Robust and Stable Small-World Topology of Brain Intrinsic Organization during Pre- and Post-Task Resting States

  • Zhijiang Wang
  • Jiming Liu
  • Ning Zhong
  • Yulin Qin
  • Haiyan Zhou
  • Kuncheng Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6889)

Abstract

Brain functional network studies have demonstrated the small-world topology as the nature of large-scale spontaneous brain activity. Studies have also revealed that the temporal coherence of spontaneous activity could be reshaped during task-dependent (or post-task) resting states within local spatial patterns such as task-related and the default-mode networks. However, to our best knowledge, it is still a lack of rigorous investigations that whether the small-world topology of spontaneous intrinsic organization remains robust and stable during different resting states. To address the problem, we recorded blood oxygen level-dependent (BOLD) signals from two rests (namely, pre- and post-task resting states) before and after a simple semantic-matching task, and investigated the preceding task influences on the topology of the large-scale spontaneous intrinsic organization during the post-task resting state. The major findings are that the small-world configuration of spontaneous intrinsic organization remains robust and stable during resting states regardless of preceding task influences.

Keywords

Functional Connectivity Temporal Coherence Characteristic Path Length Preceding Task Brain Functional Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Fox, M.D., Raichle, M.E.: Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat. Rev. Neurosci. 8, 700–711 (2007)CrossRefGoogle Scholar
  2. 2.
    Raichle, M.E., Mintun, M.A.: Brain work and brain imaging. Annu. Rev. Neurosci. 29, 449–476 (2006)CrossRefGoogle Scholar
  3. 3.
    Sporns, O., Chialvo, D.R., Kaiser, M., Hilgetag, C.C.: Organization, development and function of complex brain networks. Trends in Cognitive Sciences 8, 418–425 (2004)CrossRefGoogle Scholar
  4. 4.
    Bassett, D.S., Bullmore, E.D.: Small-world brain networks. Neuroscientist 12, 512–523 (2006)CrossRefGoogle Scholar
  5. 5.
    Bullmore, E.D., Sporns, O.: Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience 10, 186–198 (2009)CrossRefGoogle Scholar
  6. 6.
    Salvador, R., Suckling, J., Coleman, M.R., Pickard, J.D., Menon, D., Bullmore, E.D.: Neurophysiological architecture of functional magnetic resonance images of human brain. Cereb. Cortex 15, 1332–1342 (2005a)CrossRefGoogle Scholar
  7. 7.
    Salvador, R., Suckling, J., Schwarzbauser, C., Bullmore, E.D.: Undirected graphs of frequency-dependent functional connectivity in whole brain networks. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 360, 937–946 (2005b)CrossRefGoogle Scholar
  8. 8.
    Eguíluz, V.M., Chialvo, D.R., Cecchi, G.A., Baliki, M., Apkarian, A.V.: Scale-free brain functional networks. Phys. Rev. Lett. 94, 018102 (2005)CrossRefGoogle Scholar
  9. 9.
    Achard, S., Salvador, R., Whitcher, B., Suckling, J., Bullmore, E.D.: A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. J. Neurosci. 26, 63–72 (2006)CrossRefGoogle Scholar
  10. 10.
    Van den Heuvel, M.P., Stam, C.J., Boersma, M., Hulshoff Pol, H.E.: Small-world and scale-free organization of voxel-based resting-state functional connectivity in the human brain. Neuroimage 43, 528–539 (2008)CrossRefGoogle Scholar
  11. 11.
    Stephan, K.E., Hilgetag, C.C., Burns, G.A.P.C., O’Neill, M.A., Young, M.P., Kö tter, R.: Computational analysis of functional connectivity between areas of primate cerebral cortex. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 355, 111–126 (2000)CrossRefGoogle Scholar
  12. 12.
    Micheloyannis, S., Pachou, E., Stam, C.J., Vourkas, M., Erimaki, S., Tsirka, V.: Using graph theoretical analysis of multi channel EEG to evaluate the neural efficiency hypothesis. Neuroscience Letters 402, 273–277 (2006)CrossRefGoogle Scholar
  13. 13.
    Stam, C.J.: Functional connectivity patterns of human magnetoencephalographic recordings: a ’small-world’ network? Neuroscience Letters 355, 25–28 (2004)CrossRefGoogle Scholar
  14. 14.
    Bassett, D.S., Meyer-Lindenberg, A., Achard, S., Duke, T., Bullmore, E.: Adaptive reconfiguration of fractal small-world human brain functional networks. Proc. Natl. Acad. Sci. U.S.A. 103, 19518–19523 (2006)CrossRefGoogle Scholar
  15. 15.
    Watts, D.J., Strogatz, S.H.: Collective dynamics of ”small-world” networks. Nature 393, 440–442 (1998)CrossRefMATHGoogle Scholar
  16. 16.
    Sporns, O., Tononi, G., Edelman, G.M.: Theoretical neuroanatomy: relating anatomical and functional connectivity in graphs and cortical connection matrices. Cerebral Cortex 10, 127–141 (2000)CrossRefGoogle Scholar
  17. 17.
    Hilgetag, C.C., Burns, G.A.P.C., O’Neill, M.A., Scannell, J.W.: Anatomical connectivity defines the organization of clusters of cortical areas in the macaque and the cat. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 355, 91–110 (2000)CrossRefGoogle Scholar
