Abstract
Functional network connectivity (FNC) and Granger causality have been widely used to identify functional and effective connectivity for resting functional magnetic resonance imaging (fMRI) data. However, the relationship between these two approaches is still unclear, making it difficult to compare results. In this study, we investigate the relationship by constraining the FNC lags and the causality coherences for analyzing resting state fMRI data. The two techniques were applied respectively to examine the connectivity within default mode network related components extracted by group independent component analysis. The results show that FNC and Granger causality provide complementary results. In addition, when the temporal delays between two nodes were larger and the causality coherences were distinct, the two approaches exhibit consistent functional and effective connectivity. The consensus between the two approaches provides additional confidence in the results and provides a link between functional and effective connectivity.
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Acknowledgments
This work was supported by National Natural Science Foundation of China under Grants 61379012, 61671106, and 81471742, NSF grants 0840895 and 0715022, NIH grants R01EB005846 and 5P20GM103472, the Fundamental Research Funds for the Central Universities (China, DUT14RC(3)037), and the China Scholarship Council.
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Zhang, C. et al. (2017). Comparison of Functional Network Connectivity and Granger Causality for Resting State fMRI Data. In: Cong, F., Leung, A., Wei, Q. (eds) Advances in Neural Networks - ISNN 2017. ISNN 2017. Lecture Notes in Computer Science(), vol 10262. Springer, Cham. https://doi.org/10.1007/978-3-319-59081-3_65
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DOI: https://doi.org/10.1007/978-3-319-59081-3_65
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