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Characterization of local white matter microstructural alterations in Alzheimer’s disease: A reproducible study

白质 疾病 表征(材料科学) 阿尔茨海默病 病理 医学 材料科学 磁共振成像 纳米技术 放射科
作者
Caiyun Wen,Qingrun Zeng,Ronghui Zhou,Lei Xie,Jiangli Yu,Chengzhe Zhang,Jingqiang Wang,Yan Yu,Yixin Gu,Guoquan Cao,Yuanjing Feng,Meihao Wang
出处
期刊:Computers in Biology and Medicine [Elsevier]
卷期号:179: 108750-108750
标识
DOI:10.1016/j.compbiomed.2024.108750
摘要

Alzheimer's disease (AD) is a neurodegenerative disease with a close association with microstructural alterations in white matter (WM). Current studies lack the characterization and further validation of specific regions in WM fiber tracts in AD. This study subdivided fiber tracts into multiple fiber clusters on the basis of automated fiber clustering and performed quantitative analysis along the fiber clusters to identify local WM microstructural alterations in AD. Diffusion tensor imaging data from a public dataset (53 patients with AD and 70 healthy controls [HCs]) and a clinical dataset (27 patients with AD and 19 HCs) were included for mutual validation. Whole-brain tractograms were automatically subdivided into 800 clusters through the automatic fiber clustering approach. Then, 100 segments were divided along the clusters, and the diffusion properties of each segment were calculated. Results showed that patients with AD had significantly lower fraction anisotropy (FA) and significantly higher mean diffusivity (MD) in some regions of the fiber clusters in the cingulum bundle, uncinate fasciculus, external capsule, and corpus callosum than HCs. Importantly, these changes were reproducible across the two datasets. Correlation analysis revealed a positive correlation between FA and Mini-Mental State Examination (MMSE) scores and a negative correlation between MD and MMSE in these clusters. The accuracy of the constructed classifier reached 89.76% with an area under the curve of 0.93. This finding indicates that this study can effectively identify local WM microstructural changes in AD and provides new insight into the analysis and diagnosis of WM abnormalities in patients with AD.
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