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The Human Connectome: A Structural Description of the Human Brain
Open accessAbstract
The connection matrix of the human brain (the human "connectome") represents an indispensable foundation for basic and applied neurobiological research. However, the network of anatomical connections linking the neuronal elements of the human brain is still largely unknown. While some databases or collations of large-scale anatomical connection patterns exist for other mammalian species, there is currently no connection matrix of the human brain, nor is there a coordinated research effort to collect, archive, and disseminate this important information. We propose a research strategy to achieve this goal, and discuss its potential impact.
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“While a network approach to brain function is not a new idea, 25,49,68,85,88 a recent focus on brain connectivity as the underlying principle of the brain has gained eminence in the past decade, culminating in the search for the connectome, or the brain's “wiring diagram.”89”
From Graph theory analysis of complex brain networks: new concepts in brain mapping applied to neurosurgery (2016) · cites this paper as [89]
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“However, the anatomical accuracy of diffusion-weighted tractography, which infers neuronal pathways from tissue water diffusion direction, continues to be a matter of controversy, and reconstructing connectomes with both high sensitivity and high specificity remains challenging (Sporns et al. 2005; Bastiani et al. 2012; Jbabdi et al. 2013, 2015; Thomas et al. 2014; Azadbakht et al. 2015; Calabrese”
From Diffusion MRI-based cortical connectome reconstruction: dependency on tractography procedures and neuroanatomical characteristics (2018) · cites this paper as Sporns et al. 2005
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“Quantitative measures extracted for individual fiber pathways can also be used to construct a structural connectivity matrix to map the whole-brain network of structural connectivity between all pairs of gray matter regions (Hagmann et al., 2008a; Sporns et al., 2005).”
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Sporns et al., 2005
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“Medical neuralnanorobots are expected to have the capacity for real-time, non-destructive monitoring of single-neuron and single-synapse neuroelectric activity, local neuropeptide traffic, and other relevant functional data, while also allowing the acquisition of fundamental structural information from neuron surfaces, to enhance the connectome map of a living human brain”
From Human Brain/Cloud Interface (2019) · cites this paper as Sporns et al., 2005
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Cites
The sequence of the human genome
Venter, J. C., Adams, M. D., Myers, E. W., Li, P. W., Mural, R. J., Sutton, G. G., Smith, H. O., Yandell, M., Evans, C. A., Holt, R. A., et al. (2001) · Science · doi:10.1126/science.1058040 · Cited by 13,909
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
Friston, K. J., Harrison, L., & Penny, W. (2003) · NeuroImage · doi:10.1016/s1053-8119(03)00202-7 · Cited by 4,583
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 3,483
Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging
Behrens, T. E. J., Johansen-Berg, H., Woolrich, M. W., Smith, S. M., Wheeler-Kingshott, C. A. M., Boulby, P. A., Barker, G. J., Sillery, E. L., Sheehan, K., Ciccarelli, O., et al. (2003) · Nature Neuroscience · doi:10.1038/nn1075 · Cited by 2,330
The columnar organization of the neocortex
Mountcastle, V. B. (1997) · Brain · doi:10.1093/brain/120.4.701 · Cited by 2,314
Organization, development and function of complex brain networks
SPORNS, O., CHIALVO, D., KAISER, M., & HILGETAG, C. (2004) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2004.07.008 · Cited by 2,055
Self-citationTracking neuronal fiber pathways in the living human brain
Conturo, T. E., Lori, N. F., Cull, T. S., Akbudak, E., Snyder, A. Z., Shimony, J. S., McKinstry, R. C., Burton, H., & Raichle, M. E. (1999) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.96.18.10422 · Cited by 1,774
The Sequence of the Human Genome
Venter, J. C., Smith, H. O., & Adams, M. D. (2015) · Clinical Chemistry · doi:10.1373/clinchem.2014.237016 · Cited by 1,665
Looking into the functional architecture of the brain with diffusion MRI.
Le Bihan, D. (2003) · Nature Reviews Neuroscience · doi:10.1038/nrn1119 · Cited by 1,571
Flagged first, then by citations
1 of the 86 references couldn’t be matched to a known paper.
