The DOI matches its Crossref record; 56 of the 56 references it cites were found in a registry or an index.
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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
Citations
Cites
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
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
A default mode of brain function
Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.98.2.676 · Cited by 12,190
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-citationDistributed 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
The WU-Minn Human Connectome Project: An overview
Van Essen, D. C., Smith, S. M., Barch, D. M., Behrens, T. E. J., Yacoub, E., & Ugurbil, K. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.041 · Cited by 5,651
The structure of the nervous system of the nematode Caenorhabditis elegans
White, J. G., Southgate, E., Thomson, J. N., & Brenner, S. (1986) · Philosophical Transactions of the Royal Society of London. B, Biological Sciences · doi:10.1098/rstb.1986.0056 · Cited by 5,589 · cited 1×
“At the small scale of synaptically connected neurons, the connectome has only been completely mapped for the 302-neuron nervous system of the roundworm Caenorhabditis elegans, using serial electron microscopy and painstaking visual synaptic reconstruction (4).”
Genome-wide atlas of gene expression in the adult mouse brain
Lein, E. S., Hawrylycz, M. J., Ao, N., Ayres, M., Bensinger, A., Bernard, A., Boe, A. F., Boguski, M. S., Brockway, K. S., Byrnes, E. J., et al. (2007) · Nature · doi:10.1038/nature05453 · Cited by 5,572
Preface: Cerebral Cortex Has Come of Age
Goldman, P. S., & RAKIC, P. T. (1991) · Cerebral Cortex · doi:10.1093/cercor/1.1.1-a · Cited by 3,537
The economy of brain network organization
Bullmore, E., & Sporns, O. (2012) · Nature Reviews Neuroscience · doi:10.1038/nrn3214 · Cited by 3,284
Self-citationThe Human Connectome: A Structural Description of the Human Brain
Sporns, O., Tononi, G., & Kötter, R. (2005) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0010042 · Cited by 3,260
Ongur, D. (2000) · Cerebral Cortex · doi:10.1093/cercor/10.3.206 · Cited by 2,627
A mesoscale connectome of the mouse brain
Oh, S. W., Harris, J. A., Ng, L., Winslow, B., Cain, N., Mihalas, S., Wang, Q., Lau, C., Kuan, L., Henry, A. M., et al. (2014) · Nature · doi:10.1038/nature13186 · Cited by 2,612
Models of core/periphery structures
Borgatti, S. P., & Everett, M. G. (2000) · Social Networks · doi:10.1016/s0378-8733(99)00019-2 · Cited by 2,295
Statistical mechanics of community detection
Reichardt, J., & Bornholdt, S. (2006) · Physical Review E · doi:10.1103/physreve.74.016110 · Cited by 2,048
Modular and hierarchically modular organization of brain networks
Meunier, D., Lambiotte, R., & Bullmore, E. T. (2010) · Frontiers in Neuroscience · doi:10.3389/fnins.2010.00200 · Cited by 1,156
Self-citationImaging structural co-variance between human brain regions
Alexander-Bloch, A., Giedd, J. N., & Bullmore, E. (2013) · Nature Reviews Neuroscience · doi:10.1038/nrn3465 · Cited by 1,064
Self-citationCommunication in neuronal networks
Laughlin, S. B., & Sejnowski, T. J. (2003) · Science · doi:10.1126/science.1089662 · Cited by 950
Consensus clustering in complex networks
Lancichinetti, A., & Fortunato, S. (2012) · Scientific Reports · doi:10.1038/srep00336 · Cited by 905
A Weighted and Directed Interareal Connectivity Matrix for Macaque Cerebral Cortex
Markov, N. T., Ercsey-Ravasz, M. M., Ribeiro Gomes, A. R., Lamy, C., Magrou, L., Vezoli, J., Misery, P., Falchier, A., Quilodran, R., Gariel, M. A., et al. (2014) · Cerebral Cortex · doi:10.1093/cercor/bhs270 · Cited by 857
Partial least squares: a versatile tool for the analysis of high-dimensional genomic data
Boulesteix, A., & Strimmer, K. (2006) · Briefings in Bioinformatics · doi:10.1093/bib/bbl016 · Cited by 806
Neural Networks of the Mouse Neocortex
