The DOI matches its Crossref record; 85 of the 86 references it cites were found in a registry or an index.
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.
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.
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Experimental — accuracy not yet measured
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
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 3,537
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
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
Cited by
Sorted by citations
Items 701–800 of 1,014
Li, M., Chen, H., 汪俊萍, Liu, F., Long, Z., Wang, Y., Iturria‐Medina, Y., Zhang, J., Yu, C., & Chen, H. (2014) · Brain Connectivity · doi:10.1089/brain.2013.0211 · Cited by 62
Multi-stage Multi-recursive-input Fully Convolutional Networks for Neuronal Boundary Detection
Shen, W., Wang, B., Jiang, Y., Wang, Y., & Yuille, A. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.262 · Cited by 62
Wirsich, J., Ridley, B., Besson, P., Jirsa, V., Bénar, C., Ranjeva, J., & Guye, M. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.08.055 · Cited by 62
Navigable maps of structural brain networks across species
Allard, A., & Serrano, M. Á. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007584 · Cited by 62
d’Errico, F., & Colagè, I. (2018) · Biological Theory · doi:10.1007/s13752-018-0306-x · Cited by 62
Wang, S. H., Lobier, M., Siebenhühner, F., Puoliväli, T., Palva, S., & Palva, J. M. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.01.056 · Cited by 62
Oldham, S., Fulcher, B., Aquino, K. M., Arnatkevičiūtė, A., Paquola, C., Shishegar, R., & Fornito, A. (2022) · Science Advances · doi:10.1126/sciadv.abm6127 · Cited by 62
Clinical applications of magnetic resonance imaging based functional and structural connectivity
Wu, C., Ferreira, F., Fox, M., Harel, N., Hattangadi-Gluth, J. A., Horn, A., Jbabdi, S., Kahan, J., Oswal, A., Sheth, S. A., et al. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118649 · Cited by 61
Synchronization between the end stages of the dorsal and the ventral visual stream
Verhoef, B.-E., Vogels, R., & Janssen, P. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00924.2010 · Cited by 61
Gene Expression of Caenorhabditis elegans Neurons Carries Information on Their Synaptic Connectivity
Kaufman, A., Dror, G., Meilijson, I., & Ruppin, E. (2006) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0020167 · Cited by 61
Functional connectivity and oscillatory neuronal activity in the resting human brain
Ganzetti, M., & Mantini, D. (2013) · Neuroscience · doi:10.1016/j.neuroscience.2013.02.032 · Cited by 61
Clique Community Persistence: A Topological Visual Analysis Approach for Complex Networks
Rieck, B., Fugacci, U., Lukasczyk, J., & Leitte, H. (2017) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2017.2744321 · Cited by 61
Embracing variability in the search for biological mechanisms of psychiatric illness
Segal, A., Tiego, J., Parkes, L., Holmes, A. J., Marquand, A. F., & Fornito, A. (2024) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2024.09.010 · Cited by 60
MR Diffusion Histology and Micro-Tractography Reveal Mesoscale Features of the Human Cerebellum
Dell’Acqua, F., Bódi, I., Slater, D. A., Catani, M., & Modo, M. (2013) · The Cerebellum · doi:10.1007/s12311-013-0503-x · Cited by 60
Arsiwalla, X. D., Zucca, R., Betella, A., Martínez, E. F., Dalmazzo, D., Omedas, P., Deco, G., & Verschure, P. F. M. J. (2015) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2015.00002 · Cited by 60
The organisation of the elderly connectome
Perry, A., Wen, W. X., Lord, A., Thalamuthu, A., Roberts, G. M. P., Mitchell, P. B., Sachdev, P. S., & Breakspear, M. J. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.04.009 · Cited by 60
de Tommaso, M., Stramaglia, S., Marinazzo, D., Trotta, G., & Pellicoro, M. (2013) · Cephalalgia · doi:10.1177/0333102413477741 · Cited by 60
A Jones matrix formalism for simulating three-dimensional polarized light imaging of brain tissue
