The diverse club

Nature Communications

Volumn 8, Issue 1, 2017, Pages

  Bertolero, M A ^a,b   Yeo, B T T ^c   D'Esposito, M ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF PENNSYLVANIA   (United States)

^c NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; BRAIN; COMMUNICATION; COMMUNITY STRUCTURE; NERVOUS SYSTEM; NEUROLOGY;

ARTICLE; AXON; CAENORHABDITIS ELEGANS; COGNITION; COMMUNITY STRUCTURE; FUNCTIONAL MAGNETIC RESONANCE IMAGING; HUMAN; INTEGRATION; NERVE CELL; NERVE CELL NETWORK; NONHUMAN; RESTING STATE NETWORK; TIME SERIES ANALYSIS; WORKING MEMORY; ANIMAL; AVIATION; BRAIN; MACACA; POWER SUPPLY; WHITE MATTER;

UNITED STATES;

AIR TRAVEL; ANIMALS; AXONS; BRAIN; CAENORHABDITIS ELEGANS; ELECTRIC POWER SUPPLIES; HUMANS; MACACA; NERVE NET; WHITE MATTER;

EID: 85032973307     PISSN: None     EISSN: 20411723     Source Type: Journal    
DOI: 10.1038/s41467-017-01189-w     Document Type: Article

References (68)

