Generative models of the human connectome

Abstract

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.

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References 69

1. 1.

   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. 2014;369(1653).

   Not in our library10.1098/rstb.2013.0530

2. 2.

   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;711-718.

   Not in our library

3. 3.

   Barabási A.-L., Albert R. Emergence of scaling in random networks. Science. 1999;286(5439):509-512.

   Not in our library10.1126/science.286.5439.509

4. 4.

   Barthélemy M. Spatial networks. Phys. Rep. 2011;499(1):1-101.

   Not in our library

5. 5.
   Efficient Physical Embedding of Topologically Complex Information Processing Networks in Brains and Computer Circuits · 2010

   No notice
6. 6.

   Betzel R.F., Griffa A., Avena-Koenigsberger A., Goñi J., Hagmann P., Thiran J.-P., Sporns O. Multi-scale community organization of the human structural connectome and its relationship with resting-state functional connectivity. Netw. Sci. 2013;1(3):353-373.

   Not in our library

7. 7.

   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. 2014;102:345-357.

   Not in our library10.1016/j.neuroimage.2014.07.067

8. 8.

   Blondel V.D., Guillaume J.-L., Lambiotte R., Lefebvre E. Fast unfolding of communities in large networks. J. Stat. Mech: Theory Exp. 2008;2008(10):P10008.

   Not in our library

9. 9.

   Bullmore E., Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 2009;10(3):186-198.

   Not in our library10.1038/nrn2575

10. 10.

    Bullmore E., Sporns O. The economy of brain network organization. Nat. Rev. Neurosci. 2012;13(5):336-349.

    Not in our library10.1038/nrn3214

11. 11.

    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. 2012;203(2):386-397.

    Not in our library10.1016/j.jneumeth.2011.09.031

12. 12.

    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. Topological organization of the human brain functional connectome across the lifespan. Dev. Cogn. Neurosci. 2014;7:76-93.

    Not in our library10.1016/j.dcn.2013.11.004

13. 13.

    Cherniak C., Mokhtarzada Z., Rodriguez-Esteban R., Changizi K. Global optimization of cerebral cortex layout. Proc. Natl. Acad. Sci. U. S. A. 2004;101(4):1081-1086.

    Not in our library10.1073/pnas.0305212101

14. 14.

    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. 2007;1(1):16.

    Not in our library10.1186/1752-0509-1-16

15. 15.

    Costa L.d.F., Rodrigues F.A., Travieso G., Villas Boas P.R. Characterization of complex networks: a survey of measurements. Adv. Phys. 2007;56(1):167-242.

    Not in our library

16. 16.
    The Connectome Mapper: An Open-Source Processing Pipeline to Map Connectomes with MRI · 2012

    No notice
17. 17.

    Dall J., Christensen M. Random geometric graphs. Phys. Rev. E. 2002;66(1):016121.

    Not in our library10.1103/physreve.66.016121

18. 18.

    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. 2014;8:647.

    Not in our library10.3389/fnhum.2014.00647

19. 19.

    Destrieux C., Fischl B., Dale A., Halgren E. Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. NeuroImage. 2010;53(1):1-15.

    Not in our library10.1016/j.neuroimage.2010.06.010

20. 20.

    Ercsey-Ravasz M., Markov N.T., Lamy C., Van Essen D.C., Knoblauch K., Toroczkai Z., Kennedy H. A predictive network model of cerebral cortical connectivity based on a distance rule. Neuron. 2013;80(1):184-197.

    Not in our library10.1016/j.neuron.2013.07.036

21. 21.

    Fortunato S. Community detection in graphs. Phys. Rep. 2010;486(3):75-174.

    Not in our library

22. 22.

    Friedman E.J., Landsberg A.S., Owen J., Hsieh W., Kam L., Mukherjee P. Edge correlations in spatial networks. J. Complex Netw. 2015;cnv015.

    Not in our library

23. 23.

    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. 2013;80:105-124.

    Not in our library10.1016/j.neuroimage.2013.04.127

24. 24.
    Exploring the Morphospace of Communication Efficiency in Complex Networks · 2013

    No notice
25. 25.