Menzel, M., Michielsen, K. F. L., Raedt, H. A. D., Reckfort, J., Amunts, K., & Axer, M. (2015) · Journal of The Royal Society Interface · doi:10.1098/rsif.2015.0734 · Cited by 60
Connectome analysis for pre-operative brain mapping in neurosurgery
Hart, M. G., Price, S. J., & Suckling, J. (2016) · British Journal of Neurosurgery · doi:10.1080/02688697.2016.1208809 · Cited by 60
Motor skill learning induces brain network plasticity: A diffusion-tensor imaging study
Pi, Y., Wu, X.-H., Wang, F., Liu, K., Wu, Y., Zhu, H., & Zhang, J. (2019) · PLoS ONE · doi:10.1371/journal.pone.0210015 · Cited by 60
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
Mapping neurotransmitter networks with PET: An example on serotonin and opioid systems
Tuominen, L. J., Nummenmaa, L., Keltikangas‐Järvinen, L., Raitakari, O. T., & Hietala, J. A. (2013) · Human Brain Mapping · doi:10.1002/hbm.22298 · Cited by 59
Graham, D. J., & Rockmore, D. N. (2010) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.2010.21477 · Cited by 59
Atasoy, S., Vohryzek, J., Deco, G., Carhart-Harris, R. L., & Kringelbach, M. L. (2018) · Progress in brain research · doi:10.1016/bs.pbr.2018.08.009 · Cited by 59
Principles and open questions in functional brain network reconstruction
Korhonen, O., Zanin, M., & Papo, D. (2021) · Human Brain Mapping · doi:10.1002/hbm.25462 · Cited by 59
Litwińczuk, M. C., Muhlert, N., Cloutman, L. L., Trujillo‐Barreto, N. J., & Woollams, A. M. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119531 · Cited by 59
Cerebello-cerebral connectivity deficits in Friedreich ataxia
Zalesky, A., Akhlaghi, H., Corben, L. A., Bradshaw, J. L., Delatycki, M. B., Storey, E., Georgiou‐Karistianis, N., & Egan, G. F. (2013) · Brain Structure and Function · doi:10.1007/s00429-013-0547-1 · Cited by 58
Topological organization of connectivity strength in the rat connectome
van den Heuvel, M. P., Scholtens, L. H., & de Reus, M. A. (2015) · Brain Structure and Function · doi:10.1007/s00429-015-0999-6 · Cited by 58
Várkuti, B., Çavuşoğlu, M., Kullik, A., Schiffler, B. C., Veit, R., Yilmaz, Ö., Rosenstiel, W., Braun, C., Uludaǧ, K., Birbaumer, N., et al. (2011) · PLoS ONE · doi:10.1371/journal.pone.0014801 · Cited by 58
Owen, J. P., Li, Y., Yang, F., Shetty, C., Bukshpun, P., Vora, S., Wakahiro, M. L. J., Hinkley, L. B. N., Nagarajan, S. S., Sherr, E. H., et al. (2013) · Brain Connectivity · doi:10.1089/brain.2013.0175 · Cited by 58
Active learning of neuron morphology for accurate automated tracing of neurites
Gala, R., Chapeton, J. I., Jitesh, J., Bhavsar, C. V., & Stepanyants, A. (2014) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2014.00037 · Cited by 58
Cortical Network for Gaze Control in Humans Revealed Using Multimodal MRI
Anderson, E. J., Jones, D. K., Tuura, R., Leemans, A., Catani, M., & Husain, M. (2011) · Cerebral Cortex · doi:10.1093/cercor/bhr110 · Cited by 58
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
Stochastic resonance at criticality in a network model of the human cortex
Vázquez-Rodríguez, B., Avena-Koenigsberger, A., Sporns, O., Griffa, A., Hagmann, P., & Larralde, H. (2017) · Scientific Reports · doi:10.1038/s41598-017-13400-5 · Cited by 58
Self-citationScaling Principles of White Matter Connectivity in the Human and Nonhuman Primate Brain
Ardesch, D. J., Scholtens, L. H., de Lange, S. C., Roumazeilles, L., Khrapitchev, A. A., Preuss, T. M., Rilling, J. K., Mars, R. B., & van den Heuvel, M. P. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab384 · Cited by 58
Lemaire, J., Golby, A. J., Wells, W. M., Pujol, S., Tie, Y., Rigolo, L., Yarmarkovich, A., Pieper, S., Westin, C., Jolesz, F. A., et al. (2012) · Brain Topography · doi:10.1007/s10548-012-0257-7 · Cited by 57
Synchronizability of EEG-Based Functional Networks in Early Alzheimer's Disease
Tahaei, M. S., Jalili, M., & Knyazeva, M. G. (2012) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2012.2202127 · Cited by 57
Graph theoretical analysis of human brain structural networks
