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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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Published on: August 7, 2017

On directed information theory and Granger causality graphs.

Pierre-Olivier Amblard¹, Olivier J J Michel

¹GIPSAlab, Department of Images and Signals, CNRS UMR 5216, BP46, 38402, Saint Martin d'Hères Cedex, France. bidou.amblard@gipsa-lab.grenoble-inp.fr

Journal of Computational Neuroscience

|March 25, 2010

Summary

This summary is machine-generated.

Directed information theory, a framework for communication channels with feedback, can assess Granger causality in networks. This approach is crucial for neuroscience applications like connectivity inference.

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Area of Science:

Information Theory
Network Science
Neuroscience

Background:

Directed information theory analyzes communication channels with feedback.
Applying it to networks requires extensions based on causal conditioning.
Existing methods may not fully capture complex network dynamics.

Purpose of the Study:

To extend directed information theory for network applications.
To demonstrate its utility in assessing Granger causality graphs.
To establish it as a suitable framework for neuroscience connectivity inference.

Main Methods:

Developing network-based measures from directed information theory.
Applying these measures to analyze stochastic processes.
Comparing results with established Granger causality methods.

Main Results:

Directed information theory measures effectively assess Granger causality graphs.
The framework naturally incorporates measures like transfer entropy.
The approach provides a robust method for connectivity inference.

Conclusions:

Directed information theory offers a powerful framework for network analysis.
It is particularly well-suited for neuroscience applications.
This work bridges information theory and causal inference in complex systems.