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-citation
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
Brain networks and cognitive architectures
Petersen, S. E., & Sporns, O. (2015) · Neuron · doi:10.1016/j.neuron.2015.09.027 · Cited by 553
Whitaker, K. J., Vértes, P. E., Romero-García, R., Váša, F., Moutoussis, M., Prabhu, G., Weiskopf, N., Callaghan, M. F., Wagstyl, K., Rittman, T., et al. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1601745113 · Cited by 519
Self-citationThe physics of brain network structure, function and control
Lynn, C. W., & Bassett, D. S. (2019) · Nature Reviews Physics · doi:10.1038/s42254-019-0040-8 · Cited by 452
Small-world human brain networks: Perspectives and challenges
Liao, X., Vasilakos, A. V., & Yong He, A. (2017) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2017.03.018 · Cited by 443
Bridging the Gap between Connectome and Transcriptome
Fornito, A., Arnatkevičiūtė, A., & Fulcher, B. (2018) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2018.10.005 · Cited by 381
van den Heuvel, M. P., Bullmore, E. T., & Sporns, O. (2016) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2016.03.001 · Cited by 328
Self-citationRapid Reconfiguration of the Functional Connectome after Chemogenetic Locus Coeruleus Activation
Zerbi, V., Floriou‐Servou, A., Markicevic, M., Vermeiren, Y., Sturman, O., Privitera, M., von Ziegler, L., Ferrari, K. D., Weber, B., Deyn, P. P. D., et al. (2019) · Neuron · doi:10.1016/j.neuron.2019.05.034 · Cited by 343
Generative models of the human connectome
Betzel, R. F., Avena-Koenigsberger, A., Goñi, J., He, Y., de Reus, M. A., Griffa, A., Vértes, P. E., Mišic, B., Thiran, J.-P., Hagmann, P., et al. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.09.041 · Cited by 303
Self-citationArchitecture of the Mouse Brain Synaptome
Zhu, F., Cizeron, M., Qiu, Z., Benavides‐Piccione, R., Kopanitsa, M. V., Skene, N., Koniaris, B., DeFelipe, J., Fransén, E. A., Komiyama, N. H., et al. (2018) · Neuron · doi:10.1016/j.neuron.2018.07.007 · Cited by 272
Specificity and robustness of long-distance connections in weighted, interareal connectomes
Betzel, R. F., & Bassett, D. S. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1720186115 · Cited by 223
Computational network biology: Data, models, and applications
Liu, C., Ma, Y., Zhao, J., Nussinov, R., Zhang, Y., Cheng, F., & Zhang, Z. (2019) · Physics Reports · doi:10.1016/j.physrep.2019.12.004 · Cited by 230
A metabolome atlas of the aging mouse brain
Ding, J., Ji, J., Rabow, Z., Shen, T., Folz, J. S., Brydges, C. R., Fan, S., Lu, X., Mehta, S. S., Showalter, M. R., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-26310-y · Cited by 224
Vértes, P. E., Rittman, T., Whitaker, K. J., Romero-García, R., Váša, F., Kitzbichler, M. G., Wagstyl, K., Fonagy, P., Dolan, R. J., Jones, P. B., et al. (2016) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2015.0362 · Cited by 206
Self-citationThe importance of the whole: Topological data analysis for the network neuroscientist
Sizemore, A. E., Phillips‐Cremins, J. E., Ghrist, R., & Bassett, D. S. (2018) · Network Neuroscience · doi:10.1162/netn_a_00073 · Cited by 215
The contribution of geometry to the human connectome
Roberts, J. A., Perry, A., Lord, A. R., Roberts, G., Mitchell, P. B., Smith, R. E., Calamante, F., & Breakspear, M. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.09.009 · Cited by 213
Fulcher, B. D., Arnatkeviciute, A., & Fornito, A. (2021) · Nature Communications · doi:10.1038/s41467-021-22862-1 · Cited by 164
A mechanistic model of connector hubs, modularity and cognition
Bertolero, M. A., Yeo, B. T. T., Bassett, D. S., & D’Esposito, M. (2018) · Nature Human Behaviour · doi:10.1038/s41562-018-0420-6 · Cited by 213
From maps to multi-dimensional network mechanisms of mental disorders
Braun, U., Schaefer, A., Betzel, R. F., Tost, H., Meyer-Lindenberg, A., & Bassett, D. S. (2018) · Neuron · doi:10.1016/j.neuron.2017.11.007 · Cited by 177
