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.
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Acknowledgements
This work was supported by the open project of the State Key Laboratory of Robotics and System at Harbin Institute of Technology (SKLRS-2010-2D-09, SKLRS-2010-MS-10), National Natural Science Foundation of China (51307010, 61201096), University Natural Science Research Program of Jiangsu Province (13KJB510002), Natural Science Foundation of Changzhou City (CJ20110023), and Changzhou High-tech Research Key Laboratory Project (CM20123006). The authors are also very grateful to School of Information Science and Engineering, Changzhou University.
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Jiao, ZQ., Zou, L., Cao, Y. et al. Effective connectivity analysis of fMRI data based on network motifs. J Supercomput 67, 806–819 (2014). https://doi.org/10.1007/s11227-013-1010-z
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DOI: https://doi.org/10.1007/s11227-013-1010-z