The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
The human connectome: a complex network
Abstract
The human brain is a complex network. An important first step toward understanding the function of such a network is to map its elements and connections, to create a comprehensive structural description of the network architecture. This paper reviews current empirical efforts toward generating a network map of the human brain, the human connectome, and explores how the connectome can provide new insights into the organization of the brain's structural connections and their role in shaping functional dynamics. Network studies of structural connectivity obtained from noninvasive neuroimaging have revealed a number of highly nonrandom network attributes, including high clustering and modularity combined with high efficiency and short path length. The combination of these attributes simultaneously promotes high specialization and high integration within a modular small‐world architecture. Structural and functional networks share some of the same characteristics, although their relationship is complex and nonlinear. Future studies of the human connectome will greatly expand our knowledge of network topology and dynamics in the healthy, developing, aging, and diseased brain.
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
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-citationNetwork biology: understanding the cell's functional organization
Barabási, A.-L., & Oltvai, Z. N. (2004) · Nature Reviews Genetics · doi:10.1038/nrg1272 · Cited by 7,903
Distributed 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
Functional connectivity in the resting brain: a network analysis of the default mode hypothesis
Greicius, M. D., Krasnow, B., Reiss, A. L., & Menon, V. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0135058100 · Cited by 6,358
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
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-citationEpigenetic differences arise during the lifetime of monozygotic twins
Fraga, M. F., Ballestar, E., Paz, M. F., Ropero, S., Setien, F., Ballestar, M. L., Heine-Suñer, D., Cigudosa, J. C., Urioste, M., Benitez, J., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0500398102 · Cited by 3,602
Investigations into resting-state connectivity using independent component analysis
Beckmann, C. F., DeLuca, M., Devlin, J. T., & Smith, S. M. (2005) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2005.1634 · Cited by 3,398
The 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-citation
Cited by
Large-scale brain networks and psychopathology: a unifying triple network model
Menon, V. (2011) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2011.08.003 · Cited by 4,168
The structure and dynamics of multilayer networks
Boccaletti, S., Bianconi, G., Criado, R., del Genio, C. I., Gómez-Gardeñes, J., Romance, M., Sendiña-Nadal, I., Wang, Z., & Zanin, M. (2014) · Physics Reports · doi:10.1016/j.physrep.2014.07.001 · Cited by 3,125
Rich-club organization of the human connectome
van den Heuvel, M. P., & Sporns, O. (2011) · The Journal of Neuroscience · doi:10.1523/jneurosci.3539-11.2011 · Cited by 2,354
Self-citationA theoretically based index of consciousness independent of sensory processing and behavior
Casali, A. G., Gosseries, O., Rosanova, M., Boly, M., Sarasso, S., Casali, K. R., Casarotto, S., Bruno, M.-A., Laureys, S., Tononi, G., et al. (2013) · Science Translational Medicine · doi:10.1126/scitranslmed.3006294 · Cited by 1,246
Sex differences in the structural connectome of the human brain
Ingalhalikar, M., Smith, A., Parker, D., Satterthwaite, T. D., Elliott, M. A., Ruparel, K., Hakonarson, H., Gur, R. E., Gur, R. C., & Verma, R. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1316909110 · Cited by 1,223
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-citationDisrupted brain connectivity networks in drug-naive, first-episode major depressive disorder
Zhang, J., Wang, J., Wu, Q., Kuang, W., Huang, X., He, Y., & Gong, Q. (2011) · Biological Psychiatry · doi:10.1016/j.biopsych.2011.05.018 · Cited by 951
Schizophrenia, neuroimaging and connectomics
Fornito, A., Zalesky, A., Pantelis, C., & Bullmore, E. T. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.12.090 · Cited by 716
Graph analysis of the human connectome: Promise, progress, and pitfalls
Fornito, A., Zalesky, A., & Breakspear, M. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.087 · Cited by 743
Raichle, M. E. (2011) · Brain Connectivity · doi:10.1089/brain.2011.0019 · Cited by 672
Fox, M. D., Buckner, R. L., Liu, H., Chakravarty, M. M., Lozano, A. M., & Pascual-Leone, A. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1405003111 · Cited by 617
Tractography: Where Do We Go from Here?