Lo, C. Z., Yong He, A., & Lin, C. (2011) · Reviews in the Neurosciences · doi:10.1515/rns.2011.039 · Cited by 57
CONSTRUCTING BRAIN FUNCTIONAL NETWORKS FROM EEG: PARTIAL AND UNPARTIAL CORRELATIONS
Jalili, M., & Knyazeva, M. G. (2011) · Journal of Integrative Neuroscience · doi:10.1142/s0219635211002725 · Cited by 57
Convergence and Divergence of Brain Network Dysfunction in Deficit and Non-deficit Schizophrenia
Yu, M., Dai, Z., Tang, X., Wang, X., Zhang, X., Sha, W., Yao, S., Shu, N., Yang, J., Yang, J., et al. (2017) · Schizophrenia Bulletin · doi:10.1093/schbul/sbx014 · Cited by 57
How Big Data and High-Performance Computing Drive Brain Science
Chen, S., He, Z., Han, X., He, X., Li, R., Zhu, H., Zhao, D., Dai, C., Zhang, Y., Lu, Z., et al. (2019) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2019.09.003 · Cited by 57
Concurrent EEG- and fMRI-derived functional connectomes exhibit linked dynamics
Wirsich, J., Giraud, A., & Sadaghiani, S. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116998 · Cited by 57
Renz‐Polster, H., Tremblay, M., Bienzle, D., & Fischer, J. E. (2022) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2022.888232 · Cited by 56
Understanding brain states across spacetime informed by whole-brain modelling
Vohryzek, J., Cabral, J., Vuust, P., Deco, G., & Kringelbach, M. L. (2022) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2021.0247 · Cited by 56
Structural brain networks and neuropsychiatric disorders
Wen, W. X., Yong He, A., & Sachdev, P. S. (2011) · Current Opinion in Psychiatry · doi:10.1097/yco.0b013e32834591f8 · Cited by 56
Anatomical connectivity and the resting state activity of large cortical networks
Pinotsis, D. A., Hansen, E. C. A., Friston, K. J., & Jirsa, V. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.10.016 · Cited by 56
The Developing Utility of Zebrafish Models for Cognitive Enhancers Research
Stewart, A., & Kalueff, A. V. (2012) · Current Neuropharmacology · doi:10.2174/157015912803217323 · Cited by 56
Resting state functional connectivity: Its physiological basis and application in neuropharmacology
Lu, H., & Stein, E. A. (2013) · Neuropharmacology · doi:10.1016/j.neuropharm.2013.08.023 · Cited by 56
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
Effect of socioeconomic status disparity on child language and neural outcome: how early is early?
Hurt, H., & Betancourt, L. M. (2015) · Pediatric Research · doi:10.1038/pr.2015.202 · Cited by 56
Guan, X., Zhang, Y., Wei, H., Guo, T., Zeng, Q., Zhou, C., Wang, J., Gao, T., Xuan, M., Gu, Q., et al. (2018) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2018.11.013 · Cited by 56
Sparsity through evolutionary pruning prevents neuronal networks from overfitting
Gerum, R. C., Erpenbeck, A., Krauß, P., & Schilling, A. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.05.007 · Cited by 56
Emerging imaging methods to study whole-brain function in rodent models
Markicevic, M., Savvateev, I., Grimm, C., & Zerbi, V. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01575-5 · Cited by 55
Nie, J., Li, G., Wang, L., Shi, F., Lin, W., Gilmore, J. H., & Shen, D. G. (2013) · Human Brain Mapping · doi:10.1002/hbm.22432 · Cited by 55
Using joint ICA to link function and structure using MEG and DTI in schizophrenia
Stephen, J. M., Coffman, B. A., Jung, R. E., Bustillo, J., Aine, C. J., & Calhoun, V. D. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.06.038 · Cited by 55
Structural MRI connectome in development: challenges of the changing brain
Tymofiyeva, O., Hess, C. P., Xu, D., & Barkovich, A. J. (2014) · British Journal of Radiology · doi:10.1259/bjr.20140086 · Cited by 55
Wen, H., Liu, Y., Rekik, I., Wang, S., Zhang, J., Zhang, Y., Peng, Y., & He, H. (2017) · Human Brain Mapping · doi:10.1002/hbm.23643 · Cited by 55
The dimensionalities of lesion-deficit mapping
Xu, T., Jha, A., & Nachev, P. (2017) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2017.09.007 · Cited by 55
Untangling the Tauopathy for Alzheimer’s disease and parkinsonism
