1 Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
2 School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
3 Department of Neurology, Tianjin Huanhu Hospital, Tianjin University, Tianjin 300222, China
4 Department of Neurology, Tianjin Huanhu Hospital, Tianjin University, Tianjin 300222, China
5 Department of Radiology, Department of Epidemiology and Health Statistics, School of Public Health, Qilu Hospital of Shandong University, Ji’nan 250063, China
6 Department of Neurology, Qilu Hospital of Shandong University, Ji’nan 250063, China
7 Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing 100053, China
8 Branch of Chinese, PLA General Hospital, Sanya 572022, China
9 Department of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing 100089, China
10 Department of Radiology, Tianjin Huanhu Hospital, Tianjin 300222, China
11 Department of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China
12 Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing 100053, China
13 Beijing Institute of Geriatrics, Beijing 100053, China
14 National Clinical Research Center for Geriatric Disorders, Beijing 100053, China
15 Center of Alzheimer’s Disease, Beijing Institute for Brain Disorders, Beijing 100053, China
16 State Key Lab of Cognition Neuroscience & Learning, Beijing Normal University, Beijing 100091, China
17 School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China
Abstract
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.
Keywords
Alzheimer’s disease; Diffusion tensor imaging; White matter tracts; Cross-validation
