Predicting cognitive aging through brain structural covariance networks: A decade of longitudinal insights using...

Area of Science:

• Neuroscience
• Cognitive Aging Research

Background:

• Cognitive aging poses public health challenges due to increasing global life expectancy.
• While functional connectivity in aging is studied, the predictive power of structural covariance networks is less understood.

Purpose of the Study:

• To investigate if baseline structural covariance networks predict cognitive decline over a decade.
• To explore the predictive value of these networks using Source-Based Morphometry (SBM).

Main Methods:

• Longitudinal study with 37 participants (mean age ~55 years) assessed at two time points, 10 years apart.
• Structural magnetic resonance imaging and cognitive assessments were performed.
• Source-Based Morphometry (SBM) analyzed structural covariance networks, identifying twelve independent components (ICs).

Main Results:

• Network IC1 predicted working memory decline (p<0.001), and IC2 predicted global cognitive function (p=0.047), after controlling for demographics and APOE genotype.
• Brain-cognition relationships were moderated by baseline cognitive performance, sex, and APOE genotype.
• Significant interactions were found for working memory, executive function, processing speed, and verbal memory with specific networks.

Conclusions:

• Structural covariance networks serve as predictive markers for cognitive aging trajectories.
• These findings may inform early intervention strategies to maintain cognitive health in aging populations.