Complex network theory and the brain

Philosophical Transactions of the Royal Society B: Biological Sciences

Volumn 369, Issue 1653, 2014, Pages

  Papo, David ^a   Buldú, Javier M ^a,b   Boccaletti, Stefano ^c   Bullmore, Edward T ^d,e  

^a UNIVERSIDAD POLITÉCNICA DE MADRID   (Spain)

^b UNIVERSIDAD REY JUAN CARLOS   (Spain)

^c ISTITUTO DEI SISTEMI COMPLESSI   (Italy)

^d UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^e GLAXOSMITHKLINE   (United Kingdom)

Author keywords

Community structure; Connectome; Graph; Hubs; Neuroimaging; Topology

Indexed keywords

ANATOMY AND HISTOLOGY; BIOLOGICAL MODEL; BRAIN; CONNECTOME; HUMAN; NERVE CELL NETWORK; NEUROSCIENCE; PHYSIOLOGY; PROCEDURES; TRENDS;

BRAIN; CONNECTOME; HUMANS; MODELS, NEUROLOGICAL; NERVE NET; NEUROSCIENCES;

EID: 84964314150     PISSN: 09628436     EISSN: 14712970     Source Type: Journal    
DOI: 10.1098/rstb.2013.0520     Document Type: Editorial

References (45)

