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Wiring cost and topological participation of the mouse brain connectome
Open accessAbstract
Brain connectomes are topologically complex systems, anatomically embedded in 3D space. Anatomical conservation of "wiring cost" explains many but not all aspects of these networks. Here, we examined the relationship between topology and wiring cost in the mouse connectome by using data from 461 systematically acquired anterograde-tracer injections into the right cortical and subcortical regions of the mouse brain. We estimated brain-wide weights, distances, and wiring costs of axonal projections and performed a multiscale topological and spatial analysis of the resulting weighted and directed mouse brain connectome. Our analysis showed that the mouse connectome has small-world properties, a hierarchical modular structure, and greater-than-minimal wiring costs. High-participation hubs of this connectome mediated communication between functionally specialized and anatomically localized modules, had especially high wiring costs, and closely corresponded to regions of the default mode network. Analyses of independently acquired histological and gene-expression data showed that nodal participation colocalized with low neuronal density and high expression of genes enriched for cognition, learning and memory, and behavior. The mouse connectome contains high-participation hubs, which are not explained by wiring-cost minimization but instead reflect competitive selection pressures for integrated network topology as a basis for higher cognitive and behavioral functions.
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“This is consistent with other studies showing presence of hubs in the mouse brain (Liska et al., 2015; Rubinov, Ypma, Watson, & Bullmore, 2015).”
From Functional and structural connectome properties in the 5XFAD transgenic mouse model of Alzheimer’s disease (2018) · cites this paper as Rubinov, Ypma, Watson, & Bullmore, 2015
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Self-citationConnectome architecture favours within-module diffusion and between-module routingPreprint
Seguin, C., Puxeddu, M. G., Faskowitz, J., Betzel, R. F., & Sporns, O. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.10.637586 · Cited by 2
Directed connectomes across species reveal conserved and divergent pathways of neural signalingPreprint
Venkadesh, S., Linn, W.-J., Tian, Y., Johnson, G. A., & Yeh, F.-C. F. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.07.674762 · Cited by 1
A Computational Perspective on the No-Strong-Loops Principle in Brain NetworksPreprint
Hadaeghi, F., Fakhar, K., Khajehnejad, M. A., & Hilgetag, C. C. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.24.678310 · Cited by 2
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Metadata
- Authors
- Rubinov, M., Ypma, R. J. F., Watson, C., & Bullmore, E. T.
- Affiliations
- University of Cambridge (GB)
- Curtin University (AU)
- GlaxoSmithKline (United Kingdom) (GB)
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2015-07-27
- Volume
- 112
- Issue
- 32
- Pages
- 10032-10037
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC4538676
- OpenAlex
- W1900134997
- Cited by
- 219
- References
- 56
- Citations by year
- 2026: 7 · 2025: 20 · 2024: 23 · 2023: 17 · 2022: 19 · 2021: 19 · 2020: 22 · 2019: 24 · 2018: 25 · 2017: 15 · 2016: 25 · 2015: 3
- FWCI
- 13.64
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Apollo (University of Cambridge) (acceptedVersion, cc-by-nc)
- eSpace (Curtin University) (submittedVersion)
- Funders
- Wellcome Trust (095844/Z/11/Z, 095844, 093875)
- National Institute for Health and Care Research (NF-SI-0513-10051)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- brain connectome, wiring cost, mouse brain, network topology, small-world networks, modular networks, network hubs, default mode network, axonal projections, anterograde tracing, neuronal density, gene expression, cognitive function, network integration
- MeSH terms
- Animals, Nerve Net, Gene Expression Profiling, Mice, Connectome
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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