The DOI matches its Crossref record; 143 of the 143 references it cites were found in a registry or an index.
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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Cites
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
Fast unfolding of communities in large networks
Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008) · Journal of Statistical Mechanics: Theory and Experiment · doi:10.1088/1742-5468/2008/10/p10008 · Cited by 21,424
Community structure in social and biological networks
Girvan, M., & Newman, M. E. J. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.122653799 · Cited by 15,502
Finding and evaluating community structure in networks
Newman, M. E. J., & Girvan, M. (2004) · Physical Review E · doi:10.1103/physreve.69.026113 · Cited by 13,931
Complex brain networks: graph theoretical analysis of structural and functional systems
Bullmore, E., & Sporns, O. (2009) · Nature Reviews Neuroscience · doi:10.1038/nrn2575 · Cited by 11,820
Self-citationComplex network measures of brain connectivity: Uses and interpretations
Rubinov, M., & Sporns, O. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.10.003 · Cited by 11,522
Self-citationFortunato, S. (2010) · Physics Reports · doi:10.1016/j.physrep.2009.11.002 · Cited by 11,293
The organization of the human cerebral cortex estimated by intrinsic functional connectivity
Thomas Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., Roffman, J. L., Smoller, J. W., Zöllei, L., Polimeni, J. R., et al. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00338.2011 · Cited by 9,182
Distributed hierarchical processing in the primate cerebral cortex
Felleman, D. J., & Van Essen, D. C. (1991) · Cerebral Cortex · doi:10.1093/cercor/1.1.1 · Cited by 7,783
Fodor, J. A. (1983) · doi:10.7551/mitpress/4737.001.0001 · Cited by 7,734
Research works printed but not linked: 3
- MacMahon M, 2015, Phys. Rev. X, 5, 021006 [physics
- Simon HA, 1962, Proc. Am. Philos. Soc., 106, 467
- Zamora-Lopez G, 2010, Front. Neuroinform., 4, 1
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Bassett, D. S., & Sporns, O. (2017) · Nature Neuroscience · doi:10.1038/nn.4502 · Cited by 2,398
Self-citationStatistical physics of vaccination
Wang, Z., Bauch, C. T., Bhattacharyya, S., d’Onofrio, A., Manfredi, P., Perc, M., Perra, N., Salathé, M., & Zhao, D. (2016) · Physics Reports · doi:10.1016/j.physrep.2016.10.006 · Cited by 973
Shine, J. M., Bissett, P. G., Bell, P. T., Koyejo, O., Balsters, J. H., Gorgolewski, K. J., Moodie, C. A., & Poldrack, R. A. (2016) · Neuron · doi:10.1016/j.neuron.2016.09.018 · Cited by 886
Communication dynamics in complex brain networks
Avena-Koenigsberger, A., Misic, B., & Sporns, O. (2018) · Nature Reviews Neuroscience · doi:10.1038/nrn.2017.149 · Cited by 856
Self-citationSmall-World Brain Networks Revisited
Bassett, D. S., & Bullmore, E. T. (2016) · The Neuroscientist · doi:10.1177/1073858416667720 · Cited by 887
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
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