1. Albert R, Barabási A-L. 2001 Statistical mechanics of complex networks. Rev. Mod. Phys. 74, 47-97.(doi:10.1103/RevModPhys.74.47)
2. Boccaletti S, Latora V, Moreno Y, Chavez M, Hwang DU. 2006 Complex networks: structure and dynamics. Phys. Rep. 424, 175-308.(doi:10.1016/j. physrep.2005.10.009)
3. Newman MEJ. 2010 Networks: an introduction. New York, NY: Oxford University Press.
4. Bullmore E, Sporns O. 2009 Complex brain networks: graph theoretical analysis of structural functional systems. Nat. Rev. Neurosci. 10, 186.(doi:10.1038/nrn2575)
5. Bullmore ET, Bassett DS. 2011 Brain graphs: graphical models of the human brain connectome. Annu. Rev. Clin. Psychol. 7, 113-140.(doi:10.1146/ annurev-clinpsy-040510-143934)
6. Eguíluz VM, Chialvo DR, Cecchi GA, Baliki M, Apkarian AV. 2005 Scale-free brain functional networks. Phys. Rev. Lett. 94, 018102.(doi:10.1103/ PhysRevLett.94.018102)
7. Achard S, Salvador R, Whitcher B, Suckling J, Bullmore E. 2006 A resilient, low frequency, smallworld human brain functional network with highly connected association cortical hubs. J. Neurosci. 26, 63-72.(doi:10.1523/JNEUROSCI.3874-05.2006)
8. Milo R, Shen-Orr S, Itzkovitz S, Kashtan N, Chklovskii D, Alon U. 2002 Network motifs: simple building blocks of complex networks. Science 298, 824-827.(doi:10.1126/science.298.5594.824)
9. Sporns O, Kötter R. 2004 Motifs in brain networks. PLoS Biol. 2, e369.(doi:10.1371/journal.pbio. 0020369)
10. Watts DJ, Strogatz SH. 1998 Collective dynamics of small-world networks. Nature 393, 440-442.(doi:10.1038/30918)
11. Bassett DS, Meyer-Lindenberg A, Achard S, Duke T, Bullmore E. 2006 Adaptive reconfiguration of fractal small-world human brain functional networks. Proc. Natl Acad. Sci. USA 103, 19 518-19 523.(doi:10. 1073/pnas.0606005103)
12. Meunier D, Lambiotte R, Bullmore ET. 2010 Modular and hierarchically modular organization of brain networks. Front. Neurosci. 4, 200.(doi:10.3389/ fnins.2010.00200)
13. Gallos LK, Makse HA, Sigman M. 2012 A small world of weak ties provides optimal global integration of self-similar modules in functional brain networks. Proc. Natl Acad. Sci. USA 109, 2825-2830.(doi:10.1073/pnas.1106612109)
14. Towlson EK, Vértes PE, Ahnert SE, Schafer WR, Bullmore ET. 2013 The rich-club organization of the C. elegans neuronal connectome. J. Neurosci. 33, 12 929-12 939.(doi:10.1523/JNEUROSCI.3784- 12.2013)
15. Albert R, Jeong H, Barabási A-L. 2000 Error attack tolerance of complex networks. Nature 406, 378-382.(doi:10.1038/35019019)
16. Laughlin S. 2001 Energy as a constraint on the coding and processing of sensory information. Curr. Opin. Neurobiol. 11, 475-480.(doi:10.1016/S0959- 4388(00)00237-3)
17. Laughlin S, Sejnowski T. 2003 Communication in neuronal networks. Science 301, 1870-1874.(doi:10.1126/science.1089662)
18. Bullmore ET, Sporns O. 2012 The economy of brain network organization. Nat. Rev. Neurosci. 13, 336-349.
19. Hofman MA. 2012 Design principles of the human brain: an evolutionary perspective. Progr. Brain Res. 195, 373-390.(doi:10.1016/B978-0-444-53860-4. 00018-0)
20. Sarpeshkar R. 1998 Analog versus digital: extrapolating from electronics to neurobiology. Neural Comput. 10, 1601-1638.(doi:10.1162/ 089976698300017052)
21. Fries P. 2005 A mechanism for cognitive dynamics: neural communications through neuronal coherence. Trends Cogn. Neurosci. 9, 474-480.(doi:10.1016/j. tics.2005.08.011)
22. Gross T, Blasius B. 2008 Adaptive coevolutionary networks: a review. J. R. Soc. Interface 5, 259-271.(doi:10.1098/rsif.2007.1229)
23. Papo D. 2013 Why should cognitive neuroscientists study the brain's resting state? Front. Hum. Neurosci. 7, 45.(doi:10.3389/fnhum.2013.00045)
24. Kubo R. 1966 The fluctuation-dissipation theorem. Rep. Progr. Phys. 29, 255-284.(doi:10.1088/0034- 4885/29/1/306)
25. Deco G, Ponce-Alvarez A, Mantini D, Romani GL, Hagmann P, Corbetta M. 2013 Resting state functional connectivity emerges from structurally and dynamically shaped slow linear fluctuations. J. Neurosci. 33, 11 239-11 252.(doi:10.1523/ JNEUROSCI.1091-13.2013)
26. Bialonski S. 2012 Inferring complex networks from time series of dynamical systems: pitfalls, misinterpretations, and possible solutions. PhD thesis, University of Bonn, Germany. (http://arxiv. org/abs/1208.0800)
27. Fornito A, Zalesky A, Breakspear M. 2013 Graph analysis of the human connectome: promise, progress, and pitfalls. Neuroimage 80, 426-444.(doi:10.1016/j.neuroimage.2013. 04.087)
28. Papo D, Zanin M, Buldú JM. 2014 Reconstructing brain networks: have we got the basics right? Front. Hum. Neurosci. 8, 107.(doi:10.3389/fnhum.2014. 00107)
29. Zanin M, Sousa P, Papo D, Bajo R, Garcia-Prieto J, del Pozo F, Menasalvas E, Boccaletti S. 2012 Optimizing functional network representation of multivariate time series. Sci. Rep. 2, 630.(doi:10. 1038/srep00630)
30. De Vico Fallani F, Richiardi J, Chavez M, Achard S. 2014 Graph analysis of functional brain networks: practical issues in translational neuroscience. Phil. Trans. R. Soc. B 369, 20130521.(doi:10.1098/rstb. 2013.0521)
31. de Reus MA, Saenger VM, Kahn RS, van den Heuvel MP. 2014 An edge-centric perspective on the human connectome: link communities in the brain. Phil. Trans. R. Soc. B 369, 20130527.(doi:10.1098/ rstb.2013.0527)
32. Keller CJ, Honey CJ, Mégevand P, Entz L, Ulbert I, Mehta AD. 2014 Mapping human brain networks with cortico-cortical evoked potentials. Phil. Trans. R. Soc. B 369, 20130528.(doi:10.1098/rstb. 2013.0528)
33. Hütt M-T, Kaiser M, Hilgetag CC. 2014 Perspective: network-guided pattern formation of neural dynamics. Phil. Trans. R. Soc. B 369, 20130522.(doi:10.1098/rstb.2013.0522)
34. Gollo LL, Breakspear M. 2014 The frustrated brain: from dynamics on motifs to communities and networks. Phil. Trans. R. Soc. B 369, 20130532.(doi:10.1098/rstb.2013.0532)
35. Barardi A, Malagarriga D, Sancristobal B, Garcia- Ojalvo J, Pons AJ. 2014 Probing scale interaction in brain dynamics through synchronization. Phil. Trans. R. Soc. B 369, 20130533.(doi:10.1098/rstb. 2013.0533)
36. Kim JS, Kaiser M. 2014 From Caenorhabditis elegans to the human connectome: a specific modular organization increases metabolic, functional and developmental efficiency. Phil. Trans. R. Soc. B 369, 20130529.(doi:10.1098/rstb.2013.0529)
37. Vértes PE, Alexander-Bloch A, Bullmore ET. 2014 Generative models of rich clubs in Hebbian neuronal networks and large-scale human brain networks. Phil. Trans. R. Soc. B 369, 20130531.(doi:10.1098/ rstb.2013.0531)
38. Avena Koenigsberger A, Goñi J, Betzel RF, van den Heuvel MP, Griffa A, Hagmann P, Thiran J-P, Sporns O. 2014 Using Pareto optimality to explore the topology and dynamics of the human connectome. Phil. Trans. R. Soc. B 369, 20130530.(doi:10.1098/ rstb.2013.0530)
39. Bassett DS, Wymbs NF, Porter MA, Mucha PJ, Carlson JM, Grafton ST. 2011 Dynamic reconfiguration of human brain networks during learning. Proc. Natl Acad. Sci. USA 108, 7641-7646.(doi:10.1073/pnas.1018985108)
40. Krienen FM, Yeo BTT, Buckner RL. 2014 Reconfigurable task-dependent functional coupling modes cluster around a core functional architecture. Phil. Trans. R. Soc. B 369, 20130526.(doi:10.1098/ rstb.2013.0526)
41. Papo D, Zanin M, Pineda-Pardo JA, Boccaletti S, Buldú JM. 2014 Functional brain networks: great expectations, hard times and the big leap forward. Phil. Trans. R. Soc. B 369, 20130525.(doi:10.1098/ rstb.2013.0525)
42. Papo D. 2013 Time scales in cognitive neuroscience. Front. Physiol. 4, 86.(doi:10.3389/fphys.2013. 00086)
43. Corominas Murtra B, Fortuny J, Solé RV. 2014 Towards a mathematical theory of meaningful communication. Sci. Rep. 4, 4587.(doi:10.1038/ srep04587)
44. Lesne A. 2008 Robustness: confronting lessons from physics and biology. Biol. Rev. 83, 509-532.(doi:10.1111/j.1469-185X.2008.00052.x)
45. Whitacre JM. 2012 Biological robustness: paradigms, mechanisms, and systems principles. Front. Genet. 3, 67.(doi:10.3389/fgene.2012.00067)