Developing scanner change invariant brain age models for aging and dementia studies
We found that training on all CU (Model B) rather than a subset CU-noVAβ (Model A) allowed for the DenseNet model to backpropagate on more variable data which lowered MAE in prediction error between scanners and lowered MAE for MCIs. Histogram matching and scanner as an input to the brain age model accounted for scanner changes and resulted in scanner invariant longitudinal brain age estimation.
Authors
- Jeffrey L. Gunter (ORCID: https://orcid.org/0000-0001-7813-9078)
- Scott A. Przybelski (ORCID: https://orcid.org/0009-0009-3437-3773)
- Siddhartha Satpathi (ORCID: https://orcid.org/0000-0001-5359-7793)
- Robel K Gebre (ORCID: https://orcid.org/0000-0002-5746-0994)
- Matthew L. Senjem (ORCID: https://orcid.org/0000-0001-9308-9275)
- Kejal Kantarci
- Clifford R. Jack
- Prashanthi Vemuri
Institutions
- Mayo Clinic in Arizona (US)
- Mayo Clinic in Florida (US)
Publication Details
- Journal
- Alzheimer s & Dementia
- Published
- 2025-12-01
- DOI
- https://doi.org/10.1002/alz70856_097891
- Primary Topic
- Dementia and Cognitive Impairment Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00