Reproducible Abnormalities and Diagnostic Generalizability of White Matter in Alzheimer’s Disease

被引:0
|
作者
Yida Qu
Pan Wang
Hongxiang Yao
Dawei Wang
Chengyuan Song
Hongwei Yang
Zengqiang Zhang
Pindong Chen
Xiaopeng Kang
Kai Du
Lingzhong Fan
Bo Zhou
Tong Han
Chunshui Yu
Xi Zhang
Nianming Zuo
Tianzi Jiang
Yuying Zhou
Bing Liu
Ying Han
Jie Lu
Yong Liu
机构
[1] Chinese Academy of Sciences,Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation
[2] University of Chinese Academy of Sciences,School of Artificial Intelligence
[3] Tianjin University,Department of Neurology, Tianjin Huanhu Hospital
[4] Tianjin University,Department of Neurology, Tianjin Huanhu Hospital
[5] Qilu Hospital of Shandong University,Department of Radiology, Department of Epidemiology and Health Statistics, School of Public Health
[6] Qilu Hospital of Shandong University,Department of Neurology
[7] Xuanwu Hospital of Capital Medical University,Department of Radiology
[8] PLA General Hospital,Branch of Chinese
[9] Chinese PLA General Hospital,Department of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases
[10] Tianjin Huanhu Hospital,Department of Radiology
[11] Tianjin Medical University General Hospital,Department of Radiology
[12] Xuanwu Hospital of Capital Medical University,Department of Neurology
[13] Beijing Institute of Geriatrics,Center of Alzheimer’s Disease
[14] National Clinical Research Center for Geriatric Disorders,State Key Lab of Cognition Neuroscience & Learning
[15] Beijing Institute for Brain Disorders,School of Artificial Intelligence
[16] Beijing Normal University,undefined
[17] Beijing University of Posts and Telecommunications,undefined
来源
Neuroscience Bulletin | 2023年 / 39卷
关键词
Alzheimer’s disease; Diffusion tensor imaging; White matter tracts; Cross-validation;
D O I
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学科分类号
摘要
Alzheimer’s disease (AD) is associated with the impairment of white matter (WM) tracts. The current study aimed to verify the utility of WM as the neuroimaging marker of AD with multisite diffusion tensor imaging datasets [321 patients with AD, 265 patients with mild cognitive impairment (MCI), 279 normal controls (NC)], a unified pipeline, and independent site cross-validation. Automated fiber quantification was used to extract diffusion profiles along tracts. Random-effects meta-analyses showed a reproducible degeneration pattern in which fractional anisotropy significantly decreased in the AD and MCI groups compared with NC. Machine learning models using tract-based features showed good generalizability among independent site cross-validation. The diffusion metrics of the altered regions and the AD probability predicted by the models were highly correlated with cognitive ability in the AD and MCI groups. We highlighted the reproducibility and generalizability of the degeneration pattern of WM tracts in AD.
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页码:1533 / 1543
页数:10
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