Chang, H.-Y., Sang, T., & Chiang, A. (2018) · Journal of Biomedical Science · doi:10.1186/s12929-018-0457-x · Cited by 56
Cacciola, A., Naro, A., Milardi, D., Bramanti, A., Malatacca, L., Spitaleri, M., Leo, A., Muscoloni, A., Cannistraci, C. V., Bramanti, P., et al. (2019) · Journal of Clinical Medicine · doi:10.3390/jcm8030306 · Cited by 55
Reconfiguration of Cortical Networks in MDD Uncovered by Multiscale Community Detection with fMRI
He, Y., Lim, S., Fortunato, S., Sporns, O., Zhang, L., Qiu, J., Xie, P., & Zuo, X.-N. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhx335 · Cited by 54
Self-citationLlewellyn, S., & Hobson, J. A. (2015) · Neurobiology of Learning and Memory · doi:10.1016/j.nlm.2015.04.005 · Cited by 54
Rahimi, R., Dolatshahi, M., Abbasi-Feijani, F., Momtazmanesh, S., Cattarinussi, G., Aarabi, M. H., & Pini, L. (2022) · Brain Imaging and Behavior · doi:10.1007/s11682-022-00690-1 · Cited by 54
Graph lesion-deficit mapping of fluid intelligence
Cipolotti, L., Ruffle, J. K., Mole, J. A., Xu, T., Hyare, H., Shallice, T., Chan, E., & Nachev, P. (2022) · Brain · doi:10.1093/brain/awac304 · Cited by 54
Principles and Operation of Virtual Brain Twins
Hashemi, M., Depannemaecker, D., Saggio, M. L., Triebkorn, P., Rabuffo, G., Fousek, J., Ziaeemehr, A., Ší́p, V., Athanasiadis, A., Breyton, M., et al. (2025) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2025.3562951 · Cited by 54
Labels, cognomes, and cyclic computation: an ethological perspective
Murphy, E. (2015) · Frontiers in Psychology · doi:10.3389/fpsyg.2015.00715 · Cited by 54
Scale-free neocortical dynamics
Freeman, W. (2007) · Scholarpedia · doi:10.4249/scholarpedia.1357 · Cited by 54
Topographic Hub Maps of the Human Structural Neocortical Network
Nijhuis, E. H. J., van Walsum, A. V. C., & Norris, D. G. (2013) · PLoS ONE · doi:10.1371/journal.pone.0065511 · Cited by 54
Bruce, I. P., Chang, H., Petty, C., Chen, N., & Song, A. W. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.07.035 · Cited by 54
Brain connectivity alterations in early psychosis: from clinical to neuroimaging staging
Griffa, A., Baumann, P., Klauser, P., Mullier, E., Cleusix, M., Jenni, R., van den Heuvel, M. P., Kim, Q., Conus, P., & Hagmann, P. (2019) · Translational Psychiatry · doi:10.1038/s41398-019-0392-y · Cited by 54
Zhou, Y., Wang, K., Liu, Y., Song, M., Song, S. W., & Jiang, T. (2010) · Cognitive Neurodynamics · doi:10.1007/s11571-010-9126-9 · Cited by 54
Of what use is connectomics? A personal perspective on the Drosophila connectome
Meinertzhagen, I. A. (2018) · Journal of Experimental Biology · doi:10.1242/jeb.164954 · Cited by 53
Tasks activating the default mode network map multiple functional systems
Mancuso, L., Cavuoti-Cabanillas, S., Liloia, D., Manuello, J., Buzi, G., Duca, S., Cauda, F., & Costa, T. (2022) · Brain Structure and Function · doi:10.1007/s00429-022-02467-0 · Cited by 53
A framework For brain atlases: Lessons from seizure dynamics
Revell, A. Y., Silva, A. B., Arnold, T. C., Stein, J. M., Das, S. R., Shinohara, R. T., Bassett, D. S., Litt, B., & Davis, K. A. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.118986 · Cited by 53
Diffusion MRI of the neonate brain: acquisition, processing and analysis techniques
Pannek, K., Guzzetta, A., Colditz, P. B., & Rose, S. (2012) · Pediatric Radiology · doi:10.1007/s00247-012-2427-x · Cited by 53
Semantic Image Segmentation with Contextual Hierarchical Models
Seyedhosseini, M., & Taşdizen, T. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2473846 · Cited by 53
Connectomic correlates of response to treatment in first-episode psychosis
Crossley, N. A., Marques, T. R., Taylor, H., Chaddock, C., Dell’Acqua, F., Reinders, A. A. T. S., Mondelli, V., Forti, M. D., Simmons, A., David, A. S., et al. (2016) · Brain · doi:10.1093/brain/aww297 · Cited by 53
Structural Deep Brain Network Mining
Wang, S., He, L., Cao, B., Lu, C.-T., Yu, P. S., & Ragin, A. B. (2017) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3097983.3097988 · Cited by 53
Will We Ever Have Conscious Machines?
