Diversity of meso-scale architecture in human and non-human connectomes

Abstract

Brain function is reflected in connectome community structure. The dominant view is that communities are assortative and segregated from one another, supporting specialized information processing. However, this view precludes the possibility of non-assortative communities whose complex inter-community interactions could engender a richer functional repertoire. We use weighted stochastic blockmodels to uncover the meso-scale architecture of Drosophila , mouse, rat, macaque, and human connectomes. We find that most communities are assortative, though others form core-periphery and disassortative structures, which better recapitulate observed patterns of functional connectivity and gene co-expression in human and mouse connectomes compared to standard community detection techniques. We define measures for quantifying the diversity of communities in which brain regions participate, showing that this measure is peaked in control and subcortical systems in humans, and that inter-individual differences are correlated with cognitive performance. Our report paints a more diverse portrait of connectome communities and demonstrates their cognitive relevance.

Full text

Its licence does not allow us to show the full text. Read it at PubMed Central:

References 75

1. 1.

   Mišić B, Sporns O. From regions to connections and networks: new bridges between brain and behavior. Curr. Opin. Neurobiol. 2016;40:1-7.

   Not in our library10.1016/j.conb.2016.05.003 (opens in a new tab)

2. 2.

   Medaglia JD, Lynall M, Bassett DS. Cognitive network neuroscience. J. Cogn. Neurosci. 2015;27:471-1491.

   Not in our library10.1162/jocn_a_00810 (opens in a new tab)

3. 3.

   Sporns O, et al. Organization, development and function of complex brain networks. Trends Cogn. Sci. 2004;8:418-425.

   Not in our library10.1016/j.tics.2004.07.008 (opens in a new tab)

4. 4.

   Passingham RE, Stephan KE, Kötter R. The anatomical basis of functional localization in the cortex. Nat. Rev. Neurosci. 2002;3:606-616.

   Not in our library10.1038/nrn893 (opens in a new tab)

5. 5.

   Sporns O, Tononi G, Edelman GM. Theoretical neuroanatomy: relating anatomical and functional connectivity in graphs and cortical connection matrices. Cereb. Cortex. 2000;10:127-141.

   Not in our library10.1093/cercor/10.2.127 (opens in a new tab)

6. 6.

   Sporns O, Betzel RF. Modular brain networks. Annu. Rev. Psychol. 2016;67:613-640.

   Not in our library10.1146/annurev-psych-122414-033634 (opens in a new tab)

7. 7.

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

   Not in our library10.1103/physreve.69.026113 (opens in a new tab)

8. 8.

   Simon HA. The architecture of complexity. Proc. Am. Philos. Soc. 1962;106:467-482.

   Not in our library

9. 9.

   van den Heuvel MP, Sporns O. Network hubs in the human brain. Trends Cogn. Sci. 2013;17:683-696.

   Not in our library10.1016/j.tics.2013.09.012 (opens in a new tab)

10. 10.
    Rich Club Organization of Macaque Cerebral Cortex and Its Role in Network Communication · 2012

    No notice
11. 11.

    Towlson EK, et al. The rich club of the c. elegans neuronal connectome. J. Neurosci. 2013;33:6380-6387.

    Not in our library10.1523/jneurosci.3784-12.2013 (opens in a new tab)

12. 12.

    van Den Heuvel MP, Sporns O. Rich-club organization of the human connectome. J. Neurosci. 2011;31:15775-15786.

    Not in our library10.1523/jneurosci.3539-11.2011 (opens in a new tab)

13. 13.

    Benson AR, Gleich DF, Leskovec J. Higher-order organization of complex networks. Science. 2016;353:163-166.

    Not in our library10.1126/science.aad9029 (opens in a new tab)

14. 14.

    Briggman KL, Helmstaedter M, Denk W. Wiring specificity in the direction-selectivity circuit of the retina. Nature. 2011;471:183-188.

    Not in our library10.1038/nature09818 (opens in a new tab)

15. 15.

    Ding H, et al. Species-specific wiring for direction selectivity in the mammalian retina. Nature. 2016;535:105-110.

    Not in our library10.1038/nature18609 (opens in a new tab)

16. 16.

    Deco. G, et al. Rethinking segregation and integration: contributions of whole-brain modelling. Nat. Rev. Neurosci. 2015;16:430-439.

    Not in our library10.1038/nrn3963 (opens in a new tab)

17. 17.

    Aicher, C., Jacobs, A. Z. & Clauset, A. Adapting the stochastic block model to edge-weighted networks. Preprint: arXiv:1305.5782 (2013).

    Not in our library

18. 18.

