EEG Resting-State Functional Networks in Amnestic Mild Cognitive Impairment

Area of Science:

• Neuroscience
• Cognitive Science
• Medical Imaging

Background:

• Alzheimer's cognitive-behavioral syndrome stems from impaired nerve cell connectivity due to protein misfolding and subsequent disruption of brain networks.
• Electroencephalography (EEG) offers optimal temporal resolution for assessing communication within functionally related brain regions.

Purpose of the Study:

• To detect and compare resting-state networks (RSNs) using EEG in patients with amnesic mild cognitive impairment (aMCI) versus healthy elderly (HE) individuals.
• To investigate differences in brain network activity associated with early-stage Alzheimer's disease.

Main Methods:

• Recruited 125 aMCI patients and 70 healthy elderly subjects.
• Analyzed 120-second artifact-free EEG data using standard low-resolution brain electromagnetic tomography (sLORETA)-independent component analysis (ICA) to assess RSNs across delta, theta, alpha, and beta frequencies.

Main Results:

• Identified 17 common RSNs between groups.
• aMCI patients exhibited distinct network activity patterns compared to HE individuals, including altered delta/beta activity in the superior frontal gyrus and decreased alpha1 activity in the paracentral lobule (default mode network).
• Significant differences in attention networks were observed, with aMCI patients showing greater overall activity in the superior frontal gyrus and reduced alpha activity in the left superior parietal lobe.

Conclusions:

• The sLORETA-ICA method effectively detects functional RSNs and between-group connectivity differences.
• Findings support the Alzheimer's network disconnection hypothesis, highlighting altered brain network function in aMCI.