Non-Gaussian water diffusion in aging white matter.
Non-Gaussian water diffusion in aging white matter.
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DOI：
10.1016/j.neurobiolaging.2013.12.001
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发表时间：
2014-06
影响因子：
4.2
通讯作者：
Salat DH
中科院分区：
文献类型：
--
作者：
Coutu JP;Chen JJ;Rosas HD;Salat DH
脑白质相关的白色变性已被充分记录，可能是导致老年人认知能力下降的重要机制。最近的工作已经探索了一系列的非侵入性神经成像程序，以区别突出组织微环境的变化。扩散峰度成像（DKI）是扩散张量成像（DTI）的扩展，可解释非高斯水扩散，并可反映组织隔室分布和扩散特性的变化。我们使用DKI在111名年龄从33岁到91岁的参与者中产生全脑基于体素的平均、轴向和径向扩散峰度（分别为MK、AK和RK）图，这些图是组织微结构扩散异质性的定量指标。正如之前的DTI研究所表明的那样，年龄越大，白质组织微结构的改变越大，这反映在所有三个DKI指标的减少上。与相对保存的初级运动区和视觉区相比，在前额叶和相关白色物质中发现了突出的影响。虽然DKI指标与DTI指标在全球范围内存在差异，但DKI对DTI无法提供的年龄影响具有独特的区域敏感性。DKI指标还可与DTI指标结合使用，用于根据其多变量“扩散足迹”或相对年龄效应大小的模式对区域进行分类。这是可能的，特定的多变量模式的年龄相关的变化测量是代表不同类型的微观结构病理。这些结果表明，DKI为脑老化和神经系统疾病的研究提供了重要的脑微结构补充指标。
Age-associated white matter degeneration has been well-documented and is likely an important mechanism contributing to cognitive decline in older adults. Recent work has explored a range of noninvasive neuroimaging procedures to differentially highlight alterations in the tissue microenvironment. Diffusion kurtosis imaging (DKI) is an extension of diffusion tensor imaging (DTI) that accounts for non-Gaussian water diffusion and can reflect alterations in the distribution and diffusion properties of tissue compartments. We used DKI to produce whole-brain voxel-based maps of mean, axial and radial diffusional kurtoses (MK, AK and RK, respectively), quantitative indices of the tissue microstructure’s diffusional heterogeneity, in 111 participants ranging from 33 to 91 years of age. As suggested from prior DTI studies, greater age was associated with alterations in white-matter tissue microstructure, which was reflected by a reduction in all three DKI metrics. Prominent effects were found in prefrontal and association white matter compared to relatively preserved primary motor and visual areas. Although DKI metrics co-varied with DTI metrics on a global level, DKI provided unique regional sensitivity to the effects of age not available with DTI. DKI metrics were additionally useful in combination with DTI metrics for the classification of regions according to their multivariate ‘diffusion footprint’, or pattern of relative age effect sizes. It is possible that the specific multivariate patterns of age-associated changes measured are representative of different types of microstructural pathology. These results suggest that DKI provides important complementary indices of brain microstructure for the study of brain aging and neurological disease.
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影响因子：
2.9
作者：
Jensen, Jens H.;Helpern, Joseph A.
通讯作者：
Helpern, Joseph A.
影响因子：
4.4
作者：
Helpern, Joseph A.;Adisetiyo, Vitria;Falangola, Maria F.;Hu, Caixia;Di Martino, Adriana;Williams, Kathleen;Castellanos, Francisco X.;Jensen, Jens H.
通讯作者：
Jensen, Jens H.
影响因子：
5.7
作者：
Barrick, Thomas R.;Charlton, Rebecca A.;Markus, Hugh S.
通讯作者：
Markus, Hugh S.
影响因子：
4.8
作者：
Jeurissen, Ben;Leemans, Alexander;Sijbers, Jan
通讯作者：
Sijbers, Jan
影响因子：
4.4
作者：
Falangola, Maria F.;Jensen, Jens H.;Babb, James S.;Hu, Caixia;Castellanos, Francisco X.;Di Martino, Adriana;Ferris, Steven H.;Helpern, Joseph A.
通讯作者：
Helpern, Joseph A.