Anatomically interpretable deep learning of brain age captures domain-specific cognitive impairment
Abstract
The phenotypic age of the human brain, as revealed via deep learning of anatomic magnetic resonance images, reflects patterns of structural change related to cognitive decline. Our interpretable deep learning estimates that the brain ages more accurately than any other approaches to date. Furthermore, compared to chronological age, our inferred brain ages are significantly more strongly associated with early signs of Alzheimer's disease. Maps conveying the importance of each brain region for estimating brain age reveal differences in patterns of neurological aging between males and females and between persons with and without cognitive impairment. These findings provide insight into early identification of persons at high risk of Alzheimer's disease.
- Publication:
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Proceedings of the National Academy of Science
- Pub Date:
- March 2023
- DOI:
- 10.1073/pnas.2214634120
- Bibcode:
- 2023PNAS..12014634Y