Welcome to the Stanford Longevity, Equity, Accessibility, multi-Racial coNsortium (LEARN)
LEARN is a multi-institutional consortium advancing longevity and healthy aging for diverse populations
Highlights
Keynote address:
Lloyd Minor, MD, Dean of the Stanford University School of Medicine
Junior Investigator Accomplishments:
Research projects solving pressing problems in aging
Apply now for an NIH Pilot Award
The LEARN consortium is currently accepting applications for our annual pilot grant program on longevity and healthy aging topics. Each award is for $50,000. The applications are due in January 2024 and funding will start on 07/01/2024.
Center News
Virtual reality helps people with hoarding disorder practice decluttering
A first-of-its-kind study by Stanford Medicine researchers lets patients practice letting go of treasured objects in simulations of their own homes. A virtual reality simulation of a patient’s home can provide “a kind of stepping stone” toward discarding real-life possessions, according to Carolyn Rodriguez. A pilot study suggests that virtual reality therapy allows those with hoarding disorder to rehearse relinquishing possessions in a simulation of their own home could help them declutter in real life. The simulations can help patients practice organizational and decision-making skills learned in cognitive behavioral therapy. The study was published in the October issue of the Journal of Psychiatric Research.
Machine learning prediction of mild cognitive impairment and its progression to Alzheimer’s disease
Health Science Report 2023: Effective screening for mild cognitive impairment (MCI) as a risk factor for developing Alzheimer’s disease is a crucial step in helping aging population with their needs Early detection and automated screening for MCI and dementia could offer opportunities for deliberate study and recruitment into trials for developing other potentially useful therapeutics or interventions.
The study was first published in October. The results illustrate that it is possible to predict MCI onset and AD progression with moderate levels of accuracy, which suggests an opportunity for population-wide screening mechanisms to identify patients at potential risk, who could then undergo more specific evaluation to consider early treatment or recruitment into clinical trials.
Our Awardees
Click on the profiles to read their biography.