1932

Abstract

The development of new technologies for mapping structural and functional brain connectivity has led to the creation of comprehensive network maps of neuronal circuits and systems. The architecture of these brain networks can be examined and analyzed with a large variety of graph theory tools. Methods for detecting modules, or network communities, are of particular interest because they uncover major building blocks or subnetworks that are particularly densely connected, often corresponding to specialized functional components. A large number of methods for community detection have become available and are now widely applied in network neuroscience. This article first surveys a number of these methods, with an emphasis on their advantages and shortcomings; then it summarizes major findings on the existence of modules in both structural and functional brain networks and briefly considers their potential functional roles in brain evolution, wiring minimization, and the emergence of functional specialization and complex dynamics.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-psych-122414-033634
2016-01-04
2024-07-14
Loading full text...

Full text loading...

/deliver/fulltext/psych/67/1/annurev-psych-122414-033634.html?itemId=/content/journals/10.1146/annurev-psych-122414-033634&mimeType=html&fmt=ahah

Literature Cited

  1. Ahn YY, Bagrow JP, Lehmann S. 2010. Link communities reveal multiscale complexity in networks. Nature 466:7307761–64Link-clustering algorithm for obtaining an estimate of a network's overlapping community structure. [Google Scholar]
  2. Ahrens MB, Orger MB, Robson DN, Li JM, Keller PJ. 2013. Whole-brain functional imaging at cellular resolution using light-sheet microscopy. Nat. Methods 10:5413–20 [Google Scholar]
  3. Aldecoa R, Marin I. 2011. Deciphering network community structure by surprise. PLOS ONE 6:9e24195 [Google Scholar]
  4. Alexander-Bloch A, Lambiotte R, Roberts B, Giedd J, Gogtay N, Bullmore E. 2012. The discovery of population differences in network community structure: new methods and applications to brain functional networks in schizophrenia. Neuroimage 59:43889–900 [Google Scholar]
  5. Andric M, Hasson U. 2015. Global features of functional brain networks change with contextual disorder. Neuroimage 117:103–13 [Google Scholar]
  6. Arenas A, Díaz-Guilera A, Pérez-Vicente CJ. 2006. Synchronization reveals topological scales in complex networks. Phys. Rev. Lett. 96:114102 [Google Scholar]
  7. Arenas A, Fernández A, Gómez S. 2008. Analysis of the structure of complex networks at different resolution levels. New J. Phys. 10:5053039 [Google Scholar]
  8. Bassett DS, Greenfield DL, Meyer-Lindenberg A, Weinberger DR, Moore SW, Bullmore ET. 2010. Efficient physical embedding of topologically complex information processing networks in brains and computer circuits. PLOS Comp. Biol. 6:4e1000748 [Google Scholar]
  9. Bassett DS, Porter MA, Wymbs NF, Grafton ST, Carlson JM, Mucha PJ. 2013. Robust detection of dynamic community structure in networks. Chaos 23:1013142An important technical paper highlighting many useful guidelines for modularity maximization. [Google Scholar]
  10. Bassett DS, Wymbs NF, Porter MA, Mucha PJ, Carlson JM, Grafton ST. 2011. Dynamic reconfiguration of human brain networks during learning. PNAS 108:187641–46 [Google Scholar]
  11. Bassett DS, Yang M, Wymbs NF, Grafton ST. 2015. Learning-induced autonomy of sensorimotor systems. Nat. Neurosci. 18:744–51 [Google Scholar]
