A Comparison of White Matter Microstructure and its Relationship with Cognition in Younger and Older Adults

Abstract

Background: Given the growing aging population, it is crucial to better understand the neurobiological underpinnings of healthy aging and how changes in structure relate to changes in function. The current study derived diffusion tensor imaging (DTI) metrics of white matter microstructure in younger and older adults to simulate the healthy aging process. Methods: The DTI metrics of fractional anisotropy (FA) and mean diffusivity (MD) as well as the cognitive domains of memory and executive function were examined in 34 healthy participants divided into older adults (17; Mean = 70.9, SD = 5.4) and younger adults (17; Mean = 28.1, SD = 2.8). Cognitive performance on the California Verbal Learning Test 2nd Edition (CVLT-II) and the trails making test (Trails-A & Trails-B) were used to evaluate memory and executive function, respectively. The differences in white matter microstructure between older and younger adults were analyzed using tract based spatial statistics (TBSS; p < 0.05, corrected for multiple comparisons) in FSL. Associations between the DTI metrics and cognition were then evaluated for each group. Results: Older adults had lower FA and higher MD in diffuse brain regions, including major tracts such as the corticospinal tract, corpus callosum and superior and inferior longitudinal fasciculi. There was a significant negative correlation between executive function and MD in the right superior and anterior corona radiata and the body of the corpus callosum of older adults. No significant relationship was detected between memory performance and DTI metrics in older adults. Furthermore, no significant relationships were detected between memory or executive function performance and FA or MD in younger adults. Conclusions: The differences in DTI metrics between groups as well as the association between MD and executive function support further examinations into the healthy aging process. Future studies should use longitudinal designs with large sample sizes to better understand changes and trajectories during healthy aging.