Joint Aging Patterns in Brain Function and Structure Revealed Using 27,793 Samples.

Area of Science:

• Neuroscience
• Aging Research
• Brain Imaging

Background:

• Aging significantly alters brain structure and function.
• The interplay between functional network connectivity (FNC) and gray matter volume (GMV) changes during aging is not fully understood.

Purpose of the Study:

• To develop a unified framework for predicting age using multimodal brain data.
• To identify and validate aging-related changes in FNC and GMV.
• To investigate joint function-structure aging patterns and their association with cognitive decline.

Main Methods:

• Utilized a large UK Biobank dataset (27,793 healthy subjects, ages 49-76).
• Employed single-modal and multimodal approaches for age prediction.
• Developed a novel analysis to reveal joint aging patterns in FNC and GMV.

Main Results:

• Multimodal age prediction outperformed single modality.
• Identified synergistic decreases in FNC and GMV in cerebellum, frontal pole, paracingulate gyrus, and precuneus cortex.
• Observed contradictory changes (increased FNC, decreased GMV) in occipital regions, suggesting compensation.
• Joint decreases in FNC and GMV in specific regions correlated with reaction time.

Conclusions:

• Multimodal brain analysis is crucial for understanding aging.
• Aging involves diverse joint function-structure changes with region-specific patterns.
• These changes are associated with cognitive function, offering insights into age-related cognitive deterioration.