  12. Bazzi M, Porter MA, Williams S, McDonald M, Fenn DJ, Howison SD. 2014. Community detection in temporal multilayer networks, and its application to correlation networks. arXiv1501.00040 [physics.soc-ph]
  13. Beckmann CF, DeLuca M, Devlin JT, Smith SM. 2005. Investigations into resting-state connectivity using independent component analysis. Philos. Trans. R. Soc. B 360:14571001–13 [Google Scholar]
  14. Bellec P, Rosa-Neto P, Lyttelton OC, Benali H, Evans AC. 2010. Multi-level bootstrap analysis of stable clusters in resting-state fMRI. Neuroimage 51:31126–39 [Google Scholar]
  15. Betzel RF, Byrge L, He Y, Goñi J, Zuo XN, Sporns O. 2014. Changes in structural and functional connectivity among resting-state networks across the human lifespan. Neuroimage 102:2345–57 [Google Scholar]
  16. Betzel RF, Griffa A, Avena-Koenigsberger A, Goñi J, Hagmann P. et al. 2013. Multi-scale community organization of the human structural connectome and its relationship with resting-state functional connectivity. Netw. Sci. 1:3353–73 [Google Scholar]
  17. Blondel VD, Guillaume JL, Lambiotte R, Lefebvre E. 2008. Fast unfolding of communities in large networks. J. Stat. Mech. Theor. Exp. 2008:10P10008 [Google Scholar]
  18. Boccaletti S, Ivanchenko M, Latora V, Pluchino A, Rapisarda A. 2007. Detecting network modularity by dynamical clustering. Phys. Rev. E 75:4045102 [Google Scholar]
  19. Börner K, Sanyal S, Vespignani A. 2007. Network science. Annu. Rev. Inform. Sci. Technol. 41:1537–607 [Google Scholar]
  20. Bota M, Sporns O, Swanson LW. 2015. Architecture of the cerebral cortical association connectome underlying cognition. PNAS 112:16E2093–101 [Google Scholar]
  21. Bressler SL, Menon V. 2010. Large-scale brain networks in cognition: emerging methods and principles. Trends Cogn. Sci. 14:6277–90 [Google Scholar]
  22. Bullmore ET, Sporns O. 2009. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10:3186–93 [Google Scholar]
  23. Bullmore ET, Sporns O. 2012. The economy of brain network organization. Nat. Rev. Neurosci. 13:5336–49 [Google Scholar]
  24. Carrington PJ, Scott J, Wasserman S. 2005. Models and Methods in Social Network Analysis New York: Cambridge Univ. Press [Google Scholar]
  25. Chan MY, Park DC, Savalia NK, Petersen SE, Wig GS. 2014. Decreased segregation of brain systems across the healthy adult lifespan. PNAS 111:46E4997–5006 [Google Scholar]
  26. Chen BL, Hall DH, Chklovskii DB. 2006. Wiring optimization can relate neuronal structure and function. PNAS 103:127423–28 [Google Scholar]
  27. Chen Y, Wang S, Hilgetag CC, Zhou C. 2013. Trade-off between multiple constraints enables simultaneous formation of modules and hubs in neural systems. PLOS Comp. Biol. 9:3e1002937 [Google Scholar]
  28. Cherniak C, Mokhtarzada Z, Rodriguez-Esteban R, Changizi K. 2004. Global optimization of cerebral cortex layout. PNAS 101:41081–86 [Google Scholar]
  29. Clauset A, Newman MEJ, Moore C. 2004. Finding community structure in very large networks. Phys. Rev. E 70:6066111 [Google Scholar]
  30. Clune J, Mouret JB, Lipson H. 2013. The evolutionary origins of modularity. Proc. R. Soc. B 280:175520122863 [Google Scholar]
  31. Cole MW, Bassett DS, Power JD, Braver TS, Petersen SE. 2014. Intrinsic and task-evoked architectures of the human brain. Neuron 83:1238–51 [Google Scholar]
  32. Crossley NA, Mechelli A, Vertes PE, Winton-Brown TT, Patel AX. et al. 2013. Cognitive relevance of the community structure of the human brain functional coactivation network. PNAS1102811583–88 [Google Scholar]
