Resting-state functional dynamic connectivity and healthy aging: A sliding-window network analysis

Area of Science:

• Neuroscience
• Network Science
• Gerontology

Background:

• Graph theory is utilized to investigate brain connectivity in aging.
• Resting-state functional magnetic resonance imaging (fMRI) studies explore dynamic network changes.
• Understanding non-stationary patterns is key to studying the aging process.

Purpose of the Study:

• To characterize resting-state fMRI network dynamics in healthy aging.
• To identify age-related differences in brain network patterns.

Main Methods:

• 114 healthy older adults underwent resting-state fMRI.
• A sliding-window graph theory approach was applied.
• Measures included mean degree, average path length, clustering coefficient, and small-worldness.

Main Results:

• A combined effect of age and time was observed on mean degree, average path length, and small-worldness.
• Participants aged 75-79 exhibited a curvilinear trend.
• This trend showed reduced network density and increased small-worldness.

Conclusions:

• Age impacts average path length, with younger individuals showing lower scores.
• Brain network dynamics exhibit age-related changes.