Acta Biologica Hungarica

, Volume 63, Supplement 1, pp 38–53 | Cite as

What Makes The Prefrontal Cortex So Appealing in the Era of Brain Imaging? A Network Analytical Perspective

  • L. NégyessyEmail author
  • M. Bányai
  • T. Nepusz
  • F. Bazsó


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Achard, S., Salvador, R., Whitcher, B., Suckling, J., Bullmore, E. (2006) A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. J. Neurosci. 26, 63–72.CrossRefGoogle Scholar
  2. 2.
    Averbeck, B.B., Seo, M. (2008) The statistical neuroanatomy of frontal networks in the macaque. PLoS Comput. Biol. 4, e1000050.CrossRefGoogle Scholar
  3. 3.
    Baddeley, A. (2012) Working memory: Theories, models, and controversies. Annu Rev. Psychol. 63, 12.1–12.29.CrossRefGoogle Scholar
  4. 4.
    Banich, M.T., Compton, R. J. (2011) Cognitive Neuroscience (3rd ed.). Wadsworth Publishing.Google Scholar
  5. 5.
    Bányai, M., Négyessy, L., Bazsó, F. (2011) Organisation of signal flow in directed networks. Journal of Statistical Mechanics: theory and experiment. P06001. doi: 10.1088/1742-5468/2011/06/P06001Google Scholar
  6. 6.
    Bullmore, E., Sporns, O. (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10, 186–198.CrossRefGoogle Scholar
  7. 7.
    Constantinidis, C., Procyk, E. (2004) The primate working memory networks. Cogn. Affect Behav. Neurosci. 4, 444 165.Google Scholar
  8. 8.
    Csárdi, G., Nepusz, T. (2006) The igraph software package for complex network research. Inter. Journal, Complex Systems 1695.Google Scholar
  9. 9.
    Dehaene, S., Changeux, J. P. (2011) Experimental and theoretical approaches to conscious processing. Neuron. 70, 200–227.CrossRefGoogle Scholar
  10. 10.
    Fortunato, S. (2009) Community detection in graphs Physics Report. 486, 75–174.Google Scholar
  11. 11.
    Fruchterman, T. M. J., Reingold, E. M. (1991) Graph Drawing by Force-Directed Placement. Software -Practice & Experienc. 21, 1129–1164.CrossRefGoogle Scholar
  12. 12.
    Fuster, J. M. (1997) The prefrontal cortex. Anatomy, physiology and neuropsychology of the frontal lobe. Lippincott-Raven. Philadelphia, New York.Google Scholar
  13. 13.
    Gazzaniga, M.S., Ivry, R.B., Mangun, G. R. (2009) Cognitive Neuroscience: The biology of the mind (3rd ed.). New York: W. W. Norton.Google Scholar
  14. 14.
    Gisiger, T., Dehaene, S., Changeux, J. P. (2000) Computational models of association cortex. Curr Opin. Neurobiol. 10, 250–259.CrossRefGoogle Scholar
  15. 15.
    Honey, C.J., Kõtter, R., Breakspear, M., Sporns, O. (2007) Network structure of cerebral cortex shapes functional connectivity on multiple time scales. Proc. Natl. Acad. Sci. USA. 104, 10240–10245.CrossRefGoogle Scholar
  16. 16.
    Honey, C.J., Thivierge, J.P., Sporns, O. (2010) Can structure predict function in the human brain? Neuroimag. 52, 766–776.CrossRefGoogle Scholar
  17. 17.
    Marois, R., Ivanoff J. (2005) Capacity limits of information processing in the brain. Trends Cogn. Sci. 9, 296–305.CrossRefGoogle Scholar
  18. 18.
    Meyer, K., Damásio, A. (2009) Convergence and divergence in a neural architecture for recognition and memory. Trends Neurosci. 32, 376–382.CrossRefGoogle Scholar
  19. 19.
    Miller, E.K., Cohen, J. D. (2001) An integrative theory of prefrontal cortex function. Annu. Rev. Neurosci. 24, 167–202.CrossRefGoogle Scholar
  20. 20.
