Evaluating causal relations in neural systems: Granger causality, directed transfer function and statistical assessment of significance

Kamiński, Maciej; Ding, Mingzhou; Truccolo, Wilson; Bressler, Steven L.

doi:10.1007/s004220000235

Cited by 908 publications

(833 citation statements)

References 15 publications

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“…Methodologically, spectral methods have been shown to be more robust to deviations from the stationarity assumption [Granger, 1964]. It is to be noted that unlike previously reported studies Kaminski et al, 2001;Kus et al, 2004], we avoided normalizing DTF so as to allow direct comparison between the absolute values of the strengths of influence. Normalization of DTF with respect to inflows into any ROI as in Kus et al would make such a comparison untenable.…”

Section: Multivariate Granger Causality Analysismentioning

confidence: 99%

“…Analytical distributions of multivariate Granger causality are not established because they are said to have a highly nonlinear relationship with the time series data [Kaminski et al, 2001]. Therefore, to assess the significance of the Granger causality reflected by dDTF, we employed surrogate data [Kaminski et al, 2001;Kus et al, 2004;Theiler et al, 1992] to obtain an empirical null distribution.…”

Section: Statistical Significance Testingmentioning

confidence: 99%

“…Therefore, to assess the significance of the Granger causality reflected by dDTF, we employed surrogate data [Kaminski et al, 2001;Kus et al, 2004;Theiler et al, 1992] to obtain an empirical null distribution. The original time series was transformed into the frequency domain and their phase was randomized so as to be uniformly distributed over (2p, p) .…”

Section: Statistical Significance Testingmentioning

confidence: 99%

“…Simulations by Kus et al [2004] have shown that a complete set of observations from a process have to be used to obtain causal relationships between them and that pair-wise estimates may yield incorrect results. To date, multivariate measures of Granger causality have been largely limited to electrophysiological data Ding et al, 2000;Kaminski et al, 2001;Kus et al, 2004] although multivariate autoregressive models have been used to infer functional connectivity from fMRI data [Harrison et al, 2003]. We have previously presented preliminary forms of the study described here [Deshpande et al, 2006a,b].…”

Section: Introductionmentioning

confidence: 99%

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Multivariate Granger causality analysis of fMRI data

Deshpande

LaConte

James

et al. 2008

Human Brain Mapping

217

214

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This article describes the combination of multivariate Ganger causality analysis, temporal down-sampling of fMRI time series, and graph theoretic concepts for investigating causal brain networks and their dynamics. As a demonstration, this approach was applied to analyze epoch-to-epoch changes in a hand-gripping, muscle fatigue experiment. Causal influences between the activated regions were analyzed by applying the directed transfer function (DTF) analysis of multivariate Granger causality with the integrated epoch response as the input, allowing us to account for the effects of several relevant regions simultaneously. Integrated responses were used in lieu of originally sampled time points to remove the effect of the spatially varying hemodynamic response as a confounding factor; using integrated responses did not affect our ability to capture its slowly varying affects of fatigue. We separately modeled the early, middle, and late periods in the fatigue. We adopted graph theoretic concepts of clustering and eccentricity to facilitate the interpretation of the resultant complex networks. Our results reveal the temporal evolution of the network and demonstrate that motor fatigue leads to a disconnection in the related neural network.

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Section: Multivariate Granger Causality Analysismentioning

confidence: 99%

Section: Statistical Significance Testingmentioning

confidence: 99%

Section: Statistical Significance Testingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multivariate Granger causality analysis of fMRI data

Deshpande

LaConte

James

et al. 2008

Human Brain Mapping

217

214

View full text Add to dashboard Cite

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“…The majority of approaches are parametric, which rely on estimating the parameters of a model to describe the patten of interactions between the observed signals, typically using autoregressive (AR) models (Granger, 1969;Geweke, 1982). Once the AR parameters have been estimated different metrics relating to directionality can be constructed directly as a function of the model parameters (Baccala et al, 2001;Kaminski et al, 2001;Chen et al, 2006;Schelter et al, 2006;Chicharro, 2012). A number of concerns have been raised regarding the validity of AR models to accurately capture the complex structure present in multivariate neural and other time series typically encountered in scientific problems (Gersch, 1972;Thomson and Chave, 1991;Lindsay and Rosenberg, 2011).…”

Section: Introductionmentioning

confidence: 99%

Non-parametric directionality analysis – Extension for removal of a single common predictor and application to time series

Halliday

Senik

Stevenson

et al. 2016

Journal of Neuroscience Methods

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Article ReuseItems deposited in White Rose Research Online are protected by copyright, with all rights reserved unless indicated otherwise. They may be downloaded and/or printed for private study, or other acts as permitted by national copyright laws. The publisher or other rights holders may allow further reproduction and re-use of the full text version. This is indicated by the licence information on the White Rose Research Online record for the item. TakedownIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request.Non parametric directionality analysis -extension for removal of a single common predictor and application to time series. AbstractBackground. The ability to infer network structure from multivariate neuronal signals is central to computational neuroscience. Directed network analyses typically use parametric approaches based on auto-regressive (AR) models, where networks are constructed from estimates of AR model parameters. However, the validity of using low order AR models for neurophysiological signals has been questioned. A recent article introduced a non parametric approach to estimate directionality in bivariate data, non parametric approaches are free from concerns over model validity.New Method. We extend the non parametric framework to include measures of directed conditional independence, using scalar measures that decompose the overall partial correlation coefficient summatively by direction, and a set of functions that decompose the partial coherence summatively by direction. A time domain partial correlation function allows both time and frequency views of the data to be constructed. The conditional independence estimates are conditioned on a single predictor.Results. The framework is applied to simulated cortical neuron networks and mixtures of Gaussian time series data with known interactions. It is applied to experimental data consisting of local field potential recordings from bilateral hippocampus in anaesthetised rats.Comparison with Existing Method(s). The framework offers a non parametric approach to estimation of directed interactions in multivariate neuronal recordings, and increased flexibility in dealing with both spike train and time series data.Conclusions. The framework offers a novel alternative non parametric approach to estimate directed interactions in multivariate neuronal recordings, and is applicable to spike train and time series data

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