Long Longitudinal Tract Lesion Contributes to the Progression of Alzheimer's Disease

Caimei Luo; Mengchun Li; Ruomeng Qin; Haifeng Chen; Lili Huang; Dan Yang; Qing Ye; Renyuan Liu; Yun Xu; Hui Zhao; Feng Bai

doi:10.3389/fneur.2020.503235

Long Longitudinal Tract Lesion Contributes to the Progression of Alzheimer's Disease

Front Neurol. 2020 Oct 16:11:503235. doi: 10.3389/fneur.2020.503235. eCollection 2020.

Authors

Caimei Luo^{1

2

3

4}, Mengchun Li^{1

2

3

4}, Ruomeng Qin^{1

2

3

4}, Haifeng Chen^{1

2

3

4}, Lili Huang^{1

2

3

4}, Dan Yang^{1

2

3

4}, Qing Ye^{1

2

3

4}, Renyuan Liu^{1

2

3

4}, Yun Xu^{1

2

3

4}, Hui Zhao^{1

2

3

4}, Feng Bai^{1

2

3

4}

Affiliations

¹ The State Key Laboratory of Pharmaceutical Biotechnology, Department of Neurology, Affiliated Drum Tower Hospital of Medical School, Institute of Brain Science, Nanjing University, Nanjing, China.
² Jiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing, China.
³ Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.
⁴ Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China.

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.

Keywords: Alzheimer's disease; cognitive impairment; diffusion tensor imaging; support vector machine; white matter damage.