Constraints and spandrels of interareal connectomes

Nature Communications

Volumn 7, Issue , 2016, Pages

  Rubinov, Mikail ^a,b  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^b HOWARD HUGHES MEDICAL INSTITUTE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANATOMY; BRAIN; EVOLUTIONARY BIOLOGY; INTEGRATED APPROACH; INVERTEBRATE; PITFALL TRAP; VERTEBRATE;

BIOLOGY; CONNECTOME; DEFAULT MODE NETWORK; GOLD STANDARD; INVERTEBRATE; MODEL; NONHUMAN; VERTEBRATE; ALGORITHM; ANIMAL; BIOLOGICAL MODEL; BRAIN; C57BL MOUSE; DIAGNOSTIC IMAGING; DROSOPHILA MELANOGASTER; MALE; NERVE CELL NETWORK; NERVE TRACT; NUCLEAR MAGNETIC RESONANCE IMAGING; PHYSIOLOGY; PROCEDURES; WHITE MATTER;

INVERTEBRATA; VERTEBRATA;

ALGORITHMS; ANIMALS; BRAIN; CONNECTOME; DROSOPHILA MELANOGASTER; MAGNETIC RESONANCE IMAGING; MALE; MICE, INBRED C57BL; MODELS, NEUROLOGICAL; NERVE NET; NEURAL PATHWAYS; WHITE MATTER;

EID: 85006070925     PISSN: None     EISSN: 20411723     Source Type: Journal    
DOI: 10.1038/ncomms13812     Document Type: Article

References (82)