    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. 2014;111(2):833-838.

    Not in our library10.1073/pnas.1315529111

26. 26.
    Mapping the Structural Core of Human Cerebral Cortex · 2008

    No notice
27. 27.

    Henderson J., Robinson P. Geometric effects on complex network structure in the cortex. Phys. Rev. Lett. 2011;107(1):018102.

    Not in our library10.1103/physrevlett.107.018102

28. 28.

    Henderson J.A., Robinson P.A. Using geometry to uncover relationships between isotropy, homogeneity, and modularity in cortical connectivity. Brain Connect. 2013;3(4):423-437.

    Not in our library10.1089/brain.2013.0151

29. 29.

    Henderson J.A., Robinson P.A. Relations between the geometry of cortical gyrification and white-matter network architecture. Brain Connect. 2014;4(2):112-130.

    Not in our library10.1089/brain.2013.0183

30. 30.

    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. 2013;110(15):6169-6174.

    Not in our library10.1073/pnas.1219562110

31. 31.

    Kaiser M., Hilgetag C.C. Modelling the development of cortical systems networks. Neurocomputing. 2004;58:297-302.

    Not in our library

32. 32.

    Kaiser M., Hilgetag C.C. Spatial growth of real-world networks. Phys. Rev. E. 2004;69(3):036103.

    Not in our library10.1103/physreve.69.036103

33. 33.
    Nonoptimal Component Placement, but Short Processing Paths, due to Long-Distance Projections in Neural Systems · 2006

    No notice
34. 34.

    Kaiser M., Hilgetag C.C. Development of multi-cluster cortical networks by time windows for spatial growth. Neurocomputing. 2007;70(10–12):1829-1832.

    Not in our library

35. 35.

    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. 2009;19(12):3001-3010.

    Not in our library10.1093/cercor/bhp071

36. 36.

    Klimm F., Bassett D.S., Carlson J.M., Mucha P.J. Resolving structural variability in network models and the brain. PLoS Comput. Biol. 2014;10(3):e1003491.

    Not in our library10.1371/journal.pcbi.1003491

37. 37.

    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;57-65.

    Not in our library

38. 38.

    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. 2015;162(1):120-133.

    Not in our library10.1016/j.cell.2015.05.055

39. 39.

    Laughlin S.B., Sejnowski T.J. Communication in neuronal networks. Science. 2003;301(5641):1870-1874.

    Not in our library10.1126/science.1089662

40. 40.

    Lim S., Kaiser M. Developmental time windows for axon growth influence neuronal network topology. Biol. Cybern. 2015;109(2):275-286.

    Not in our library10.1007/s00422-014-0641-3

41. 41.

    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. 2015;25(6):1477-1489.

    Not in our library10.1093/cercor/bht333

42. 42.

    Menezes T., Roth C. Symbolic regression of generative network models. Sci. Rep. 2014;4:6284.

    Not in our library10.1038/srep06284

43. 43.

    Middendorf M., Ziv E., Wiggins C.H. Inferring network mechanisms: the Drosophila melanogaster protein interaction network. Proc. Natl. Acad. Sci. U. S. A. 2005;102(9):3192-3197.

    Not in our library10.1073/pnas.0409515102

44. 44.

    Mišić B., Betzel R.F., Nematzadeh A., Goñi J., Griffa A., Hagmann P., Flammini A., Ahn Y.-Y., Sporns O. Cooperative and competitive spreading dynamics on the human connectome. Neuron. 2015;86:1518-1529.

    Not in our library10.1016/j.neuron.2015.05.035

45. 45.

    Mitchison G. Neuronal branching patterns and the economy of cortical wiring. Proc. R. Soc. Lond. B Biol. Sci. 1991;245(1313):151-158.

    Not in our library10.1098/rspb.1991.0102

46. 46.

    Newman M.E. Assortative mixing in networks. Phys. Rev. Lett. 2002;89(20):208701.

    Not in our library10.1103/physrevlett.89.208701

47. 47.

    Newman M.E. Communities, modules and large-scale structure in networks. Nat. Phys. 2012;8(1):25-31.

