上海科技大学知识管理系统

["Background: The detection of amyloid-beta (A beta) 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 A beta 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 A beta deposition levels in the AD continuum based on clustering of PET data, and 2) to explore the feasibility of predicting individual A beta deposition grades with non-invasive functional magnetic resonance imaging (fMRI).","Methods: 1) Individual whole-brain A beta-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 A beta-PET grades for each individual.","Results: We found three clearly separated clusters, indicating three A beta-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 A beta-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 A beta deposition grading."]