Evidence for hubs in human functional brain networks
Open accessAbstract
Hubs integrate and distribute information in powerful ways due to the number and positioning of their contacts in a network. Several resting-state functional connectivity MRI reports have implicated regions of the default mode system as brain hubs; we demonstrate that previous degree-based approaches to hub identification may have identified portions of large brain systems rather than critical nodes of brain networks. We utilize two methods to identify hub-like brain regions: (1) finding network nodes that participate in multiple subnetworks of the brain, and (2) finding spatial locations in which several systems are represented within a small volume. These methods converge on a distributed set of regions that differ from previous reports on hubs. This work identifies regions that support multiple systems, leading to spatially constrained predictions about brain function that may be tested in terms of lesions, evoked responses, and dynamic patterns of activity.
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Node strength and betweenness in functional networks may serve as proxies for modular size and synchronization.
“This is consistent with the notion that node strength and betweenness in functional networks may serve, at least to some extent, as proxies for modular size and synchronization (Power et al., 2013).”
From Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature (2017) · cites this paper as Power et al., 2013
The participation coefficient BT has previously been used successfully to characterize hubs in brain networks.
“The participation coefficient, BT, quantifies the extent to which a region connects across all modules (i.e. between-module strength) and has previously been used to successfully characterize hubs within brain networks (e.g. see Power et al., 2013).”
From The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance (2016) · cites this paper as Power et al., 2013
Participation coefficients were calculated to determine the degree to which nodes participated in multiple networks, based on arguments that this measure is the most appropriate way to identify hubs in correlation-based networks.
“Participation coefficients were calculated to determine the degree to which nodes participated in multiple networks, based on arguments that this measure is the most appropriate way to identify “hubs” in correlation-based networks32.”
From Insular cortex mediates approach and avoidance responses to social affective stimuli (2018) · cites this paper as [32]
Recent work suggests that hubs identified using degree-centrality may be confounded by community size.
“This is especially important given recent work that suggests that hubs identified using degree-centrality may be confounded by community size47.”
From Functional properties of resting state networks in healthy full-term newborns (2015) · cites this paper as [47]
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Dynamic causal brain circuits during working memory and their functional controllability
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Functional properties of resting state networks in healthy full-term newborns
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Précis on <i>The Cognitive-Emotional Brain</i>
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Large-Scale Functional Brain Organization
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Altered predictive capability of the brain network EEG model in schizophrenia during cognition
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- Authors
- Power, J. D., Schlaggar, B. L., Lessov-Schlaggar, C. N., & Petersen, S. E.
- Affiliations
- Washington University in St. Louis (US)
- Corresponding author
- Power, Jonathan D.
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2013-08
- Volume
- 79
- Issue
- 4
- Pages
- 798-813
- ISSN
- 0896-6273 (ISSN-L)
- PMC
- PMC3838673
- OpenAlex
- W2084479203
- Cited by
- 716
- References
- 64
- Citations by year
- 2026: 29 · 2025: 48 · 2024: 40 · 2023: 50 · 2022: 53 · 2021: 65 · 2020: 63 · 2019: 82 · 2018: 77 · 2017: 63 · 2016: 59 · 2015: 47 · 2014: 34 · 2013: 4 · 2012: 1
- FWCI
- 26.33
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Neuron (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (UL1TR000448)
- National Institute of Mental Health (F30MH094032, F30 MH940322)
- National Institute on Drug Abuse (K01DA027046)
- National Eye Institute (K12EY016336, K12 EY16336)
- National Cancer Institute (P30 CA 91842, P30CA091842)
- National Institute of Neurological Disorders and Stroke (R21NS061144)
- National Center for Advancing Translational Sciences
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD057076, 5R01 HD057076-03-S1, P30HD062171)
- 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 network hubs, functional brain networks, resting-state functional connectivity, default mode network, network subnetworks
- MeSH terms
- Adult, Algorithms, Brain, Brain Mapping, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Models, Neurological, Nerve Net, Neural Pathways, Oxygen, Regression Analysis, Rest, Cohort Studies, Case-Control Studies, Young Adult
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