  33. Danon L, Díaz-Guilera A, Duch J, Arenas A. 2005. Comparing community structure identification. J. Stat. Mech. Theor. Exp. 9:P09008 [Google Scholar]
  34. de Reus MA, van den Heuvel MP. 2013. Rich club organization and intermodule communication in the cat connectome. J. Neurosci. 33:3212929–39 [Google Scholar]
  35. Delvenne JC, Yaliraki SN, Barahona M. 2010. Stability of graph communities across time scales. PNAS 107:2912755–60 [Google Scholar]
  36. Doron KW, Bassett DS, Gazzaniga MS. 2012. Dynamic network coordination of interhemispheric coordination. PNAS 109:4618661–68 [Google Scholar]
  37. Duch J, Arenas A. 2005. Community detection in complex networks using extremal optimization. Phys. Rev. E 72:2027104 [Google Scholar]
  38. Ellefsen KO, Mouret JB, Clune J. 2015. Neural modularity helps organisms evolve to learn new skills without forgetting old skills. PLOS Comp. Biol. 11:4e1004128 [Google Scholar]
  39. Espinosa-Soto C, Wagner A. 2010. Specialization can drive the evolution of modularity. PLOS Comp. Biol. 6:3e1000719 [Google Scholar]
  40. Evans TS, Lambiotte R. 2009. Line graphs, link partitions, and overlapping communities. Phys. Rev. E 80:1016105 [Google Scholar]
  41. Expert P, Evans TS, Blondel VD, Lambiotte R. 2011. Uncovering space-independent communities in spatial networks. PNAS 108:197663–68 [Google Scholar]
  42. Felleman DJ, van Essen DC. 1991. Distributed hierarchical processing in the primate cerebral cortex. Cereb. Cortex 1:11–47 [Google Scholar]
  43. Fodor JA. 1983. The Modularity of Mind: An Essay on Faculty Psychology Cambridge, MA: MIT Press [Google Scholar]
  44. Fornito A, Zalesky A, Breakspear M. 2015. The connectomics of brain disorders. Nat. Rev. Neurosci. 16:3159–72 [Google Scholar]
  45. Fortunato S. 2010. Community detection in graphs. Phys. Rep. 486:375–174An encyclopedic reference on community detection algorithms. [Google Scholar]
  46. Fortunato S, Barthelemy M. 2007. Resolution limit in community detection. PNAS 104:136–41 [Google Scholar]
  47. Friston KJ. 2011. Functional and effective connectivity: a review. Brain Connect. 1:113–36 [Google Scholar]
  48. Gallos LK, Makse HA, Sigman M. 2012. A small world of weak ties provides optimal global integrations of self-similar modules in functional brain networks. PNAS 109:82825–30 [Google Scholar]
  49. Geerligs L, Renken RJ, Saliasi E, Maruits NM, Lorist MM. 2015. A brain-wide study of age-related changes in functional connectivity. Cereb. Cortex. 25:71987–99 [Google Scholar]
  50. Gfeller D, Chappelier JC, De Los Rios P. 2005. Finding instabilities in the community structure of complex networks. Phys. Rev. E 72:5056135 [Google Scholar]
  51. Gibson G, Dworkin I. 2004. Uncovering cryptic genetic variation. Nat. Rev. Genet. 5:9681–90 [Google Scholar]
  52. Girvan M, Newman MEJ. 2002. Community structure in social and biological networks. PNAS 99:127821–26 [Google Scholar]
  53. Godwin G, Barry RL, Marois R. 2015. Breakdown of the brain's functional network modularity with awareness. PNAS 112:123799–804 [Google Scholar]
  54. Gómez S, Jensen P, Arenas A. 2009. Analysis of community structure in networks of correlated data. Phys. Rev. E 80:1016114 [Google Scholar]
  55. Good BH, de Montjoye YA, Clauset A. 2010. Performance of modularity maximization in practical contexts. Phys. Rev. E 81:4046106 [Google Scholar]