Jbabdi, S., & Johansen-Berg, H. (2011) · Brain Connectivity · doi:10.1089/brain.2011.0033 · Cited by 614
Brain hyperconnectivity in children with autism and its links to social deficits
Supekar, K., Uddin, L. Q., Khouzam, A., Phillips, J., Gaillard, W. D., Kenworthy, L. E., Yerys, B. E., Vaidya, C. J., & Menon, V. (2013) · Cell Reports · doi:10.1016/j.celrep.2013.10.001 · Cited by 538
Johnson, M. W., Hendricks, P. S., Barrett, F. S., & Griffiths, R. R. (2019) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2018.11.010 · Cited by 506
Functional network disruption in the degenerative dementias
Pievani, M., de Haan, W., Wu, T., Seeley, W. W., & Frisoni, G. B. (2011) · The Lancet Neurology · doi:10.1016/s1474-4422(11)70158-2 · Cited by 481
Disrupted Functional Brain Connectome in Individuals at Risk for Alzheimer's Disease
Wang, J., Zuo, X., Dai, Z., Xia, M., Zhao, Z., Zhao, X., Jia, J., Han, Y., & He, Y. (2013) · Biological Psychiatry · doi:10.1016/j.biopsych.2012.03.026 · Cited by 432
Human brain mapping: A systematic comparison of parcellation methods for the human cerebral cortex
Arslan, S., Ktena, S. I., Makropoulos, A., Robinson, E. C., Rueckert, D., & Parisot, S. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2017.04.014 · Cited by 374
Cognitive Network Neuroscience
Medaglia, J. D., Lynall, M.-E., & Bassett, D. S. (2015) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00810 · Cited by 419
The 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
“…the presence of a small-world modular organization with functionally coupled communities, short communication pathways, and the formation of central connectivity hubs facilitating efficient global communication (Bassett and Bullmore 2006; Hagmann et al. 2008; van den Heuvel and Sporns 2011).”
BackgroundMethodInfluentialConvolutional Networks Can Learn to Generate Affinity Graphs for Image Segmentation
Turaga, S. C., Murray, J. F., Jain, V., Roth, F., Helmstaedter, M., Briggman, K., Denk, W., & Seung, H. S. (2010) · Neural Computation · doi:10.1162/neco.2009.10-08-881 · Cited by 376
A tutorial in connectome analysis: Topological and spatial features of brain networks
Kaiser, M. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.05.025 · Cited by 371
Di Martino, A., Zuo, X.-N., Kelly, C., Grzadzinski, R., Mennes, M., Schvarcz, A., Rodman, J., Lord, C., Castellanos, F. X., & Milham, M. P. (2013) · Biological Psychiatry · doi:10.1016/j.biopsych.2013.02.011 · Cited by 345
Mathematical framework for large-scale brain network modeling in The Virtual Brain
Sanz-Leon, P., Knock, S. A., Spiegler, A., & Jirsa, V. K. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.01.002 · Cited by 356
Brain correlates of hypnosis: A systematic review and meta-analytic exploration
Landry, M., Lifshitz, M., & Raz, A. (2017) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2017.02.020 · Cited by 295
Beyond the Tripartite Cognition–Emotion–Interoception Model of the Human Insular Cortex
Uddin, L. Q., Kinnison, J., Pessoa, L., & Anderson, M. L. (2014) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00462 · Cited by 275
Connectivity differences in brain networks
Zalesky, A., Cocchi, L., Fornito, A., Murray, M. M., & Bullmore, E. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.01.068 · Cited by 260
Disrupted Small-World Brain Networks in Moderate Alzheimer's Disease: A Resting-State fMRI Study
Zhao, X., Liu, Y., Wang, X., Liu, B., Xi, Q., Guo, Q., Jiang, H., Jiang, T., & Wang, P. (2012) · PLoS ONE · doi:10.1371/journal.pone.0033540 · Cited by 215
Mapping Connectivity Damage in the Case of Phineas Gage
Van Horn, J. D., Irimia, A., Torgerson, C. M., Chambers, M. C., Kikinis, R., & Toga, A. W. (2012) · PLoS ONE · doi:10.1371/journal.pone.0037454 · Cited by 196
Mapping Structural Connectivity Using Diffusion MRI : Challenges and Opportunities
Yeh, C., Jones, D. K., Liang, X., Descoteaux, M., & Connelly, A. (2021) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.27188 · Cited by 187
Noninvasive brain stimulation treatments for addiction and major depression
Dunlop, K., Hanlon, C. A., & Downar, J. (2017) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.12985 · Cited by 171
Intrinsic connectomes are a predictive biomarker of remission in major depressive disorder
Korgaonkar, M. S., Goldstein-Piekarski, A. N., Fornito, A., & Williams, L. M. (2020) · Molecular Psychiatry · doi:10.1038/s41380-019-0574-2 · Cited by 163
Shin, D.-J., Jung, W. H., He, Y., Wang, J., Shim, G., Byun, M. S., Jang, J. H., Kim, S. N., Lee, T. Y., Park, H. Y., et al. (2014) · Biological Psychiatry · doi:10.1016/j.biopsych.2013.09.002 · Cited by 162