    Aicher C, Jacobs AZ, Clauset A. Learning latent block structure in weighted networks. J. Complex Netw. 2014;3:221-248.

    Not in our library10.1093/comnet/cnu026 (opens in a new tab)

19. 19.

    Van Veen V, Carter CS. Separating semantic conflict and response conflict in the stroop task: a functional mri study. Neuroimage. 2005;27:497-504.

    Not in our library10.1016/j.neuroimage.2005.04.042 (opens in a new tab)

20. 20.

    Navon D. Forest before trees: The precedence of global features in visual perception. Cogn. Psychol. 1977;9:353-383.

    Not in our library10.1016/0010-0285(77)90012-3 (opens in a new tab)

21. 21.

    Meilă, M. in Learning Theory and Kernel Machines (eds Schölkopf, B. & Warmuth, M. K.) 173–187 (Springer, Berlin, 2003).

    Not in our library

22. 22.

    Ramsay, J. O. & Silverman, B. W. Applied Functional Data Analysis: Methods and Case Studies (Springer, New York, 2002).

    Not in our library

23. 23.

    Yeo BTT, et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophys. 2011;106:1125-1165.

    Not in our library10.1152/jn.00338.2011 (opens in a new tab)

24. 24.

    Hilgetag, C. C., Kötter, R., Stephan, K. E. & Sporns, O. in Computational Neuroanatomy: Principles and Methods (ed. Ascoli, G. A.) 295–335 (Springer, New York, 2002).

    Not in our library

25. 25.

    Hric D, Darst RK, Fortunato S. Community detection in networks: structural communities versus ground truth. Phys. Rev. E. 2014;90:062805.

    Not in our library10.1103/physreve.90.062805 (opens in a new tab)

26. 26.

    Yang, H., McAuley, J. & Leskovec, J. Community detection in networks with node attributes. In Data Mining (ICDM), 2013 IEEE 13th International Conference on Data Mining (eds Xiong, H., Karypis, G., Thuraisingham, T., Cook, D. & Wu, X.) 1151–1156 (IEEE, Dallas, Texas, 2013).

    Not in our library

27. 27.

    Honey CJ, et al. Network structure of cerebral cortex shapes functional connectivity on multiple time scales. Proc. Natl Acad. Sci. USA. 2007;104:10240-10245.

    Not in our library10.1073/pnas.0701519104 (opens in a new tab)

28. 28.

    Zalesky A, Fornito A, Bullmore E. On the use of correlation as a measure of network connectivity. Neuroimage. 2012;60:2096-2106.

    Not in our library10.1016/j.neuroimage.2012.02.001 (opens in a new tab)

29. 29.

    McGhee, G. R. The Geometry of Evolution: Adaptive Landscapes and Theoretical Morphospaces (Cambridge University Press, Cambridge, 2006).

    Not in our library

30. 30.

    Avena-Koenigsberger A, et al. Network morphospace. J. R. Soc. Interface. 2015;12:20140881.

    Not in our library10.1098/rsif.2014.0881 (opens in a new tab)

31. 31.

    Buckner RL, Krienen FM. The evolution of distributed association networks in the human brain. Trends Cogn. Sci. 2013;17:648-665.

    Not in our library10.1016/j.tics.2013.09.017 (opens in a new tab)

32. 32.

    Cole MW, et al. Multi-task connectivity reveals flexible hubs for adaptive task control. Nat. Neurosci. 2013;16:1348-1355.

    Not in our library10.1038/nn.3470 (opens in a new tab)

33. 33.

    Davis M, Whalen PJ. The amygdala: vigilance and emotion. Mol. Psychiatry. 2001;6:13.

    Not in our library10.1038/sj.mp.4000812 (opens in a new tab)

34. 34.

    Grahn JA, Parkinson JA, Owen AM. The cognitive functions of the caudate nucleus. Prog. Neurobiol. 2008;86:141-155.

    Not in our library10.1016/j.pneurobio.2008.09.004 (opens in a new tab)

35. 35.

    Haber SN. Corticostriatal circuitry. Dialogues Clin. Neurosci. 2016;18:7-21.

    Not in our library10.31887/dcns.2016.18.1/shaber (opens in a new tab)

36. 36.

    Levy N, et al. Distributed synchrony in a cell assembly of spiking neurons. Neural Netw. 2001;14:815-824.

    Not in our library10.1016/s0893-6080(01)00044-2 (opens in a new tab)

37. 37.

    Rajan K, Harvey CD, Tank DW. Recurrent network models of sequence generation and memory. Neuron. 2016;90:128-142.

    Not in our library10.1016/j.neuron.2016.02.009 (opens in a new tab)

38. 38.