  56. Goulas A, Schaefer A, Margulies DS. 2015. The strength of weak connections in the macaque cortico-cortical network. Brain Struct. Funct. 2202939–51 [Google Scholar]
  57. Guimerà R, Amaral LAN. 2005. Functional cartography of complex metabolic networks. Nature 433:7028895–900 [Google Scholar]
  58. Hagmann P, Cammoun L, Gigandet X, Meuli R, Honey CJ. et al. 2008. Mapping the structural core of human cerebral cortex. PLOS Biol. 6:7e159 [Google Scholar]
  59. Harriger L, van den Heuvel MP, Sporns O. 2012. Rich club organization of macaque cerebral cortex and its role in network communication. PLOS ONE 7:9e46497 [Google Scholar]
  60. Hastie T, Tibshirani R, Friedman J. 2009. The Elements of Statistical Learning 2 New York: Springer [Google Scholar]
  61. Hastings MB. 2006. Community detection as an inference problem. Phys. Rev. E 74:3035102 [Google Scholar]
  62. Henderson JA, Robinson PA. 2013. Using geometry to uncover relationships between isotropy, homogeneity, and modularity in cortical connectivity. Brain Connect. 3:4423–37 [Google Scholar]
  63. Hilgetag CC, Burns GA, O'Neill MA, Scannell JW, Young MP. 2000. Anatomical connectivity defines the organization of clusters of cortical areas in the macaque and the cat. Philos. Trans. R. Soc. B 355:139391–110 [Google Scholar]
  64. Hinne M, Ekman M, Janssen RJ, Heskes T, van Gerven MAJ. 2014. Probabilistic clustering of the human connectome identifies communities and hubs. PLOS ONE 10:e0117179 [Google Scholar]
  65. Hutchison MR, Womelsdorf T, Allen EA, Bandettini PA, Calhoun VD. et al. 2013. Dynamic functional connectivity: promise, issues, and interpretations. Neuroimage 80:360–78 [Google Scholar]
  66. Jarrell TA, Wang Y, Bloniarz AE, Brittin CA, Xu M. et al. 2012. The connectome of a decision-making neural network. Science 337:6093437–44 [Google Scholar]
  67. Jirsa VK, McIntosh AR. 2007. Handbook of Brain Connectivity 1 Berlin: Springer [Google Scholar]
  68. Kaiser M, Hilgetag CC, Kötter R. 2010. Hierarchy and dynamics in neural networks. Front. Neuroinform. 4:112 [Google Scholar]
  69. Karrer B, Levina E, Newman MEJ. 2008. Robustness of community structure in networks. Phys. Rev. E 77:4046119 [Google Scholar]
  70. Kashtan N, Alon U. 2005. Spontaneous evolution of modularity and network motifs. PNAS 102:3913773–78 [Google Scholar]
  71. Kashtan N, Noor E, Alon U. 2007. Varying environments can speed up evolution. PNAS 104:3413711–16 [Google Scholar]
  72. Kirschner M, Gerhart J. 1998. Evolvability. PNAS 95:158420–27 [Google Scholar]
  73. Kötter R. 2004. Online retrieval, processing, and visualization of primate connectivity data from the CoCoMac database. Neuroinformatics 2:2127–44 [Google Scholar]
  74. Laird AR, Lancaster JJ, Fox PT. 2005. BrainMap: the social evolution of a human brain mapping database. Neuroinformatics 3:165–78 [Google Scholar]
  75. Lancichinetti A, Fortunato S. 2009. Community detection algorithms: a comparative analysis. Phys. Rev. E 80:5056117 [Google Scholar]
  76. Lancichinetti A, Fortunato S. 2011. Limits of modularity maximization in community detection. Phys. Rev. E 84:066122 [Google Scholar]
  77. Lancichinetti A, Fortunato S. 2012. Consensus clustering in complex networks. Sci. Rep. 2:336A general method for obtaining consensus communities through iterative thresholding and reclustering of association matrices. [Google Scholar]
  78. Lancichinetti A, Fortunato S, Radicchi F. 2008. Benchmark graphs for testing community detection algorithms. Phys. Rev. E 78:4046110 [Google Scholar]
