Deep-learning based multi-modal models for brain age, cognition and amyloid pathology prediction
These findings indicate that the present predictive models can identify subtle changes in brain structure, enabling precise estimates of brain age, cognitive status, and amyloid pathology. Such models could facilitate the use of MRI as a non-invasive diagnostic tool for neurodegenerative diseases, including AD.
Authors
- Chang Liu (ORCID: https://orcid.org/0000-0003-1004-590X)
- Feng Gao (ORCID: https://orcid.org/0000-0002-4902-7429)
- Mengguo Zhang
- Linbin Dai
- Qiong Wang (ORCID: https://orcid.org/0000-0002-1043-7826)
- Chenxi Wang (ORCID: https://orcid.org/0009-0003-8705-8546)
- Weiwei Zhang (ORCID: https://orcid.org/0000-0002-4314-7657)
- Yong Shen (ORCID: https://orcid.org/0000-0002-5048-8134)
- Ming Ni (ORCID: https://orcid.org/0000-0003-1357-6263)
Institutions
- University of Science and Technology of China (CN)
- Hefei National Center for Physical Sciences at Nanoscale (CN)
Publication Details
- Journal
- Alzheimer s Research & Therapy
- Published
- 2025-05-31
- DOI
- https://doi.org/10.1186/s13195-025-01773-z
- Citations
- 2
- Primary Topic
- Dementia and Cognitive Impairment Research
- Type
- article
- Field-Weighted Citation Impact
- 2.94
Funders
- Chinese Academy of Sciences
- University of Science and Technology of China