During deep sleep, water-like fluids circulate through the brain and help remove metabolic waste linked to neurological diseases such as Alzheimer’s disease. This natural cleaning process is part of the glymphatic system, which was first described in 2012 by pioneering neuroscientist Maiken Nedergaard, co-director of the Center for Translational Neuromedicine at the University of Rochester.
Although the glymphatic system plays an important role in brain health, scientists still do not fully understand how it works. One major unanswered question is how quickly fluids move through the brain. Measuring this slow circulation in a living brain is challenging because researchers need to observe fluid movement without causing permanent tissue damage.
The challenge of measuring brain fluid flow
“If you put a microscope in a small part of the brain, you can see in great detail what is happening there. We have worked with that type of data in the past, but it only captures one part of the overall process,” says Douglas Kelly, professor of mechanical engineering at the University of Rochester. “If you want to image the entire brain, MRI is an excellent option because it provides a three-dimensional view. However, MRI also has important limitations, including its inability to measure fluid-flow velocities at extremely slow speeds.”
To overcome this limitation, Kelly and researchers from the University of Rochester, Brown University, and the University of Copenhagen used artificial intelligence. In a new study published in Science Advances, the team describes a physics-based AI method for calculating fluid-flow velocity from magnetic resonance imaging (MRI) data.
The researchers trained a neural network using videos that showed dye spreading through brain tissue over time. By analyzing the movement of the dye, the AI system estimated both the speed of fluid flow and the permeability of the surrounding brain tissue.
Two very different fluid-flow speeds in the brain
The findings identify two major pathways through which the glymphatic system clears particles such as amyloid beta, a protein associated with Alzheimer’s disease. The study also shows that fluid moves at dramatically different speeds along these pathways.
In more open areas around the brain, including the space between the skull and the brain’s surface, water-like fluids move at several microns per second. However, fluid movement deep within brain tissue is significantly slower. Researchers found that this deeper flow is approximately 50 times slower than movement in the surrounding spaces.
The research team is now studying animals such as mice to establish baseline measurements of normal fluid circulation in the brain. These measurements will help researchers develop and refine AI tools for analyzing brain fluid flow. Ultimately, the scientists hope to compare circulation patterns in healthy and diseased brains, as well as in younger and older individuals.
Moving toward brain fluid-flow measurements in humans
A key long-term goal is to adapt this AI-powered MRI approach for use in people. Measuring fluid circulation in and around the human brain could create new opportunities to study neurological diseases, brain injuries, and conditions associated with impaired waste removal.
“We are working to measure the flow of water-like fluids in and around the human brain because this could make clinical applications even more important and exciting,” Kelly says. “One day, we hope to determine whether people with Alzheimer’s disease have impaired brain circulation or identify poor circulation early in life to help prevent the disease. We may also be able to investigate whether brain fluid flow is disrupted after a concussion. This study brings us one step closer.”
This research was supported by the NIH National Center for Complementary and Integrative Health and the NIH BRAIN Initiative. Kelly’s collaborators included Brown University doctoral student Juan Diego Toscano, University of Rochester computational scientist Yisen Guo, Brown University doctoral student Jibo Wang, University of Rochester doctoral student Mohammad Vaezi, Yuki Mori of the University of Copenhagen, George Karniadakis of Brown University, and Kimberly Boster of the University of Rochester.
Source: www.sciencedaily.com