  79. Lancichinetti A, Radicchi F, Ramasco JJ. 2010. Statistical significance of communities in networks. Phys. Rev. E 81:4046110 [Google Scholar]
  80. Lancichinetti A, Radicchi F, Ramasco JJ, Fortunato. 2011. Finding statistically significant communities in networks. PLOS ONE 6:4e18961OSLOM method for detecting statistically significant communities. [Google Scholar]
  81. Laumann TO, Gordon EM, Adeyemo B, Snyder AZ, Joo SJ. et al. 2015. Functional system and areal organization of a highly sampled individual human brain. Neuron 87:3657–70 [Google Scholar]
  82. Leicht EA, Newman MEJ. 2008. Community structure in directed networks. Phys. Rev. Lett. 100:11118703 [Google Scholar]
  83. Lohse C, Bassett DS, Lim KO, Carlson JM. 2014. Resolving anatomical and functional structure in human brain organization: identifying mesoscale organization in weighted network representations. PLOS Comp. Biol. 10:10e1003712 [Google Scholar]
  84. MacMahon M, Garlaschelli D. 2015. Community detection for correlation matrices. Phys. Rev. X 5:021006 [physics.data-an] [Google Scholar]
  85. Markov NT, Ercsey-Ravasz MM, Ribeiro Gomes AR, Lamy C, Magrou L. et al. 2014. A weighted and directed interareal connectivity matrix for macaque cerebral cortex. Cereb. Cortex 24:17–36 [Google Scholar]
  86. McIntosh AR, Mišić B. 2013. Multivariate statistical analysis for neuroimaging data. Annu. Rev. Psychol. 64:499–525 [Google Scholar]
  87. Meunier D, Achard S, Morcom A, Bullmore E. 2009a. Age-related changes in modular organization of brain functional networks. Neuroimage 44:3715–23 [Google Scholar]
  88. Meunier D, Lambiotte R, Fornito A, Ersche KD, Bullmore ET. 2009b. Hierarchical modularity in human brain functional networks. Front. Neuroinform. 3:37 [Google Scholar]
  89. Modha DS, Singh R. 2010. Network architecture of the long-distance pathways in the macaque brain. PNAS 107:3013485–90 [Google Scholar]
  90. Mucha PJ, Richardson T, Macon K, Porter MA, Onnela JP. 2010. Community structure in time-dependent, multiscale, and multiplex networks. Science 328:5980876–78A multilayer analog of the standard single-layer modularity quality function. [Google Scholar]
  91. Newman MEJ. 2004. Fast algorithm for detecting community structure in networks. Phys. Rev. E 69:6066133 [Google Scholar]
  92. Newman MEJ, Girvan M. 2004. Finding and evaluating community structure in networks. Phys. Rev. E 69:2026113Seminal work in which the modularity quality function was first formalized. [Google Scholar]
  93. Nicosia V, Vértes PE, Schafer WR, Latora V, Bullmore ET. 2013. Phase transition in the economically modeled growth of a cellular nervous system. PNAS 110:197880–85 [Google Scholar]
  94. Oh SW, Harris JA, Ng L, Winslow B, Cain N. et al. 2014. A mesoscale connectome of the mouse brain. Nature 508:7495207–14 [Google Scholar]
  95. Palla G, Derényi I, Farkas I, Vicsek T. 2005. Uncovering the overlapping community structure of complex networks in nature and society. Nature 435:7043814–18Clique percolation algorithm for obtaining overlapping community structure. [Google Scholar]
  96. Pan RK, Chatterjee N, Sinha S. 2010. Mesoscopic organization reveals the constraints governing Caenorhabditis elegans nervous system. PLOS ONE 5:2e9240 [Google Scholar]
  97. Pan RK, Sinha S. 2009. Modularity produces small-world networks with dynamical time-scale separation. Europhys. Lett. 85:68006 [Google Scholar]
