AI reveals a striking two-speed cleanup system that helps the sleeping brain clear away harmful waste.
During deep sleep, a waterlike fluid moves through and around the brain, helping remove metabolic waste associated with disorders including Alzheimer’s disease. This cleanup process is known as the glymphatic system. It was first described in 2012 by Maiken Nedergaard, a pioneering neuroscientist and co-director of the University of Rochester Center for Translational Neuromedicine.
Although scientists have learned a great deal about the glymphatic system, major questions remain about how it actually works. One of the biggest uncertainties is the speed at which fluid travels through different parts of the brain. Measuring such slow circulation in a living brain is especially challenging because researchers need to observe it without causing permanent damage.
The Challenge of Measuring Brain Fluid Flow
“You can put a microscope on a small patch of the brain and watch what’s happening there with a lot of detail, and we’ve worked with that type of data in the past, but it’s only a tiny view of the overall process,” says Professor Douglas Kelley from URochester’s Department of Mechanical Engineering. “If you want to image whole brains, an MRI is a great approach because it gives you a three-dimensional view. But an MRI has serious limitations too, the biggest of which is that it does not capture the fluid flow velocity, at least not for flows this slow.”
To overcome that limitation, Kelley and researchers from URochester, Brown University, and the University of Copenhagen turned to artificial intelligence. Their new study, published in Science Advances, describes a method that uses physics-informed artificial intelligence to extract fluid flow speeds from magnetic resonance imaging (MRI) data.
The researchers trained neural networks using videos that showed dye spreading through brain tissue over time. From those changes, the AI models could estimate both the speed of the fluid and the permeability of the surrounding brain tissue.
The Brain’s Cleanup System Has Two Speeds
The results revealed two major pathways by which the glymphatic system helps remove particles from the brain, including amyloid beta proteins associated with Alzheimer’s disease. The researchers found that these pathways operate at dramatically different speeds.
In more open areas around the brain, including the region between the skull and the brain’s surface, the waterlike fluid travels at a few microns per second. Deeper inside brain tissue, however, the fluid moves far more slowly, flowing at a rate roughly 50 times lower.
This large difference in speed gives researchers a clearer picture of how waste may be transported through different brain environments. Fluid can move relatively quickly through open spaces, while its movement through dense brain tissue is much more gradual.
From Animal Brains to Human Health
For now, the research team is working to establish baseline measurements of brain fluid flow in animals such as mice. Those measurements are helping researchers refine and improve the AI tools.
Eventually, they hope to compare glymphatic circulation in healthy and diseased brains, as well as in young and old brains. A major long-term goal is to extend the technique to humans.
“We’re working hard toward being able to measure the flow of waterlike fluids in and around human brains because then the clinical applications get a lot more important and exciting,” says Kelley. “We hope to someday be able to see whether an Alzheimer’s patient has poor circulation in their brain or even screen for poor circulation earlier in life to try to stave off Alzheimer’s. Or we could check when somebody has been concussed to see whether the fluid circulation in their brain is disrupted. This study gets us a step closer.”
If researchers can eventually measure these slow fluid movements reliably in people, the technique could offer a new way to investigate whether the brain’s natural waste-clearing circulation changes with Alzheimer’s disease, aging, or traumatic brain injury.
Reference: “MR-AIV reveals in vivo brain-wide fluid flow with physics-informed AI” by Juan Diego Toscano, Yisen Guo, Zhibo Wang, Mohammad Vaezi, Yuki Mori, George Em Karniadakis, Kimberly A. S. Boster and Douglas H. Kelley, 27 May 2026, Science Advances.
DOI: 10.1126/sciadv.aeb0404
The research is supported by the NIH National Center for Complementary and Integrative Health and the NIH BRAIN Initiative.
Kelley’s collaborators on the study include Brown University PhD student Juan Diego Toscano, URochester computational scientist Yisen Guo, Brown University PhD student Zhibo Wang, URochester PhD student Mohammad Vaezi, University of Copenhagen Associate Professor Yuki Mori, Brown University Professor George Karniadakis, and URochester Assistant Professor Kimberly Boster.
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