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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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Robust unified Granger causality analysis: a normalized maximum likelihood form.

Zhenghui Hu1, Fei Li2,3, Minjia Cheng2

  • 1College of Science, Zhejiang University of Technology, Hangzhou, China. zhenghui@zjut.edu.cn.

Brain Informatics
|August 7, 2021
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Summary
This summary is machine-generated.

Unified Granger causality analysis (uGCA) offers a new framework for causal discovery. The study recommends uGCA-NML as a robust method for general scenarios, showing its stability in mental arithmetic experiments.

Keywords:
FMRIGranger causality analysisInherent redundancyNormalized maximum likelihoodUnified Granger causality analysis

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Area of Science:

  • * Causal inference and network analysis
  • * Information theory and machine learning

Background:

  • * Conventional Granger causality analysis involves a two-stage process.
  • * Various forms of unified Granger causality analysis (uGCA) have been developed, each with specific strengths for different contexts.
  • * A need exists to compare these uGCA methods to identify the most robust approach for general applications.

Purpose of the Study:

  • * To compare different forms of unified Granger causality analysis (uGCA).
  • * To identify and recommend a more robust uGCA method for general scenarios.
  • * To demonstrate the advantages and stability of the recommended method.

Main Methods:

  • * Comparative analysis of several uGCA methods.
  • * Introduction and evaluation of uGCA-NML.
  • * Validation using a synthetic 6-node network.
  • * Application in mental arithmetic experiments with visual/auditory stimuli.

Main Results:

  • * Detailed comparison of various uGCA forms.
  • * Identification of uGCA-NML as a robust method for general causal network investigation.
  • * Demonstrated advantages of uGCA-NML on a synthetic network.
  • * Confirmed robustness and stability of uGCA-NML in mental arithmetic tasks, revealing stable causal network similarities under different stimuli.

Conclusions:

  • * uGCA provides a unified framework for causal analysis, improving upon traditional methods.
  • * uGCA-NML is a stable and accurate method, making it a preferred choice for general causal investigations within the uGCA paradigm.
  • * The study highlights the practical applicability and reliability of uGCA-NML in complex experimental settings.