Now at Princeton, @clairebedbrook.bsky.social and @ravi-nath.bsky.social led a study during their time as postdocs at Stanford that mapped the full arc of aging in individual vertebrates for the first time, finding that activity levels in young adults may serve as an early predictor of longevity.
On today's podcast, we talk with neuroscientists Claire Bedbrook and Ravi Nath about their new study, which found that an animal's lifespan can be predicted surprisingly early by observing its behavior. @brunetlab.bsky.social @deisseroth.bsky.social
🔗 neuroscience.stanford.edu/news/why-do-...
These findings suggest an architecture of vertebrate aging where adulthood progresses through a sequence of ordered behavioral stages.
Behavior provides a powerful, non-invasive readout of the aging process.
One interesting result:
Early-life behavior predicted future lifespan.
Using machine learning, we could identify animals likely to become short-lived vs long-lived long before death.