Wiener-Granger Causality: A well established methodology

By Steven L. Bressler, Anil K. Seth. NeuroImage, 2011

867 citations recorded by the source corpus.

For decades, the main ways to study the effect of one part of the nervous system upon another have been either to stimulate or lesion the first part and investigate the outcome in the second. This article describes a fundamentally different approach to identifying causal connectivity in neuroscience: a focus on the predictability of ongoing activity in one part from that in another. This approach was made possible by a new method that comes from the pioneering work of Wiener (1956) and Granger (1969). The Wiener-Granger method, unlike stimulation and ablation, does not require direct intervention in the nervous system. Rather, it relies on the estimation of causal statistical influences between simultaneously recorded neural time series data, either in the absence of identifiable behavioral events or in the context of task performance. Causality in the Wiener-Granger sense is based on the statistical predictability of one time series that derives from knowledge of one or more others. This article defines Wiener-Granger Causality, discusses its merits and limitations in neuroscience, and outlines recent developments in its implementation.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/fa2e7cd122a56f814daa1234275aa196ada4d3a7

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In Cognitive Neuroscience

Citations in the corpus

Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review (2019, methodology): “Model selection in DCM is based on a direct comparison between all models (Penny, 2012), whereas in GC this involves testing for the presence of GC followed by selecting the VAR model order using Akaike or Bayesian information criteria (Bressler and Seth, 2011).”
Analysing connectivity with Granger causality and dynamic causal modelling (2013, methodology): “com also arises in the selection of VAR model order, using standard approximations to model evidence, such as the Akaike or Bayesian information criteria [42].”
A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula (2016, background): “…and following the arguments developed by Wiener and Granger [Granger, 1969; Wiener, 1956] can be used to infer causal relationships between brain signals, with some caveats [Bressler and Seth, 2011; Chicharro and Ledberg, 2012; Chicharro and Panzeri, 2014; Quinn et al., 2011; Wibral et al., 2014].”
Analysis of sampling artifacts on the Granger causality analysis for topology extraction of neuronal dynamics (2014, methodology): “When time series are linear and Gaussian, GC is equivalent to the transfer entropy (Barnett et al., 2009; Bressler and Seth, 2011).”
Data Based Identification and Prediction of Nonlinear and Complex Dynamical Systems (2016, methodology): “WGC is widely used in measuring the functional connectivity among subdomains of brain based on EEG, MEG, SEEG data [311].”
Is There Really Granger Causality between Energy Use and Output? (2014, background): “…widely applied in many areas of economics including monetary policy (Lee and Yang, 2012), ﬁnance and economic development (Ang, 2008a), and energy economics (Ozturk, 2010), as well as in other ﬁelds such as climate change (e.g. Kaufmann and Stern, 1997) and neuroscience (Bressler and Seth, 2011).”
A Tutorial Review of Functional Connectivity Analysis Methods and Their Interpretational Pitfalls (2016, background): “In neuroscience, a rich and growing literature has evolved that used these particular methods to quantify neuronal interactions, which has been reviewed elsewhere (Bressler and Seth, 2011; Sakkalis, 2011; Seth et al., 2015).”
Phase transfer entropy: A novel phase-based measure for directed connectivity in networks coupled by oscillatory interactions (2014, background): “Although this definition makes no assumptions on signal or interaction structure, Granger Causality (GC) metrics (Bressler and Seth, 2011) that implement it in the time or the frequency domain are largely based on autoregressive modeling of signals and their interactions.”
Effective connectivity: Influence, causality and biophysical modeling (2011, neutral): “058 Type of equation: GCM has been predominantly based on linear stochastic recurrence (autoregressive) models (Bressler and Seth, 2010).”
Working Memory 2.0 (2018, background): “Granger causality is a statistical measure of time series prediction that is indicative of functional connectivity (Bressler and Seth, 2011).”
Granger Causality: A Review and Recent Advances (2021, background): “Other researchers have emphasized the limitations of the approach and have tried to distinguish it as Granger causality or G-causality (Holland, 1986; Bressler and Seth, 2011).”