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Related Experiment Video

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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

Evaluating the effective connectivity of resting state networks using conditional Granger causality.

Wei Liao¹, Dante Mantini, Zhiqiang Zhang

¹Key Laboratory for Neuroinformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.

Biological Cybernetics

|November 26, 2009

Summary

The self-referential network influences other brain networks, while the default-mode network integrates information. This study reveals causal interactions within resting state networks (RSNs) using fMRI data.

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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Area of Science:

Neuroscience
Cognitive Science
Systems Biology

Background:

The human brain exhibits spatial organization into distinct resting state networks (RSNs).
Traditional correlation methods may not fully capture dynamic and directional interactions among RSNs.

Purpose of the Study:

To investigate effective connectivity and causal influences among RSNs.
To elucidate the distinct roles of self-referential and default-mode networks in brain architecture.

Main Methods:

Applied conditional Granger causality analysis (CGCA) to RSNs.
Utilized resting state functional magnetic resonance imaging (fMRI) data.
RSNs were identified using independent component analysis (ICA).

Main Results:

Identified specific causal influences among default-mode, dorsal attention, core, central-executive, self-referential, somatosensory, visual, and auditory networks.
The self-referential network (SRN) demonstrated the strongest top-down causal influence on other RSNs.
The default-mode network (DMN) was significantly influenced by other RSNs, suggesting information integration.

Conclusions:

RSNs exhibit complex causal interactions, revealing hierarchical processing levels.
The SRN modulates sensory and cognitive processing, while the DMN integrates information from various networks.
Findings enhance understanding of brain network dynamics and functional architecture.