“In this study, we combined some measurements from inside the animal models with a novel AI technique that allowed us to effectively measure things that nobody’s ever been able to measure before,” says Kelley, a faculty member in Rochester’s Department of Mechanical Engineering.
The findings are the result of years of research undertaken by study co-author Maiken Nedergaard, co-director of Rochester’s Center for Translational Neuromedicine. Previously, the research was able to perform two-dimensional investigations on fluid flow in perivascular regions by infusing microscopic particles into the fluid and analyzing their position and velocity over time. However, more detailed measurements were required to fully comprehend the system’s complexitiesand investigating such a crucial, fluid system is difficult.
To address this issue, the team teamed with Brown University’s George Karniadakis to use artificial intelligence. They used existing 2D data with physics-based neural networks to generate unprecedented high-resolution views of the system.
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“This is a way to reveal pressures, forces, and the three-dimensional flow rate with much more accuracy than we can otherwise do,” says Kelley. “The pressure is important because nobody knows for sure quite what pumping mechanism drives all these flows around the brain yet. This is a new field.”
Reference:
- Artificial intelligence velocimetry reveals in vivo flow rates, pressure gradients, and shear stresses in murine perivascular flows – (https:www.pnas.org/doi/10.1073/pnas.2217744120)
Source: Medindia
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