Short-window spectral analysis of cortical event-related potentials by adaptive multivariate autoregressive modeling: data preprocessing, model validation, and variability assessment

Ding, Mingzhou; Bressler, Steven L.; Yang, Weiming; Liang, Hualou

doi:10.1007/s004229900137

Cited by 541 publications

(499 citation statements)

References 17 publications

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“…The shorttime directed DTF (SDTF) [11,21] enables the construction of a time-variant GC and can be used to visualize the dynamics of transmissions using the DTF in combination with a short window spectral technique [26].…”

Section: (I) Linear Methodsmentioning

confidence: 99%

Cardiovascular and cardiorespiratory coupling analyses: a review

Schulz

Adochiei

Edu

et al. 2013

Phil. Trans. R. Soc. A.

136

113

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Recently, methods have been developed to analyse couplings in dynamic systems. In the field of medical analysis of complex cardiovascular and cardiorespiratory systems, there is growing interest in how insights may be gained into the interaction between regulatory mechanisms in healthy and diseased persons. The couplings within and between these systems can be linear or nonlinear. However, the complex mechanisms involved in cardiovascular and cardiorespiratory regulation very likely interact with each other in a nonlinear way. Recent advances in nonlinear dynamics and information theory have allowed the multivariate study of information transfer between time series. They therefore might be able to provide additional diagnostic and prognostic information in medicine and might, in particular, be able to complement traditional linear coupling analysis techniques. In this review, we describe the approaches (Granger causality, nonlinear prediction, entropy, symbolization, phase synchronization) most commonly applied to detect direct and indirect couplings between time series, especially focusing on nonlinear approaches. We will discuss their capacity to quantify direct and indirect couplings and the direction (driver–response relationship) of the considered interaction between different biological time series. We also give their basic theoretical background, their basic requirements for application, their main features and demonstrate their usefulness in different applications in the field of cardiovascular and cardiorespiratory coupling analyses.

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Section: (I) Linear Methodsmentioning

confidence: 99%

Cardiovascular and cardiorespiratory coupling analyses: a review

Schulz

Adochiei

Edu

et al. 2013

Phil. Trans. R. Soc. A.

136

113

View full text Add to dashboard Cite

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“…This implies a tradeoff between likelihood of stationarity (shorter is better) and accuracy of model fit (longer is better). An advantage of windowingassuming there is sufficient data-is that time-varying Gcausality can be analysed (Ding et al, 2000). This is particularly useful given a large number of temporallyaligned trials: then so-called "vertical regression" can be implemented using extremely short windows.…”

Section: Potential Problems and Some Solutionsmentioning

confidence: 99%

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

Barnett

Seth

2014

Journal of Neuroscience Methods

801

828

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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 c 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.

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“…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%

Multivariate Granger causality analysis of fMRI data

Deshpande

LaConte

James

et al. 2008

Human Brain Mapping

210

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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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Short-window spectral analysis of cortical event-related potentials by adaptive multivariate autoregressive modeling: data preprocessing, model validation, and variability assessment

Cited by 541 publications

References 17 publications

Cardiovascular and cardiorespiratory coupling analyses: a review

Cardiovascular and cardiorespiratory coupling analyses: a review

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

Multivariate Granger causality analysis of fMRI data

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