    Fiete IR, et al. Spike-time-dependent plasticity and heterosynaptic competition organize networks to produce long scale-free sequences of neural activity. Neuron. 2010;65:563-576.

    Not in our library10.1016/j.neuron.2010.02.003 (opens in a new tab)

39. 39.

    Jensen MH, Krishna S, Pigolotti S. Repressor lattice: feedback, commensurability, and dynamical frustration. Phys. Rev. Lett. 2009;103:118101.

    Not in our library10.1103/physrevlett.103.118101 (opens in a new tab)

40. 40.

    Dosenbach NUF, et al. Distinct brain networks for adaptive and stable task control in humans. Proc. Natl Acad. Sci. USA. 2007;104:11073-11078.

    Not in our library10.1073/pnas.0704320104 (opens in a new tab)

41. 41.

    Kasthuri N, et al. Saturated reconstruction of a volume of neocortex. Cell. 2015;162:648-661.

    Not in our library10.1016/j.cell.2015.06.054 (opens in a new tab)

42. 42.

    Kim TH, et al. Long-term optical access to an estimated one million neurons in the live mouse cortex. Cell Rep. 2016;17:3385-3394.

    Not in our library10.1016/j.celrep.2016.12.004 (opens in a new tab)

43. 43.

    Simpson SL, Hayasaka S, Laurienti PJ. Exponential random graph modeling for complex brain networks. PLoS ONE. 2011;6:e20039.

    Not in our library10.1371/journal.pone.0020039 (opens in a new tab)

44. 44.

    Moyer, D. et al. Mixed membership stochastic blockmodels for the human connectome. In MICCAI–Workshop on Bayesian and Graphical Models for Biomedical Imaging (2015).

    Not in our library

45. 45.

    Murphy, A. C. et al. Explicitly linking regional activation and function connectivity: community structure of weighted networks with continuous annotation. Preprint: arXiv:1611.07962 (2016).

    Not in our library

46. 46.

    Baldassano C, Beck DM, Fei-Fei L. Parcellating connectivity in spatial maps. PeerJ. 2015;3:e784.

    Not in our library10.7717/peerj.784 (opens in a new tab)

47. 47.

    Felleman DJ, Van Essen DC. Distributed hierarchical processing in the primate cerebral cortex. Cereb. Cortex. 1991;1:1-47.

    Not in our library10.1093/cercor/1.1.1 (opens in a new tab)

48. 48.

    Seung HS, Sümbül U. Neuronal cell types and connectivity: lessons from the retina. Neuron. 2014;83:1262-1272.

    Not in our library10.1016/j.neuron.2014.08.054 (opens in a new tab)

49. 49.

    Pavlovic DM, et al. Stochastic blockmodeling of the modules and core of the Caenorhabditis elegans connectome. PLoS ONE. 2014;9:e97584.

    Not in our library10.1371/journal.pone.0097584 (opens in a new tab)

50. 50.

    Noori HR, et al. A multiscale cerebral neurochemical connectome of the rat brain. PLoS Biol. 2017;15:e2002612.

    Not in our library10.1371/journal.pbio.2002612 (opens in a new tab)

51. 51.

    Ercsey-Ravasz M, et al. A predictive network model of cerebral cortical connectivity based on a distance rule. Neuron. 2013;80:184-197.

    Not in our library10.1016/j.neuron.2013.07.036 (opens in a new tab)

52. 52.

    Dalley JW, Everitt BJ, Robbins TW. Impulsivity, compulsivity, and top-down cognitive control. Neuron. 2011;69:680-694.

    Not in our library10.1016/j.neuron.2011.01.020 (opens in a new tab)

53. 53.

    Fries P. Rhythms for cognition: communication through coherence. Neuron. 2015;88:220-235.

    Not in our library10.1016/j.neuron.2015.09.034 (opens in a new tab)

54. 54.

    Santarnecchi E, et al. Efficiency of weak brain connections support general cognitive functioning. Hum. Brain. Mapp. 2014;35:4566-4582.

    Not in our library10.1002/hbm.22495 (opens in a new tab)

55. 55.

    Bassett DS, et al. Altered resting state complexity in schizophrenia. Neuroimage. 2012;59:2196-2207.

    Not in our library10.1016/j.neuroimage.2011.10.002 (opens in a new tab)

56. 56.
    Controllability of structural brain networks · 2015

    No notice
57. 57.

    Forstmann BU, et al. Cortico-striatal connections predict control over speed and accuracy in perceptual decision making. Proc. Natl Acad. Sci. USA. 2010;107:15916-15920.

