Enhancing Brain Age Estimation with a Multimodal 3D CNN Approach Combining Structural MRI and AI-Synthesized Cerebral Blood Volume Measures Jordan Jomsky, Kay C. Igwe, Zongyu Li, Yiren Zhang, Max Lashley, Tal Nuriel, Andrew Laine, Jia Guo, et al.
Official preprint: https://arxiv.org/abs/2412.01865
This repository contains a VGG-style 3D CNN architecture for Brain Age estimation from T1-weighted MRI and AI-Synthesized Cerebral Blood Volume data and two pretrained weight files (BrainAGE_T1_Model_Weights.pkl, T1-only model and BrainAGE_AICBV_Model_Weights.pkl, AICBV-only model).
The multimodal approach (T1 + AICBV) improves predictive performance (MAE ≈ 3.95 years, R² ≈ 0.943 on held-out test set) vs. unimodal T1 or AICBV models . The project and manuscript were developed in Jia Guo’s Lab at Columbia University. Contact: Jia Guo, jg3400@columbia.edu.
If you use this code or models, please cite the arXiv preprint: Jomsky J, Igwe KC, Li Z, Zhang Y, Lashley M, Nuriel T, Laine A, Guo J, et al. Enhancing Brain Age Estimation with a Multimodal 3D CNN Approach Combining Structural MRI and AI-Synthesized Cerebral Blood Volume Measures. Preprint. arXiv:2412.01865. 2024. https://arxiv.org/abs/2412.01865
This study used and aggregated many public neuroimaging datasets (ADNI, AIBL, OASIS, IXI, PPMI, BGSP, SLIM, DLBS, SALD, CoRR, SchizConnect, and FTLDNI). See the manuscript for the complete acknowledgements and investigator lists.