The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Rich Club Organization of Macaque Cerebral Cortex and Its Role in Network Communication
Open accessAbstract
Graph-theoretical analysis of brain connectivity data has revealed significant features of brain network organization across a range of species. Consistently, large-scale anatomical networks exhibit highly nonrandom attributes including an efficient small world modular architecture, with distinct network communities that are interlinked by hub regions. The functional importance of hubs motivates a closer examination of their mutual interconnections, specifically to examine the hypothesis that hub regions are more densely linked than expected based on their degree alone, i.e. forming a central rich club. Extending recent findings of rich club topology in the cat and human brain, this report presents evidence for the existence of rich club organization in the cerebral cortex of a non-human primate, the macaque monkey, based on a connectivity data set representing a collation of numerous tract tracing studies. Rich club regions comprise portions of prefrontal, parietal, temporal and insular cortex and are widely distributed across network communities. An analysis of network motifs reveals that rich club regions tend to form star-like configurations, indicative of their central embedding within sets of nodes. In addition, rich club nodes and edges participate in a large number of short paths across the network, and thus contribute disproportionately to global communication. As rich club regions tend to attract and disperse communication paths, many of the paths follow a characteristic pattern of first increasing and then decreasing node degree. Finally, the existence of non-reciprocal projections imposes a net directional flow of paths into and out of the rich club, with some regions preferentially attracting and others dispersing signals. Overall, the demonstration of rich club organization in a non-human primate contributes to our understanding of the network principles underlying neural connectivity in the mammalian brain, and further supports the hypothesis that rich club regions and connections have a central role in global brain communication.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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-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
Mapping the structural core of human cerebral cortex
Hagmann, P., Cammoun, L., Gigandet, X., Meuli, R., Honey, C. J., Wedeen, V. J., & Sporns, O. (2008) · PLoS Biology · doi:10.1371/journal.pbio.0060159 · Cited by 4,173
Self-citationThe 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
Self-citationMesulam, M. (1998) · Brain · doi:10.1093/brain/121.6.1013 · Cited by 3,072
Towards a cognitive neuroscience of consciousness: basic evidence and a workspace framework
Dehaene, S. (2001) · Cognition · doi:10.1016/s0010-0277(00)00123-2 · Cited by 2,498 · cited 1×
“The connection topology of a central collective of densely interconnected regions that attract and disperse large volumes of signal traffic is reminiscent of proposals of an integrative core or ‘‘workspace’’ in modern theories of cognition and consciousness [71–75].”
BackgroundTononi, G., Sporns, O., & Edelman, G. M. (1994) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.91.11.5033 · Cited by 1,626
Self-citationNetwork ‘Small-World-Ness’: A Quantitative Method for Determining Canonical Network Equivalence
Humphries, M. D., & Gurney, K. (2008) · PLoS ONE · doi:10.1371/journal.pone.0002051 · Cited by 1,442
Cited by
Network hubs in the human brain
van den Heuvel, M. P., & Sporns, O. (2013) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2013.09.012 · Cited by 2,123
Self-citationStructural and Functional Brain Networks: From Connections to Cognition
Park, H.-J., & Friston, K. (2013) · Science · doi:10.1126/science.1238411 · Cited by 2,106
The connectomics of brain disorders
Fornito, A., Zalesky, A., & Breakspear, M. (2015) · Nature Reviews Neuroscience · doi:10.1038/nrn3901 · Cited by 1,734
Function in the human connectome: task-fMRI and individual differences in behavior
Barch, D. M., Burgess, G. C., Harms, M. P., Petersen, S. E., Schlaggar, B. L., Corbetta, M., Glasser, M. F., Curtiss, S., Dixit, S., Feldt, C., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.033 · Cited by 1,529
Sporns, O., & Betzel, R. F. (2016) · Annual Review of Psychology · doi:10.1146/annurev-psych-122414-033634 · Cited by 1,334
Self-citationThe hubs of the human connectome are generally implicated in the anatomy of brain disorders
Crossley, N. A., Mechelli, A., Scott, J., Carletti, F., Fox, P. T., McGuire, P., & Bullmore, E. T. (2014) · Brain · doi:10.1093/brain/awu132 · Cited by 1,166
Network attributes for segregation and integration in the human brain
Sporns, O. (2013) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2012.11.015 · Cited by 1,067
Self-citationEvidence for hubs in human functional brain networks
Power, J. D., Schlaggar, B. L., Lessov-Schlaggar, C. N., & Petersen, S. E. (2013) · Neuron · doi:10.1016/j.neuron.2013.07.035 · Cited by 716
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
Donahue, C. J., Sotiropoulos, S. N., Jbabdi, S., Hernandez-Fernandez, M., Behrens, T. E., Dyrby, T. B., Coalson, T., Kennedy, H., Knoblauch, K., Van Essen, D. C., et al. (2016) · The Journal of Neuroscience · doi:10.1523/jneurosci.0493-16.2016 · Cited by 380
An anatomical substrate for integration among functional networks in human cortex
van den Heuvel, M. P., & Sporns, O. (2013) · The Journal of Neuroscience · doi:10.1523/jneurosci.2128-13.2013 · Cited by 404
Self-citationThe Neonatal Connectome During Preterm Brain Development
van den Heuvel, M. P., Kersbergen, K. J., de Reus, M. A., Keunen, K., Kahn, R. S., Groenendaal, F., de Vries, L. S., & Benders, M. J. N. L. (2015) · Cerebral Cortex · doi:10.1093/cercor/bhu095 · Cited by 386
“High consistency of connectivity patterns across mammalian species (Markov et al. 2011; Goulas et al. 2014; Harriger et al. 2012; Shi et al. 2012; de Reus and Van den Heuvel 2013), together with findings of a genetic background of brain networks (Glahn et al.”