    Nepusz, T., Négyessy, L., Tusnády, G., Bazsó, F. (2009) Reconstructing cortical networks: case of directed graphs with high level of reciprocity. In: Bollobás, B., Kozma, R., Miklós D. (ed.) Handbook of Large-scale Random Networks. Springer, pp. 325–368.Google Scholar
  21. 21.
    Nepusz, T., Petróczi, A., Négyessy, L., Bazsó, F. (2008) Fuzzy communities and the concept of brid-geness in complex networks. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 77, 016107.CrossRefGoogle Scholar
  22. 22.
    Négyessy, L., Nepusz, T., Kocsis, L., Bazsó, F. (2006) Prediction of the main cortical areas and connections involved in the tactile function of the visual cortex by network analysis. Eur. J. Neurosci. 23, 1919–1930.CrossRefGoogle Scholar
  23. 23.
    Négyessy, L., Nepusz, T., Zalányi, L., Bazsó, F. (2008) Convergence and divergence are mostly reciprocated properties of the connections in the network of cortical areas. Proc. Biol. Sci. 275, 2403–2410.CrossRefGoogle Scholar
  24. 24.
    Sporns, O. (2002) Graph theory methods for the analysis of neural connectivity patterns. In: Kõtter, R. (ed.) Neuroscience Databases. A Practical Guide. Klüwer, Boston, MA. pp. 171–186.Google Scholar
  25. 25.
    Sporns, O., Kõtter, R. (2004) Motifs in brain networks. PLoSBiol. 2, e369.CrossRefGoogle Scholar
  26. 26.
    Tombu, M.N., Asplund, C.L., Dux, P.E., Godwin, D., Martin, J.W., Marois, R. (2011) A unified attentional bottleneck in the human brain. Proc. Natl. Acad. Sci. USA. 108, 13426–13431.CrossRefGoogle Scholar
  27. 27.
    Sigman, M., Dehaene, S. (2008) Brain mechanisms of serial and parallel processing during dual-task performance. J. Neurosci. 28, 7585–7598.CrossRefGoogle Scholar
  28. 28.
    Sporns, O. (2011) The non-random brain: efficiency, economy, and complex dynamics. Front Comput. Neurosci. 5, 5.CrossRefGoogle Scholar
  29. 29.
    Strogatz, S. H. (2001) Exploring complex networks. Natur. 410, 268–276.CrossRefGoogle Scholar
  30. 30.
    Watts, D. J. (2004) The “New” Science of Networks. Annual Review of Sociolog. 30, 243–270.CrossRefGoogle Scholar
  31. 31.
    Wood, J.N., Grafman, J. (2003) Human prefrontal cortex: processing and representational perspectives. Nat. Rev. Neurosci. 4, 139–147.CrossRefGoogle Scholar
  32. 32.
    Yan, C., He, Y (2011) Driving and driven architectures of directed small-world human brain functional networks. PLoS One. 6, e23460.Google Scholar
  33. 33.
    Zylberberg, A., Fernandez Slezak, D., Roelfsema, P.R., Dehaene, S., Sigman, M. (2010) The brain’s router: a cortical network model of serial processing in the primate brain. PLoS Comput. Biol. 6, e1000765.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest 2012

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  • L. Négyessy
    • 1
    Email author
  • M. Bányai
    • 2
    • 3
  • T. Nepusz
    • 4
    • 5
  • F. Bazsó
    • 2
    • 6
  1. 1.Neurobionics Research GroupHungarian Academy of Sciences — Péter Pázmány Catholic University — Semmelweis UniversityBudapestHungary
  2. 2.Department of BiophysicsKFKI Research Institute for Particle and Nuclear Physics of the Hungarian Academy of SciencesBudapestHungary
  3. 3.Department of Measurement and Information SystemsBudapest University of Technology and Economics, Faculty of Electrical Engineering and InformaticsBudapestHungary
  4. 4.Department of Biological PhysicsEõtvõs Loránd UniversityBudapestHungary
  5. 5.Statistical and Biological Physics Research Group of the Hungarian Academy of SciencesBudapestHungary
  6. 6.SU-Tech College of Applied SciencesSuboticaSerbia

Personalised recommendations