This repository contains the data and scripts required to reproduce the results of the paper titled: Ultra-fast MRI for brain-age prediction in a real-world cognitive disorders clinic. IDs are obfuscated for privacy reasons.
The models used in the paper can be found at:
The model weights and training scripts for MCCQR_MLP are available in this repository. It builds upon the work of:
Hahn, Tim, et al. "An uncertainty-aware, shareable, and transparent neural network architecture for brain-age modeling." Science advances 8.1 (2022): eabg9471. DOI: 10.1126/sciadv.abg9471
Read the paper at:
Navarro-González, R., de Luis-García, R., Aja-Fernández, S., Liu, W., Alexander, D. C., Barkhof, F., Beament, M., Chughtai, H. R., Fox, N. C., Mummery, C. J., Rosa-Grilo, M., Thomas, D. L., Parker, G. J. M., & Cole, J. H. (2026). Ultra-fast MRI for brain-age prediction in a real-world cognitive disorders clinic. Frontiers in Aging Neuroscience, 18, 1731909. https://doi.org/10.3389/fnagi.2026.1731909
BibTeX
@article{navarro2026ultra,
title = {Ultra-fast MRI for brain-age prediction in a real-world cognitive disorders clinic},
author = {Navarro-Gonz{\\'a}lez, Rafael and
de Luis-Garc{\\'i}a, Rodrigo and
Aja-Fern{\\'a}ndez, Santiago and
Liu, Wei and
Alexander, Daniel C. and
Barkhof, Frederik and
Beament, Millie and
Chughtai, Haroon R. and
Fox, Nick C. and
Mummery, Catherine J. and
Rosa-Grilo, M. and
Thomas, D. L. and
Parker, G. J. M. and
Cole, J. H.},
journal = {Frontiers in Aging Neuroscience},
volume = {18},
pages = {1731909},
year = {2026},
publisher = {Frontiers Media SA},
doi = {10.3389/fnagi.2026.1731909},
url = {https://doi.org/10.3389/fnagi.2026.1731909}
}