Noninvasive imaging of brain CSF waves using AI-powered MRI tagging
Official title: Quantification of Low-Frequency Oscillations in Cerebrospinal Fluid Dynamics with Rapidly Acquired Tagging MRI and Learning-based Spatiotemporal Pattern Recognition
Funded at State University New York Stony Brook · NIH-11440409 · Data updated 2026-11-12
A noninvasive MRI approach using fast tagging and AI-based pattern recognition to measure tiny CSF pulsations in people, which may relate to brain waste clearance.
Quick facts
| Grant type | R21 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | State University New York Stony Brook NIH-funded |
| Lab location | 1 site (Stony Brook, United States) |
| Project start | August 15, 2026 |
| Project end | June 30, 2029 |
| Funding year | 2026 |
| Award (this year) | $194,819 |
| NIH project number | 1R21EB040046-01 |
| Project ID | NIH-11440409 on NIH RePORTER |
Who could benefit from this research
✓Could help
Individuals at risk for or living with Alzheimer’s disease/dementia who can undergo MRI scanning.
✕Less likely to help
People unable to undergo MRI or who do not have a condition related to Alzheimer's may not benefit.
What this research studies
This project aims to create and test a fast MRI method that uses AI to pull out low-frequency CSF motion signals from brain scans. By combining rapid tagging MRI with a robust learning-based pattern recognition system, researchers hope to quantify CSF oscillations that help clear metabolic wastes. The work focuses on improving temporal resolution and reducing interference from heart and breathing, so results could be more reliable for understanding brain health. If successful, this could pave the way for better imaging biomarkers related to aging and dementia.
Why it matters
Potential benefit: If successful, patients may benefit from more accurate brain imaging markers tied to waste clearance and dementia, aiding future diagnosis and monitoring.
How similar studies have gone: Prior imaging approaches have shown promise, and this project builds on successful use of AI and fast MRI tagging, indicating potential but still early-stage.
Where this research is happening
-
State University New York Stony Brook
Stony Brook, United States Active
Researchers
- Principal investigator: Wang, Yicun — State University New York Stony Brook
- Study coordinator: Wang, Yicun
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About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.