Cortical Graph Neural Networks to Predict Dementia Risk Based on MRI‐Derived Cortical Surface Morphonology
This project demonstrated the effectiveness of using cortical GCN to achieve early prediction for future dementia onset for subjects with risk of AD. Future work will focus on independent validation of NACC/ADRC data to evaluate the model's generalizability, as well as enhance the model's explainability through techniques such as Grad-CAM and integrated gradient. These efforts will provide deeper insights into AD's neuroanatomical aspects and contribute to developing more accurate, transparent, and effective diagnostic tools.
Authors
- Suzanne Craft (ORCID: https://orcid.org/0000-0003-0591-8657)
- Marc D. Rudolph (ORCID: https://orcid.org/0000-0001-7046-1169)
- Sheng Luo (ORCID: https://orcid.org/0000-0003-4214-5809)
- Metin N. Gürcan (ORCID: https://orcid.org/0000-0002-2421-8229)
- Da Ma (ORCID: https://orcid.org/0000-0001-8628-3029)
- Trey Bateman
- Harinishree Sathu
- Timothy M. Hughes
- Guanlin Guo
Institutions
- Georgia Institute of Technology (US)
- Duke University (US)
- Wake Forest University (US)
- The University of Texas Health Science Center at Houston (US)
Publication Details
- Journal
- Alzheimer s & Dementia
- Published
- 2025-12-01
- DOI
- https://doi.org/10.1002/alz70862_109938
- Primary Topic
- Dementia and Cognitive Impairment Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00