Predicting and promoting resilient brain aging trajectories (renewed)
This project combines brain circuit and molecular approaches with artificial intelligence (machine learning) to understand, predict, and manipulate individual differences by stratifying their behavioral trajectories, which can potentially be transformative in identifying ways to promote brain resilience. This is an extension from a Catalyst Grant Award.
Research Summary
Predicting and promoting resilient brain aging trajectories
We plan to understand how brain aging trajectories differ between individuals, with the goal
of boosting brain resilience. Excitingly, our data obtained during the Catalyst phase of this award have shown that it is possible to predict future aging trajectories based solely on early non-invasive recording, making it feasible for the first time to identify and manipulate different brain aging trajectories. These experiments have also enabled us to identify specific features of cognitive that are predictive of youthful states. In parallel, we have also started to dissect the molecular features involved in aging, including transcriptomics and proteomics changes. In this highly collaborative project between the Brunet, Deisseroth, Jarosz, and Linderman labs at Stanford, we want to 1) Identify the molecular features linked to cognitive fitness throughout life and 2) Uncover the brain circuitry associated with longevity to boost brain resilience during aging. Results from these studies should have a critical impact in understanding the mechanisms underlying different brain aging trajectories and identifying new strategies to boost brain resilience.
