Journal article
Granger-Geweke causality: Estimation and interpretation
NeuroImage (Orlando, Fla.), v 175, pp 460-463
15 Jul 2018
PMID: 29684646
Featured in Collection : UN Sustainable Development Goals @ Drexel
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.
•GGC (Geweke-Granger causality) can be reliably done with the non-para and parametric approaches.•The reliability of GGC is illustrated with simulation examples.•‘Receiver’-independence of GGC is not problematic for neuroscience applications.
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Details
- Title
- Granger-Geweke causality: Estimation and interpretation
- Creators
- Mukesh Dhamala - Department of Physics and Astronomy, Neuroscience Institute, Georgia State University, Atlanta, GA, USAHualou Liang - School of Biomedical Engineering, Science & Health Systems, Drexel University, Philadelphia, PA, USASteven L Bressler - Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, FL, USAMingzhou Ding - J Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA
- Publication Details
- NeuroImage (Orlando, Fla.), v 175, pp 460-463
- Publisher
- Elsevier
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- School of Biomedical Engineering, Science, and Health Systems
- Web of Science ID
- WOS:000432949000038
- Scopus ID
- 2-s2.0-85046121574
- Other Identifier
- 991014878465004721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Neuroimaging
- Neurosciences
- Radiology, Nuclear Medicine & Medical Imaging