  98. Park HJ, Friston K. 2013. Structural and functional brain networks: from connections to cognition. Science 342:61581238411 [Google Scholar]
  99. Pavlovic DM, Vértes PE, Bullmore ET, Schafer WR, Nichols TE. 2014. Stochastic blockmodeling of the modules and core of the Caenorhabditis elegans connectome. PLOS ONE 9:7e97584 [Google Scholar]
  100. Pons P, Latapy M. 2005. Computing communities in large networks using random walks. Computer and Information Sciences—ISCIS 2005 P Yolum, T Güngör, F Gürgen, C Özturan 284–93 Berlin: Springer [Google Scholar]
  101. Power JD, Cohen AL, Nelson SM, Wig GS, Barnes KA. et al. 2011. Functional network organization of the human brain. Neuron 72:4665–78Application of Infomap and modularity maximization to functional brain networks to uncover modules. [Google Scholar]
  102. Reichardt J, Bornholdt S. 2006. Statistical mechanics of community detection. Phys. Rev. E 74:1016110 [Google Scholar]
  103. Ronhovde P, Nussinov Z. 2009. Multiresolution community detection for megascale networks by information-based replica correlations. Phys. Rev. E 80:1016109 [Google Scholar]
  104. Rosvall M, Axelsson, Bergstrom CT. 2009. The map equation. Eur. Phys. J. Special Top. 178:113–23 [Google Scholar]
  105. Rosvall M, Bergstrom CT. 2008. Maps of random walks on complex networks reveal community structure. PNAS10541118–23Infomap algorithm for uncovering nonoverlapping community structure based on a random walk over a network. [Google Scholar]
  106. Rubinov M, Sporns O. 2010. Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52:1059–69 [Google Scholar]
  107. Rubinov M, Sporns O. 2011. Weight-conserving characterization of complex functional brain networks. Neuroimage 56:42068–79 [Google Scholar]
  108. Rubinov M, Sporns O, Thivierge JP, Breakspear M. 2011. Neurobiologically realistic determinants of self-organized criticality in networks of spiking neurons. PLOS Comp. Biol. 7:6e1002038 [Google Scholar]
  109. Samu D, Seth AK, Nowotny T. 2014. Influence of wiring cost on the large-scale architecture of human cortical connectivity. PLOS Comp. Biol. 10:4e1003557 [Google Scholar]
  110. Scannell JW, Blackmore C, Young MP. 1995. Analysis of connectivity in the cat cerebral cortex. J. Neurosci. 15:21463–83 [Google Scholar]
  111. Shih CT, Sporns O, Yuan SL, Su TS, Lin YJ. et al. 2015. Connectomics-based analysis of information flow in the Drosophila brain. Curr. Biol. 25:101249–58 [Google Scholar]
  112. Shimono M, Beggs JM. 2015. Functional clusters, hubs, and communities in the cortical microconnectome. Cereb. Cortex 253743–57 [Google Scholar]
  113. Simon HA. 1962. The architecture of complexity. Proc. Am. Philos. Soc. 106:467–82 [Google Scholar]
  114. Smith SM, Fox PT, Miller KL, Glahn DC, Fox PM. et al. 2009. Correspondence of the brain's functional architecture during activations and rest. PNAS 106:3113040–45 [Google Scholar]
  115. Smith SM, Miller KL, Salimi-Khorshidi G, Webster M, Beckmann CF. et al. 2011. Network modeling methods for fMRI. Neuroimage 54:2875–91 [Google Scholar]
  116. Sohn Y, Choi MK, Ahn YY, Lee J, Jeong J. 2011. Topological cluster analysis reveals the systemic organization of Caenorhabditis elegans connectome. PLOS Comp. Biol. 7:5e1001139 [Google Scholar]
  117. Sporns O. 2011. Networks of the Brain. Cambridge, MA: MIT Press [Google Scholar]
  118. Sporns O. 2013. The human connectome: origins and challenges. Neuroimage 80:53–61 [Google Scholar]
  119. Sporns O. 2014. Contributions and challenges for network models in cognitive neuroscience. Nat. Neurosci. 17:5652–60 [Google Scholar]
