Age-Related Changes in Micro Brain Characteristics Based on Relaxed Mean-Field Model

Area of Science:

• Neuroscience
• Computational Neuroscience
• Aging Research

Background:

• Brain health is crucial, with aging posing a significant threat alongside diseases.
• Few studies utilize dynamic models to analyze micro-level brain changes during healthy aging.

Purpose of the Study:

• To investigate the impact of normal aging on brain characteristics using a dynamic model.
• To analyze the sensitivity of the relaxed mean-field model (rMFM) to initial parameter values.
• To identify age-related differences in brain connectivity and their dynamic changes.

Main Methods:

• Employed the relaxed mean-field model (rMFM) to simulate normal aging effects.
• Conducted numerical experiments to determine a suitable initial parameter range for the rMFM.
• Utilized statistical methods to identify significant age-related differences in brain regions of interest (ROIs).
• Performed detailed timescale analysis on the dynamic changes within identified ROIs.

Main Results:

• Established a reliable initial parameter range for the rMFM through sensitivity analysis.
• Identified specific ROIs showing significant differences in recurrent connection strength and subcortical input strength between young and old groups.
• Discovered that aging alters the dynamic trends of change in ROIs, even when final states are similar.

Conclusions:

• Dynamic modeling provides novel insights into healthy brain aging.
• Focusing on the trends of change in brain regions, not just end-point results, is critical for understanding aging.
• These findings could inform the development of strategies to prevent or delay age-related cognitive decline.