The MVGC multivariate Granger causality toolbox: a new approach to Granger-causal inference.

By Lionel C. Barnett, Anil K. Seth. Journal of Neuroscience Methods, 2014

973 citations recorded by the source corpus.

BACKGROUND Wiener-Granger causality ("G-causality") is a statistical notion of causality applicable to time series data, whereby cause precedes, and helps predict, effect. It is defined in both time and frequency domains, and allows for the conditioning out of common causal influences. Originally developed in the context of econometric theory, it has since achieved broad application in the neurosciences and beyond. Prediction in the G-causality formalism is based on VAR (vector autoregressive) modelling. NEW METHOD The MVGC Matlab© Toolbox approach to G-causal inference is based on multiple equivalent representations of a VAR model by (i) regression parameters, (ii) the autocovariance sequence and (iii) the cross-power spectral density of the underlying process. It features a variety of algorithms for moving between these representations, enabling selection of the most suitable algorithms with regard to computational efficiency and numerical accuracy. RESULTS In this paper we explain the theoretical basis, computational strategy and application to empirical G-causal inference of the MVGC Toolbox. We also show via numerical simulations the advantages of our Toolbox over previous methods in terms of computational accuracy and statistical inference. COMPARISON WITH EXISTING METHOD(S) The standard method of computing G-causality involves estimation of parameters for both a full and a nested (reduced) VAR model. The MVGC approach, by contrast, avoids explicit estimation of the reduced model, thus eliminating a source of estimation error and improving statistical power, and in addition facilitates fast and accurate estimation of the computationally awkward case of conditional G-causality in the frequency domain. CONCLUSIONS The MVGC Toolbox implements a flexible, powerful and efficient approach to G-causal inference.

Subfields: Cognitive Neuroscience, Computer Networks and Communications, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/baea8a540cd96e7e6b7ab52872a629881272c6f6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

In Computer Networks and Communications

In Statistics and Probability

Citations in the corpus

Alpha and gamma oscillations characterize feedback and feedforward processing in monkey visual cortex (2014, methodology): “We used the MVGC toolbox (23) to compute Granger causality with a model order of”
Granger Causality Analysis in Neuroscience and Neuroimaging (2015, methodology): “Several freely available software toolboxes can now facilitate the application of G-causality (Cui et al., 2008; Barnett and Seth, 2014).”
Advancing functional connectivity research from association to causation (2019, methodology): “…FC measures that, unlike pairwise correlation and coherence, take confounding into account via fitting all measured time series simultaneously, such as partial correlation, multiple regression 91 , dynamic causal modeling 40 , multivariate Granger causality 92 , and Bayesian search approaches 88 .”
A Tutorial Review of Functional Connectivity Analysis Methods and Their Interpretational Pitfalls (2016, methodology): “To estimate the absolute value of a connectivity metric like Granger causality, one can subtract the estimated bias to obtain a de-biased estimate (Barnett and Seth, 2014).”
A study of problems encountered in Granger causality analysis from a neuroscience perspective (2017, background): “Seth AK (2010) A MATLAB toolbox for Granger causal connectivity analysis.”
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).”
Theta oscillations between the ventromedial prefrontal cortex and amygdala support dynamic representations of threat and safety (2025, unknown): “The model order was ascertained based on the Bayesian information criterion (BIC) using the Multivariate Granger Causality Toolbox (Barnett & Seth, 2014).”