White matter tract covariance patterns predict age-declining cognitive abilities

Abstract

Previous studies investigating the relationship of white matter (WM) integrity to cognitive abilities and aging have either focused on a global measure or a few selected WM tracts. Ideally, contribution from all of the WM tracts should be evaluated at the same time. However, the high collinearity among WM tracts precludes systematic examination of WM tracts simultaneously without sacrificing statistical power due to stringent multiple-comparison corrections. Multivariate covariance techniques enable comprehensive simultaneous examination of all WM tracts without being penalized for high collinearity among observations.

Method: In this study, Scaled Subprofile Modeling (SSM) was applied to the mean integrity of 18 major WM tracts to extract covariance patterns that optimally predicted four cognitive abilities (perceptual speed, episodic memory, fluid reasoning, and vocabulary) in 346 participants across ages 20 to 79years old. Using expression of the covariance patterns, age-independent effects of white matter integrity on cognition and the indirect effect of WM integrity on age-related differences in cognition were tested separately, but inferences from the indirect analyses were cautiously made given that cross-sectional data set was used in the analysis.

Results: A separate covariance pattern was identified that significantly predicted each cognitive ability after controlling for age except for vocabulary, but the age by WM covariance pattern interaction was not significant for any of the three abilities. Furthermore, each of the patterns mediated the effect of age on the respective cognitive ability. A distinct set of WM tracts was most influential in each of the three patterns. The WM covariance pattern accounting for fluid reasoning showed the most number of influential WM tracts whereas the episodic memory pattern showed the least number.

Conclusion: Specific patterns of WM tracts make significant contributions to the age-related differences in perceptual speed, episodic memory, and fluid reasoning but not vocabulary. Other measures of brain health will need to be explored to reveal the major influences on the vocabulary ability.

Keywords: Cognition; Diffusion tensor imaging; Mediation; Multivariate statistics; Principal component analysis.

Figure 1

Mean z-scores for each cognitive ability across decades. Z-scores were calculated across all age groups within each task and then averaged within each decade to show the relative trend in performance across age groups. Solid and dashed lines helped to distinguish the overlapping lines from each other.

Figure 2

A. Mediation models for the three cognitive abilities: S = Perceptual Speed, M = Episodic Memory, R = Fluid Reasoning. The numbers above the arrows are the regression coefficients. IE 95% CI = 95% Confidence interval for the indirect effect. c = total effect. c’ = direct effect. B. Loadings for the 18 WM tracts. Blue circles indicate point estimate for the loading and red bars indicate the variability of the loadings for 10,000 bootstrapped samples. Tracts with red bars that do not overlap with zero suggest significant contribution from the WM tract in the covariance pattern. PCs = the number of Principal components linearly combined to create the covariance pattern. R = right. L = Left. Please refer to section 2.5 for WM tract abbreviations.