Forecasting individualized progression of Alzheimer’s disease using structural MRI and population spatiotemporal priors
Incorporating group-level progression priors into the generative model can significantly improve the accuracy and anatomical fidelity of predicted MRIs, enhance the visualization of disease progression at the voxel level, and advance the development of precision treatment for AD.
Authors
- Songze Li (ORCID: https://orcid.org/0000-0003-3168-644X)
- Tongtong Che (ORCID: https://orcid.org/0000-0003-4993-0586)
- Yan Zhao
Institutions
- The University of Sydney (AU)
- University of Shanghai for Science and Technology (CN)
- Beijing Normal University (CN)
Publication Details
- Journal
- Frontiers in Aging Neuroscience
- Published
- 2026-02-04
- DOI
- https://doi.org/10.3389/fnagi.2026.1691084
- Primary Topic
- Functional Brain Connectivity Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- U.S. Department of Defense
- Alzheimer's Disease Neuroimaging Initiative
- National Natural Science Foundation of China
- Beijing Normal University
- China Scholarship Council
- National Institutes of Health