Early-Stage Identification and Pathological Development of Alzheimer’s Disease Using Multimodal MRI

Article type: Research Article

Authors: Yan, Tianyi^{a; *} | Wang, Yonghao^a | Weng, Zizheng^b | Du, Wenying^c | Liu, Tiantian^a | Chen, Duanduan^a | Li, Xuesong^d | Wu, Jinglong^e | Han, Ying^{c; f; g; h; *}

Affiliations: [a] School of Life Science, Beijing Institute of Technology, Beijing, China | [b] Daniel Felix Ritchie School of Engineering and Computer Science, University of Denver, Denver, CO, USA | [c] Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China | [d] School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China | [e] Beijing Advanced Innovation Center for Intelligent Robots and Systems; Beijing Institute of Technology, Beijing, China | [f] Center of Alzheimer’s Disease, Beijing Institute for Brain Disorders, Beijing, China | [g] Beijing Institute of Geriatrics, Beijing, China | [h] National Clinical Research Center for Geriatric Disorders, Beijing, China

Correspondence: [*] Correspondence to: Dr. Tianyi Yan, School of Life Science, Beijing Institute of Technology, 5 South Zhongguancun Street, Haidian District, Beijing 100081, China. Tel.: +86 13070188777; E-mail: [email protected] and Dr. Ying Han, Department of Neurology, XuanWu Hospital of Capital Medical University, 45 Changyi Street, Xicheng District, Beijing, China. Tel.: +86 18515692701; E-mail: [email protected].

Abstract: Alzheimer’s disease (AD) is one of the most common progressive and irreversible neurodegenerative diseases. The study of the pathological mechanism of AD and early-stage diagnosis is essential and important. Subjective cognitive decline (SCD), the first at-risk stage of AD occurring prior to amnestic mild cognitive impairment (aMCI), is of great research value and has gained our interest. To investigate the entire pathological development of AD pathology efficiently, we proposed a machine learning classification method based on a multimodal support vector machine (SVM) to investigate the structural and functional connectivity patterns of the three stages of AD (SCD, aMCI, and AD). Our experiments achieved an accuracy of 98.58% in the AD group, 97.76% in the aMCI group, and 80.24% in the SCD group. Moreover, in our experiments, we identified the most discriminating brain regions, which were mainly located in the default mode network and subcortical structures (SCS). Notably, with the development of AD pathology, SCS regions have become increasingly important, and structural connectivity has shown more discriminative power than functional connectivity. The current study may shed new light on the pathological mechanism of AD and suggests that whole-brain connectivity may provide potential effective biomarkers for the early-stage diagnosis of AD.

Keywords: Alzheimer’s disease, diffusion tensor imaging, machine learning, multimodal MRI, resting-state fMRI

DOI: 10.3233/JAD-181049

Journal: Journal of Alzheimer's Disease, vol. 68, no. 3, pp. 1013-1027, 2019