Predicting Brain Amyloid-β PET Grades with Graph Convolutional Networks Based on Functional MRI and Multi-Level Functional Connectivity

J Alzheimers Dis. 2022;86(4):1679-1693. doi: 10.3233/JAD-215497.


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.

Keywords: Amyloid-β; brain network; functional connectivity; graph convolutional neural network; positron emission tomography.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Alzheimer Disease* / pathology
  • Amyloid beta-Peptides* / metabolism
  • Brain / pathology
  • Humans
  • Magnetic Resonance Imaging / methods
  • Positron-Emission Tomography / methods


  • Amyloid beta-Peptides