Zingg, B., Hintiryan, H., Gou, L., Song, M. Y., Bay, M., Bienkowski, M. S., Foster, N. N., Yamashita, S., Bowman, I., Toga, A. W., et al. (2014) · Cell · doi:10.1016/j.cell.2014.02.023 · Cited by 797
High-cost, high-capacity backbone for global brain communication
van den Heuvel, M. P., Kahn, R. S., Goñi, J., & Sporns, O. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1203593109 · Cited by 781
Cartography of complex networks: modules and universal roles
Guimerà, R., & Amaral, L. A. N. (2005) · Journal of Statistical Mechanics Theory and Experiment · doi:10.1088/1742-5468/2005/02/p02001 · Cited by 768
Thomas, C., Ye, F. Q., Irfanoglu, M. O., Modi, P., Saleem, K. S., Leopold, D. A., & Pierpaoli, C. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1405672111 · Cited by 725
Neocortex Patterning by the Secreted Signaling Molecule FGF8
Fukuchi-Shimogori, T., & Grove, E. (2001) · Science · doi:10.1126/science.1064252 · Cited by 707
Kaiser, M., & Hilgetag, C. C. (2005) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0020095 · Cited by 653
Histology Of The Nervous System
Cajal, S. R. Y., Azoulay, D. L., swanson, N., & Swanson, L. W. (1995) · doi:10.1093/oso/9780195074017.001.0001 · Cited by 634
Rat brains also have a default mode network
Lu, H., Zou, Q., Gu, H., Raichle, M. E., Stein, E. A., & Yang, Y. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1200506109 · Cited by 622
Cortical high-density counterstream architectures
Markov, N. T., Ercsey-Ravasz, M., Van Essen, D. C., Knoblauch, K., Toroczkai, Z., & Kennedy, H. (2013) · Science · doi:10.1126/science.1238406 · Cited by 616
A Predictive Network Model of Cerebral Cortical Connectivity Based on a Distance Rule
Ercsey-Ravasz, M., Markov, N. T., Lamy, C., Van Essen, D. C., Knoblauch, K., Toroczkai, Z., & Kennedy, H. (2013) · Neuron · doi:10.1016/j.neuron.2013.07.036 · Cited by 487
Atlas of Human Brain Connections
Catani, M., & Thiebaut de Schotten, M. (2012) · doi:10.1093/med/9780199541164.001.0001 · Cited by 467
Regional Dendritic and Spine Variation in Human Cerebral Cortex: a Quantitative Golgi Study
Jacobs, B. (2001) · Cerebral Cortex · doi:10.1093/cercor/11.6.558 · Cited by 423
Advanced database methodology for the Collation of Connectivity data on the Macaque brain (CoCoMac).
Stephan, K. E., Kamper, L., Bozkurt, A., Burns, G. A., Young, M. P., & Kötter, R. (2001) · Philosophical transactions of the Royal Society of London. Series B, Biological sciences · doi:10.1098/rstb.2001.0908 · Cited by 394
Density of neurons and synapses in the cerebral cortex of the mouse
Schüz, A., & Palm, G. (1989) · Journal of Comparative Neurology · doi:10.1002/cne.902860404 · Cited by 376
Neuron densities vary across and within cortical areas in primates
Collins, C. E., Airey, D. C., Young, N. A., Leitch, D. B., & Kaas, J. H. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1010356107 · Cited by 361
Simple models of human brain functional networks
Vértes, P. E., Alexander‐Bloch, A. F., Gogtay, N., Giedd, J. N., Rapoport, J. L., & Bullmore, E. T. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1111738109 · Cited by 356
Self-citationThe Rich Club of the C. elegans Neuronal Connectome
Towlson, E. K., Vértes, P. E., Ahnert, S. E., Schafer, W. R., & Bullmore, E. T. (2013) · The Journal of Neuroscience · doi:10.1523/jneurosci.3784-12.2013 · Cited by 343
Self-citationA High-Resolution Spatiotemporal Atlas of Gene Expression of the Developing Mouse Brain
Thompson, C. L., Ng, L., Menon, V., Martı́nez, S., Lee, C., Glattfelder, K. J., Sunkin, S. M., Henry, A. M., Lau, C., Dang, C., et al. (2014) · Neuron · doi:10.1016/j.neuron.2014.05.033 · Cited by 332
Network measures predict neuropsychological outcome after brain injury
Warren, D. E., Power, J. D., Bruss, J., Denburg, N. L., Waldron, E. J., Sun, H., Petersen, S. E., & Tranel, D. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1322173111 · Cited by 249
