A study of problems encountered in Granger causality analysis from a neuroscience perspective
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- Patrick A. Stokes
- Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139;
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- Patrick L. Purdon
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, MA 02144
説明
<jats:title>Significance</jats:title> <jats:p>Granger causality analysis is a statistical method for investigating the flow of information between time series. Granger causality has become more widely applied in neuroscience, due to its ability to characterize oscillatory and multivariate data. However, there are ongoing concerns regarding its applicability in neuroscience. When are these methods appropriate? How reliably do they recover the functional structure of the system? Also, what do they tell us about oscillations in neural systems? In this paper, we analyze fundamental properties of Granger causality and illustrate statistical and conceptual problems that make Granger causality difficult to apply and interpret in neuroscience studies. This work provides important conceptual clarification of Granger causality methods and suggests ways to improve analyses of neuroscience data in the future.</jats:p>
収録刊行物
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- Proceedings of the National Academy of Sciences
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Proceedings of the National Academy of Sciences 114 (34), E7063-, 2017-08-04
Proceedings of the National Academy of Sciences