Characterizing White Matter Connectivity in Alzheimer's Disease and Mild Cognitive Impairment: Automated Fiber Quantification Analysis with Two Independent Datasets

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 method that can rapidly and reliably identify major WM fiber tracts and evaluate white matter 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 abnormities in a cohort of patients with 120 participants (39 NCs, 34 aMCI and 47 AD) in the discovery dataset, as well as 122 participants (43 NCs, 37aMCI and 42 AD) in the replicated dataset. Pointwise differences along the WM tracts were identified in the discovery dataset and simultaneously confirmed by the replicated dataset. Next, we investigated the utility of DTI measures along WM tracts as features to classify patients with AD from NCs with multilevel cross validations using a support vector machine. The correlation analysis revealed that the identified impaired microstructural WM alterations and classification output were highly associated with cognitive ability in the patient groups, indicating that this credible, robust potential early biomarker may be useful for clinical application in AD. This systematic study provides a pipeline to examine WM integrity and its potential clinical application in AD and may be useful in studying other neurological and psychiatric disorders. Funding: This work was partially supported by the National Key Research and Development Program of China (No. 2016YFC1305904), the National Natural Science Foundation of China (Grant Nos. 81871438, 81571062, 81471120), and the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDB32020200). Declaration of Interest: The authors declare no conflicts of interest. Ethical Approval: This study was approved by the Medical Ethics Committee of the Chinese PLA General Hospital.