1. Sporns, O., Tononi, G. & Kotter, R. The human connectome: a structural description of the human brain. PLoS Comput. Biol. 1, e42 (2005).
2. Masse, I. O., Regnier, P. & Boire, D. in Axons and Brain Architecture (ed. Rockland, K) (Academic Press, 2016).
3. Markov, N. T. et al. A weighted and directed interareal connectivity matrix for macaque cerebral cortex. Cereb. Cortex 24, 17-36 (2014).
4. Oh, S. W. et al. A mesoscale connectome of the mouse brain. Nature 508, 207-214 (2014).
5. Bota, M., Sporns, O. & Swanson, L. W. Architecture of the cerebral cortical association connectome underlying cognition. Proc. Natl Acad. Sci. USA 112, E2093-E2101 (2015).
6. Kasthuri, N. & Lichtman, J. W. The rise of the 'projectome'. Nat. Meth. 4, 307-308 (2007).
7. Swanson, L. W. & Lichtman, J. W. From Cajal to connectome and beyond. Ann. Rev. Neurosci. 39, 197-216 (2016).
8. Rubinov, M., Ypma, R. J., Watson, C. & Bullmore, E. T. Wiring cost and topological participation of the mouse brain connectome. Proc. Natl Acad. Sci. USA 112, 10032-10037 (2015).
9. Chiang, A.-S. et al. Three-dimensional reconstruction of brain-wide wiring networks in Drosophila at single-cell resolution. Curr. Biol. 21, 1-11 (2011).
10. Shih, C. T. et al. Connectomics-based analysis of information flow in the Drosophila brain. Curr. Biol. 25, 1249-1258 (2015).
11. Ito, M., Masuda, N., Shinomiya, K., Endo, K. & Ito, K. Systematic analysis of neural projections reveals clonal composition of the Drosophila brain. Curr. Biol. 23, 644-655 (2013).
12. Zingg, B. et al. Neural networks of the mouse neocortex. Cell 156, 1096-1111 (2014).
13. Modha, D. S. & Singh, R. Network architecture of the long-distance pathways in the macaque brain. Proc. Natl Acad. Sci. USA 107, 13485-13490 (2010).
14. Hagmann, P. et al. Mapping the structural core of human cerebral cortex. PLoS Biol. 6, e159 (2008).
15. Wedeen, V. J. et al. The geometric structure of the brain fiber pathways. Science 335, 1628-1634 (2012).
16. Bullmore, E. & Sporns, O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10, 186-198 (2009).
17. Newman, M. E. J. Networks: An Introduction (Oxford University Press, Inc., 2010).
18. Sporns, O. & Betzel, R. F. Modular brain networks. Annu. Rev. Psychol. 67, 613-640 (2016).
19. van den Heuvel, M. P. & Sporns, O. Network hubs in the human brain. Trends Cogn. Sci. 17, 683-696 (2013).
20. Bassett, D. S. & Bullmore, E. Small-world brain networks. Neuroscientist 12, 512-523 (2006).
21. Meunier, D., Lambiotte, R. & Bullmore, E. T. Modular and hierarchically modular organization of brain networks. Front Neurosci. 4, 200 (2010).
22. Petersen, S. E. & Sporns, O. Brain networks and cognitive architectures. Neuron 88, 207-219 (2015).
23. Zamora-Lopez, G., Zhou, C. & Kurths, J. Cortical hubs form a module for multisensory integration on top of the hierarchy of cortical networks. Front Neuroinform. 4, 1 (2010).
24. van den Heuvel, M. P. & Sporns, O. Rich-club organization of the human connectome. J. Neurosci. 31, 15775-15786 (2011).
25. Raichle, M. E. The brain's default mode network. Annu. Rev. Neurosci. 38, 433-447 (2015).
26. Buckner, R. L., Andrews-Hanna, J. R. & Schacter, D. L. The brain's default network: anatomy, function, and relevance to disease. Ann. N. Y. Acad. Sci. 1124, 1-38 (2008).
27. Whitfield-Gabrieli, S. & Ford, J. M. Default mode network activity and connectivity in psychopathology. Annu. Rev. Clin. Psychol. 8, 49-76 (2012).
28. Stafford, J. M. et al. Large-scale topology and the default mode network in the mouse connectome. Proc. Natl Acad. Sci. USA 111, 18745-18750 2014.
29. Lu, H. et al. Rat brains also have a default mode network. Proc. Natl Acad. Sci. USA 109, 3979-3984 (2012).
30. Ramon y Cajal, S. Histology of the Nervous System of Man and Vertebrates (Oxford University Press, 1995).
31. Bullmore, E. & Sporns, O. The economy of brain network organization. Nat. Rev. Neurosci. 13, 336-349 (2012).
32. Kaiser, M. & Hilgetag, C. C. Nonoptimal component placement, but short processing paths, due to long-distance projections in neural systems. PLoS Comput. Biol. 2, e95 (2006).
33. Henderson, J. A. & Robinson, P. A. Geometric effects on complex network structure in the cortex. Phys. Rev. Lett. 107, 018102 (2011).
34. Vertes, P. E. et al. Simple models of human brain functional networks. Proc. Natl Acad. Sci. USA 109, 5868-5873 (2012).
35. Chen, Y., Wang, S., Hilgetag, C. C. & Zhou, C. Trade-off between multiple constraints enables simultaneous formation of modules and hubs in neural systems. PLoS Comput. Biol. 9, e1002937 (2013).
36. Samu, D., Seth, A. K. & Nowotny, T. Influence of wiring cost on the large-scale architecture of human cortical connectivity. PLoS Comput. Biol. 10, e1003557 (2014).
37. Klimm, F., Bassett, D. S., Carlson, J. M. & Mucha, P. J. Resolving structural variability in network models and the brain. PLoS Comput. Biol. 10, e1003491 (2014).
38. Song, H. F., Kennedy, H. & Wang, X. J. Spatial embedding of structural similarity in the cerebral cortex. Proc. Natl Acad. Sci. USA 111, 16580-16585 (2014).
39. Beul, S. F., Grant, S. & Hilgetag, C. C. A predictive model of the cat cortical connectome based on cytoarchitecture and distance. Brain Struct. Funct. 220, 3167-3184 (2015).
40. Roberts, J. A. et al. The contribution of geometry to the human connectome. Neuroimage 124, 379-393 (2016).
