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A Multivariate Granger Causality Concept towards Full Brain Functional Connectivity

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  • Christoph Schmidt
  • Britta Pester
  • Nicole Schmid-Hertel
  • Herbert Witte
  • Axel Wismüller
  • Lutz Leistritz

Abstract

Detecting changes of spatially high-resolution functional connectivity patterns in the brain is crucial for improving the fundamental understanding of brain function in both health and disease, yet still poses one of the biggest challenges in computational neuroscience. Currently, classical multivariate Granger Causality analyses of directed interactions between single process components in coupled systems are commonly restricted to spatially low- dimensional data, which requires a pre-selection or aggregation of time series as a preprocessing step. In this paper we propose a new fully multivariate Granger Causality approach with embedded dimension reduction that makes it possible to obtain a representation of functional connectivity for spatially high-dimensional data. The resulting functional connectivity networks may consist of several thousand vertices and thus contain more detailed information compared to connectivity networks obtained from approaches based on particular regions of interest. Our large scale Granger Causality approach is applied to synthetic and resting state fMRI data with a focus on how well network community structure, which represents a functional segmentation of the network, is preserved. It is demonstrated that a number of different community detection algorithms, which utilize a variety of algorithmic strategies and exploit topological features differently, reveal meaningful information on the underlying network module structure.

Suggested Citation

  • Christoph Schmidt & Britta Pester & Nicole Schmid-Hertel & Herbert Witte & Axel Wismüller & Lutz Leistritz, 2016. "A Multivariate Granger Causality Concept towards Full Brain Functional Connectivity," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-25, April.
  • Handle: RePEc:plo:pone00:0153105
    DOI: 10.1371/journal.pone.0153105
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    Cited by:

    1. Angeliki Papana & Catherine Kyrtsou & Dimitris Kugiumtzis & Cees Diks, 2017. "Assessment of resampling methods for causality testing: A note on the US inflation behavior," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-20, July.
    2. Wenjie Zhao & Raquel Prado, 2020. "Efficient Bayesian PARCOR approaches for dynamic modeling of multivariate time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(6), pages 759-784, November.
    3. Mite Mijalkov & Joana B Pereira & Giovanni Volpe, 2020. "Delayed correlations improve the reconstruction of the brain connectome," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-22, February.
    4. Torun, Erdost & Chang, Tzu-Pu & Chou, Ray Y., 2020. "Causal relationship between spot and futures prices with multiple time horizons: A nonparametric wavelet Granger causality test," Research in International Business and Finance, Elsevier, vol. 52(C).

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