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Related Experiment Video

Updated: Jul 3, 2026

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

Kernel-Granger causality and the analysis of dynamical networks.

D Marinazzo¹, M Pellicoro, S Stramaglia

¹Dipartimento Interateneo di Fisica, Università di Bari, I-70126 Bari, Italy. daniele.marinazzo@ba.infn.it

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics

|July 23, 2008

Summary

This study introduces a novel multivariate kernel-Granger causality method for analyzing dynamical networks. The approach reconstructs network topology from time series data and identifies causal relationships in biological systems.

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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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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

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Published on: December 7, 2021

Area of Science:

Dynamical systems analysis
Network inference
Computational biology

Background:

Granger causality is a fundamental tool for time series analysis.
Existing methods often struggle with nonlinearity and false causalities in complex networks.
Kernel methods offer a flexible framework for capturing nonlinear relationships.

Purpose of the Study:

To generalize kernel-Granger causality to the multivariate case for dynamical network analysis.
To develop a method capable of reconstructing network topology from time series data.
To identify causal relationships in both simulated and real biological networks.

Main Methods:

Kernel-based Granger causality extension to multivariate time series.
Eigenvector selection strategy for addressing false causalities.
Application to chaotic maps, simulated genetic regulatory networks, and real gene expression data.

Main Results:

Successful reconstruction of network topology from time series data with sufficient samples.
Demonstration that bivariate Granger causality is superior to L1 minimization for linear networks with limited data.
Identification of 19 causal relationships in HeLa cell gene expression data, linked to tumor development.

Conclusions:

The proposed multivariate kernel-Granger causality method effectively analyzes dynamical networks without assuming directed acyclic graphs.
The method can reconstruct underlying network topology and identify significant causal links.
This approach has potential applications in understanding complex biological systems and disease mechanisms.