Generative models of the human connectome
Open accessAbstract
The human connectome represents a network map of the brain's wiring diagram and the pattern into which its connections are organized is thought to play an important role in cognitive function. The generative rules that shape the topology of the human connectome remain incompletely understood. Earlier work in model organisms has suggested that wiring rules based on geometric relationships (distance) can account for many but likely not all topological features. Here we systematically explore a family of generative models of the human connectome that yield synthetic networks designed according to different wiring rules combining geometric and a broad range of topological factors. We find that a combination of geometric constraints with a homophilic attachment mechanism can create synthetic networks that closely match many topological characteristics of individual human connectomes, including features that were not included in the optimization of the generative model itself. We use these models to investigate a lifespan dataset and show that, with age, the model parameters undergo progressive changes, suggesting a rebalancing of the generative factors underlying the connectome across the lifespan.
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
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?
Connectome networks strike a balance between wiring minimization and homophily.
“In addition, connectome networks are known to strike a balance between wiring minimization (shortest paths) and formation of connections between brain regions with similar connectivity profiles (homophily) (Betzel et al., 2016).”
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Betzel et al., 2016
The Results section reports that the best-fitting model combines a penalty against long-distance connections with a greater probability of connecting regions that have similar connectivity profiles. However, the parenthetical “shortest paths” is inaccurate: the paper describes minimizing physical wiring distance, and its Results > Geometric model section says forming only the shortest connections increases characteristic path length. (LLM, conf=0.88)
Experimental — accuracy not yet measuredGraph theory and network science provide a framework to quantify network properties of brain connectivity and investigate whole-brain models of structural wiring.
“The disciplines of graph theory and network science provide a framework to quantify network properties of brain connectivity (Rubinov and Sporns, 2010) and investigate whole-brain models of structural wiring (Betzel et al., 2016).”
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Betzel et al., 2016
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Emergence of Scaling in Random Networks
Barabási, A.-L., & Albert, R. (1999) · Science · doi:10.1126/science.286.5439.509 · Cited by 36,135 · see citing works
Individual Comparisons by Ranking Methods
Wilcoxon, F. (1945) · Biometrics Bulletin · doi:10.2307/3001968 · Cited by 15,354 · see citing works
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 · see citing works
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 · see citing works
Self-citationAutomatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature
Destrieux, C., Fischl, B., Dale, A., & Halgren, E. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.06.010 · Cited by 2,956 · see citing works
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 · cited 1× · see citing works
“…brain function, including hierarchical and multi-scale modules (Bassett et al., 2010; Betzel et al., 2013), highly connected, highly central hubs (Hagmann et al., 2008; van den Heuvel and Sporns, 2013), and a rich club of mutually connected, high-degree regions (van den Heuvel and Sporns, 2011).”
Self-citationSporns, O., & Betzel, R. F. (2016) · Annual Review of Psychology · doi:10.1146/annurev-psych-122414-033634 · Cited by 1,334 · see citing works
Self-citationA 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 · see citing works
Topological organization of the human brain functional connectome across the lifespan
Cao, M., Wang, J.-H., Dai, Z.-J., Cao, X.-Y., Jiang, L.-L., Fan, F.-M., Song, X.-W., Xia, M.-R., Shu, N., Dong, Q., et al. (2014) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2013.11.004 · Cited by 440 · see citing works
