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


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.


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


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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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