Canonical Granger causality applied to functional brain data

Ashrafulla, Syed; Haldar, Justin P.; Joshi, Anand A.; Leahy, Richard M.

doi:10.1109/isbi.2012.6235919

Cited by 5 publications

(5 citation statements)

References 5 publications

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“…In addition, the researcher must arbitrarily choose a number of lags taken into consideration to estimate regression coefficients. Ashrafulla et al (2012) point out background knowledge as useful in making this choice. Another solution consists in using autocorrelation functions (ACF) or Akaike Information Criterion (AIC) (Liu and Bahadori 2012).…”

Section: Methodsmentioning

confidence: 99%

A review of the Granger-causality fallacy

Maziarz

2015

Journal of Philosophical Economics

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Methods used to infer causal relations from data rather than knowledge of mechanisms are most helpful and exploited only if the theoretical background is insufficient or experimentation impossible. The review of literature shows that when an investigator has no prior knowledge of the researched phenomenon, no result of the Grangercausality test has any epistemic utility due to different possible interpretations. (1) Rejecting the null in one of the tests can be interpreted as either a true causal relation, opposite direction of the true causation, instant causality, time series cointegration, not frequent enough sampling, etc. (2) Bi-directional Granger causality can be read either as instant causality or common cause fallacy. (3) Non-rejection of both nulls possibly means either indirect or nonlinear causality, or no causal relation.

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Section: Methodsmentioning

confidence: 99%

A review of the Granger-causality fallacy

Maziarz

2015

Journal of Philosophical Economics

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“…A preliminary version of CGC was presented by Ashrafulla et al (2012). This paper expands substantially upon the results presented in that work, presenting a refined procedure for computing CGC, using extensive simulations to evaluate and characterize the approach relative to methods like MGC and GCCA, and applying the method to identify causality in real LFP data.…”

Section: Introductionmentioning

confidence: 96%

Canonical granger causality between regions of interest

et al. 2013

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Estimating and modeling functional connectivity in the brain is a challenging problem with potential applications in the understanding of brain organization and various neurological and neuropsychological conditions. An important objective in connectivity analysis is to determine the connections between regions of interest in the brain. However, traditional functional connectivity analyses have frequently focused on modeling interactions between time series recordings at individual sensors, voxels, or vertices despite the fact that a single region of interest will often include multiple such recordings. In this paper, we present a novel measure of interaction between regions of interest rather than individual signals. The proposed measure, termed canonical Granger causality, combines ideas from canonical correlation and Granger causality analysis to yield a measure that reflects directed causality between two regions of interest. In particular, canonical Granger causality uses optimized linear combinations of signals from each region of interest to enable accurate causality measurements from substantially less data compared to alternative multivariate methods that have previously been proposed for this scenario. The optimized linear combinations are obtained using a variation of a technique developed for optimization on the Steifel manifold. We demonstrate the advantages of canonical Granger causality in comparison to alternative causality measures for a range of different simulated datasets. We also apply the proposed measure to local field potential data recorded in a macaque brain during a visuomotor task. Results demonstrate that canonical Granger causality can be used to identify causal relationships between striate and prestriate cortex in cases where standard Granger causality is unable to identify statistically significant interactions.

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“…Despite its popularity, several studies, even in neuroscience [8] , have questioned its trustworthiness. Up to now these criticisms have only concentrated on establishing the right conditions in which the Granger test could be successfully applied, focusing for example on the need of constructing the right hypothesis tests [9] or even having a prior knowledge of the researched phenomenon [10] (due to the possible different interpretations when rejecting or accepting them [11] , [12] and the fact that one could easily produce conflicting conclusions by employing a battery of causality tests on the same data sets [13] , [14] ). Many papers, finally, investigated which kind of data can possibly be studied with this method [15] .…”

Section: Introductionmentioning

confidence: 99%

New considerations on the validity of the Wiener-Granger causality test

Grassmann

2020

Heliyon

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The Wiener-Granger causality test is used to predict future experimental results from past observations in a purely mathematical way. For instance, in many scientific papers this test has been used to study the causality relations in the case of neuronal activities. Albeit some papers reported repeatedly about problems or open questions related to the application of the Granger causality test on biological systems, these criticisms were always related to some kind of assumptions to be made before the test's application. In our paper instead we investigate the Granger method itself, making use exclusively of fundamental mathematical tools like Fourier transformation and differential calculus. We find that the ARMA method reconstructs any time series from any time series, regardless of their properties, and that the quality of the reconstruction is given by the properties of the Fourier transform. In literature several definitions of “causality” have been proposed in order to maintain the idea that the Granger test might be able to predict future events and prove causality between time series. We find instead that not even the most fundamental requirement underlying any possible definition of causality is met by the Granger causality test. No matter of the details, any definition of causality should refer to the prediction of the future from the past; instead by inverting the time series we find that Granger also allows one to “predict” the past from the future.

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Canonical Granger causality applied to functional brain data

Cited by 5 publications

References 5 publications

A review of the Granger-causality fallacy

A review of the Granger-causality fallacy

Canonical granger causality between regions of interest

New considerations on the validity of the Wiener-Granger causality test

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