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[The Granger causality models and their applications in brain effective connectivity networks]
Summary
Granger causality analysis reveals directional brain connectivity without prior knowledge. This review covers its principles, methods, and applications in brain effective networks.
Area of Science:
- Neuroscience
- Network Science
- Computational Biology
Background:
- Effective brain connectivity describes directed interactions between neural regions.
- Granger causality is a time-series analysis method for inferring directed relationships.
- Understanding brain networks is crucial for neuroscience research.
Purpose of the Study:
- To review the principles and methodologies of the Granger causality model.
- To analyze the applications of Granger causality in brain effective connectivity networks.
- To discuss the limitations and future directions of this analytical approach.
Main Methods:
- Review of Granger causality principles and statistical foundations.
- Examination of basic and advanced Granger causality test steps.
- Analysis of existing literature on Granger causality in brain network studies.
Main Results:
- Granger causality provides a data-driven approach to infer directional influences in neural systems.
- The model's application in brain networks offers insights into functional and effective connectivity.
- Identified challenges include model assumptions, data requirements, and interpretation complexities.
Conclusions:
- Granger causality is a valuable tool for exploring brain effective connectivity.
- Further methodological refinements are needed to address existing limitations.
- Continued application and development will enhance our understanding of neural dynamics.