Self-citationCooperative and competitive spreading dynamics on the human connectome
Mišić, B., Betzel, R. F., Nematzadeh, A., Goñi, J., Griffa, A., Hagmann, P., Flammini, A., Ahn, Y.-Y., & Sporns, O. (2015) · Neuron · doi:10.1016/j.neuron.2015.05.035 · Cited by 342 · see citing works
Self-citationWiring cost and topological participation of the mouse brain connectome
Rubinov, M., Ypma, R. J. F., Watson, C., & Bullmore, E. T. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1420315112 · Cited by 219 · see citing works
Self-citationThe 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 · see citing works
Exploring the Morphospace of Communication Efficiency in Complex Networks
Goñi, J., Avena-Koenigsberger, A., Velez de Mendizabal, N., van den Heuvel, M. P., Betzel, R. F., & Sporns, O. (2013) · PLoS ONE · doi:10.1371/journal.pone.0058070 · Cited by 158 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 45
- Avena-Koenigsberger A. Goñi J. Betzel R.F. van den Heuvel M.P. Griffa A. Hagmann P. Thiran J.-P. Sporns O. Using Pareto optimality to explore the topology and dynamics of the human connectome Philos. Trans. R. Soc. B-Biol. Sci. 369 1653 2014 20130530 PMC4150305 25180308
- Bailey A. Ventresca M. Ombuki-Berman B. Automatic generation of graph models for complex networks by genetic programming Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation 2012 ACM 711 718
- Bassett D.S. Greenfield D.L. Meyer-Lindenberg A. Weinberger D.R. Moore S.W. Bullmore E.T. Efficient physical embedding of topologically complex information processing networks in brains and computer circuits PLoS Comput. Biol. 6 4 2010 e1000748 20421990 PMC2858671
- Betzel R.F. Byrge L. He Y. Goñi J. Zuo X.-N. Sporns O. Changes in structural and functional connectivity among resting-state networks across the human lifespan NeuroImage 102 2014 345 357 25109530
- Bullmore E. Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems Nat. Rev. Neurosci. 10 3 2009 186 198 19190637
- Bullmore E. Sporns O. The economy of brain network organization Nat. Rev. Neurosci. 13 5 2012 336 349 22498897
- Cammoun L. Gigandet X. Meskaldji D. Thiran J.P. Sporns O. Do K.Q. Maeder P. Meuli R. Hagmann P. Mapping the human connectome at multiple scales with diffusion spectrum MRI J. Neurosci. Methods 203 2 2012 386 397 22001222
- Cherniak C. Mokhtarzada Z. Rodriguez-Esteban R. Changizi K. Global optimization of cerebral cortex layout Proc. Natl. Acad. Sci. U. S. A. 101 4 2004 1081 1086 14722353 PMC327154
- Costa L.d.F. Kaiser M. Hilgetag C.C. Predicting the connectivity of primate cortical networks from topological and spatial node properties BMC Syst. Biol. 1 1 2007 16 17408506 PMC1831788
- Daducci A. Gerhard S. Griffa A. Lemkaddem A. Cammoun L. Gigandet X. Meuli R. Hagmann P. Thiran J.-P. The connectome mapper: an open-source processing pipeline to map connectomes with mri PLoS ONE 7 12 2012 e48121 23272041 PMC3525592
- Dall J. Christensen M. Random geometric graphs Phys. Rev. E 66 1 2002 016121 12241440
- de Reus M.A. van den Heuvel M.P. Simulated rich club lesioning in brain networks: a scaffold for communication and integration? Front. Hum. Neurosci. 8 2014 647 25191259 PMC4139657
- Glasser M.F. Sotiropoulos S.N. Wilson J.A. Coalson T.S. Fischl B. Andersson J.L. Xu J. Jbabdi S. Webster M. Polimeni J.R. The minimal preprocessing pipelines for the human connectome project NeuroImage 80 2013 105 124 23668970 PMC3720813
- Goñi J. van den Heuvel M.P. Avena-Koenigsberger A. de Mendizabal N.V. Betzel R.F. Griffa A. Hagmann P. Corominas-Murtra B. Thiran J.-P. Sporns O. Resting-brain functional connectivity predicted by analytic measures of network communication Proc. Natl. Acad. Sci. U. S. A. 111 2 2014 833 838 24379387 PMC3896172
- Henderson J. Robinson P. Geometric effects on complex network structure in the cortex Phys. Rev. Lett. 107 1 2011 018102 21797575
- Henderson J.A. Robinson P.A. Using geometry to uncover relationships between isotropy, homogeneity, and modularity in cortical connectivity Brain Connect. 3 4 2013 423 437 23802922
- Henderson J.A. Robinson P.A. Relations between the geometry of cortical gyrification and white-matter network architecture Brain Connect. 4 2 2014 112 130 24437717
