The aging brain experiences structural and cellular changes, impacting its function and susceptibility to neurodegenerative disorders like Alzheimer’s disease. Age acceleration, the variance between biological and chronological age, provides insights into brain mechanisms and normal functions.
Mount Sinai researchers have introduced “HistoAge,” an AI-driven algorithm predicting age at death based on brain tissue composition with remarkable 5.45-year accuracy. It also identifies brain regions vulnerable to age-related changes, signifying potential cognitive diseases.
Researchers analyzed 700 digitized images of aged brain specimens, focusing on the hippocampus due to its relevance to aging and neurodegenerative diseases. A machine learning model estimated age at death and calculated age acceleration by comparing predicted and actual ages.
Comparison with existing age acceleration measures revealed HistoAge’s strongerassociations with cognitive impairment, cerebrovascular disease, and Alzheimer’s-related protein aggregation. HistoAge offers a reliable metric for understanding neurodegeneration and can be widely deployed in research laboratories.
This groundbreaking research paves the way for an entirely new paradigm in assessing aging and neurodegeneration using AI-ready datasets. AI’s potential to enhance our understanding of brain diseases is evident, offering hope for future cures.