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Directionality indices: Testing information transfer with surrogate correction.

Beth Jelfs1, Rosa H M Chan1

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Summary
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Accurate directionality indices require significance testing. This study proposes a surrogate corrected directionality index, improving the reliability of coupling analysis in complex systems.

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Area of Science:

  • Neuroscience
  • Information Theory
  • Complex Systems Analysis

Background:

  • Directionality indices measure asymmetric coupling between systems, particularly in neurological studies.
  • These indices rely on information transfer measures in both directions.
  • Current methods may lack robust significance testing, potentially leading to inaccurate inferences.

Purpose of the Study:

  • To emphasize the necessity of significance testing before inferring directionality.
  • To introduce a novel surrogate corrected directionality index incorporating significance testing.
  • To differentiate between testing the overall index versus individual transfer measures.

Main Methods:

  • Proposed a surrogate corrected directionality index.
  • Compared two coupling estimation methods: evolution map and conditional mutual information (CMI).
  • Evaluated two CMI estimators: equiquantization-based and k-nearest neighbors.

Main Results:

  • Demonstrated the importance of significance testing for accurate directionality inference.
  • Validated the proposed surrogate corrected directionality index.
  • Highlighted differences in significance testing approaches for directionality indices.

Conclusions:

  • Significance testing is crucial for reliable directionality index interpretation.
  • The surrogate corrected directionality index offers improved accuracy.
  • The study provides a framework for robust coupling analysis in complex systems.