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Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
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Assessing the performance of Granger-Geweke causality: Benchmark dataset and simulation framework.

Mattia F Pagnotta1, Mukesh Dhamala2,3, Gijs Plomp1

  • 1Perceptual Networks Group, Department of Psychology, University of Fribourg, Fribourg CH-1701, Switzerland.

Data in Brief
|November 13, 2018
PubMed
Summary

This study benchmarks nonparametric Granger-Geweke Causality (GGC) methods using rat EEG data. Time reversal testing for GGC (tr-GGC) effectively mitigates noise and reference issues, improving causal inference accuracy.

Keywords:
Additive noiseBarrel cortexBrain connectivityCommon reference problemConditional Granger causalityEEGNonparametric Granger causalitySNR imbalance

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Signal Processing

Background:

  • Nonparametric methods using spectral factorization are established for Granger-Geweke Causality (GGC) estimation.
  • Previous work benchmarked these methods with rat EEG data during whisker stimulation.

Purpose of the Study:

  • To provide detailed information on a benchmark dataset for GGC analysis.
  • To offer code for nonparametric GGC estimation and a simulation framework.
  • To evaluate the impact of common reference, SNR differences, and additive noise on GGC.

Main Methods:

  • Benchmarking nonparametric GGC methods using EEG data from rat whisker stimulation.
  • Developing a simulation framework to test GGC under various noise conditions.
  • Utilizing time reversal testing for GGC (tr-GGC) to assess robustness.

Main Results:

  • Time reversal testing for GGC (tr-GGC) mitigates detrimental effects from SNR imbalance and additive noise.
  • tr-GGC unambiguously identifies dominant causal spectral components even with common reference signals.
  • Nonparametric methods demonstrate potential to overcome limitations in conditional GGC implementations.

Conclusions:

  • Nonparametric GGC methods, particularly with time reversal testing, offer robust causal inference in neuroscience.
  • The provided framework and code facilitate the evaluation and application of GGC methods.
  • Addressing common reference and noise issues is crucial for accurate Granger-Geweke Causality analysis.