- Hermundstad A.M. Bassett D.S. Brown K.S. Aminoff E.M. Clewett D. Freeman S. Frithsen A. Johnson A. Tipper C.M. Miller M.B. Structural foundations of resting-state and task-based functional connectivity in the human brain Proc. Natl. Acad. Sci. U. S. A. 110 15 2013 6169 6174 23530246 PMC3625268
- Kaiser M. Hilgetag C.C. Spatial growth of real-world networks Phys. Rev. E 69 3 2004 036103 15089357
- Kaiser M. Hilgetag C.C. Nonoptimal component placement, but short processing paths, due to long-distance projections in neural systems PLoS Comput. Biol. 2 7 2006 e95 16848638 PMC1513269
- Kaiser M. Hilgetag C.C. Van Ooyen A. A simple rule for axon outgrowth and synaptic competition generates realistic connection lengths and filling fractions Cereb. Cortex 19 12 2009 3001 3010 19435708
- Klimm F. Bassett D.S. Carlson J.M. Mucha P.J. Resolving structural variability in network models and the brain PLoS Comput. Biol. 10 3 2014 e1003491 24675546 PMC3967917
- Kumar R. Raghavan P. Rajagopalan S. Sivakumar D. Tomkins A. Upfal E. Stochastic models for the web graph Proceedings of the 41st Annual Symposium on the Foundations of Computer Science 2000 IEEE 57 65
- Langen M. Agi E. Altschuler D.J. Wu L.F. Altschuler S.J. Hiesinger P.R. The developmental rules of neural superposition in drosophila Cell 162 1 2015 120 133 26119341 PMC4646663
- Laughlin S.B. Sejnowski T.J. Communication in neuronal networks Science 301 5641 2003 1870 1874 14512617 PMC2930149
- Lim S. Kaiser M. Developmental time windows for axon growth influence neuronal network topology Biol. Cybern. 109 2 2015 275 286 25633181 PMC4366563
- Lim S. Han C.E. Uhlhaas P.J. Kaiser M. Preferential detachment during human brain development: age-and sex-specific structural connectivity in diffusion tensor imaging (DTI) data Cereb. Cortex 25 6 2015 1477 1489 24343892 PMC4428296
- Menezes T. Roth C. Symbolic regression of generative network models Sci. Rep. 4 2014 6284 25190000 PMC4155339
- Middendorf M. Ziv E. Wiggins C.H. Inferring network mechanisms: the Drosophila melanogaster protein interaction network Proc. Natl. Acad. Sci. U. S. A. 102 9 2005 3192 3197 15728374 PMC552930
- Mitchison G. Neuronal branching patterns and the economy of cortical wiring Proc. R. Soc. Lond. B Biol. Sci. 245 1313 1991 151 158 1682939
- Newman M.E. Assortative mixing in networks Phys. Rev. Lett. 89 20 2002 208701 12443515
- Nicosia V. Vértes P.E. Schafer W.R. Latora V. Bullmore E.T. Phase transition in the economically modeled growth of a cellular nervous system Proc. Natl. Acad. Sci. U. S. A. 110 19 2013 7880 7885 23610428 PMC3651470
- Nooner K.B. Colcombe S.J. Tobe R.H. Mennes M. Benedict M.M. Moreno A.L. Panek L.J. Brown S. Zavitz S.T. Li Q. The nki-rockland sample: a model for accelerating the pace of discovery science in psychiatry Front. Neurosci. 6 2012 152 23087608 PMC3472598
- Rubinov M. Sporns O. Complex network measures of brain connectivity: uses and interpretations NeuroImage 52 3 2010 1059 1069 19819337
- Samu D. Seth A.K. Nowotny T. Influence of wiring cost on the large-scale architecture of human cortical connectivity PLoS Comput. Biol. 10 2014 e1003557 24699277 PMC3974635
- Song H.F. Kennedy H. Wang X.-J. Spatial embedding of structural similarity in the cerebral cortex Proc. Natl. Acad. Sci. U. S. A. 111 46 2014 16580 16585 25368200 PMC4246295
- Sporns O. Contributions and challenges for network models in cognitive neuroscience Nat. Neurosci. 17 5 2014 652 660 24686784
- Thomas C. Frank Q.Y. Irfanoglu M.O. Modi P. Saleem K.S. Leopold D.A. Pierpaoli C. Anatomical accuracy of brain connections derived from diffusion MRI tractography is inherently limited Proc. Natl. Acad. Sci. U. S. A. 111 46 2014 16574 16579 25368179 PMC4246325
- van den Heuvel M.P. Sporns O. Rich-club organization of the human connectome J. Neurosci. 31 44 2011 15775 15786 22049421 PMC6623027
- Van Essen D.C. Ugurbil K. Auerbach E. Barch D. Behrens T. Bucholz R. Chang A. Chen L. Corbetta M. Curtiss S.W. The human connectome project: a data acquisition perspective NeuroImage 62 4 2012 2222 2231 22366334 PMC3606888
- Vértes P.E. Alexander-Bloch A.F. Gogtay N. Giedd J.N. Rapoport J.L. Bullmore E.T. Simple models of human brain functional networks Proc. Natl. Acad. Sci. U. S. A. 109 15 2012 5868 5873 22467830 PMC3326510
- Watts D. Strogatz S. Collective dynamics of small-world networks Nature 393 1998 440 442 9623998
