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...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.展开更多
基金This work was partially supported by the Science and Technology Innovation 2030 Major Projects(2022ZD0211600)the Beijing Natural Science Funds for Distinguished Young Scholars(JQ20036),the Beijing Nova Program(20220484177)+2 种基金the Fundamental Research Funds for the Central Universities(2021XD-A03)the National Natural Science Foundation of China(82172018 and 81871438)In addition,data collection and sharing for this project were funded by the National Natural Science Foundation of China(61633018,81571062,81400890,81471120,and 81701781).
文摘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.
