Age-Related Alterations in EEG Network Connectivity in Healthy Aging.

Area of Science:

• Neuroscience
• Computational Neuroscience
• Graph Theory Applications

Background:

• Emerging research indicates age-related changes in functional brain networks.
• Graph theory and electroencephalography (EEG) are used to study age-related differences in brain function.
• The impact of normal aging on functional networks and inter-regional synchronization during working memory (WM) tasks requires further investigation.

Purpose of the Study:

• To investigate the effect of aging on brain network topology using graph theory.
• To classify aging EEG signals based on resting-state and visual working memory task performance.
• To analyze age-related differences in functional connectivity and network characteristics.

Main Methods:

• Recorded EEG data from healthy middle-aged and elderly subjects under eyes-open, eyes-closed, and visual WM task conditions.
• Constructed functional brain networks using EEG signals, with electrodes as nodes and functional connectivity as edges.
• Calculated graph theory metrics (global efficiency, local efficiency, clustering coefficient, etc.) and applied K-nearest neighbor (KNN), support vector machine (SVM), and random forest (RF) classifiers.

Main Results:

• Significantly reduced network topology features were observed in the elderly group compared to the middle-aged group.
• Local efficiency, global efficiency, and clustering coefficient were significantly lower in the elderly group across all tested conditions.
• The KNN classifier achieved the highest accuracy (98.89%) in classifying age groups during the visual WM task.

Conclusions:

• Functional network connectivity and topological characteristics analyzed via graph theory provide an effective method for exploring normal age-related changes in the human brain.
• Age-related decline in network efficiency and clustering is evident in EEG functional connectivity.
• Machine learning classifiers, particularly KNN, can effectively distinguish between age groups based on EEG network properties during cognitive tasks.