The use of time-variant EEG Granger causality for inspecting directed interdependencies of neural assemblies

Hesse, Wolfram; Möller, Eva; Arnold, Matthias; Schack, Bärbel

doi:10.1016/s0165-0270(02)00366-7

Cited by 339 publications

(264 citation statements)

References 28 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.

149

127

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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.

149

127

View full text Add to dashboard Cite

show abstract

“…A number of studies have appeared in the neuroscience literature where the issue of causal effects in neural data is examined [6,8,9,10,11,12,13,14,15]. Three of these studies [9,10,15] used the measures presented in this article.…”

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confidence: 99%

Granger Causality: Basic Theory and Application to Neuroscience

Ding

Chen

Bressler

2006

Handbook of Time Series Analysis

550

521

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Multi-electrode neurophysiological recordings produce massive quantities of data. Multivariate time series analysis provides the basic framework for analyzing the patterns of neural interactions in these data. It has long been recognized that neural interactions are directional. Being able to assess the directionality of neuronal interactions is thus a highly desired capability for understanding the cooperative nature of neural computation. Research over the last few years has shown that Granger causality is a key technique to furnish this capability. The main goal of this article is to provide an expository introduction to the concept of Granger causality. Mathematical frameworks for both bivariate Granger causality and conditional Granger causality are developed in detail with particular emphasis on their spectral representations. The technique is demonstrated in numerical examples where the exact answers of causal influences are known.It is then applied to analyze multichannel local field potentials recorded from monkeys performing a visuomotor task. Our results are shown to be physiologically interpretable and yield new insights into the dynamical organization of large-scale oscillatory cortical networks.

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“…A number of studies have appeared in the neuroscience literature where the issue of causal effects in neural data is examined (Chen et al 2008, in press;Baccala and Sameshima 2001;Freiwald et al 1999;Bernasconi and Konig 1999;Bernasconi et al 2000;Kaminski et al 2001;Goebel et al 2003;Hesse et al 2003;Brovelli et al 2004). Three of these studies (Bernasconi and Konig 1999;Bernasconi et al 2000;Brovelli et al 2004) used the measures presented in this article.…”

Section: Analysis Of a Beta Oscillation Network In Sensorimotor Cortexmentioning

confidence: 99%

Dynamics on networks: assessing functional connectivity with Granger causality

Chen

Bressler

Ding

2008

Comput Math Organ Theory

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We consider the analysis of brain networks based on multi-electrode neural recordings. Granger causality and its spectral decomposition are used to assess the directions of dynamic interactions. The effectiveness of the method is illustrated by applying it to simulated data. Then multichannel local field potential recordings from monkeys performing a visuomotor pattern recognition task are analyzed to gain deeper understanding of the organization and functionality of large-scale oscillatory cortical networks.

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The use of time-variant EEG Granger causality for inspecting directed interdependencies of neural assemblies

Cited by 339 publications

References 28 publications

Cardiovascular and cardiorespiratory coupling analyses: a review

Cardiovascular and cardiorespiratory coupling analyses: a review

Granger Causality: Basic Theory and Application to Neuroscience

Dynamics on networks: assessing functional connectivity with Granger causality

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