When we sleep deeply, a watery liquid moves around our brain. This liquid clears away waste that can cause diseases like Alzheimer’s. The cleaning system is called the glymphatic system, first described in 2012.
Scientists still do not fully know how fast this liquid travels. Watching the flow inside a living brain is hard because the movement is very slow and any tool must not damage the tissue.
The Trouble with Measuring Brain Liquids
“A microscope can show tiny details on a small brain patch, but it can’t see the whole picture,” says Professor Douglas Kelley from the University of Rochester. “MRI gives us a 3‑D view, but it can’t tell us the speed of such slow flow.”
To fix this, Kelley teamed up with researchers from Brown University and the University of Copenhagen. They used a special kind of artificial intelligence that learns from physics. The AI looked at MRI scans and guessed how fast the liquid was moving.
First, the team taught the AI using videos that showed a colored dye spreading through brain tissue. By watching the dye, the AI learned to estimate both the flow speed and how easily the liquid passes through the tissue.
Two Very Different Speeds
The study found two main routes for the brain’s cleanup. Near the surface, between the skull and the brain, the liquid moves a few microns each second. Deeper inside, the flow is about 50 times slower.
Researchers are now measuring these speeds in mice to set a baseline. This data helps improve the AI model. In the future, they hope to compare healthy brains with diseased ones and see how age changes the flow.
Looking Ahead to Human Brains
The big goal is to use this method on people. If doctors can watch fluid flow in human brains, they could find new ways to study Alzheimer’s, brain injuries, and other disorders.
“We want to see if an Alzheimer’s patient has poor fluid circulation or if we can spot problems early,” says Kelley. “We could also check concussion patients to see if their brain flow is disrupted.” This research brings that vision a step closer.
The project was funded by the NIH National Center for Complementary and Integrative Health and the NIH BRAIN Initiative.