AI Maps Brain Fluid Flow, Revealing How the Glymphatic System Clears Alzheimer's-Linked Waste

Mapping the Glymphatic System with AI - Neuroscience News

Summary: A multidisciplinary neuroengineering study has broken a major imaging barrier by mapping the exact flow velocity of the brain's waste-clearing infrastructure. The research utilizes physics-informed artificial intelligence to decode magnetic resonance imaging (MRI) data, revealing the hidden mechanics of the glymphatic system, the fluid network that washes away metabolic wastes like the amyloid-beta proteins linked to Alzheimer's disease. The AI models uncovered a dual-speed drainage blueprint, demonstrating that protective fluid moves 50 times faster across the brain's outer surfaces than it does when trickling through deep brain tissue. When a person goes into deep sleep, waterlike fluid circulates around the brain, washing away metabolic waste that is linked to diseases such as Alzheimer's. This process, known as the glymphatic system, was first described in 2012 by Maiken Nedergaard -- a pioneering neuroscientist and co-director of the University of Rochester Center for Translational Neuromedicine. But questions remain about the system's mechanics -- notably, how quickly the fluid circulates around the brain. Studying the circulation within a living brain is difficult to do without causing irreparable harm to a subject. "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." Kelley and his colleagues from URochester, Brown University, and the University of Copenhagen turned to artificial intelligence for help. In a new study published in Science Advances, they outline how they used physics-informed artificial intelligence to determine fluid flow velocities from magnetic resonance imaging (MRI) data. Using videos of dye spreading across brain tissue over time, the neural networks the researchers built were able to deduce how fast the fluid flows and how permeable the brain tissue is. The results showed that there are two main ways that the glymphatic system washes away particles in the brain such as the amyloid beta proteins linked to Alzheimer's disease -- and one of these ways is much faster than the other. The fast flow of the glymphatic system's waterlike fluid moves at a few microns per second around the brain's open regions such as the surface between the skull and the brain, while the slower flow of the waterlike fluid trickles through the brain's deep tissue at a rate about 50 times slower. So far, the researchers have been working to get baseline measurements of fluid flow in the brains of animals such as mice to inform the AI tools. In the future, they hope to be able to compare the fluid flow in healthy and sick brains as well as young and old brains, with aspirations to eventually study circulation in 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." Funding: 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. Author: Luke Auburn Source: University of Rochester Contact: Luke Auburn - University of Rochester Image: The image is credited to Neuroscience News Original Research: Open access. "MR-AIV reveals in vivo brain-wide fluid flow with physics-informed AI" byJuan Diego Toscano, Yisen Guo, Zhibo Wang, Mohammad Vaezi, Yuki Mori, George Em Karniadakis, Kimberly A. S. Boster, and Douglas H. Kelley. Science Advances DOI:10.1126/sciadv.aeb0404 Abstract MR-AIV reveals in vivo brain-wide fluid flow with physics-informed AI The circulation of cerebrospinal and interstitial fluid plays a vital role in clearing metabolic waste from the brain, and its disruption has been linked to neurological disorders. However, directly measuring brain-wide fluid transport, especially in the deep brain, has remained elusive. Here, we introduce magnetic resonance artificial intelligence velocimetry (MR-AIV), a framework featuring a specialized physics-informed architecture and optimization method that reconstructs three-dimensional fluid velocity fields from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). MR-AIV unveils brain-wide velocity maps while providing estimates of tissue permeability and pressure fields, quantities inaccessible to other methods. Applied to the brain, MR-AIV reveals a functional landscape of interstitial and perivascular flow, quantitatively distinguishing slow diffusion-driven transport [∼0.1 micrometers per second (μm/s)] from rapid advective flow (∼3 μm/s). This approach enables new investigations into brain clearance mechanisms and fluid dynamics in health and disease, with broad potential applications to other porous medium systems, from geophysics to tissue mechanics.