Research works printed but not linked: 77
- Kötter, 2001, Phil Trans R Soc Lond B Biol Sci, 356, 1111
- Amari, 2003, Neural Netw, 16, 1235
- 2003, Human brain function, 2nd ed
- Tononi, 1998, Trends Cogn Sci, 2, 44
- Friston, 2002, Annu Rev Neurosci, 25, 221
- McIntosh, 2000, Neural Netw, 13, 861
- Bressler, 1995, Brain Res Brain Res Rev, 20, 288
- Bressler, 2001, Trends Cogn Sci, 5, 26
- Tononi, 1999, Proc Natl Acad Sci U S A, 96, 3257
- Price, 2002, Trends Cogn Sci, 6, 416
- Crick, 1993, Nature, 361, 109
- Tootell, 2003, J Neurosci, 23, 3981
- Young, 1993, Proc R Soc Lond B Biol Sci, 252, 13
- Stephan, 2001, Philos Trans R Soc Lond B Biol Sci, 356, 1159
- Murre, 1995, Biol Cybern, 73, 529
- Engert, 1999, Nature, 399, 66
- Stepanyants, 2002, Neuron, 34, 275
- Kalisman, 2005, Proc Natl Acad Sci U S A, 102, 880
- Van Essen, 1998, Proc Natl Acad U S A, 95, 788
- Felleman, 1991, Cereb Cortex, 1, 1
- Scannell, 1999, Cereb Cortex, 9, 277
- Burns, 2000, Phil Trans R Soc Lond B Biol Sci, 355, 55
- Sporns, 2004, Trends Cogn Sci, 8, 418
- Hilgetag, 2000, Phil Trans R Soc Lond B Biol Sci, 355, 91
- Hilgetag, 2000, Phil Trans R Soc Lond B Biol Sci, 355, 71
- Hilgetag, 1996, Science, 271, 776
- Sporns, 2000, Cereb Cortex, 10, 127
- Sporns, 2004, Neuroinformatics, 2, 145
- Young, 1992, Nature, 358, 152
- Kötter, 2003, Neural Netw, 16, 1261
- Chklovskii, 2002, Neuron, 34, 341
- Köbbert, 2000, Prog Neurobiol, 62, 327
- Kötter, 2004, Neuroinformatics, 2, 127
- Galuske, 2000, Science, 289, 1946
- Tardif, 2001, Eur J Neurosci, 13, 1045
- Saleem, 2002, Neuron, 34, 685
- Conturo, 1999, Proc Natl Acad Sci U S A, 96, 10422
- LeBihan, 2003, Nature Rev Neurosci, 4, 469
- Catani, 2003, Brain, 126, 2093
- Dougherty, 2005, Proc Natl Acad Sci USA, 102, 7350
- Tuch, 2003, Neuron, 40, 885
- Tuch, 2005, Phil Trans R Soc B Biol Sci, 360, 869
- Passingham, 2002, Nat Rev Neurosci, 3, 606
- Behrens, 2005, Phil Trans R Soc B Biol Sci, 360, 903
- Behrens, 2003, Nat Neurosci, 6, 750
- Johansen-Berg, 2004, Proc Natl Acad Sci U S A, 101, 13335
- Mountcastle, 1978, The mindful brain, 7
- Mountcastle, 1997, Brain, 120, 701
- Buxhoeveden, 2002, Brain, 125, 935
- Rockland, 2004, Exp Brain Res, 158, 265
- Jones, 2000, Proc Natl Acad Sci U S A, 97, 5019
- Douglas, 1991, J Physiol, 440, 735
- Yoshimura, 2005, Nature, 433, 868
- Ohki, 2005, Nature, 433, 597
- Zhao, 2005, Neuroimage, 27, 416
- Vanzetta, 2004, Neuron, 42, 843
- Callaway, 1990, J Neurosci, 10, 1134
- Amirikian, 2003, Proc Natl Acad Sci U S A, 100, 12474
- Kötter, 2005, Phil Trans R Soc Lond B Biol Sci, 360, 751
- Thompson, 1996, J Neurosci, 16, 4261
- Thompson, 2001, Nat Neurosci, 4, 1
- Amunts, 1999, J Comp Neurol, 412, 319
- Westbury, 1999, Cereb Cortex, 9, 392
- Watson, 1993, Cereb Cortex, 3, 79
- Hasnain, 1998, Hum Brain Mapp, 6, 301
- Lahaye, 2003, Neuroimage, 20, 962
- Bartels, 2005, Neuroimage, 24, 339
- Stephan, 2000, Phil Trans R Soc Lond B Biol Sci, 355, 111
- Kötter, 2000, Phil Trans R Soc Lond B Biol Sci, 355, 127
- McIntosh, 1994, Hum Brain Mapp, 2, 2
- Friston, 2003, Neuroimage, 19, 1273
- Bota, 2003, Nat Neuroscience, 6, 795
- Horwitz, 1994, Hum Brain Mapp, 1, 269
- Horwitz, 1999, Trends Cogn Sci, 3, 91
- Horwitz, 2004, Neuroinformatics, 2, 251
- Chadderdon, 2005, J Cogn Neurosci
- Bota, 2004, Neuroinformatics, 2, 19
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Gu, S., Cieslak, M., Baird, B., Muldoon, S. F., Grafton, S. T., Pasqualetti, F., & Bassett, D. S. (2018) · Scientific Reports · doi:10.1038/s41598-018-20123-8 · Cited by 118
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Jie, B., Zhang, D., Chong-yaw Wee, A., & Shen, D. G. (2013) · Human Brain Mapping · doi:10.1002/hbm.22353 · Cited by 116
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Niu, H., Wang, J., Zhao, T., Shu, N., & Yong He, A. (2012) · PLoS ONE · doi:10.1371/journal.pone.0045771 · Cited by 116
Verkhratsky, A., Marutle, A., Rodríguez-Arellano, J. J., & Nordberg, A. (2015) · The Neuroscientist · doi:10.1177/1073858414547132 · Cited by 95
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Self-citationEssen, D. C. V. (2013) · Neuron · doi:10.1016/j.neuron.2013.10.027 · Cited by 112
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Metadata
- Authors
- Sporns, O., Tononi, G., & Kötter, R.
- Affiliations
- Indiana University Bloomington (US)
- University of Wisconsin–Madison (US)
- Corresponding author
- Sporns, Olaf
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2005
- Volume
- 1
- Issue
- 4
- Pages
- e42
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC1239902
- OpenAlex
- W2146693559
- Cited by
- 3,260
- References
- 86
- Citations by year
- 2026: 127 · 2025: 184 · 2024: 188 · 2023: 191 · 2022: 232 · 2021: 231 · 2020: 244 · 2019: 235 · 2018: 194 · 2017: 216 · 2016: 203 · 2015: 220 · 2014: 166 · 2013: 168 · 2012: 134
- FWCI
- 2.12
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- James S. McDonnell Foundation
- Deutsche Forschungsgemeinschaft
- National Institutes of Health
- National Institute on Drug Abuse (1R21 DA 15647-01, R21DA015647)
- 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
- human connectome, structural connectivity, structural brain networks, research strategies
- MeSH terms
- Animals, Brain, Humans, Nerve Net, Neurons, Synapses
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