The role of long-range connections on the specificity of the macaque interareal cortical network
Markov, N. T., Ercsey-Ravasz, M., Lamy, C., Gomes, A. R. R., Magrou, L., Misery, P., Giroud, P., Barone, P., Dehay, C., Toroczkai, Z., et al. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1218972110 · Cited by 217
CoCoMac 2.0 and the future of tract-tracing databases
Bakker, R., Wächtler, T., & Diesmann, M. (2012) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2012.00030 · Cited by 176
Herculano‐Houzel, S., WATSON, C. R. R., & Paxinos, G. (2013) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2013.00035 · Cited by 164
Self-citationScholtens, L. H., Schmidt, R., de Reus, M. A., & van den Heuvel, M. P. (2014) · The Journal of Neuroscience · doi:10.1523/jneurosci.0752-14.2014 · Cited by 160
(2012) · doi:10.1016/c2009-0-00185-8 · Cited by 152
Self-citationRelationships between Gene Expression and Brain Wiring in the Adult Rodent Brain
French, L., & Pavlidis, P. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1001049 · Cited by 129
Improved community structure detection using a modified fine-tuning strategy
Sun, Y., Danila, B., Josić́, K., & Bassler, K. E. (2009) · Europhysics Letters (EPL) · doi:10.1209/0295-5075/86/28004 · Cited by 125
Neuronal Density Determines Network Connectivity and Spontaneous Activity in Cultured Hippocampus
Ivenshitz, M., & Segal, M. (2010) · Journal of Neurophysiology · doi:10.1152/jn.00914.2009 · Cited by 112
The connectional organization of neural systems in the cat cerebral cortex
Scannell, J. W., & Young, M. P. (1993) · Current Biology · doi:10.1016/0960-9822(93)90331-h · Cited by 104
A predictive model of the cat cortical connectome based on cytoarchitecture and distance
Beul, S. F., Grant, S., & Hilgetag, C. C. (2014) · Brain Structure and Function · doi:10.1007/s00429-014-0849-y · Cited by 103
Synapse-to-neuron ratio is inversely related to neuronal density in mature neuronal cultures
Cullen, D. K., Gilroy, M. E., Irons, H. R., & LaPlaca, M. C. (2010) · Brain Research · doi:10.1016/j.brainres.2010.08.058 · Cited by 86
Phase transition in the economically modeled growth of a cellular nervous system
Nicosia, V., Vértes, P. E., Schafer, W. R., Latora, V., & Bullmore, E. T. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1300753110 · Cited by 83
Self-citationCommunication and wiring in the cortical connectome
Budd, J. M. L., & Kisvárday, Z. F. (2012) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2012.00042 · Cited by 71
Optimally wired subnetwork determines neuroanatomy of Caenorhabditis elegans
Pérez‐Escudero, A., & de Polavieja, G. G. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0703183104 · Cited by 58
Gene Expression in the Rodent Brain is Associated with Its Regional Connectivity
Wolf, L., Goldberg, C., Manor, N., Sharan, R., & Ruppin, E. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002040 · Cited by 56
Why data coherence and quality is critical for understanding interareal cortical networks
Kennedy, H., Knoblauch, K., & Toroczkai, Z. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.031 · Cited by 47
Cited by
Sorted by citations
Bassett, D. S., & Sporns, O. (2017) · Nature Neuroscience · doi:10.1038/nn.4502 · Cited by 2,398
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
Small-World Brain Networks Revisited
Bassett, D. S., & Bullmore, E. T. (2016) · The Neuroscientist · doi:10.1177/1073858416667720 · Cited by 887
Self-citationHierarchical organization of cortical and thalamic connectivity
Harris, J. A., Mihalas, S., Hirokawa, K. E., Whitesell, J. D., Choi, H., Bernard, A., Bohn, P., Caldejon, S., Casal, L., Cho, A., et al. (2019) · Nature · doi:10.1038/s41586-019-1716-z · Cited by 785
Betzel, R. F., & Bassett, D. S. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.11.006 · Cited by 587
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 ›
Are you an author, or do you see a problem with this page? Request a review