Artificial intelligence reveals hidden fluid flow patterns in sleeping brains

University of RochesterMay 27 2026 When a person goes into deep sleep, waterlike fluid circulates around the brain, washing away metabolic waste that is linked to diseases such as Alzheimer's. This process, known as the glymphatic system, was first described in 2012 by Maiken Nedergaard-a pioneering neuroscientist and co-director of the University of Rochester Center for Translational Neuromedicine. But questions remain about the system's mechanics-notably, how quickly the fluid circulates around the brain. Studying the circulation within a living brain is difficult to do without causing irreparable harm to a subject. 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. 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." Professor Douglas Kelley from URochester's Department of Mechanical Engineering Kelley and his colleagues from URochester, Brown University, and the University of Copenhagen turned to artificial intelligence for help. In a new study published in Science Advances, they outline how they used physics-informed artificial intelligence to determine fluid flow velocities from magnetic resonance imaging (MRI) data. Using videos of dye spreading across brain tissue over time, the neural networks the researchers built were able to deduce how fast the fluid flows and how permeable the brain tissue is. The results showed that there are two main ways that the glymphatic system washes away particles in the brain such as the amyloid beta proteins linked to Alzheimer's disease-and one of these ways is much faster than the other. The fast flow of the glymphatic system's waterlike fluid moves at a few microns per second around the brain's open regions such as the surface between the skull and the brain, while the slower flow of the waterlike fluid trickles through the brain's deep tissue at a rate about 50 times slower. So far, the researchers have been working to get baseline measurements of fluid flow in the brains of animals such as mice to inform the AI tools. In the future, they hope to be able to compare the fluid flow in healthy and sick brains as well as young and old brains, with aspirations to eventually study circulation in 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." 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. University of Rochester Journal reference: Toscano, J. D., et al. (2026). MR-AIV reveals in vivo brain-wide fluid flow with physics-informed AI. Science Advances. DOI: 10.1126/sciadv.aeb0404. https://www.science.org/doi/10.1126/sciadv.aeb0404

AI shows how your brain cleans out harmful waste

A new approach combines MRI scans and AI tools to measure fluid flow linked to diseases such as Alzheimer's. When a person goes into deep sleep, water-like fluid circulates around the brain, washing away metabolic waste linked to diseases such as Alzheimer's. This process, known as the glymphatic system, was first described in 2012 by Maiken Nedergaard -- a pioneering neuroscientist and codirector of the University of Rochester's Center for Translational Neuromedicine. But questions remain about the system's mechanics -- notably, how quickly the fluid circulates. Studying the circulation within a living brain is difficult without causing irreparable harm to a subject. "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 mechanical engineering department. "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." Kelley and his colleagues turned to artificial intelligence for help. In a new study in Science Advances, they outline how they used physics-informed AI to determine fluid flow velocities from magnetic resonance imaging (MRI) data. Using videos of dye spreading across brain tissue over time, the neural networks the researchers built were able to deduce how fast the fluid flows and how permeable the brain tissue is. The results showed that there are two main ways that the glymphatic system washes away particles in the brain such as the amyloid beta proteins linked to Alzheimer's disease -- and one of these ways is much faster than the other. The fast flow of the glymphatic system's waterlike fluid moves at a few microns per second around the brain's open regions such as the surface between the skull and the brain, while the slower flow of the waterlike fluid trickles through the brain's deep tissue at a rate about 50 times slower. So far, the researchers have been working to get baseline measurements of fluid flow in the brains of animals such as mice to inform the AI tools. In the future, they hope to be able to compare the fluid flow in healthy and sick brains as well as young and old brains, with aspirations to eventually study circulation in 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." Additional collaborators on the study are from Brown University, URochester, and University of Copenhagen. The NIH National Center for Complementary and Integrative Health and the NIH BRAIN Initiative supported this research.