    Not in our library

48. 48.

    Newman M.E., Girvan M. Finding and evaluating community structure in networks. Phys. Rev. E. 2004;69(2):026113.

    Not in our library10.1103/physreve.69.026113

49. 49.

    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. 2013;110(19):7880-7885.

    Not in our library10.1073/pnas.1300753110

50. 50.
    The NKI-Rockland Sample: A Model for Accelerating the Pace of Discovery Science in Psychiatry · 2012

    No notice
51. 51.

    Roberts J.A., Perry A., Lord A.R., Roberts G., Mitchell P.B., Smith R.E., Calamante F., Breakspear M. The contribution of geometry to the human connectome. NeuroImage. 2016;124:379-393.

    Not in our library10.1016/j.neuroimage.2015.09.009

52. 52.

    Rubinov M., Sporns O. Complex network measures of brain connectivity: uses and interpretations. NeuroImage. 2010;52(3):1059-1069.

    Not in our library10.1016/j.neuroimage.2009.10.003

53. 53.

    Rubinov M., Ypma R., Watson C., Bullmore E. Wiring cost and topological participation of the mouse brain connectome. Proc. Natl. Acad. Sci. U. S. A. 2015.

    Not in our library10.1073/pnas.1420315112

54. 54.

    Samu D., Seth A.K., Nowotny T. Influence of wiring cost on the large-scale architecture of human cortical connectivity. PLoS Comput. Biol. 2014;10:e1003557.

    Not in our library10.1371/journal.pcbi.1003557

55. 55.

    Solé R., Satorras R.P., Smith E., Kepler T. A model of large-scale proteome evolution. Adv. Complex Syst. 2002;5(01):43-54.

    Not in our library

56. 56.

    Song H.F., Kennedy H., Wang X.-J. Spatial embedding of structural similarity in the cerebral cortex. Proc. Natl. Acad. Sci. U. S. A. 2014;111(46):16580-16585.

    Not in our library10.1073/pnas.1414153111

57. 57.

    Sporns O. Contributions and challenges for network models in cognitive neuroscience. Nat. Neurosci. 2014;17(5):652-660.

    Not in our library10.1038/nn.3690

58. 58.

    Sporns O., Betzel R.F. Modular brain networks. Annu. Rev. Psychol. 2016;67(1).

    Not in our library10.1146/annurev-psych-122414-033634

59. 59.

    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. 2014;111(46):16574-16579.

    Not in our library10.1073/pnas.1405672111

60. 60.

    van den Heuvel M.P., Sporns O. Rich-club organization of the human connectome. J. Neurosci. 2011;31(44):15775-15786.

    Not in our library10.1523/jneurosci.3539-11.2011

61. 61.

    van den Heuvel M.P., Sporns O. Network hubs in the human brain. Trends Cogn. Sci. 2013;17(12):683-696.

    Not in our library10.1016/j.tics.2013.09.012

62. 62.

    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. 2012;62(4):2222-2231.

    Not in our library10.1016/j.neuroimage.2012.02.018

63. 63.

    Vázquez A., Flammini A., Maritan A., Vespignani A. Modeling of protein interaction networks. Complexus. 2003;1(1):38-44.

    Not in our library

64. 64.

    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. 2012;109(15):5868-5873.

    Not in our library10.1073/pnas.1111738109

65. 65.

    Watts D., Strogatz S. Collective dynamics of small-world networks. Nature. 1998;393:440-442.

    Not in our library10.1038/30918

66. 66.

    Wilcoxon F. Individual comparisons by ranking methods. Biom. Bull. 1945;80-83.

    Not in our library

67. 67.

    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. 2014;89:45-56.

    Not in our library10.1016/j.neuroimage.2013.10.039

68. 68.

    Yeh F.-C., Wedeen V.J., Tseng W.-Y.I. Generalized-sampling imaging. IEEE Trans. Med. Imaging. 2010;29(9):1626-1635.

    Not in our library10.1109/tmi.2010.2045126

69. 69.

    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. 2010;50(3):970-983.

    Not in our library10.1016/j.neuroimage.2009.12.027

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