Predicting Therapeutic Interventions for Resilience to Brain Aging

We will apply our AI model for human aging to understand the aging process across brain cell types and predict interventions to promote resilience to age-related brain dysfunction.

Alzheimer disease (AD) is a disease of the aging brain, where cells progress along a degenerative trajectory, leading to devastating effects on quality of life and longevity. Most studies test for interventions that shift cells towards healthy at that point in time but fall short of testing therapies for their long-term efficacy across extended lifespans. To address this, we will apply our novel artificial intelligence models to map how cells change over human brain aging and enable in silico longitudinal trials to predict treatments that promote an AD-resilient long-term trajectory.

Our proposal is unique in its application of AI to model how cells change across the long timelapses of human aging. We apply the model to a unique dataset of brain samples across the human lifespan to gain insights into the drivers of normal brain aging and predict interventions to promote resilience to age-related brain dysfunction.

The proposed work will map the key drivers of brain aging and predict rejuvenating targets to promote resilience to age-related brain dysfunction. This will represent an important advance towards disease-modifying interventions for Alzheimer disease, which would have a tremendous impact in addressing this major public health crisis that is only increasing in prevalence with the aging population.