1. Bertolero, M. A., Yeo, B. T. T. & D'Esposito, M. The modular and integrative functional architecture of the human brain. Proc. Natl Acad. Sci. USA 112, E6798-E6807 (2015).
2. Newman, M. E. J. Modularity and community structure in networks. Proc. Natl Acad. Sci. USA 103, 8577-8582 (2006).
3. Power, J. D. et al. Functional network organization of the human brain. Neuron 72, 665-678 (2011).
4. Yeo, B. T. T. et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophysiol. 106, 1125-1165 (2011).
5. van den Heuvel, M. P. & Sporns, O. Rich-club organization of the human connectome. J. Neurosci. 31, 15775-15786 (2011).
6. Power, J. D., Schlaggar, B. L., Lessov-Schlaggar, C. N. & Petersen, S. E. Evidence for Hubs in human functional brain. Networks 79, 798-813 (2013).
7. Schmidt, R., LaFleur, K. J. R., de Reus, M. A., van den Berg, L. H. & van den Heuvel, M. P. Kuramoto model simulation of neural hubs and dynamic synchrony in the human cerebral connectome. BMC Neurosci. 16, 143 (2015).
8. Liska, A., Galbusera, A., Schwarz, A. J. & Gozzi, A. Functional connectivity hubs of the mouse brain. Neuroimage 115, 281-291 (2015).
9. Scholtens, L. H., Schmidt, R., de Reus, M. A. & van den Heuvel, M. P. Linking macroscale graph analytical organization to microscale neuroarchitectonics in the macaque connectome. J. Neurosci. 34, 12192-12205 (2014).
10. Guimerà, R., Guimera, R., Amaral, L. A. N. & Amaral, L. Functional cartography of complex metabolic networks. Nature 433, 895-900 (2005).
11. Guimera, R., Mossa, S., Turtschi, A. & Amaral, L. A. N. The worldwide air transportation network: Anomalous centrality, community structure, and cities' global roles. Proc. Natl Acad. Sci. USA 102, 7794-7799 (2005).
12. Guimerà, R., Sales-Pardo, M. & Amaral, L. A. N. Classes of complex networks defined by role-to-role connectivity profiles. Nat. Phys. 3, 63-69 (2007).
13. Towlson, E. K., Vértes, P. E., Ahnert, S. E., Schafer,W. R. & Bullmore, E. T. The Rich Club of the C. elegans neuronal connectome. J. Neurosci. 33, 6380-6387 (2013).
14. Grayson, D. S. et al. Structural and functional rich club organization of the brain in children and adults. PLoS ONE 9, e88297 (2014).
15. Crossley, N. A. et al. The hubs of the human connectome are generally implicated in the anatomy of brain disorders. Brain 137, 2382-2395 (2014).
16. Bacik, K. A., Schaub, M. T., Beguerisse-Díaz, M., Billeh, Y. N. & Barahona, M. Flow-based network analysis of the Caenorhabditis elegans connectome. PLoS Comput. Biol. 12, e1005055 (2016).
17. Sporns, O., Honey, C. J. & Kötter, R. Identification and classification of hubs in brain networks. PLoS ONE 2, e1049 (2007).
18. van den Heuvel, M. P. & Sporns, O. An anatomical substrate for integration among functional networks in human cortex. J. Neurosci 33, 14489-14500 (2013).
19. Alstott, J., Panzarasa, P., Rubinov, M., Bullmore, E. T. & Vértes, P. E. A unifying framework for measuring weighted rich clubs. Sci. Rep. 4, 7258 (2014).
20. Colizza, V., Flammini, A., Serrano, M. A. & Vespignani, A. Detecting rich-club ordering in complex networks. Nat. Phys. 2, 110-115 (2006).
21. de Reus, M. A. & van den Heuvel, M. P. Rich club organization and intermodule communication in the cat connectome. J. Neurosci. 33, 12929-12939 (2013).
22. Bullmore, E. & Sporns, O. The economy of brain network organization. Nat. Rev. Neurosci. 74, 47-14 (2012).
23. Bassett, D. S., Yang, M., Wymbs, N. F. & Grafton, S. T. Learning-induced autonomy of sensorimotor systems. Nat. Neurosci. 18, 744-751 (2015).
24. Spadone, S. et al. Dynamic reorganization of human resting-state networks during visuospatial attention. Proc. Natl Acad. Sci. USA 112, 8112-8117 (2015).
25. Gratton, C., Laumann, T. O., Gordon, E. M., Adeyemo, B. & Petersen, S. E. Evidence for two independent factors that modify brain networks to meet task goals. Cell Rep. 17, 1276-1288 (2016).
26. Cole, M. W. et al. Multi-task connectivity reveals flexible hubs for adaptive task control. Nat. Neurosci. 16, 1348-1355 (2013).
27. Yeo, B. T. T. et al. Functional specialization and flexibility in human association cortex. Cereb. Cortex 26, 465-465 (2015).
28. Gratton, C., Nomura, E. M., Pérez, F. & D'Esposito, M. Focal brain lesions to critical locations cause widespread disruption of the modular organization of the brain. J. Cogn. Neurosci. 24, 1275-1285 (2012).
29. Warren, D. E. et al. Network measures predict neuropsychological outcome after brain injury. Proc. Natl Acad. Sci. USA 111, 14247-14252 (2014).
30. Jacomy, M., Venturini, T., Heymann, S. & Bastian, M. ForceAtlas2, a continuous graph layout algorithm for handy network visualization designed for the gephi software. PLoS ONE 9, e98679 (2014).
31. Cajal, S. R. Y. Texture of the Nervous System of Man and the Vertebrates (Springer Vienna, 2000). doi:10.1007/978-3-7091-6315-3
32. Bassett, D. S. & Bullmore, E. Small-world brain networks. Neuroscientist 12, 512-523 (2006).
