title: 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.
  
  
  METHODS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Cognitive Neuroscience Division, Department of Neurology, Columbia University Medical Center, New York, NY, USA. Electronic address: YL2107@cumc.columbia.edu.
authors.forename: Yunglin
authors.initials: Y
authors.lastname: Gazes
authors.affiliation: Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA.
authors.forename: F DuBois
authors.initials: FD
authors.lastname: Bowman
authors.affiliation: Cognitive Neuroscience Division, Department of Neurology, Columbia University Medical Center, New York, NY, USA.
authors.forename: Qolamreza R
authors.initials: QR
authors.lastname: Razlighi
authors.affiliation: Cognitive Neuroscience Division, Department of Neurology, Columbia University Medical Center, New York, NY, USA.
authors.forename: Deirdre
authors.initials: D
authors.lastname: O'Shea
authors.affiliation: Cognitive Neuroscience Division, Department of Neurology, Columbia University Medical Center, New York, NY, USA.
authors.forename: Yaakov
authors.initials: Y
authors.lastname: Stern
authors.affiliation: Cognitive Neuroscience Division, Department of Neurology, Columbia University Medical Center, New York, NY, USA.
authors.forename: Christian
authors.initials: C
authors.lastname: Habeck
mesh_terms: D000328:Adult; D000368:Aged; D000375:Aging / Q000473:pathology*; D003071:Cognition / Q000502:physiology*; D003430:Cross-Sectional Studies; D056324:Diffusion Tensor Imaging; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008297:Male; D008875:Middle Aged; D009434:Neural Pathways / Q000473:pathology*; D025341:Principal Component Analysis; D066127:White Matter / Q000473:pathology*; D055815:Young Adult
keywords: Cognition; Diffusion tensor imaging; Mediation; Multivariate statistics; Principal component analysis