Dynamic network structure of interhemispheric coordination
Doron, K. W., Bassett, D. S., & Gazzaniga, M. S. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1216402109 · Cited by 153
The Cortico-Basal Ganglia-Cerebellar Network: Past, Present and Future Perspectives
Milardi, D., Quartarone, A., Bramanti, A., Anastasi, G., Bertino, S., Basile, G. A., Buonasera, P., Pilone, G., Celeste, G., Rizzo, G., et al. (2019) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2019.00061 · Cited by 162
Ongoing physiological processes in the cerebral cortex
Leopold, D. A., & Maier, A. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.10.059 · Cited by 127
Rayner, G., Jackson, G., & Wilson, S. (2016) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2015.09.022 · Cited by 104
Brain connectivity in normally developing children and adolescents
Khundrakpam, B. S., Lewis, J. D., Zhao, L., Chouinard-Decorte, F., & Evans, A. C. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.03.062 · Cited by 91
Fiber clustering versus the parcellation-based connectome
O'Donnell, L. J., Golby, A. J., & Westin, C.-F. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.066 · Cited by 83
Intrinsic Brain Network Biomarkers of Antidepressant Response: a Review
Dunlop, K., Talishinsky, A., & Liston, C. (2019) · Current Psychiatry Reports · doi:10.1007/s11920-019-1072-6 · Cited by 80
Large-Scale Functional Brain Organization
Menon, V. (2015) · Brain Mapping · doi:10.1016/b978-0-12-397025-1.00024-5 · Cited by 76
Rudrauf, D. (2014) · Advances in Neuroscience · doi:10.1155/2014/462765 · Cited by 74
Whole mouse brain structural connectomics using magnetic resonance histology
Wang, N., Anderson, R. J., Badea, A., Cofer, G., Dibb, R., Qi, Y., & Johnson, G. A. (2018) · Brain Structure and Function · doi:10.1007/s00429-018-1750-x · Cited by 71
Lohse, C., Bassett, D. S., Lim, K. O., & Carlson, J. M. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003712 · Cited by 62
Naro, A., Bramanti, P., Leo, A., Russo, M., & Calabrò, R. S. (2016) · Brain Topography · doi:10.1007/s10548-016-0489-z · Cited by 61
Sato, J. R., Salum, G. A., Gadelha, A., Vieira, G., Zugman, A., Picon, F. A., Pan, P. M., Hoexter, M. Q., Anés, M., Moura, L. M., et al. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2014.09.063 · Cited by 54
Madole, J. W., Ritchie, S. J., Cox, S. R., Buchanan, C. R., Hernández, M. V., Maniega, S. M., Wardlaw, J. M., Harris, M. A., Bastin, M. E., Deary, I. J., et al. (2021) · Biological Psychiatry · doi:10.1016/j.biopsych.2020.06.010 · Cited by 45
Aberrant spontaneous and task-dependent functional connections in the anxious brain
MacNamara, A., DiGangi, J., & Phan, K. L. (2016) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2015.12.004 · Cited by 34
Hilger, K., & Fiebach, C. J. (2019) · Network Neuroscience · doi:10.1162/netn_a_00083 · Cited by 31
Intrinsic Connectivity Networks of Glutamate-Mediated Antidepressant Response: A Neuroimaging Review
Demchenko, I., Tassone, V. K., Kennedy, S. H., Dunlop, K., & Bhat, V. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.864902 · Cited by 29
“One of the major advances in the field of neuroimaging is the demonstration that the activity of brain regions is organized into coherent networks that are functionally distinct (21).”
BackgroundLarge-scale, closed-loop interrogation of neural circuits underlying cognition.
Khodagholy, D., Ferrero, J. J., Park, J., Zhao, Z., & Gelinas, J. N. (2022) · Trends in Neurosciences · doi:10.1016/j.tins.2022.10.003 · Cited by 27
Basile, G. A., Tatti, E., Bertino, S., Milardi, D., Genovese, G., Bruno, A., Muscatello, M. R. A., Ciurleo, R., Cerasa, A., Quartarone, A., et al. (2024) · Brain Structure and Function · doi:10.1007/s00429-024-02781-9 · Cited by 24
“…functions within brain regions, the last decade has seen a paradigmatic shift toward a network-based approach, setting the focus on how distinct brain regions are interconnected together and interact in space and time from a structural and functional point of view (Sporns 2011; Uddin 2013).”
BackgroundGendron, M., Mesquita, B., & Barrett, L. F. (2020) · Culture, Mind, and Brain · doi:10.1017/9781108695374.010 · Cited by 20
Metadata
- Authors
- Sporns, O.
- Venue
- Annals of the New York Academy of Sciences
- Publisher
- Wiley
- Published
- 2011-01-04
- Volume
- 1224
- Issue
- 1
- Pages
- 109-125
- ISSN
- 0077-8923 (ISSN-L)
- 1749-6632
- OpenAlex
- W2124953625
- Cited by
- 1,394
- References
- 39
- FWCI
- 41.60
- Type
- journal article
- Language
- en
- Open access
- closed
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.