Alteration of Neural Network Activity With Aging Focusing on Temporal Complexity and Functional Connectivity Within...

Area of Science:

• Neuroscience
• Computational Biology
• Gerontology

Background:

• Aging leads to cognitive decline, impacting attention and memory.
• Understanding neural activity alterations is key for preventing age-related brain dysfunction.
• Temporal neural fluctuations and functional connectivity are crucial for brain information processing.

Purpose of the Study:

• To investigate the relationship between neural complexity and functional connectivity in aging.
• To determine if combining these measures improves the detection of age-related spatiotemporal patterns in EEG.
• To develop machine learning models for classifying aging participants based on EEG data.

Main Methods:

• Electroencephalography (EEG) data from young and older adults.
• Multi-fractal (MF) and multi-scale entropy (MSE) analyses for temporal complexity.
• Phase lag index (PLI) analysis for functional connectivity.
• Machine learning for combining complexity and connectivity profiles.

Main Results:

• The complementary relationship between neural complexity and functional connectivity significantly improved classification accuracy among aging participants.
• MF and MSE analyses effectively captured temporal complexity, while PLI assessed functional connectivity.
• Combined analysis demonstrated superior detection of age-related changes in neural activity patterns.

Conclusions:

• The integration of neural complexity and functional connectivity provides a powerful approach to detect age-related brain alterations.
• This combined method enhances the accuracy of identifying changes in spatiotemporal patterns from EEG.
• Findings support the development of targeted interventions for preventing age-related cognitive decline.