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Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
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Confounding effects of phase delays on causality estimation.

Vasily A Vakorin1, Bratislav Mišić, Olga Krakovska

  • 1Rotman Research Institute, Baycrest Centre, Toronto, Ontario, Canada. vvakorin@research.baycrest.org

Plos One
|January 26, 2013
PubMed
Summary

Inferring brain connectivity using Granger causality can be challenging due to phase synchronization. An information-theoretic approach is most robust to phase effects when analyzing coupled non-linear systems.

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Area of Science:

  • Neuroscience
  • Complex Systems

Background:

  • Brain connectivity analysis often uses Granger causality, relying on temporal precedence.
  • Phase synchronization is a key mechanism for neural integration in cognitive tasks.
  • Phase lag can complicate causal inference, especially in non-linear systems with time delays.

Purpose of the Study:

  • To evaluate the robustness of different Granger causality inference methods to phase effects.
  • To compare spectral, information-theoretic, and standard Granger causality pipelines.
  • To investigate causal inference in coupled non-linear systems with synchronized signals.

Main Methods:

  • Utilized a prototypical model of coupled non-linear systems.
  • Compared three Granger causality inference pipelines: spectral, information-theoretic, and standard.
  • Analyzed signal phase differences at frequencies of synchronization.

Main Results:

  • Phase lag significantly interferes with causal inference in coupled non-linear systems.
  • Classical linear methods struggle with phase ambiguity.
  • The information-theoretic Granger causality approach demonstrated superior robustness to phase effects.

Conclusions:

  • Information-theoretic Granger causality is a more reliable method for inferring causal brain connectivity in the presence of phase synchronization.
  • Accounting for varying time lags is crucial for accurate causal inference in complex neural systems.
  • Phase effects present a significant challenge for traditional Granger causality methods.