Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
460-02-013 ISMRM Abstract

BrainAGE Modeling from In Vivo T1w MRI in Mice Across the Lifespan with Deep Learning

Primary: Analysis Methods - Classification and Prediction
Secondary: Neuro - Aging
460-02-013 · From Bench to Bedside: All About the Brain in Health and Alzheimer’s Disease · Tuesday, 12 May, 9:15 AM–10:10 AM · Digital Posters Row A
Keywords: Brain aging Structural T1 MRI Animal mouse model Contrastive Learning Brain age prediction
Accepted
Zongyu Li 1, Taisheng Wu2, Tal Nuriel3, Jia Guo1,4
1Biomedical Engineering, Columbia University, New York, United States of America
2William A. Shine Great Neck South High School, Lake Success, United States of America
3Pathology & Cell Biology, Columbia University Irving Medical Center, New York, United States of America
4Psychiatry, Columbia University, New York, United States of America
Presenting Author: Zongyu Li

Synopsis

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References

1. Elder GA, Gama Sosa MA, De Gasperi R. Transgenic mouse models of Alzheimer’s disease. Mt Sinai J Med. 2010;77(1):69-81.
2. Franke K, Ziegler G, Klöppel S, Gaser C. Estimating the age of healthy subjects from T1-weighted MRI using kernel methods. NeuroImage. 2010;50:883-892.
3. Feng X, Lipton ZC, Yang J, Small SA, Provenzano FA. Estimating brain age with deep learning and MRI. Neurobiol Aging. 2020;91:15-25.
4. Feng X, Guo J, Sigmon HC, et al. Brain regions vulnerable and resistant to aging without Alzheimer’s disease. PLoS One. 2020;15(7):e0234255.
5. Jordan J, Zongyu L, Yiren Z, et al. Enhancing Brain Age Estimation with a Multimodal 3D CNN Approach Combining Structural MRI and AI-Synthesized CBV. *arXiv:*2412.01865.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/460-02-013