A mechanistic model of connector hubs, modularity and cognition
Bertolero, M. A., Yeo, B. T. T., Bassett, D. S., & D’Esposito, M. (2018) · RePEc: Research Papers in Economics · Cited by 181
Seguin, C., van den Heuvel, M. P., & Zalesky, A. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1801351115 · Cited by 173
Subcortical contributions to large-scale network communication
Bell, P. T., & Shine, J. M. (2016) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2016.08.036 · Cited by 168
Ardesch, D. J., Scholtens, L. H., Li, L., Preuss, T. M., Rilling, J. K., & van den Heuvel, M. P. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1818512116 · Cited by 152
Vanni, M. P., Chan, A. W., Balbi, M., Silasi, G., & Murphy, T. H. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.3560-16.2017 · Cited by 152
Regional, Layer, and Cell-Type-Specific Connectivity of the Mouse Default Mode Network
Whitesell, J. D., Liska, A., Coletta, L., Hirokawa, K. E., Bohn, P., Williford, A., Groblewski, P. A., Graddis, N., Kuan, L., Knox, J. E., et al. (2020) · Neuron · doi:10.1016/j.neuron.2020.11.011 · Cited by 139
Role of graph architecture in controlling dynamical networks with applications to neural systems
Kim, J. Z., Soffer, J., Kahn, A. E., Vettel, J. M., Pasqualetti, F., & Bassett, D. S. (2017) · Nature Physics · doi:10.1038/nphys4268 · Cited by 135
Prefrontal connectomics: From anatomy to human imaging
Haber, S. N., Liu, H., Seidlitz, J., & Bullmore, E. (2022) · Neuropsychopharmacology · doi:10.1038/s41386-021-01156-6 · Cited by 129
Self-citationNetworks behind the morphology and structural design of living systems
Gosak, M., Milojević, M., Duh, M., Skok, K., & Perc, M. (2022) · Physics of Life Reviews · doi:10.1016/j.plrev.2022.03.001 · Cited by 128
Betzel, R. F., Medaglia, J. D., Kahn, A. E., Soffer, J., Schonhaut, D. R., & Bassett, D. S. (2019) · Nature Biomedical Engineering · doi:10.1038/s41551-019-0404-5 · Cited by 129
Micro-connectomics: probing the organization of neuronal networks at the cellular scale
Schröter, M. S., Paulsen, O., & Bullmore, E. T. (2017) · Nature reviews. Neuroscience · doi:10.1038/nrn.2016.182 · Cited by 128
Self-citationHigh-resolution data-driven model of the mouse connectome
Knox, J. E., Harris, K. D., Graddis, N., Whitesell, J. D., Zeng, H., Harris, J. A., Shea‐Brown, E., & Mihalaş, Ş. (2018) · Network Neuroscience · doi:10.1162/netn_a_00066 · Cited by 117
Toward Best Practices for Imaging Transcriptomics of the Human Brain
Arnatkevičiūtė, A., Markello, R. D., Fulcher, B., Mišić, B., & Fornito, A. (2022) · Biological Psychiatry · doi:10.1016/j.biopsych.2022.10.016 · Cited by 119
Seguin, C., Tian, Y., & Zalesky, A. (2020) · Network Neuroscience · doi:10.1162/netn_a_00161 · Cited by 120
Majka, P., Bai, S., Bakola, S., Bednarek, S., Chan, J. M., Jermakow, N., Passarelli, L., Reser, D. H., Theodoni, P., Worthy, K. H., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-14858-0 · Cited by 112
Network structure of the mouse brain connectome with voxel resolution
Coletta, L., Pagani, M., Whitesell, J. D., Harris, J. A., Bernhardt, B. C., & Gozzi, A. (2020) · Science Advances · doi:10.1126/sciadv.abb7187 · Cited by 113
Diversity of meso-scale architecture in human and non-human connectomes
Betzel, R. F., Medaglia, J. D., & Bassett, D. S. (2018) · RePEc: Research Papers in Economics · Cited by 122
Towards a biologically annotated brain connectome
Bazinet, V., Hansen, J. Y., & Mišić, B. (2023) · Nature reviews. Neuroscience · doi:10.1038/s41583-023-00752-3 · Cited by 118
Neural correlates of sparse coding and dimensionality reduction
Beyeler, M., Rounds, E. L., Carlson, K. D., Dutt, N., & Krichmar, J. L. (2019) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1006908 · Cited by 104
Principles of ipsilateral and contralateral cortico-cortical connectivity in the mouse
