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Modular Brain Networks
Open accessAbstract
The development of new technologies for mapping structural and functional brain connectivity has led to the creation of comprehensive network maps of neuronal circuits and systems. The architecture of these brain networks can be examined and analyzed with a large variety of graph theory tools. Methods for detecting modules, or network communities, are of particular interest because they uncover major building blocks or subnetworks that are particularly densely connected, often corresponding to specialized functional components. A large number of methods for community detection have become available and are now widely applied in network neuroscience. This article first surveys a number of these methods, with an emphasis on their advantages and shortcomings; then it summarizes major findings on the existence of modules in both structural and functional brain networks and briefly considers their potential functional roles in brain evolution, wiring minimization, and the emergence of functional specialization and complex dynamics.
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“At the mesocale, structural connectivity can be partitioned into modules (also known as communities) that reflect the presence of clusters embedded in the brain’s wiring (Betzel, 2020; Sporns and Betzel, 2016).”
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Sporns and Betzel, 2016
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“The hierarchical segregation of neural networks into distributed neighbourhoods has been found to stretch the parameter range for self-organised criticality by allowing subcritical and supercritical dynamics to co-exist simultaneously [75].”
From The depressed brain: an evolutionary systems theory (2017) · cites this paper as [75]
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Metadata
- Authors
- Sporns, O., & Betzel, R. F.
- Affiliations
- Indiana University Bloomington (US)
- Venue
- Annual Review of Psychology
- Publisher
- Annual Reviews
- Published
- 2016-01-04
- Volume
- 67
- Issue
- 1
- Pages
- 613-640
- ISSN
- 0066-4308 (ISSN-L)
- 1545-2085
- PMC
- PMC4782188
- OpenAlex
- W2143502460
- Cited by
- 1,334
- References
- 142
- Citations by year
- 2026: 102 · 2025: 147 · 2024: 138 · 2023: 133 · 2022: 137 · 2021: 145 · 2020: 135 · 2019: 113 · 2018: 129 · 2017: 93 · 2016: 53
- FWCI
- 9.31
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Annual Review of Psychology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Science Foundation (0903495)
- James S. McDonnell Foundation (#22002082)
- National Institutes of Health
- Division of Graduate Education
- National Center for Complementary and Integrative Health (R01AT009036)
- 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, network modules, community detection, structural connectivity, functional connectivity, graph theory, network neuroscience, brain evolution, functional specialization, complex dynamics
- MeSH terms
- Brain, Brain Mapping, Humans, Nerve Net
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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