Cortical connectivity and memory performance in cognitive decline: A study via graph theory from EEG data.

Area of Science:

• Neuroscience
• Cognitive Science
• Biomarker Discovery

Background:

• Alzheimer's disease (AD) and mild cognitive impairment (MCI) are associated with functional brain abnormalities and memory loss.
• Pathological changes in neural connectivity and network structures underlie cognitive decline in AD and MCI.

Purpose of the Study:

• To investigate the correlation between resting-state brain network connectivity and memory performance in AD and MCI patients.
• To explore the potential of brain network characteristics as biomarkers for memory impairment.

Main Methods:

• Recruited 144 subjects: 70 AD, 50 MCI, and 24 healthy controls.
• Constructed weighted cortical brain networks using electroencephalogram (EEG) signals and eLORETA lagged linear connectivity.
• Evaluated graph theory measures, specifically Small World parameters, to assess network topology.

Main Results:

• A significant correlation was found between Small World network characteristics and memory performance.
• Higher Small World patterns in the EEG gamma frequency band during resting state were linked to better short-term memory (digit span tests).

Conclusions:

• The Small World pattern in EEG gamma activity may serve as a potential biomarker for working memory impairment.
• This finding applies to both physiological aging and pathological conditions like AD and MCI.