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Microstructural white matter changes in normal aging: A diffusion tensor imaging study with higher-order polynomial regression models

Hsu, Jung-Lung, Van Hecke, Wim, Bai, Chyi-Huey, Lee, Cheng-Hui, Tsai, Yuh-Feng, Chiu, Hou-Chang, Jaw, Fu-Shan, Hsu, Chien-Yeh, Leu, Jyu-Gang, Chen, Wei-Hung and Leemans, Alexander 2010. Microstructural white matter changes in normal aging: A diffusion tensor imaging study with higher-order polynomial regression models. NeuroImage 49 (1) , pp. 32-43. 10.1016/j.neuroimage.2009.08.031

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Abstract

Diffusion tensor imaging (DTI) has already proven to be a valuable tool when investigating both global and regional microstructural white matter (WM) brain changes in the human aging process. Although subject to many criticisms, voxel-based analysis is currently one of the most common and preferred approaches in such DTI aging studies. In this context, voxel-based DTI analyses have assumed a ‘linear’ correlation when finding the significant brain regions that relate age with a particular diffusion measure of interest. Recent literature, however, has clearly demonstrated ‘non-linear’ relationships between age and diffusion metrics by using region-of-interest and tractography-based approaches. In this work, we incorporated polynomial regression models in the voxel-based DTI analysis framework to assess age-related changes in WM diffusion properties (fractional anisotropy and axial, transverse, and mean diffusivity) in a large cohort of 346 subjects (25 to 81 years old). Our novel approach clearly demonstrates that voxel-based DTI analyses can greatly benefit from incorporating higher-order regression models when investigating potential relationships between aging and diffusion properties.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Cardiff University Brain Research Imaging Centre (CUBRIC)
Psychology
Subjects: R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Uncontrolled Keywords: Aging; DTI; Voxel-based analysis; Higher-order polynomial regression
Publisher: Elsevier
ISSN: 1053-8119
Last Modified: 12 Jun 2019 02:28
URI: http://orca.cf.ac.uk/id/eprint/26739

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