    Not in our library10.1073/pnas.1004932107 (opens in a new tab)

58. 58.

    Wei W, Wang XJ. Inhibitory control in the cortico-basal ganglia-thalamocortical loop: complex regulation and interplay with memory and decision processes. Neuron. 2016;92:1093-1105.

    Not in our library10.1016/j.neuron.2016.10.031 (opens in a new tab)

59. 59.

    Sepulcre J, et al. Stepwise connectivity of the modal cortex reveals the multimodal organization of the human brain. J. Neurosci. 2012;32:10649-10661.

    Not in our library10.1523/jneurosci.0759-12.2012 (opens in a new tab)

60. 60.

    Fornito A, Zalesky A, Breakspear M. The connectomics of brain disorders. Nat. Rev. Neurosci. 2015;16:159-172.

    Not in our library10.1038/nrn3901 (opens in a new tab)

61. 61.

    Zuo XN, et al. Human connectomics across the life span. Trends Cogn. Sci. 2016;21:32-45.

    Not in our library10.1016/j.tics.2016.10.005 (opens in a new tab)

62. 62.

    Barch DM, et al. Function in the human connectome: task-fmri and individual differences in behavior. Neuroimage. 2013;80:169-189.

    Not in our library10.1016/j.neuroimage.2013.05.033 (opens in a new tab)

63. 63.

    Peel L, Larremore DB, Clauset A. The ground truth about metadata and community detection in networks. Sci. Adv. 2017;3:e1602548.

    Not in our library10.1126/sciadv.1602548 (opens in a new tab)

64. 64.
    The challenge of mapping the human connectome based on diffusion tractography · 2017

    Corrected
65. 65.

    Fernandez-Miranda JC, et al. High-definition fiber tractography of the human brain: neuroanatomical validation and neurosurgical applications. Neurosurgery. 2012;71:430-453.

    Not in our library10.1227/neu.0b013e3182592faa (opens in a new tab)

66. 66.

    Takemura H, et al. Ensemble tractography. PLoS Comp. Biol. 2016;12:e1004692.

    Not in our library10.1371/journal.pcbi.1004692 (opens in a new tab)

67. 67.

    Smith RE, et al. Sift: spherical-deconvolution informed filtering of tractograms. Neuroimage. 2013;67:298-312.

    Not in our library10.1016/j.neuroimage.2012.11.049 (opens in a new tab)

68. 68.
    The modular organization of human anatomical brain networks: Accounting for the cost of wiring · 2017

    No notice
69. 69.

    Yeh F, Wedeen VJ, Tseng WYI. Estimation of fiber orientation and spin density distribution by diffusion deconvolution. Neuroimage. 2011;55:1054-1062.

    Not in our library10.1016/j.neuroimage.2010.11.087 (opens in a new tab)

70. 70.

    Cammoun L, et al. Mapping the human connectome at multiple scales with diffusion spectrum mri. J. Neurosci. Methods. 2012;203:386-397.

    Not in our library10.1016/j.jneumeth.2011.09.031 (opens in a new tab)

71. 71.

    Cieslak M, Grafton ST. Local termination pattern analysis: a tool for comparing white matter morphology. Brain. Imaging Behav. 2014;8:292-299.

    Not in our library10.1007/s11682-013-9254-z (opens in a new tab)

72. 72.

    Roberts JA, et al. Consistency-based thresholding of the human connectome. Neuroimage. 2016;145:118-129.

    Not in our library10.1016/j.neuroimage.2016.09.053 (opens in a new tab)

73. 73.

    Medaglia, J. D. et al. Functional alignment with anatomical networks is associated with cognitive flexibility. Nat. Hum. Behav.10.1038/s41562-017-0260-9 (2016).

    Not in our library10.1038/s41562-017-0260-9 (opens in a new tab)

74. 74.

    Opsahl T, et al. Prominence and control: the weighted rich-club effect. Phys. Rev. Lett. 2008;101:168702.

    Not in our library10.1103/physrevlett.101.168702 (opens in a new tab)

75. 75.

    Tyszka J. Morphospace of foraminiferal shells: results from the moving reference model. Lethaia. 2006;39:1-12.

    Not in our library10.1080/00241160600575808 (opens in a new tab)

Cited by papers we hold 4

• The neuropeptidergic connectome of C. elegans · 2023
  No notice
• Neuronal classification from network connectivity via adjacency spectral embedding · 2021
  Not checked
• Multimodal network dynamics underpinning working memory · 2020
  No notice
• Distance-dependent consensus thresholds for generating group-representative structural brain networks · 2019
  No notice

Questions about this paper

Reads

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.