[Study on Brain Functional Connectivity Using Resting State Electroencephalogram Based on Synchronization Likelihood...

Area of Science:

• Neuroscience
• Medical Imaging
• Biophysics

Background:

• Alzheimer's disease (AD) is a common dementia characterized by progressive cognitive decline.
• Early identification and treatment are crucial for managing dementia onset.
• AD is linked to abnormalities in brain network organization.

Purpose of the Study:

• To investigate brain functional connectivity in Alzheimer's disease patients.
• To compare network topological parameters between AD patients and healthy controls.
• To explore the utility of electroencephalogram (EEG) for quantifying AD-related brain network changes.

Main Methods:

• Recorded 16-channel resting-state, eyes-closed EEG from 15 AD patients and 15 controls.
• Evaluated synchronization likelihood for full-band and alpha-band (8-13 Hz) data.
• Converted synchronization likelihood matrices to binary graphs and analyzed topological parameters (clustering coefficient, global efficiency) at various thresholds.

Main Results:

• Full-band EEG showed significantly lower global efficiency in AD patients at thresholds T=0.06 and T=0.07.
• No significant difference in clustering coefficients for full-band EEG across thresholds T=0.05-0.07.
• Alpha-band analysis revealed significantly lower clustering coefficient and global efficiency in AD patients compared to controls.

Conclusions:

• Resting-state alpha-band EEG suggests decreased brain connectivity strength in Alzheimer's disease.
• Brain network analysis offers a method for quantifying functional brain states in AD.
• Findings support the potential of EEG-based network metrics for AD assessment.