The Journal of Supercomputing

, Volume 67, Issue 3, pp 806–819

Effective connectivity analysis of fMRI data based on network motifs

  • Zhu-Qing Jiao
  • Ling Zou
  • Yin Cao
  • Nong Qian
  • Zheng-Hua Ma
Article

Abstract

Exploring effective connectivity between neuronal assemblies at different temporal and spatial scales is an important issue in human brain research from the perspective of pervasive computing. At the same time, network motifs play roles in network classification and analysis of structural network properties. This paper develops a method of analyzing the effective connectivity of functional magnetic resonance imaging (fMRI) data by using network motifs. Firstly, the directed interactions between fMRI time-series are analyzed based on Granger causality analysis (GCA), by which the complex network is built up to reveal the causal relationships among different brain regions. Then the effective connectivity in complex network is described with a variety of network motifs, and the statistical properties of fMRI data are characterized according to the network motifs topological parameters. Finally, the experimental results demonstrate that the proposed method is feasible in testing and measuring the effective connectivity of fMRI data.

Keywords

Functional magnetic resonance imaging Network motifs Time-series Effective connectivity Granger causality analysis 

References

  1. 1.
    Uludag K, Dubowitz DJ, Yoder EJ et al (2004) Coupling of cerebral blood flow and oxygen consumption during physiological activation and deactivation measured with fMRI. Neuroimage 23:148–155 CrossRefGoogle Scholar
  2. 2.
    Song, M., Jiang, T (2012) A review of functional magnetic resonance imaging for Brainnetome. Neurosci Bull 28:389–398 CrossRefGoogle Scholar
  3. 3.
    Rombouts SA, Goekoop R, Stam CJ et al (2005) Delayed rather than decreased BOLD response as a marker for early Alzheimer’s disease. Neuroimage 26:1078–1085 CrossRefGoogle Scholar
  4. 4.
    Ceballos-Baumann AO (2003) Functional imaging in Parkinson’s disease: activation studies with PET, fMRI and SPECT. J Neurol 250:15–23 CrossRefGoogle Scholar
  5. 5.
    D’Esposito M, Deouell LY, Gazzaley A (2003) Alterations in the BOLD fMRI signal with ageing and disease: a challenge for neuroimaging. Nat Rev Neurosci 4:863–872 CrossRefGoogle Scholar
  6. 6.
    Smith SM (2012) The future of fMRI connectivity. NeuroImage 62:1257–1266 CrossRefGoogle Scholar
  7. 7.
    Klaas, ES, Roebroeck A (2012) A short history of causal modeling of fMRI data. NeuroImage 62:856–863 CrossRefGoogle Scholar
  8. 8.
    Waldorp L, Christoffels I, van de Ven V (2011) Effective connectivity of fMRI data using ancestral graph theory: dealing with missing regions. NeuroImage 54:2695–2705 CrossRefGoogle Scholar
  9. 9.
    Valdes-Sosa PA, Roebroeck A, Daunizeau J et al (2011) Effective connectivity: influence, causality and biophysical modeling. NeuroImage 58:339–361 CrossRefGoogle Scholar
  10. 10.
    Marinazzo D, Liao W, Chen H et al (2011) Nonlinear connectivity by Granger causality. NeuroImage 58:330–338 CrossRefGoogle Scholar
  11. 11.
    Zang Z-X, Yan C-G, Dong Z-Y et al (2012) Granger causality analysis implementation on MATLAB: a graphic user interface toolkit for fMRI data processing. J Neurosci Methods 203:418–426 CrossRefGoogle Scholar
  12. 12.
    Hamilton JP, Chen G, Thomason ME et al (2011) Investigating neural primacy in major depressive disorder: multivariate Granger causality analysis of resting-state fMRI time-series data. Mol Psychiatry 16:763–772 CrossRefGoogle Scholar
  13. 13.
    Jiao Q, Lu GM, Zhang Z, Zhong Y et al (2011) Granger causal influence predicts BOLD activity levels in the default mode network. Hum Brain Mapp 32:154–161 CrossRefGoogle Scholar
  14. 14.
    Handwerker DA, Roopchansingh V, Gonzalez-Castillo J et al (2012) Periodic changes in fMRI connectivity. NeuroImage 63:1712–1719 CrossRefGoogle Scholar
  15. 15.
    Liao, W, Ding J, Marinazzo D (2011) Small-world directed networks in the human brain: multivariate Granger causality analysis of resting-state fMRI. NeuroImage 54:2683–2694 CrossRefGoogle Scholar
  16. 16.
    Waldorp L, Christoffels I, van de Ven V (2011) Effective connectivity of fMRI data using ancestral graph theory: dealing with missing regions. NeuroImage 54:2695–2705 CrossRefGoogle Scholar
  17. 17.
    Bullmore E, Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10:186–198 CrossRefGoogle Scholar
  18. 18.
    Milo R, Shen-Orr S, Itzkovitz S et al (2002) Network motifs: simple building blocks of complex networks. Science 298:824–827 CrossRefGoogle Scholar
  19. 19.
    Milo R, Itzkovitz S, Kashtan N et al (2004) Superfamilies of evolved and designed networks. Science 303:1538–1542 CrossRefGoogle Scholar
  20. 20.
    Huang C-Y, Cheng C-Y, Sun C-T (2007) Bridge and brick network motifs: identifying significant building blocks from complex biological systems. Artif Intell Med 41:117–127 CrossRefGoogle Scholar
  21. 21.
    Sato JR, Fujita A, Cardoso EF (2010) Analyzing the connectivity between regions of interest: an approach based on cluster Granger causality for fMRI data analysis. NeuroImage 52:1444–1455 CrossRefGoogle Scholar
  22. 22.
    Roebroeck A, Formisano E, Goebel R (2005) Mapping directed influence over the brain using Granger causality and fMRI. NeuroImage 25:230–242 CrossRefGoogle Scholar
  23. 23.
    Jones RH (2011) Bayesian information criterion for longitudinal and clustered data. Stat Med 30:3050–3056 CrossRefMathSciNetGoogle Scholar
  24. 24.
    Watanabe S (2010) Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory. J Mach Learn Res 11:3571–3594 MATHMathSciNetGoogle Scholar
  25. 25.
    Salehi M, Rabiee HR, Jalili M (2010) Motif structure and cooperation in real-world complex networks. Physica A 389:5521–5529 CrossRefGoogle Scholar
  26. 26.
    Ribeiro P, Silva F, Lopes L (2012) Parallel discovery of network motifs. J Parallel Distrib Comput 72:144–154 CrossRefGoogle Scholar
  27. 27.
    Itzhack R, Mogilevski Y, Louzoun Y (2007) An optimal algorithm for counting network motifs. Physica A 381:482–490 CrossRefGoogle Scholar
  28. 28.
    Castro NC, Azevedo PJ (2012) Significant motifs in time series. Stat Anal Data Min 5:35–53 CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Zhu-Qing Jiao
    • 1
    • 2
  • Ling Zou
    • 1
    • 2
  • Yin Cao
    • 2
  • Nong Qian
    • 2
  • Zheng-Hua Ma
    • 2
  1. 1.State Key Laboratory of Robotics and System (HIT)Harbin Institute of TechnologyHarbinChina
  2. 2.Changzhou Key Laboratory of Biomedical Information TechnologyChangzhou UniversityChangzhouChina

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