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Updated: Jun 11, 2025

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

An information-theoretic framework for conditional causality analysis of brain networks.

Lipeng Ning^1,2

¹Brigham and Women's Hospital, Boston, MA, USA.

Network Neuroscience (Cambridge, Mass.)

|October 2, 2024

Summary

This summary is machine-generated.

This study introduces advanced information-theoretic methods to improve Granger causality measures (GCM) for analyzing time series networks. The new techniques offer more accurate network structure identification compared to existing approaches.

Keywords:

Brain network Granger causality Minimum entropy Spectral factorization State-space representation

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Published on: May 7, 2014

Area of Science:

Data Science
Network Analysis
Information Theory

Background:

Identifying directed network models in multivariate time series is crucial.
Granger causality measure (GCM) and conditional GCM (cGCM) are common but lack rigorous theoretical grounding.
Previous work introduced minimum-entropy (ME) estimation to generalize GCM/cGCM.

Purpose of the Study:

To further generalize the information-theoretic framework for conditional causality analysis.
To develop three novel conditional causal measures using control theory techniques.
To theoretically analyze the relationships between these new measures.

Main Methods:

Utilized state-space representations and spectral factorizations from control theory.
Developed three conditional causal measures based on distinct ME estimation procedures.
Motivated ME procedures by equivalent formulations of minimum mean squared error estimation.
Analyzed the theoretical relationship between the three proposed conditional causality measures.

Main Results:

The proposed methods provide more accurate network structures than the original GCM/cGCM.
Evaluated performance using simulations and real neuroimaging data for brain network analysis.
Demonstrated the efficacy of the generalized information-theoretic framework.

Conclusions:

The developed conditional causality measures offer a more rigorous and accurate approach to network identification.
The information-theoretic framework, enhanced by control theory, advances time series network analysis.
These methods have practical applications in fields like neuroimaging.