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Related Experiment Video

Updated: May 9, 2026

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
08:43

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

Published on: August 7, 2017

Assessing thalamocortical functional connectivity with Granger causality.

Cheng Chen1, Anil Maybhate1, David Israel1

  • 1C. Chen was with the Department of Biomedical Engineering, the Johns Hopkins University, Baltimore, MD 21218 USA.

IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
|July 19, 2013
PubMed
Summary
This summary is machine-generated.

Granger causality (GC) analysis effectively captures directional brain network interactions. This method monitored thalamocortical activity changes after cardiac arrest, revealing altered information flow crucial for understanding brain injury.

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Last Updated: May 9, 2026

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08:43

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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Systems Neuroscience

Background:

  • Assessing brain network connectivity is vital for understanding neurological disorders.
  • Conventional methods like coherence analysis lack directional information flow insights.
  • Granger causality (GC) analysis offers a method to characterize directional influences between neural systems.

Purpose of the Study:

  • To test the capability of Granger causality (GC) analysis in capturing directional interactions in simulated and in vivo neural networks.
  • To apply GC analysis to monitor thalamocortical interactions following cardiac arrest (CA)-induced brain injury.
  • To characterize the relationship between electrical synaptic strength and GC-estimated interactions in model networks.

Main Methods:

  • Simulated neural networks using Hindmarsh-Rose neurons to estimate causal influences.
  • Application of GC analysis to in vivo rat models of cardiac arrest (CA) to monitor thalamocortical interactions.
  • Analysis of frequency-dependent dynamics of GC interactions between thalamus and cortex.

Main Results:

  • GC analysis successfully detected asymmetrical interactions in simulated networks.
  • In a rat model, GC revealed significantly higher thalamus-to-cortex interactions post-CA (1.983±0.278 times higher, p=0.021).
  • Thalamocortical GC interactions demonstrated frequency-dependent dynamics.

Conclusions:

  • Granger causality is a feasible method for monitoring dynamic thalamocortical interactions after global brain injury like CA-induced ischemia.
  • GC analysis provides a valuable tool for characterizing inter-regional brain interactions in injured brains.
  • This study highlights GC's potential for advancing research in neurological disorders and brain injury.