Granger-Geweke causality: Estimation and interpretation

作者：Dhamala Mukesh^*; Liang Hualou; Bressler Steven L; Ding Mingzhou

来源：NeuroImage, 2018, 175: 460-463.

DOI：10.1016/j.neuroimage.2018.04.043

In a recent PNAS article(1), 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.

• 出版日期2018-7-15