Krauß, P., & Maier, A. (2020) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2020.556544 · Cited by 53
Bota, M., Dong, H.-W., & Swanson, L. W. (2012) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2012.00002 · Cited by 52
Cao, H., Dixson, L., Meyer‐Lindenberg, A., & Tost, H. (2015) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2015.07.008 · Cited by 52
Zhong, S., Yong He, A., & Gong, G. (2015) · Human Brain Mapping · doi:10.1002/hbm.22751 · Cited by 52
Schmidt, B., Ghuman, A. S., & Huppert, T. J. (2014) · Frontiers in Neuroscience · doi:10.3389/fnins.2014.00141 · Cited by 52
Emerging Evidence of Connectomic Abnormalities in Schizophrenia
Rubinov, M., & Bassett, D. S. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.0382-11.2011 · Cited by 52
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
Sauerbeck, A. D., Gangolli, M., Reitz, S. J., Salyards, M. H., Kim, S., Hemingway, C., Gratuze, M., Makkapati, T., Kerschensteiner, M., Holtzman, D. M., et al. (2020) · Neuron · doi:10.1016/j.neuron.2020.04.012 · Cited by 53
Counterfactual Graphs for Explainable Classification of Brain Networks
Abrate, C., & Bonchi, F. (2021) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3447548.3467154 · Cited by 52
Canolty, R. T., Ganguly, K., & Carmena, J. M. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002809 · Cited by 52
neuroVIISAS: Approaching Multiscale Simulation of the Rat Connectome
Schmitt, O., & Eipert, P. (2012) · Neuroinformatics · doi:10.1007/s12021-012-9141-6 · Cited by 51
Singh, P., Saxena, K., Singhania, A., Sahoo, P., Ghosh, S. K., Chhajed, R., Ray, K., Fujita, D., & Bandyopadhyay, A. (2020) · Information · doi:10.3390/info11050238 · Cited by 51
Barbati, S. A., Podda, M. V., & Grassi, C. (2022) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2022.945777 · Cited by 51
Spatiotemporal Data Processing with Memristor Crossbar‐Array‐Based Graph Reservoir
Jang, Y. H., Lee, S. H., Han, J., Kim, W., Shim, S. K., Cheong, S., Woo, K. S., Han, J., & Hwang, C. S. (2023) · Advanced Materials · doi:10.1002/adma.202309314 · Cited by 51
Saykin, A. J., de Ruiter, M. B., McDonald, B. C., Deprez, S., & Silverman, D. H. S. (2013) · Brain Imaging and Behavior · doi:10.1007/s11682-013-9283-7 · Cited by 51
Anatomical Brain Networks on the Prediction of Abnormal Brain States
Iturria‐Medina, Y. (2012) · Brain Connectivity · doi:10.1089/brain.2012.0122 · Cited by 51
Can neuroimaging disentangle bipolar disorder?
Hozer, F., & Houenou, J. (2016) · Journal of Affective Disorders · doi:10.1016/j.jad.2016.01.039 · Cited by 51
Hodotopy, neuroplasticity and diffuse gliomas
Duffau, H. (2017) · Neurochirurgie · doi:10.1016/j.neuchi.2016.12.001 · Cited by 51
Cao, H., McEwen, S. C., Forsyth, J. K., Gee, D. G., Bearden, C. E., Addington, J., Goodyear, B. G., Cadenhead, K. S., Mirzakhanian, H., Cornblatt, B. A., et al. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy032 · Cited by 51
Li, K., Guo, L., Zhu, D., Hu, X., Han, J., & Liu, T. (2012) · Neuroinformatics · doi:10.1007/s12021-012-9142-5 · Cited by 50
Meeting the Memory Challenges of Brain-Scale Network Simulation
Kunkel, S., Potjans, T. C., Eppler, J. M., Pleßer, H. E., Morrison, A., & Diesmann, M. (2012) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2011.00035 · Cited by 50
Advanced Neuroimaging in Traumatic Brain Injury
Wu, O., & Edlow, B. L. (2013) · Seminars in Neurology · doi:10.1055/s-0032-1331810 · Cited by 50
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
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
