Optimizing Harmonized Cortical Morphometric Graph Neural Networks (GNN) For Identifying Risk of Alzheimer’s Disease

Research conducted during my internship at Wake Forest University, Center for Artificial Intelligence Research.

This poster presents work on optimizing graph neural network architectures for predicting Alzheimer’s disease risk using harmonized cortical morphometric features. The approach addresses challenges in multi-site neuroimaging data and improves predictive performance for early disease detection.

Recommended citation: Liu, I., Guo, E., Sathu, H., Rudolph, M. D., Bateman, J. R., Hughes, T. M., Craft, S., Metin, G., Luo, S., & Ma, D. (2025). Optimizing Harmonized Cortical Morphometric Graph Neural Networks (GNN) For Identifying Risk of Alzheimer's Disease. Poster presented at the Alzheimer's Association International Conference (AAIC).