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Neuroimage. 2018 Jul 15;175:460-463. doi: 10.1016/j.neuroimage.2018.04.043. Epub 2018 Apr 21.

Granger-Geweke causality: Estimation and interpretation.

Author information

1
Department of Physics and Astronomy, Neuroscience Institute, Georgia State University, Atlanta, GA, USA. Electronic address: mdhamala@gsu.edu.
2
School of Biomedical Engineering, Science & Health Systems, Drexel University, Philadelphia, PA, USA.
3
Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, FL, USA.
4
J Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA.

Abstract

In a recent PNAS article1, Stokes and Purdon performed numerical simulations to argue that Granger-Geweke causality (GGC) estimation is severely biased, or of high variance, and GGC application to neuroscience is problematic because the GGC measure is independent of 'receiver' dynamics. Here, we use the same simulation examples to show that GGC measures, when properly estimated either via the spectral factorization-enabled nonparametric approach or the VAR-model based parametric approach, do not have the claimed bias and high variance problems. Further, the receiver-independence property of GGC does not present a problem for neuroscience applications. When the nature and context of experimental measurements are taken into consideration, GGC, along with other spectral quantities, yield neurophysiologically interpretable results.

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