UC Riverside

Objective

Late-life changes in cognition and brain integrity are both highly multivariate, time-dependent processes that are essential for understanding cognitive aging and neurodegenerative disease outcomes. The present study seeks to identify a latent variable model capable of efficiently reducing a multitude of structural brain change magnetic resonance imaging (MRI) measurements into a smaller number of dimensions. We further seek to demonstrate the validity of this model by evaluating its ability to reproduce patterns of coordinated brain volume change and to explain the rate of cognitive decline over time.

Method

We used longitudinal cognitive data and structural MRI scans, obtained from a diverse sample of 358 participants (M_age = 74.81, SD = 7.17), to implement latent variable models for measuring brain change and to estimate the effects of these brain change factors on cognitive decline.

Results

Results supported a bifactor model for brain change with four group factors (prefrontal, temporolimbic, medial temporal, and posterior association) and one general change factor (global atrophy). Atrophy in the global (β = 0.434, SE = 0.070), temporolimbic (β = 0.275, SE = 0.085), and medial temporal (β = 0.240, SE = 0.085) factors were the strongest predictors of global cognitive decline. Overall, the brain change model explained 59% of the variance in global cognitive slope.

Conclusions

The current results suggest that brain change across 27 bilateral regions of interest can be grouped into five change factors, three of which (global gray matter, temporolimbic, and medial temporal lobe atrophy) are strongly associated with cognitive decline. (PsycInfo Database Record (c) 2021 APA, all rights reserved).