Abstract
Background: The degenerative pattern of white matter (WM) microstructures during Alzheimer's disease (AD) and its relationship with cognitive function have not yet been clarified. The present research aimed to explore the alterations of the WM microstructure and its impact on amnestic mild cognitive (aMCI) and AD patients. Mechanical learning methods were used to explore the validity of WM microstructure lesions on the classification in AD spectrum disease.Methods: Neuropsychological data and diffusion tensor imaging (DTI) images were collected from 28 AD subjects, 31 aMCI subjects, and 27 normal controls (NC). Tract-based spatial statistics (TBSS) were used to extract diffusion parameters in WM tracts. We performed ANOVA analysis to compare diffusion parameters and clinical features among the three groups. Partial correlation analysis was used to explore the relationship between diffusion metrics and cognitive functions controlling for age, gender, and years of education. Additionally, we performed the support vector machine (SVM) classification to determine the discriminative ability of DTI metrics in the differentiation of aMCI and AD patients from controls.Results: As compared to controls or aMCI patients, AD patients displayed widespread WM lesions, including in the inferior longitudinal fasciculus, inferior fronto-occipital fasciculi, and superior longitudinal fasciculus. Significant correlations between fractional anisotropy (FA), mean diffusivity (MD), and radial diffusion (RD) of the long longitudinal tract and memory deficits were found in aMCI and AD groups, respectively. Furthermore, through SVM classification, we found DTI indicators generated by FA and MD parameters can effectively distinguish AD patients from the control group with accuracy rates of up to 89 and 85%, respectively.Conclusion: The WM microstructure is extensively disrupted in AD patients, and the WM integrity of the long longitudinal tract is closely related to memory, which would hold potential value for monitoring the progression of AD. The method of classification based on SVM and WM damage features may be objectively helpful to the classification of AD diseases.
Highlights
Alzheimer’s disease (AD) is a chronic neurodegenerative disease characterized by progressive cognitive decline in multiple domains, including memory, language, executive function, and attention
The clinical diagnosis of Amnesic mild cognitive impairment (aMCI) was based on the recommendations of previous studies [19,20,21], which were as follows: [1] chief complaint of memory impairment, corroborated by the subject and/or an informant; [2] objective impaired memory function documented by an auditory verbal learning test—Huashan 20-min delayed recall (AVLT-DR) score ≤ 1.5 SD of age and education adjusted norms; clinical dementia rating scale—sum of the boxes (CDR-SB) score = 0.5; [3] normal general cognitive functions evaluated by a mini-mental state examination (MMSE) score ≥24; [4] preserved basic activities of daily living or minimal impairment in complex instrumental functions; [5] not diagnosed with dementia
Significant differences in the MMSE, Montreal cognitive assessment (MoCA), activities of daily living scale (ADL), CDR, AVLT-DR, auditory verbal learning test recognition (AVLT-R), and Zlang scores were found among the three groups
Summary
Alzheimer’s disease (AD) is a chronic neurodegenerative disease characterized by progressive cognitive decline in multiple domains, including memory, language, executive function, and attention. There has been an increasing interest in the potential contributions of white matter (WM) integrity damage to the pathogenesis of AD [4, 5]. Few studies have investigated the role of cortico-cortical WM integrity on memory processes in patients with aMCI or AD. The degenerative pattern of white matter (WM) microstructures during Alzheimer’s disease (AD) and its relationship with cognitive function have not yet been clarified. The present research aimed to explore the alterations of the WM microstructure and its impact on amnestic mild cognitive (aMCI) and AD patients. Mechanical learning methods were used to explore the validity of WM microstructure lesions on the classification in AD spectrum disease
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