  18. 18.
    Latora, V., Marchiori, M.: Economic small-world behaviour in weighted networks. Euro. Phys. JB 32, 249–263 (2003)CrossRefGoogle Scholar
  19. 19.
    Humphries, M.D., Gurney, K., Prescott, T.J.: The brainstem reticular formation is a small-world, not scale-free, network. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 273, 503–511 (2006)CrossRefGoogle Scholar
  20. 20.
    Hagmann, P., Kurant, M., Gigandet, X., Thiran, P., Wedeen, V.J., Meuli, R., Thiran, J.P.: Mapping the structural core of human cerebral cortex. PLoS ONE 2, e597 (2007)CrossRefGoogle Scholar
  21. 21.
    Iturria-Medina, Y., Sotero, R.C., Canales-Rodriguez, E.J., Aleman-Gomez, Y., Melie-Garcia, L.: Studying the human brain ananatomical network via diffusion-weighted MRI and graph theory. NeuroImage 40, 1064–1076 (2008)CrossRefGoogle Scholar
  22. 22.
    Sporns, O., Zwi, J.D.: The small world of the cerebral cortex. Neuroinformatics 2, 145–162 (2004)CrossRefGoogle Scholar
  23. 23.
    Zeki, S., Shipp, S.: The functional logic of cortical connections. Nature 335, 311–317 (1988)CrossRefGoogle Scholar
  24. 24.
    Tononi, G., Edelman, G.M., Sporns, O.: Complexity and coherency: integrating information in the brain. Trends in Cognitive Sciences 2, 474–484 (1998)CrossRefGoogle Scholar
  25. 25.
    Waites, A.B., Stanislavsky, A., Abbott, D.F., Jackson, G.D.: Effect of prior cognitive state on resting state networks measured with functional connectivity. Human Brain Mapping 24, 59–68 (2005)CrossRefGoogle Scholar
  26. 26.
    Albert, N.B., Robertson, E.M., Miall, R.C.: The resting human brain and motor learning. Curr. boil. 19, 1023–1027 (2009)CrossRefGoogle Scholar
  27. 27.
    Lewis, G.M., Baldassarre, A., Committeri, G., Romania, G.L., Corbetta, M.: Learning sculpts the spontaneous activity of the resting human brain. Proc. Natl. Acad. Sci. U.S.A. 106, 17558–17563 (2009)CrossRefGoogle Scholar
  28. 28.
    Wang, Z.J., Liu, J.M., Zhong, N., Qin, Y.L., Zhou, H.Y.: Two Perspectives to Investigate the Intrinsic Organization of the Dynamic and Ongoing Spontaneous Brain Activity in Humans. In: Proc. the 2010 International Joint Conference on Neural Networks (IJCNN 2010), pp. 1–4 (2010)Google Scholar
  29. 29.
    Zhou, H. Y., Liu, J. Y., Jing, W., Qin, Y.L., Lu, S.F., Yao, Y.Y., Zhong, N.: Basic level advantage and its switching during information retrieval: An fMRI study. In: Yao, Y., Sun, R., Poggio, T., Liu, J., Zhong, N., Huang, J. (eds.) BI 2010. LNCS, vol. 6334, pp. 427–436. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  30. 30.
    Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., Joliot, M.: Automated anatomical labelling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15, 273–289 (2002)CrossRefGoogle Scholar
  31. 31.
    Fox, M.D., Snyder, A.Z., Vincent, J.L., Corbetta, M., Van Essen, D.C., Raichle, M.E.: The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc. Natl. Acad. Sci. U.S.A. 102, 9673–9678 (2005)CrossRefGoogle Scholar
  32. 32.
    Jenkins, G.M., Watts, D.G.: Spectral Analysis and Its Applications. Holden-Day, San Francisco (1968)MATHGoogle Scholar
  33. 33.
    Newman, M.E.J.: The structure and function of complex networks. SIAM Rev. 45, 167–256 (2003)MathSciNetCrossRefMATHGoogle Scholar
  34. 34.
    Latora, V., Marchiori, M.: Efficient behaviour of small-world networks. Phys. Rev. Lett. 87, 198701 (2001)CrossRefGoogle Scholar
  35. 35.
    Achard, S., Bullmore, E.: Efficiency and cost of economical brain functional networks. PLoS Comput. Biol. 3, e17(2007)CrossRefGoogle Scholar
  36. 36.
    Ledberg, A., Akerman, S., Poland, P.F.: Estimation of the probabilities of 3D clusters in functional brain images. NeuroImage 8, 113–128 (1998)CrossRefGoogle Scholar
  37. 37.
    Maslov, S., Sneppen, K.: Specificity and stability in topology of protein networks. Science 296, 910–913 (2002)CrossRefGoogle Scholar
  38. 38.
    Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., Alon, U.: Network motifs: simple building blocks of complex networks. Science 298, 824–827 (2002)CrossRefGoogle Scholar
  39. 39.
    Rubinov, M., Sporns, O.: Complex network measures of brain connectivity: Uses and interpretations. NeuroImage 52, 1059–1069 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Zhijiang Wang
    • 1
    • 2
  • Jiming Liu
    • 1
    • 2
    • 3
  • Ning Zhong
    • 1
    • 2
    • 4
  • Yulin Qin
    • 1
    • 2
    • 5
  • Haiyan Zhou
    • 1
    • 2
  • Kuncheng Li
    • 6
    • 2
  1. 1.International WIC InstituteBeijing University of TechnologyChina
  2. 2.Beijing Municipal Lab of Brain InformaticsChina
  3. 3.Dept. of Computer ScienceHong Kong Baptist UniversityChina
  4. 4.Dept. of Life Science and InformaticsMaebashi Institute of TechnologyJapan
  5. 5.Dept. of PsychologyCarnegie Mellon UniversityUSA
  6. 6.Dept. of RadiologyXuanwu Hospital, Capital Medical UniversityChina

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