Gazes Yunglin, Bowman F DuBois, Razlighi Qolamreza R, O'Shea Deirdre, Stern Yaakov, Habeck Christian
Cognitive Neuroscience Division, Department of Neurology, Columbia University Medical Center, New York, NY, USA.
Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA.
Neuroimage. 2016 Jan 15;125:53-60. doi: 10.1016/j.neuroimage.2015.10.016. Epub 2015 Oct 19.
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.
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.
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.
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.
以往研究白质(WM)完整性与认知能力及衰老之间的关系时,要么聚焦于整体测量,要么关注少数几个选定的WM束。理想情况下,应同时评估所有WM束的贡献。然而,WM束之间的高共线性使得在不牺牲统计效力(由于严格的多重比较校正)的情况下,无法对WM束进行系统的同时检查。多变量协方差技术能够对所有WM束进行全面的同时检查,而不会因观测值之间的高共线性而受到惩罚。
在本研究中,将缩放子轮廓建模(SSM)应用于18条主要WM束的平均完整性,以提取协方差模式,这些模式能最佳预测346名年龄在20至79岁之间参与者的四种认知能力(感知速度、情景记忆、流体推理和词汇)。利用协方差模式的表达式,分别测试了白质完整性对认知的年龄无关效应以及WM完整性对认知中与年龄相关差异的间接效应,但鉴于分析中使用的是横断面数据集,对间接分析的推断需谨慎。
识别出一种单独的协方差模式,在控制年龄后,该模式能显著预测除词汇外的每种认知能力,但年龄与WM协方差模式的交互作用对这三种能力中的任何一种都不显著。此外,每种模式都介导了年龄对相应认知能力的影响。在这三种模式中,每组WM束都有各自最具影响力的子集。解释流体推理的WM协方差模式显示出最具影响力的WM束数量最多,而情景记忆模式显示的数量最少。
WM束的特定模式对感知速度、情景记忆和流体推理中与年龄相关的差异有显著贡献,但对词汇没有影响。需要探索其他脑健康指标,以揭示对词汇能力的主要影响因素。