Goulas, A., Uylings, H. B. M., & Hilgetag, C. C. (2016) · Brain Structure and Function · doi:10.1007/s00429-016-1277-y · Cited by 102
Brain rhythms define distinct interaction networks with differential dependence on anatomy
Vezoli, J., Vinck, M. A., Bosman, C. A., Bastos, A. M., Lewis, C., Kennedy, H., & Fries, P. (2021) · Neuron · doi:10.1016/j.neuron.2021.09.052 · Cited by 93
Graph theoretical modeling of baby brain networks
Zhao, T., Xu, Y., & Yong He, A. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.06.038 · Cited by 108
Inferring neural signalling directionality from undirected structural connectomes
Seguin, C., Razi, A., & Zalesky, A. (2019) · Nature Communications · doi:10.1038/s41467-019-12201-w · Cited by 96
Spatial Embedding Imposes Constraints on Neuronal Network Architectures
Stiso, J., & Bassett, D. S. (2018) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2018.09.007 · Cited by 98
Rittman, T., Rubinov, M., Vértes, P. E., Patel, A. X., Ginestet, C. E., Ghosh, B. C. P., Barker, R. A., Spillantini, M. G., Bullmore, E. T., & Rowe, J. B. (2016) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2016.09.001 · Cited by 94
Self-citationRich-club neurocircuitry: function, evolution, and vulnerability
Griffa, A., & van den Heuvel, M. P. (2018) · Dialogues in Clinical Neuroscience · doi:10.31887/dcns.2018.20.2/agriffa · Cited by 94
Characterizing the role of the structural connectome in seizure dynamics
Shah, P., Ashourvan, A., Mikhail, F., Pines, A. R., Kini, L. G., Oechsel, K. F., Das, S. R., Stein, J. M., Shinohara, R. T., Bassett, D. S., et al. (2019) · Brain · doi:10.1093/brain/awz125 · Cited by 94
Multiscale Neuroscience of Psychiatric Disorders
van den Heuvel, M. P., Scholtens, L. H., & Kahn, R. S. (2019) · Biological Psychiatry · doi:10.1016/j.biopsych.2019.05.015 · Cited by 79
The fractal brain: scale-invariance in structure and dynamics
Grosu, G. F., Hopp, A. V., Moca, V. V., Bârzan, H., Ciuparu, A., Ercsey-Ravasz, M., Winkel, M., Linde, H., & Muresan, R. C. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac363 · Cited by 87
Structural MRI of brain similarity networks
Sebenius, I., Dorfschmidt, L., Seidlitz, J., Alexander‐Bloch, A. F., Morgan, S. E., & Bullmore, E. T. (2024) · Nature reviews. Neuroscience · doi:10.1038/s41583-024-00882-2 · Cited by 82
Self-citationOpportunities and Challenges for Psychiatry in the Connectomic Era
Fornito, A., Bullmore, E. T., & Zalesky, A. (2017) · Biological Psychiatry: Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2016.08.003 · Cited by 74
Self-citationCan One Concurrently Record Electrical Spikes from Every Neuron in a Mammalian Brain?
Kleinfeld, D., Luan, L., Mitra, P. P., Robinson, J. T., Sarpeshkar, R., Shepard, K. L., Xie, C., & Harris, T. D. (2019) · Neuron · doi:10.1016/j.neuron.2019.08.011 · Cited by 78
Brain network reconfiguration for language and domain-general cognitive control in bilinguals
Wu, J., Yang, J., Chen, M., Li, S., Zhang, Z., Kang, C., Ding, G., & Guo, T. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.06.022 · Cited by 72
Diversity of information pathways drives sparsity in real-world networks
Ghavasieh, A., & Domenico, M. D. (2024) · Nature Physics · doi:10.1038/s41567-023-02330-x · Cited by 73
Structural connectome topology relates to regional BOLD signal dynamics in the mouse brain
Sethi, S. S., Zerbi, V., Wenderoth, N., Fornito, A., & Fulcher, B. (2017) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.4979281 · Cited by 72
Constraints and spandrels of interareal connectomes
Rubinov, M. (2016) · Nature Communications · doi:10.1038/ncomms13812 · Cited by 69
Self-citationSpatiotemporal ontogeny of brain wiring
Goulas, A., Betzel, R. F., & Hilgetag, C. C. (2019) · Science Advances · doi:10.1126/sciadv.aav9694 · Cited by 63
Mapping and comparing fMRI connectivity networks across species
Pagani, M., Gutierrez‐Barragan, D., de Guzman, A. E., Xu, T., & Gozzi, A. (2023) · Communications Biology · doi:10.1038/s42003-023-05629-w · Cited by 68
