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- Title
Histopathologic brain age estimation via multiple instance learning.
- Authors
Marx, Gabriel A.; Kauffman, Justin; McKenzie, Andrew T.; Koenigsberg, Daniel G.; McMillan, Cory T.; Morgello, Susan; Karlovich, Esma; Insausti, Ricardo; Richardson, Timothy E.; Walker, Jamie M.; White 3rd, Charles L.; Babrowicz, Bergan M.; Shen, Li; McKee, Ann C.; Stein, Thor D.; Farrell, Kurt; Crary, John F.
- 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.
- Subjects
ALZHEIMER'S disease; AGE; NEUROANATOMY; DEEP learning; PHYSIOLOGY
- Publication
Acta Neuropathologica, 2023, Vol 146, Issue 6, p785
- ISSN
0001-6322
- Publication type
Article
- DOI
10.1007/s00401-023-02636-3