A Machine-Learning Approach Identifies Rejuvenating Interventions in the Human Brain

The increase in life expectancy has caused a rise in age-related brain disorders. Although brain rejuvenation is a promising strategy to counteract brain functional decline, systematic discovery methods for efficient interventions are lacking. We developed a computational platform based on a transcriptional brain aging clock capable of detecting age- and neurodegeneration-related changes. Applied to neurodegeneration positive samples, it revealed that neurodegenerative disease presence and severity significantly increased predicted age. By screening 43840 transcriptional profiles of chemical and genetic perturbations, it identified 453 unique rejuvenating interventions, several of which are known to extend lifespan in animal models. Additionally, the identified interventions included drugs already used to treat neurological disorders, Alzheimer's disease among them. A combination of compounds predicted by our platform reduced anxiety, improved memory and rejuvenated the brain cortex transcriptome in aged mice. These results demonstrate our platform's ability to identify brain-rejuvenating interventions, offering potential treatments for neurodegenerative diseases.