A study of problems encountered in Granger causality analysis from a neuroscience perspective

By Patrick A. Stokes, Patrick L. Purdon. Proceedings of the National Academy of Sciences of the United States of America, 2017

303 citations recorded by the source corpus.

Significance 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. Granger causality methods were developed to analyze the flow of information between time series. These methods have become more widely applied in neuroscience. Frequency-domain causality measures, such as those of Geweke, as well as multivariate methods, have particular appeal in neuroscience due to the prevalence of oscillatory phenomena and highly multivariate experimental recordings. Despite its widespread application in many fields, there are ongoing concerns regarding the applicability of Granger causality methods in neuroscience. When are these methods appropriate? How reliably do they recover the system structure underlying the observed data? What do frequency-domain causality measures tell us about the functional properties of oscillatory neural systems? In this paper, we analyze fundamental properties of Granger–Geweke (GG) causality, both computational and conceptual. Specifically, we show that (i) GG causality estimates can be either severely biased or of high variance, both leading to spurious results; (ii) even if estimated correctly, GG causality estimates alone are not interpretable without examining the component behaviors of the system model; and (iii) GG causality ignores critical components of a system’s dynamics. Based on this analysis, we find that the notion of causality quantified is incompatible with the objectives of many neuroscience investigations, leading to highly counterintuitive and potentially misleading results. Through the analysis of these problems, we provide important conceptual clarification of GG causality, with implications for other related causality approaches and for the role of causality analyses in neuroscience as a whole.

Subfields: Artificial Intelligence, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/5bd84422c7320236ceb0568fad8dfcea133ef100

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Citations in the corpus

Granger Causality: A Review and Recent Advances (2021, background): “The above requirements were discussed in Granger’s original and followup papers (Granger, 1969, 1980, 2001) and extensively by other authors (Stokes and Purdon, 2017; Maziarz, 2015); see also the recent review by Glymour et al. (2019).”
Memory Matters: A Case for Granger Causality in Climate Variability Studies (2018, methodology): “…to Granger causality that has been used in climate sciences in recent years to great success; it is worth noting, however, that there are alternative ways of calculating Granger causality, many of which have been developed in neuroscience (e.g., Barnett and Seth 2014; Stokes and Purdon 2017).”
Neural Granger Causality (2021, background): “For example, in neuroscience it is important to determine how brain activation spreads through brain regions [1], [2], [3], [4]; in ﬁnance it is important to determine groups of stocks with low covariance to design low risk portfolios [5]; and, in biology, it is of great interest to infer gene…”