Histopathologic brain age estimation via multiple instance learning.
Acta Neuropathol
; 146(6): 785-802, 2023 12.
Article
en En
| MEDLINE
| ID: mdl-37815677
ABSTRACT
Understanding age acceleration, the discordance between biological and chronological age, in the brain can reveal mechanistic insights into normal physiology as well as elucidate pathological determinants of age-related functional decline and identify early disease changes in the context of Alzheimer's and other disorders. Histopathological whole slide images provide a wealth of pathologic data on the cellular level that can be leveraged to build deep learning models to assess age acceleration. Here, we used a collection of digitized human post-mortem hippocampal sections to develop a histological brain age estimation model. Our model predicted brain age within a mean absolute error of 5.45 ± 0.22 years, with attention weights corresponding to neuroanatomical regions vulnerable to age-related changes. We found that histopathologic brain age acceleration had significant associations with clinical and pathologic outcomes that were not found with epigenetic based measures. Our results indicate that histopathologic brain age is a powerful, independent metric for understanding factors that contribute to brain aging.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Encéfalo
/
Envejecimiento
Tipo de estudio:
Prognostic_studies
Límite:
Child, preschool
/
Humans
Idioma:
En
Revista:
Acta Neuropathol
Año:
2023
Tipo del documento:
Article
País de afiliación:
Estados Unidos