Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
October 11, 2025NeuroImageOpen Access

MR-Guided Graph Learning of 18F-Florbetapir PET Enables Accurate and Interpretable Alzheimer’s Disease Staging

View Full Paper
Ask AI
Bookmark
Share

Authors

XCXinyi ChenShanghai Jiao Tong UniversityLCLijuan ChenLinyi UniversityWYWeiheng YaoShenzhen Institutes of Advanced Technology

Discussion

Loading...

Member takes

Overview

Retrospective analysis demonstrates improved accuracy in Alzheimer's staging using graph convolutional networks, suggesting enhanced early detection strategies.

Key Points

  • The graph learning framework achieved AUCs of 89.8% for distinguishing MCI from normal controls, indicating precise diagnostic capabilities.
  • Using receiver operating characteristic analysis, the method significantly outperformed cortical SUVR with p<0.001, highlighting its clinical relevance.
  • The GCN_score provided superior group differentiation compared to cortical SUVR, showcasing the innovative approach's effectiveness in differentiating Alzheimer's stages.
  • This novel methodology integrates PET and MRI features, offering a promising avenue for early detection and timely intervention in Alzheimer's disease.

Cite This Study

Chen et al. (2025) studied this question.

synapsesocial.com/papers/68ea72339f1bd4df558cede3https://doi.org/10.1016/j.neuroimage.2025.121510
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Recurrent Graph Attention over Longitudinal Brain Networks Predicts Conversion from Mild Cognitive Impairment to Alzheimer’s Disease2026
  2. 2A novel spatiotemporal graph convolutional network framework for functional connectivity biomarkers identification of Alzheimer’s disease2024 · 8 citations
  3. 3Advancing Alzheimer's Disease Detection Harnessing Graph Convolutional Networks For Enhanced Early Identification2024 · 2 citations
  4. 4Early diagnosis of Alzheimer’s Disease: Graph theoretical analysis of cerebellar network features based on 18F-AV45 PET2026
  5. 5Feature integration of [18F]FDG PET brain imaging using deep learning for sensitive cognitive decline detection2026