Characterizing white matter connectivity in Alzheimer's disease and mild cognitive impairment: An automated fiber quantification analysis with two independent datasets. (August 2020)

• Record Type: Journal Article
• Authors: Dou, Xuejiao; Yao, Hongxiang; Feng, Feng; Wang, Pan; Zhou, Bo; Jin, Dan; Yang, Zhengyi; Li, Jin; Zhao, Cui; Wang, Luning; An, Ningyu; Liu, Bing; Zhang, Xi; Liu, Yong
• Is Part Of: Cortex. Volume 129(2020)
• Page Start: 390
• Page End: 405
• Publication Date: 2020-08
• Subjects: Alzheimer's disease -- Diffusion-weighted MRI -- Tract-specific analysis -- White matter -- Support vector machine; Neuropsychology -- Periodicals; Nervous system -- Periodicals; Neurology -- Periodicals; Psychophysiology -- Periodicals; Behavior -- Periodicals; Neurology -- Periodicals; 612.825
• DOI: 10.1016/j.cortex.2020.03.032
• Languages: English
• ISSNs: 0010-9452

Abstract

Abstract: Alzheimer's disease (AD) is a chronic neurodegenerative disease characterized by progressive dementia. Diffusion tensor imaging (DTI) has been widely used to show structural integrity and delineate white matter (WM) degeneration in AD. The automated fiber quantification (AFQ) method is a fully automated approach that can rapidly and reliably identify major WM fiber tracts and evaluate WM properties. The main aim of this study was to assess WM integrity and abnormities in a cohort of patients with amnestic mild cognitive impairment (aMCI) and AD as well as normal controls (NCs). For this purpose, we first used AFQ to identify 20 major WM tracts and assessed WM integrity and abnormalities in a cohort of 120 subjects (39 NCs, 34 aMCI patients and 47 AD patients) in a discovery dataset and 122 subjects (43 NCs, 37 aMCI patients and 42 AD patients) in a replicated dataset. Pointwise differences along WM tracts were identified in the discovery dataset and simultaneously confirmed in the replicated dataset. Next, we investigated the utility of DTI measures along WM tracts as features to distinguish patients with AD from NCs via multilevel cross validation using a support vector machine. Correlation analysis revealed the identified microstructural WM alterations and classification output to be highly associated with cognitive ability in the patient groups, suggesting that they may be a robust biomarker of AD. This systematic study provides a pipeline to examine WM integrity and its potential clinical application in AD and may be useful for studying other neurological and psychiatric disorders.