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Criteria for Causality: Bradford Hill Criteria - II01:28

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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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Local Granger causality.

Sebastiano Stramaglia1, Tomas Scagliarini1, Yuri Antonacci2

  • 1Dipartimento Interateneo di Fisica, Universitá degli Studi di Bari Aldo Moro, and INFN, Sezione di Bari, 70126 Bari, Italy.

Physical Review. E
|March 19, 2021
PubMed
Summary
This summary is machine-generated.

We introduce local Granger causality (GC), a method to track information transfer over time. This approach reveals transient causal insights missed by traditional average GC analysis in complex systems.

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Area of Science:

  • Neuroscience
  • Information Theory
  • Statistical Modeling

Background:

  • Granger causality (GC) is a statistical method for inferring causal relationships based on predictive accuracy in time series data.
  • For Gaussian variables, GC is equivalent to transfer entropy, measuring directed information flow between processes.
  • Existing GC methods provide an average measure of causality, potentially masking dynamic, short-lived causal interactions.

Purpose of the Study:

  • To develop and validate a method for calculating local Granger causality (lGC), representing time-resolved information transfer.
  • To investigate how the variability of lGC relates to underlying system dynamics, including driver and innovation processes.
  • To demonstrate the utility of lGC in identifying transient causal events not apparent in average GC values.

Main Methods:

  • Exploiting the equivalence between Granger causality and transfer entropy for Gaussian variables.
  • Calculating the exact local Granger causality profile, representing information transfer at each discrete time point.
  • Analyzing the variability of local Granger causality around its mean to understand system dynamics.

Main Results:

  • Local Granger causality provides a time-resolved profile of information transfer between processes.
  • The variability of local Granger causality is linked to the interplay between driving and noise processes.
  • Transient instances of information transfer, undetectable by average GC, can be revealed by local GC analysis.

Conclusions:

  • Local Granger causality offers a robust and computationally efficient method for analyzing time-directed information transfer.
  • This approach is applicable to both linear stochastic processes and nonlinear complex systems approximated as Gaussian.
  • Local GC enhances the understanding of dynamic causal interactions within complex systems over their temporal evolution.