33. Fornito, A. et al. Genetic influences on cost-efficient organization of human cortical functional networks. J. Neurosci. 31, 3261-3270 (2011).
34. Bassett, D. S. et al. Cognitive fitness of cost-efficient brain functional networks. Proc. Natl Acad. Sci. USA 106, 11747-11752 (2009).
35. Langer, N. et al. Functional brain network efficiency predicts intelligence. Hum. Brain Mapp. 33, 1393-1406 (2011).
36. Khundrakpam, B. S. et al. Imaging structural covariance in the development of intelligence. Neuroimage 144, 227-240 (2017).
37. Li, Y. et al. Brain anatomical network and intelligence. Neuroimage 47, S105 (2009).
38. Watts, D. J. & Strogatz, S. H. Collective dynamics of 'small-world' networks. Nature 393, 440-442 (1998).
39. Deco, G. et al. Rethinking segregation and integration: contributions of wholebrain modelling. Nat. Rev. Neurosci. 16, 430-439 (2015).
40. Zamora-López, G., Chen, Y., Deco, G., Kringelbach, M. L. & Zhou, C. Functional complexity emerging from anatomical constraints in the brain: the significance of network modularity and rich-clubs. Sci Rep 6, 38424 (2016).
41. Simon, H. A. in Facets of Systems Science 457-476 (Springer, 1991). doi:10.1007/978-1-4899-0718-9-31
42. Fodor, J. & Fodor, J. The Modularity of Mind (MIT Press, 1983).
43. Coltheart, M. Modularity and cognition. Trends Cogn. Sci. 3, 115-120 (1999).
44. Robinson, P. A., Henderson, J. A., Matar, E., Riley, P. & Gray, R. T. Dynamical reconnection and stability constraints on cortical network architecture. Phys. Rev. Lett. 103, 108104 (2009).
45. Clune, J., Mouret, J.-B. & Lipson, H. The evolutionary origins of modularity. Proc. Biol. Sci. 280, 20122863-20122863 (2013).
46. Tosh, C. R., Tosh, C. R., McNally, L. & McNally, L. The relative efficiency of modular and non-modular networks of different size. Proc. Biol. Sci. 282, 20142568-20142568 (2015).
47. Stevens, A. A., Tappon, S. C., Garg, A. & Fair, D. A. Functional brain network modularity captures inter-and intra-individual variation in working memory capacity. PLoS ONE 7, e30468 (2012).
48. Arnemann, K. L. et al. Functional brain network modularity predicts response to cognitive training after brain injury. Neurology 84, 1568-1574 (2015).
49. Modular brain network organization predicts response to cognitive training in older adults. PLoS ONE (2016).
50. Gollo, L. L., Zalesky, A., Hutchison, R. M., van den Heuvel, M. & Breakspear, M. Dwelling quietly in the rich club: brain network determinants of slow cortical fluctuations. Philos. Trans. R. Soc. Lond. B Biol. Sci. 370, 20140165-20140165 (2015).
51. Margulies, D. S. et al. Situating the default-mode network along a principal gradient of macroscale cortical organization. Proc. Natl Acad. Sci. USA 113, 12574-12579 (2016).
52. Nguyen, J. P. et al. Whole-brain calcium imaging with cellular resolution in freely behaving Caenorhabditis elegans. Proc. Natl Acad. Sci. USA 113, E1074-E1081 (2016).
53. White, J. G., Southgate, E., Thomson, J. N. & Brenner, S. The structure of the nervous system of the nematode Caenorhabditis elegans. Phil. Trans. R. Soc. B 314, 1-340 (1986).
54. Van Essen, D. C. et al. The WU-Minn human connectome project: an overview. Neuroimage 80, 62-79 (2013).
55. Cox, R. W. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput. Biomed. Res. 29, 162-173 (1996).
56. Young, M. P. The organization of neural systems in the primate cerebral cortex. Proc. R. Soc. B Biol. Sci. 252, 13-18 (1993).
57. Newman, M. E. J. & Girvan, M. Finding and evaluating community structure in networks. Phys. Rev. E 69, 026113 (2004).
58. Reichardt, J. & Bornholdt, S. Statistical mechanics of community detection. Phys. Rev. E 74, 016110 (2006).
59. Newman, M. E. J. Finding community structure in networks using the eigenvectors of matrices. Phys. Rev. E 74, 036104 (2006).
60. Blondel, V. D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 2008, P10008 (2008).
61. Raghavan, U. N., Albert, R. & Kumara, S. Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E 76, 036106 (2007).
62. Girvan, M. & Newman, M. E. J. Community structure in social and biological networks. Proc. Natl Acad. Sci. USA 99, 7821-7826 (2002).
63. Muldoon, S. F., Bridgeford, E. W. & Bassett, D. S. Small-world propensity and weighted brain networks. Sci. Rep. 6, 22057 (2016).
64. Pons, P. & Latapy, M. Computing communities in large networks using random walks. J. Graph Algorithms Appl. 11, 259-276 (2006).
65. Rosvall, M., Rosvall, M., Bergstrom, C. T. & Bergstrom, C. T. Maps of random walks on complex networks reveal community structure. Proc. Natl Acad. Sci. USA 105, 1118-1123 (2008).
66. Fortunato, S. & Barthélemy, M. Resolution limit in community detection. Proc. Natl Acad. Sci. USA 104, 36-41 (2007).
67. Bassett, D. S. et al. Efficient physical embedding of topologically complex information processing networks in brains and computer circuits. PLoS Comput. Biol. 6, e1000748 (2010).
68. Delvenne, J. C., Yaliraki, S. N. & Barahona, M. Stability of graph communities across time scales. Proc. Natl Acad. Sci. USA 107, 12755-12760 (2010).