Chen, Y., Wang, S.-J., Hilgetag, C. C., & Zhou, C. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005776 · Cited by 60
Emerging Frontiers of Neuroengineering: A Network Science of Brain Connectivity
Bassett, D. S., Khambhati, A. N., & Grafton, S. T. (2017) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-071516-044511 · Cited by 61
Where the genome meets the connectome: Understanding how genes shape human brain connectivity
Arnatkevičiūtė, A., Fulcher, B., Bellgrove, M. A., & Fornito, A. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118570 · Cited by 60
Neuron density fundamentally relates to architecture and connectivity of the primate cerebral cortex
Beul, S. F., & Hilgetag, C. C. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.01.010 · Cited by 59
Weight thresholding on complex networks
Yan, X., Jeub, L. G. S., Flammini, A., Radicchi, F., & Fortunato, S. (2018) · Physical review. E · doi:10.1103/physreve.98.042304 · Cited by 61
The genetic relationships between brain structure and schizophrenia
Stauffer, E.-M., Bethlehem, R. A. I., Dorfschmidt, L., Won, H., Warrier, V., & Bullmore, E. T. (2023) · Nature Communications · doi:10.1038/s41467-023-43567-7 · Cited by 61
Self-citationThe cerebellum and cognition: further evidence for its role in language control
Yuan, Q., Li, H., Du, B., Dang, Q., Chang, Q., Zhang, Z., Zhang, M., Ding, G., Lu, C., & Guo, T. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac051 · Cited by 56
Connectome-based reservoir computing with the conn2res toolbox
Suárez, L. E., Mihalik, Á., Milisav, F., Marshall, K., Li, M., Vértes, P. E., Lajoie, G., & Mišić, B. (2024) · Nature Communications · doi:10.1038/s41467-024-44900-4 · Cited by 63
Liang, X., Hsu, L., Lu, H., Sumiyoshi, A., Yong He, A., & Yang, Y. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw416 · Cited by 51
Gradients of connectivity distance in the cerebral cortex of the macaque monkey
Oligschläger, S., Xu, T., Bączkowski, B. M., Falkiewicz, M., Falchier, A., Linn, G. S., & Margulies, D. S. (2018) · Brain Structure and Function · doi:10.1007/s00429-018-1811-1 · Cited by 45
Organizing principles of whole-brain functional connectivity in zebrafish larvae
Betzel, R. F. (2020) · Network Neuroscience · doi:10.1162/netn_a_00121 · Cited by 52
Cortical 3-hinges could serve as hubs in cortico-cortical connective network
Zhang, T., Li, X., Jiang, X., Ge, F., Zhang, S., Zhao, L., Liu, H., Huang, Y., Wang, X., Yang, J., et al. (2020) · Brain Imaging and Behavior · doi:10.1007/s11682-019-00204-6 · Cited by 42
Wiedermann, M., Donges, J. F., Kurths, J., & Donner, R. V. (2016) · Physical review. E · doi:10.1103/physreve.93.042308 · Cited by 37
Alpha Band Resting-State EEG Connectivity Is Associated With Non-verbal Intelligence
Zakharov, I., Олеговна, Т. А., Adamovich, T., Kovas, Y. V., & Malykh, S. B. (2020) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2020.00010 · Cited by 39
Ypma, R. J. F., & Bullmore, E. T. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005104 · Cited by 35
Self-citationWu, T., Grandjean, J., Bosshard, S. C., Rudin, M., Reutens, D. C., & Jiang, T. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.01.074 · Cited by 34
Synchronization dependent on spatial structures of a mesoscopic whole-brain network
Choi, H., & Mihalaş, Ş. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006978 · Cited by 36
Fukushima, M., & Sporns, O. (2020) · Communications Biology · doi:10.1038/s42003-020-01331-3 · Cited by 34
Kesler, S. R., Acton, P., Rao, V., & Ray, W. J. (2018) · Network Neuroscience · doi:10.1162/netn_a_00048 · Cited by 34
Brain network architecture constrains age-related cortical thinning
Petersen, M., Nägele, F. L., Mayer, C., Schell, M., Rimmele, D. L., Petersen, E. L., Kühn, S., Gallinat, J., Hanning, U. M. P., Fiehler, J., et al. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119721 · Cited by 31