  120. Sporns O, Chialvo DR, Kaiser M, Hilgetag CC. 2004. Organization, development, and function of complex brain networks. Trends Cogn. Sci. 8:9418–25 [Google Scholar]
  121. Sporns O, Tononi G, Edelman GM. 2000. Theoretical neuroanatomy: relating anatomical and functional connectivity in graphs and cortical connection matrices. Cereb. Cortex 10:2127–41 [Google Scholar]
  122. Sporns O, Tononi G, Kötter R. 2005. The human connectome: a structural description of the human brain. PLOS Comp. Biol. 1:e42 [Google Scholar]
  123. Stephan KE, Kamper L, Bozkurt A, Burns GA, Young MP, Kötter R. 2001. Advanced database methodology for the Collation of Connectivity data on the Macaque brain (CoCoMac). Philos. Trans. R. Soc. B 356:14121159–86 [Google Scholar]
  124. Towlson EK, Vértes PE, Ahnert SE, Schafer WR, Bullmore ET. 2013. The rich club of the C. elegans neuronal connectome. J. Neurosci. 33:156380–87 [Google Scholar]
  125. Traag VA, Bruggeman J. 2009. Community detection in networks with positive and negative links. Phys. Rev. E 80:3036115 [Google Scholar]
  126. Traag VA, Krings G, Van Dooren P. 2014. Significant scales in community structure. Sci. Rep. 3:2930 [Google Scholar]
  127. Traud AL, Kelsic ED, Mucha PJ, Porter MA. 2011. Comparing community structure to characteristics in online collegiate social networks. SIAM Rev. 53:3526–43 [Google Scholar]
  128. van den Heuvel MP, Sporns O. 2011. Rich-club organization of the human connectome. J. Neurosci. 31:4415775–86 [Google Scholar]
  129. van den Heuvel MP, Sporns O. 2013. An anatomical substrate for integration among functional networks in human cortex. J. Neurosci. 33:3614489–500 [Google Scholar]
  130. Varshney LR, Chen BL, Paniagua E, Hall DH, Chklovskii DB. 2011. Structural properties of the Caenorhabditis elegans neuronal network. PLOS Comput. Biol. 7:2e1001066 [Google Scholar]
  131. Wang Q, Sporns O, Burkhalter A. 2012. Network analysis of corticocortical connections reveals ventral and dorsal processing streams in mouse visual cortex. J. Neurosci. 32:134386–99 [Google Scholar]
  132. White JG, Southgate E, Thomson JN, Brenner S. 1986. The structure of the nervous system of the nematode Caenorhabditis elegans. Philos. Trans. R. Soc. B 314:11651–340 [Google Scholar]
  133. Wildie M, Shanahan M. 2012. Metastability and chimera states in modular delay and pulse-coupled oscillator networks. Chaos 22:4043131 [Google Scholar]
  134. Wu F, Huberman BA. 2004. Finding communities in linear time: a physics approach. Eur. Phys. J. B 38:2331–38 [Google Scholar]
  135. Yamaguti Y, Tsuda I. 2015. Mathematical modeling for evolution of heterogeneous modules in the brain. Neural Netw. 62:3–10 [Google Scholar]
  136. Yeo BTT, Krienen FM, Sepulcre J, Sabuncu MR, Lashkari D. et al. 2011. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophys. 106:31125–65 [Google Scholar]
  137. Zamora-Lopez G, Zhou C, Kurths J. 2010. Cortical hubs form a module for multisensory integration on top of the hierarchy of cortical networks. Front. Neuroinform. 4:1 [Google Scholar]
  138. Zhang P, Moore C. 2014. Scalable detection of statistically significant communities and hierarchies using message passing for modularity. PNAS 111:5118144–49 [Google Scholar]
/content/journals/10.1146/annurev-psych-122414-033634
Loading
/content/journals/10.1146/annurev-psych-122414-033634
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error