BackgroundRich-club organization of the newborn human brain
Ball, G., Aljabar, P., Zebari, S., Tusor, N., Arichi, T., Merchant, N., Robinson, E. C., Ogundipe, E., Rueckert, D., Edwards, A. D., et al. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1324118111 · Cited by 360
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-citationThe Multilayer Connectome of Caenorhabditis elegans
Bentley, B., Branicky, R., Barnes, C. L., Chew, Y. L., Yemini, E., Bullmore, E. T., Vértes, P. E., & Schafer, W. R. (2016) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005283 · Cited by 269
Redefining the role of limbic areas in cortical processing
Chanes, L., & Barrett, L. F. (2016) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2015.11.005 · Cited by 259
Adolfi, F., Couto, B., Richter, F., Decety, J., Lopez, J., Sigman, M., Manes, F., & Ibáñez, A. (2017) · Cortex · doi:10.1016/j.cortex.2016.12.019 · Cited by 238
Optimally controlling the human connectome: the role of network topology
Betzel, R. F., Gu, S., Medaglia, J. D., Pasqualetti, F., & Bassett, D. S. (2016) · Scientific Reports · doi:10.1038/srep30770 · Cited by 247
Architecture of the cerebral cortical association connectome underlying cognition
Bota, M., Sporns, O., & Swanson, L. W. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1504394112 · Cited by 231
Self-citationConnectomics-Based Analysis of Information Flow in the Drosophila Brain
Shih, C.-T., Sporns, O., Yuan, S.-L., Su, T.-S., Lin, Y.-J., Chuang, C.-C., Wang, T.-Y., Lo, C.-C., Greenspan, R. J., & Chiang, A.-S. (2015) · Current Biology · doi:10.1016/j.cub.2015.03.021 · Cited by 200
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
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
Self-citationNetwork structure shapes spontaneous functional connectivity dynamics
Shen, K., Hutchison, R. M., Bezgin, G., Everling, S., & McIntosh, A. R. (2015) · The Journal of Neuroscience · doi:10.1523/jneurosci.4903-14.2015 · Cited by 171
Developmental changes in brain network hub connectivity in late adolescence
Baker, S. T. E., Lubman, D. I., Yucel, M., Allen, N. B., Whittle, S., Fulcher, B. D., Zalesky, A., & Fornito, A. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.5043-14.2015 · Cited by 168
van den Heuvel, M. P., de Reus, M. A., Feldman Barrett, L., Scholtens, L. H., Coopmans, F. M. T., Schmidt, R., Preuss, T. M., Rilling, J. K., & Li, L. (2015) · Human Brain Mapping · doi:10.1002/hbm.22828 · Cited by 131
Mapping putative hubs in human, chimpanzee and rhesus macaque connectomes via diffusion tractography
Li, L., Hu, X., Preuss, T. M., Glasser, M. F., Damen, F. W., Qiu, Y., & Rilling, J. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.024 · Cited by 109
Opportunities 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
Rich-club in the brain’s macrostructure: Insights from graph theoretical analysis
Kim, D.-J., & Min, B.-K. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.06.039 · Cited by 73
“The cortical connectivity matrix (binarized with 242 nodes and 4090 connections) defined from 410 neural tracing studies of the macaque revealed rich-club regions in the prefrontal, paracingu-late, anterior cingulate, parietal, and temporal cortices [55].”
BackgroundThe integrated information theory of consciousness: A case of mistaken identity
Merker, B., Williford, K., & Rudrauf, D. (2022) · Behavioral and Brain Sciences · doi:10.1017/s0140525x21000881 · Cited by 49
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
Self-citation
Metadata
- Authors
- Harriger, L., van den Heuvel, M. P., & Sporns, O.
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2012-09-28
- Volume
- 7
- Issue
- 9
- Pages
- e46497
- ISSN
- 1932-6203 (ISSN-L)
- OpenAlex
- W1984988256
- Cited by
- 303
- References
- 19
- FWCI
- 11.82
- Type
- journal article
- Language
- en
- Open access
- gold
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