Optimized connectome architecture for sensory-motor integration
Worrell, J. C., Rumschlag, J., Betzel, R. F., Sporns, O., & Mišić, B. (2017) · Network Neuroscience · doi:10.1162/netn_a_00022 · Cited by 32
A connectomics-based taxonomy of mammals
Suárez, L. E., Yovel, Y., van den Heuvel, M. P., Sporns, O., Assaf, Y., Lajoie, G., & Mišić, B. (2022) · eLife · doi:10.7554/elife.78635 · Cited by 33
A simple generative model of the mouse mesoscale connectome
Henriksen, S., Pang, R., & Wronkiewicz, M. (2016) · eLife · doi:10.7554/elife.12366 · Cited by 30
Architecture and evolution of semantic networks in mathematics texts
Christianson, N., Blevins, A. S., & Bassett, D. S. (2020) · Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rspa.2019.0741 · Cited by 30
Ashourvan, A., Shah, P., Pines, A. R., Gu, S., Lynn, C. W., Bassett, D. S., Davis, K. A., & Litt, B. (2021) · Communications Biology · doi:10.1038/s42003-021-01700-6 · Cited by 30
The human connectome from an evolutionary perspective
Ardesch, D. J., Scholtens, L. H., & van den Heuvel, M. P. (2019) · Progress in brain research · doi:10.1016/bs.pbr.2019.05.004 · Cited by 33
Edwards, T. J. C., Fenlon, L. R., Dean, R. J., Bunt, J., Sherr, E. H., & Richards, L. J. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116868 · Cited by 28
Critical scaling of whole-brain resting-state dynamics
Ponce‐Alvarez, A., Kringelbach, M. L., & Deco, G. (2023) · Communications Biology · doi:10.1038/s42003-023-05001-y · Cited by 27
Brain transcriptome atlases: a computational perspective
Mahfouz, A. E., Huisman, S. M. H., Lelieveldt, B. P. F., & Reinders, M. J. T. (2016) · Brain Structure and Function · doi:10.1007/s00429-016-1338-2 · Cited by 27
Colón-Pérez, L. M., Pino, J. A., Saha, K., Pompilus, M., Kaplitz, S., Choudhury, N., Jagnarine, D. A., Geste, J. R., Levin, B., Wilks, I., et al. (2018) · Neuropharmacology · doi:10.1016/j.neuropharm.2018.04.031 · Cited by 26
Emergence of phase clusters and coexisting states reveals the structure-function relationship
Yu, D., Wu, Y., Ding, Q., Li, T., & Jia, Y. (2024) · Physical review. E · doi:10.1103/physreve.109.054312 · Cited by 25
Cytoarchitectonic similarity is a wiring principle of the human connectomePreprint
Goulas, A., Werner, R., Beul, S. F., Säring, D., van den Heuvel, M. P., Triarhou, L. C., & Hilgetag, C. C. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/068254 · Cited by 27
Implementing a concept network model
Solomon, S. H., Medaglia, J. D., & Thompson‐Schill, S. L. (2019) · Behavior Research Methods · doi:10.3758/s13428-019-01217-1 · Cited by 24
Patterns and networks of language control in bilingual language production
Yuan, Q., Wu, J., Zhang, M., Zhang, Z., Chen, M., Ding, G., Lu, C., & Guo, T. (2021) · Brain Structure and Function · doi:10.1007/s00429-021-02218-7 · Cited by 25
Language context tunes brain network for language control in bilingual language production
Wu, J., Zhang, Z., Chen, M., Yuan, Q., Zhang, M., Yang, J., Lu, C., & Guo, T. (2020) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2020.107592 · Cited by 24
Froudist‐Walsh, S., Browning, P. G. F., Young, J., Murphy, K. L., Mars, R. B., Fleysher, L., & Croxson, P. L. (2018) · eLife · doi:10.7554/elife.34354 · Cited by 23
The Hidden Control Architecture of Complex Brain Networks
Lee, B., Kang, U., Chang, H., & Cho, K. (2019) · iScience · doi:10.1016/j.isci.2019.02.017 · Cited by 24
Uncovering the Transcriptional Correlates of Hub Connectivity in Neural Networks
Arnatkevičiūtė, A., Fulcher, B., & Fornito, A. (2019) · Frontiers in Neural Circuits · doi:10.3389/fncir.2019.00047 · Cited by 22
A multi-modal, asymmetric, weighted, and signed description of anatomical connectivity
Tanner, J., Faskowitz, J., Teixeira, A. S., Seguin, C., Coletta, L., Gozzi, A., Mišić, B., & Betzel, R. F. (2024) · Nature Communications · doi:10.1038/s41467-024-50248-6 · Cited by 23
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
