

Brain networks are increasingly understood as one of a large class of information processing systems that share important organizational principles in common, including the property of a modular community structure. A module is topologically defined as a subset of highly inter-connected nodes which are relatively sparsely connected to nodes in other modules. In brain networks, topological modules are often made up of anatomically neighboring and/or functionally related cortical regions, and inter-modular connections tend to be relatively long distance. Moreover, brain networks and many other complex systems demonstrate the property of hierarchical modularity, or modularity on several topological scales: within each module there will be a set of sub-modules, and within each sub-module a set of sub-sub-modules, etc. There are several general advantages to modular and hierarchically modular network organization, including greater robustness, adaptivity, and evolvability of network function. In this context, we review some of the mathematical concepts available for quantitative analysis of (hierarchical) modularity in brain networks and we summarize some of the recent work investigating modularity of structural and functional brain networks derived from analysis of human neuroimaging data.

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  • Free energy may be most effectively minimized-and integrated information maximized-if synchronized couplings take the form of hierarchically organized modules, thus affording robustness, separable optimization, balanced integration and differentiation, evolvability via degeneracy, efficient communication via small-world connectivity, and flexible multi-scale responsivity via critical dynamics.

    “Free energy may be most effectively minimized—and integrated information maximized (Marshall et al., 2016)—if synchronized couplings take the form of hierarchically organized modules, thus affording robustness, separable optimization, balanced integration and differentiation, evolvability via degeneracy, efficient communication via small-world connectivity, and flexible multi-scale responsivity”

    From An Integrated World Modeling Theory (IWMT) of Consciousness (2020) · cites this paper as Meunier et al., 2010

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Cited by

Sorted by citations

Authors
Meunier, D., Lambiotte, R., & Bullmore, E. T.
Affiliations
  • Imperial College London (GB)
  • University of Cambridge (GB)
Corresponding author
Bullmore, Edward T.
ORCID
Venue
Frontiers in Neuroscience
Publisher
Frontiers Media SA
Published
2010
Volume
4
Pages
200-200
ISSN
  • 1662-453X (ISSN-L)
  • 1662-4548
DOI
10.3389/fnins.2010.00200
PMC
PMC3000003
OpenAlex
W2021947606
Cited by
1,156
References
6
Citations by year
2026: 50 · 2025: 75 · 2024: 81 · 2023: 88 · 2022: 79 · 2021: 99 · 2020: 92 · 2019: 78 · 2018: 86 · 2017: 70 · 2016: 86 · 2015: 70 · 2014: 68 · 2013: 65 · 2012: 39
FWCI
25.99
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Frontiers in Neuroscience (publishedVersion, cc-by)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Repository of the University of Namur (submittedVersion)
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
brain networks, hierarchical modularity, modular organization, community structure, structural brain networks, functional brain networks, network topology, network robustness, cortical regions, complex networks

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