[Brain Efficient Connectivity Analysis of Attention Based on the Granger Causality Method].

Area of Science:

• Neuroscience
• Cognitive Neuroscience
• Brain Connectivity

Background:

• Understanding brain function relies on mapping information flow.
• Granger causality analysis, using multivariate autoregressive models, is a key method for assessing functional connectivity.
• Electroencephalogram (EEG) provides high-resolution data for brain activity analysis.

Purpose of the Study:

• To investigate directed information flow between brain regions during a visual selective attention task.
• To apply Granger causality analysis to high-resolution EEG data.
• To identify changes in causal connectivity patterns under attentional and unattentional conditions.

Main Methods:

• Acquired high-resolution EEG signals from ten healthy subjects performing a visual selective attention task.
• Utilized independent component analysis (ICA) to isolate independent components from frontal, parietal, and occipital cortices.
• Computed Granger causal connectivity between these components, with significance tested using independent sample t-tests and bootstrap methods.

Main Results:

• Significant Granger causal connectivity was observed from the frontal to the occipital cortex and from the parietal to the occipital cortex during the attentional condition.
• The causal connection from the frontal to the occipital cortex was found to disappear in the unattentional condition.

Conclusions:

• Brain information flow exhibits directional patterns between cortical regions, particularly during attention.
• Visual selective attention modulates causal influences between frontal, parietal, and occipital brain regions.
• Granger causality analysis is effective in revealing dynamic changes in functional brain networks.