41. Betzel, R. F. et al. Generative models of the human connectome. Neuroimage 124, 1054-1064 (2016).
42. Corominas-Murtra, B., Goni, J., Sole, R. V. & Rodriguez-Caso, C. On the origins of hierarchy in complex networks. Proc. Natl Acad. Sci. USA 110, 13316-13321 (2013).
43. Avena-Koenigsberger, A., Goni, J., Sole, R. & Sporns, O. Network morphospace. J. R. Soc. Interface 12, 20140881 (2015).
44. Williams, G. C. Adaptation and Natural Selection (Princeton University Press, 2008).
45. Horvath, C. D. in eLS (John Wiley & Sons, Ltd., 2001).
46. Gould, S. J. & Lewontin, R. C. The spandrels of San Marco and the Panglossian paradigm: a critique of the adaptationist programme. Proc. R. Soc. Lond. B Biol. Sci. 205, 581-598 (1979).
47. Gould, S. J. The exaptive excellence of spandrels as a term and prototype. Proc. Natl Acad. Sci. USA 94, 10750-10755 (1997).
48. Kriegeskorte, N., Simmons, W. K., Bellgowan, P. S. F. & Baker, C. I. Circular analysis in systems neuroscience: the dangers of double dipping. Nat. Neurosci. 12, 535-540 (2009).
49. Rubinov, M. & Sporns, O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52, 1059-1069 (2010).
50. Woodger, J. H. Biological Principles: A Critical Study (Harcourt, 1929).
51. Ercsey-Ravasz, M. et al. A predictive network model of cerebral cortical connectivity based on a distance rule. Neuron 80, 184-197 (2013).
52. Horvat, S. et al. Spatial embedding and wiring cost constrain the functional layout of the cortical network of rodents and primates. PLoS Biol. 14, e1002512 (2016).
53. Orsini, C. et al. Quantifying randomness in real networks. Nat. Commun. 6, 8627 (2015).
54. Schreiber, T. Constrained randomization of time series data. Phys. Rev. Lett. 80, 2105-2108 (1998).
55. Squartini, T. & Garlaschelli, D. Analytical maximum-likelihood method to detect patterns in real networks. New J. Phys. 13, 083001 (2011).
56. Park, J. Newman MEJ. Statistical mechanics of networks. Phys. Rev. E 70, 066117 (2004).
57. Robins, G., Pattison, P., Kalish, Y. & Lusher, D. An introduction to exponential random graph (p∗) models for social networks. Soc. Networks 29, 173-191 (2007).
58. Ebbesson S. O. E. The parcellation theory and its relation to interspecific variability in brain organization, evolutionary and ontogenetic development, and neuronal plasticity. Cell Tissue Res. 213, 179-212 (1980).
59. Striedter, G. F. Principles of Brain Evolution (Sinauer Associates, 2005).
60. Deacon, T. W. Rethinking mammalian brain evolution. Amer Zool 30, 629-705 (1990).
61. van den Heuvel, M. P., Bullmore, E. T. & Sporns, O. Comparative connectomics. Trends Cogn. Sci. 20, 345-361 (2016).
62. Braga, R. M. & Leech, R. Echoes of the brain: local-scale representation of whole-brain functional networks within transmodal cortex. Neuroscientist. pii: 1073858415585730 (2015).
63. Gould, S. J. & Vrba, E. S. Exaptation; a missing term in the science of form. Paleobiology 8, 4-15 (1982).
64. Buckner, R. L. & Krienen, F. M. The evolution of distributed association networks in the human brain. Trends Cogn. Sci. 17, 648-665 (2013).
65. Finlay, B. L. & Darlington, R. B. Linked regularities in the development and evolution of mammalian brains. Science 268, 1578-1584 (1995).
66. Behrens, T. E. J. et al. Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging. Nat. Neurosci. 6, 750-757 (2003).
67. Wolff, T., Iyer, N. A. & Rubin, G. M. Neuroarchitecture and neuroanatomy of the Drosophila central complex: A GAL4-based dissection of protocerebral bridge neurons and circuits. J. Comp. Neurol. 523, 997-1037 (2015).
68. Puelles, L., Harrison, M., Paxinos, G. & Watson, C. A developmental ontology for the mammalian brain based on the prosomeric model. Trends Neurosci. 36, 570-578 (2013).
69. Thompson, C. L. et al. A high-resolution spatiotemporal atlas of gene expression of the developing mouse brain. Neuron 83, 309-323 (2014).
70. Lein, E. S. et al. Genome-wide atlas of gene expression in the adult mouse brain. Nature 445, 168-176 (2007).
71. Ito, K. et al. A systematic nomenclature for the insect brain. Neuron 81, 755-765 (2014).
72. Jenett, A. et al. A GAL4-driver line resource for Drosophila neurobiology. Cell Rep. 2, 991-1001 (2012).
73. Milyaev, N. et al. The Virtual Fly Brain browser and query interface. Bioinformatics 28, 411-415 (2012).
74. Newman, M. E. J. & Barkema, G. T. Monte Carlo Methods in Statistical Physics (Clarendon Press, 1999).
75. Jaynes, E. T. Information theory and statistical mechanics. Phys. Rev. 106, 620-630 (1957).
76. Garlaschelli, D. The weighted random graph model. New J. Phys. 11, 073005 (2009).
77. Newman, M. E. J. & Girvan, M. Finding and evaluating community structure in networks. Phys. Rev. E 69, 026113 (2004).
78. Lancichinetti, A. & Fortunato, S. Consensus clustering in complex networks. Sci. Rep. 2, 336 (2012).
79. Blondel, V. D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. Fast unfolding of communities in large networks. J. Stat. Mech. P10008 (2008).
80. Reichardt, J. & Bornholdt, S. Statistical mechanics of community detection. Phys. Rev. E 74, 016110 (2006).
81. Colizza, V., Flammini, A., Serrano, M. A. & Vespignani, A. Detecting rich-club ordering in complex networks. Nat. Phys. 2, 110-115 (2006).
82. Alstott, J., Panzarasa, P., Rubinov, M., Bullmore, E. T. & Vertes, P. E. A unifying framework for measuring weighted rich clubs. Sci. Rep. 4, 7258 (2014).