Abstract Details

Title Histopathologic Brain Age Estimation via Deep Multiple Instance Learning

Topic Aging, Dementia, and Behavioral Neurology

Presentation(s) P3 - Poster Session 3 (5:30 PM-6:30 PM)

Poster/Presentation
Number 7-008

Objective To identify features of histologic brain aging and clinical correlates of brain age acceleration.

Background The discordance between cellular and chronologic aging is useful for understanding diseases in the brain and biology at large. One established method for analyzing the factors that contribute to brain aging is to train machine learning models that predict an individual’s age based on an MRI image of their brain. While this approach has yielded important insights, it is inherently constrained by the information provided by an MRI. However, age-dependent pathologic change has the potential to be assessed at greater detail histologically. Histopathological whole slide images provide more granular information regarding cellular structure, injury, dysfunction, and morphology. Recent technological advances in whole slide image digitization has paved the way for large scale analysis of histologic data via artificially intelligent based computer vision techniques.

Design/Methods We leveraged a large novel collection of uniformly processed digitized human post-mortem brain tissue sections to create a histological brain age estimation model. We further investigated the effect of cognitive impairment and exogenous stress on the model. This was accomplished by developing a context-aware attention-based deep multiple instance learning model on 702 human brain tissues sections (ages 50-110 yr) from the hippocampus stained with Luxol Fast Blue counterstained with hematoxylin and eosin on a brain age estimation task.

Results This model estimated brain age within a mean absolute error of 5.2 years. Learned attention weights corresponded to neuroanatomical regions vulnerable to age-related change. We found that histopathologic brain age acceleration had significant associations with cognitive impairment, MMSE, p-tau burden, chronic traumatic encephalopathy, and cerebrovascular disease. These associations were not found when using epigenetic-based measures of age acceleration.

Conclusions These data indicate that estimated histopathologic brain age can be used as a reliable pathologic correlate to identify factors that contribute to accelerated or decelerated brain aging.

Authors/Disclosures
PRESENTER	No disclosure on file
	No disclosure on file
	No disclosure on file
	No disclosure on file
	No disclosure on file
John Crary	John Crary has nothing to disclose.

��ɫ��

��ɫ��