Affiliations: [a]
School of Education, Guangzhou University, Guangzhou, China
| [b]
School of Biomedical Engineering, Shanghai Tech University, Shanghai, China
| [c]
Institute of Brain-Intelligence Technology, Zhangjiang Lab, Shanghai, China
| [d]
Department of Research and Development, United Imaging Intelligence Co., Ltd., Shanghai, China
Correspondence:
[*]
Correspondence to: Han Zhang and Dinggang Shen, School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China. Tel.: +86 21 20685265; Fax: +86 21 20685265; E-mails: zhanghan2@shanghaitech.edu.cn (H. Zhang) and dinggang.shen@gmail.com (D. Shen).
Note: [1] These authors contributed equally to this work.
Abstract: Background:The detection of amyloid-β (Aβ) deposition in the brain provides crucial evidence in the clinical diagnosis of Alzheimer’s disease (AD). However, the current positron emission tomography (PET)-based brain Aβ examination suffers from the problems of coarse visual inspection (in many cases, with 2-class stratification) and high scanning cost. Objective:1) To characterize the non-binary Aβ deposition levels in the AD continuum based on clustering of PET data, and 2) to explore the feasibility of predicting individual Aβ deposition grades with non-invasive functional magnetic resonance imaging (fMRI). Methods:1) Individual whole-brain Aβ-PET images from the OASIS-3 dataset (N = 258) were grouped into three clusters (grades) with t-SNE and k-means. The demographical data as well as global and regional standard uptake value ratios (SUVRs) were compared among the three clusters with Chi-square tests or ANOVA tests. 2) From resting-state fMRI, both conventional functional connectivity (FC) and high-order FC networks were constructed and the topological architectures of the two networks were jointly learned with graph convolutional networks (GCNs) to predict the Aβ-PET grades for each individual. Results:We found three clearly separated clusters, indicating three Aβ-PET grades. There were significant differences in gender, age, cognitive ability, APOE type, as well as global and regional SUVRs among the three grades we found. The prediction of Aβ-PET grades with GCNs on FC for the 258 samples in the AD continuum reached a satisfactory averaged accuracy (78.8%) in the two-class classification tasks. Conclusion:The results demonstrated the feasibility of using deep learning on a non-invasive brain functional imaging technique to approximate PET-based Aβ deposition grading.
