Neurological

Using light and sound to reveal rapid brain activity in unprecedented detail

Biomedical engineers at Duke University have developed a method to scan and image the blood flow and oxygen levels inside a mouse brain in real time with enough resolution to view the activity of both individual vessels and the entire brain at once. This new imaging approach breaks long-standing speed and resolution barriers in brain imaging technologies and could uncover new insights into neurovascular diseases like stroke, dementia and even acute brain injury.

The research appeared May 17 in the Nature journal Light: Science & Applications.

Imaging the brain is a balancing act. Tools need to be fast enough to capture rapid events, like a neuron firing or blood flowing through a capillary, and they need to show activity at different scales, whether it’s across the entire brain or at the level of a single artery.

You can achieve these things individually, but it’s very difficult to do them all together. It’s like choosing between having a fast car that is small and uncomfortable to sit in, or a large, spacious car that doesn’t go over 30 miles an hour. For a long time, there wasn’t a way to get everything you wanted at once.

Junjie Yao, an assistant professor of biomedical engineering at Duke.

In their new study, Yao and his team discuss how they’ve solved this long-standing trade-off by developing ultrafast photoacoustic microscopy, or UFF-PAM.

Looking ahead, the team aims to use UFF-PAM to explore additional brain disease models, like dementia, Alzheimer’s disease or even Long COVID. They also plan to expand the tool’s use outside of the brain to image organs like the heart, liver and placenta. These organs have traditionally been challenging to image because they are always in motion, so imaging tools need to operate at a faster speed.

There’s a lot that we can do with this technology now that we’ve addressed these long-standing roadblocks. We’re trying to pick the most challenging projects to work on to maximize the impact of this technology.

Junjie Yao

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