- Yang Z. Chang C. Xu T. Jiang L. Handwerker D.A. Castellanos F.X. Milham M.P. Bandettini P.A. Zuo X.-N. Connectivity trajectory across lifespan differentiates the precuneus from the default network NeuroImage 89 2014 45 56 24287438 PMC3944140
- Yeh F.-C. Wedeen V.J. Tseng W.-Y.I. Generalized-sampling imaging IEEE Trans. Med. Imaging 29 9 2010 1626 1635 20304721
- Zalesky A. Fornito A. Harding I.H. Cocchi L. Yücel M. Pantelis C. Bullmore E.T. Whole-brain anatomical networks: does the choice of nodes matter? NeuroImage 50 3 2010 970 983 20035887
Cited by
Sorted by citations
Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI
Schaefer, A., Kong, R., Gordon, E. M., Laumann, T. O., Zuo, X.-N., Holmes, A. J., Eickhoff, S. B., & Yeo, B. T. T. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhx179 · Cited by 3,657 · see citing works
Self-citationGenerative modeling of brain maps with spatial autocorrelation
Burt, J. B., Helmer, M., Shinn, M., Anticevic, A., & Murray, J. D. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117038 · Cited by 528 · see citing works
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 · see citing works
Self-citationZhang, F., Daducci, A., He, Y., Schiavi, S., Seguin, C., Smith, R. E., Yeh, C.-H., Zhao, T., & O’Donnell, L. J. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2021.118870 · Cited by 302 · see citing works
“framework to quantify network properties of brain connectivity [403] and investigate whole-brain models of structural wiring [55].”
BackgroundSelf-citationConnectome sensitivity or specificity: which is more important?
Zalesky, A., Fornito, A., Cocchi, L., Gollo, L. L., van den Heuvel, M. P., & Breakspear, M. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.06.035 · Cited by 275 · see citing works
Self-citationOptimally 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 · see citing works
Self-citationThe 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 · see citing works
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 · see citing works
Self-citationDetecting hierarchical genome folding with network modularity
Norton, H. K., Emerson, D. J., Huang, H., Kim, J., Titus, K. R., Gu, S., Bassett, D. S., & Phillips-Cremins, J. E. (2018) · Nature Methods · doi:10.1038/nmeth.4560 · Cited by 146 · see citing works
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 · see citing works
Self-citationOsadchiy, V., Mayer, E. A., Bhatt, R., Labus, J. S., Gao, L., Kilpatrick, L. A., Liu, C., Tillisch, K., Naliboff, B., Chang, L., et al. (2019) · Obesity Science & Practice · doi:10.1002/osp4.362 · Cited by 44 · see citing works
- Authors
- 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., van den Heuvel, M., Zuo, X.-N., Bullmore, E. T., & Sporns, O.
- Affiliations
- Indiana University Bloomington (US)
- Chinese Academy of Sciences (CN)
- Centre Hospitalier Universitaire Vaudois (CH)
- University of Cambridge (GB)
- Corresponding author
- Sporns, Olaf
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2016-01
- Volume
- 124
- Issue
- Pt A
- Pages
- 1054-1064
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC4655950
- OpenAlex
- W2182367256
- Cited by
- 303
- References
- 13
- Citations by year
- 2026: 18 · 2025: 26 · 2024: 20 · 2023: 28 · 2022: 24 · 2021: 26 · 2020: 34 · 2019: 31 · 2018: 35 · 2017: 31 · 2016: 25 · 2015: 4
- FWCI
- 19.03
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- NeuroImage (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Pure Amsterdam UMC (publishedVersion, other-oa)
- Amsterdam UMC (VU Amsterdam) - Institutional Repository (publishedVersion, other-oa)
- SERVAL (Université de Lausanne) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- IRIS (submittedVersion, cc-by)
- Institutional Repository of Institute of Psychology, Chinese Academy of Sciences (Institute of Psychology, Chinese Academy of Sciences) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Funders
- 16 NIH Institutes and Centers (1U54MH091657)
- McDonnell Center for Systems Neuroscience
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- human connectome, generative network models, brain network topology, homophily
- MeSH terms
- Adolescent, Adult, Aged, Aged, 80 and over, Aging, Algorithms, Brain, Child, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Models, Neurological, Neural Networks, Computer, Young Adult, 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
