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Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
Published on: August 7, 2017
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A study of problems encountered in Granger causality analysis from a neuroscience perspective
Patrick A Stokes1,2, Patrick L Purdon3
1Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139; patrickp@nmr.mgh.harvard.edu pstokes2@mgh.harvard.edu.
Summary
Granger-Geweke causality analysis in neuroscience can yield biased or high-variance estimates, leading to misleading findings. These methods may not align with neuroscience goals, potentially offering counterintuitive results.
Area of Science:
- Neuroscience
- Time Series Analysis
- Computational Neuroscience
Background:
- Granger causality methods are widely used to infer information flow in time series data.
- Frequency-domain and multivariate Granger causality measures are particularly appealing in neuroscience due to oscillatory phenomena and complex recordings.
- Concerns exist regarding the reliability and interpretation of Granger causality in neuroscience applications.
Purpose of the Study:
- To critically analyze the fundamental computational and conceptual properties of Granger-Geweke (GG) causality.
- To assess the appropriateness and reliability of GG causality for recovering underlying system structures in neuroscience.
- To clarify the implications of GG causality for understanding oscillatory neural systems and neuroscience investigations.
Main Methods:
- Conceptual and computational analysis of Granger-Geweke causality.
- Examination of bias and variance in GG causality estimates.
- Analysis of GG causality's interpretability in relation to system model components and dynamics.
Main Results:
- GG causality estimates can be severely biased or have high variance, leading to spurious results.
- GG causality estimates require examination of component behaviors for proper interpretation.
- GG causality overlooks critical system dynamics, potentially misrepresenting neural system properties.
Conclusions:
- The notion of causality quantified by GG methods is often incompatible with neuroscience research objectives.
- GG causality can produce counterintuitive and misleading results in neuroscience.
- Conceptual clarification of GG causality is crucial for its appropriate application and for causality analyses in neuroscience.