AI reveals how the brain clears harmful waste during sleep

Engineers routinely measure how fluids move through pipes, rivers, and blood vessels. But tracking the fluid that carries waste out of the brain has proved far more difficult. The fluid that cleans the brain during sleep moves far too slowly for any standard scanner to track. No one had measured its speed deep inside a living animal - until now. Washing the sleeping brain Scientists call this overnight cleaning network the glymphatic system. During deep sleep, waterlike fluid moves through the brain, carrying away the debris generated by a day's worth of activity. Neuroscientist Maiken Nedergaard first described the system in 2012, and the stakes became clear quickly. Among the waste it clears are sticky amyloid beta proteins - the same fragments that clump into plaques in the brains of people with Alzheimer's. Falter in that cleanup and the junk piles up. A growing body of research ties weak clearance of these proteins to the disease, which leaves one basic question unanswered: how fast does the fluid actually move? The measurement problem Seeing that flow is harder than it sounds. Douglas Kelley, a professor of mechanical engineering at the University of Rochester, has spent years on brain fluids, and every available tool came with a catch. A microscope shows a tiny patch of tissue in fine detail, but only that patch. An MRI scan captures a whole brain in three dimensions, yet goes blind to motion this slow. "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," said Kelley. Peering directly into a live brain, meanwhile, risks damaging it. Teaching AI to see In collaboration with an international team, Professor Kelley handed the problem to artificial intelligence. Not the chatbot kind. They built neural networks with the laws of fluid motion wired into them, so the system couldn't invent physics that doesn't exist. Then they fed it what an MRI can record: videos of injected dye seeping through brain tissue over time. From how the dye spread, frame by frame, the networks deduced the flow believed to push it. Out came a three-dimensional map of the moving fluid - its speed at every point, the pressure nudging it along, and how easily it slipped through each kind of tissue. A scanner measures none of that on its own. Two very different speeds The map held a surprise. Rather than one steady current, the fluid appeared to move at two very different speeds, depending on where in the brain it sat. Near the surface, in the gap between the skull and the brain, it moved fastest, at a few microns per second. That is slow by any ordinary measure, yet inside a brain it counts as a rush. Deep in the dense tissue it barely budged - roughly 50 times slower, a faint seep rather than a flow. Two systems, then, sharing one brain and doing the same job at wildly different rates. No one had measured this inside a whole living brain before. A landmark study confirmed that sleep drives the cleanup, but the actual speeds through the deep tissue had never been clocked. Until now. Mapping the deep brain Speed wasn't the only thing the method pulled out of the dark. It also charted the pressure driving the fluid and how leaky or tight the tissue was at each spot - readings no existing scan could deliver. That deep-brain view is the piece that had eluded everyone. Fluid in the brain's core threads through impossibly small spaces - deep beyond any microscope's reach in a living animal. Filling that blank turns a guess into a measurement. Researchers could now pin actual speeds to every region of the fluid network, deep and shallow, for the first time. Could this work in humans? So far, the researchers have tested the technique in five mice that were kept calm and sedated during scanning. These initial measurements establish a baseline that future studies can compare against. The ultimate goal is to compare young and aging brains, as well as healthy and diseased ones, to determine whether changes in fluid flow are linked to neurological disorders. The dye technique already runs in clinical settings and needs no surgery, putting human studies within reach. A concussion could be another target. Separate research has tied head injury to weaker brain drainage, and a scan like this might one day reveal how badly a hit disrupted the flow. Broader implications of the study What's new here is concrete. Researchers can now measure how fast waste-clearing fluid moves through a living brain, surface to core - the pressure pushing it, the resistance shaping it. All without cutting anything open. For the first time, scientists can begin tackling questions that were previously out of reach. They can investigate whether reduced fluid flow helps drive Alzheimer's disease or merely reflects it, how the system changes with age, and whether it can recover after brain injury. "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," concluded Professor Kelley. The study is published in the journal Science Advances. -- - Like what you read? Subscribe to our newsletter for engaging articles, exclusive content, and the latest updates. Check us out on EarthSnap, a free app brought to you by Eric Ralls and Earth.com.