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AI-aided Implant Captures Deep Brain Images

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A neural implant developed at the University of California San Diego could help advance the path to minimally invasive brain-computer interface (BCI) technology. The implant provides high-resolution data about deep neural activity by recording at the brain’s surface.

Built by a team led by professor Duygu Kuzum, the implant consists of a thin, transparent, flexible polymer strip that conforms to the brain’s surface. The strip is embedded with high-density arrays of graphene microelectrodes that enable up to 256 channels.

The graphene microelectrodes have ultrasmall openings and large, transparent recording areas. The diameter of the microelectrodes is scaled down to 20 μm. Each electrode in the implant is connected to a circuit board by a micrometer-thin graphene wire.
When placed on the surface of the brain, this thin, flexible implant enables researchers to capture high-resolution information about neural activity deep inside the brain without damaging its delicate tissue. Courtesy of David Baillot/UC San Diego Jacobs School of Engineering.
When placed on the surface of the brain, this thin, flexible implant enables researchers to capture high-resolution information about neural activity deep inside the brain without damaging its delicate tissue. Courtesy of David Baillot/UC San Diego Jacobs School of Engineering.

Researcher Mehrdad Ramezani said that fabricating a single layer of graphene as a thin, long wire presented a challenge, because any defect in the wire would make it nonfunctional. To prevent open-circuit failures, the researchers used an interlayer-doped, double-layer graphene. Instead of fabricating the wires as a single layer of graphene, they constructed the graphene wires as a double layer doped with nitric acid in the middle.

“By having two layers of graphene on top of one another, there’s a good chance that defects in one layer will be masked by the other layer, ensuring the creation of fully functional, thin, and long graphene wires with improved conductivity,” Ramezani said.

The use of graphene wires instead of traditional metal wires to connect the electrodes to the circuit board ensures transparency and provides a clear field of view for a microscope during imaging experiments.

To achieve high density arrays, the researchers used a microfabrication technique that involved depositing platinum nanoparticles onto the graphene electrodes. This approach improved electron flow without affecting the transparency or extremely small size of the electrodes. To the best of the researchers’ knowledge, they have developed the most densely packed, transparent electrode array for any surface-sitting neural implant to date. The high-density arrays enable neural activity to be recorded with high spatial resolution across large areas.

“This new generation of transparent graphene electrodes embedded at high density enables us to sample neural activity with higher spatial resolution,” Kuzum said. “As a result, the quality of signals improves significantly.”

The researchers also integrated machine learning methods into the technology. This made it possible to predict deep neural activity from the implant’s surface signals.


In tests on transgenic mice, the implant enabled the researchers to capture high-resolution information about two types of neural activity simultaneously. When the researchers placed the implant on the surface of the brain, it recorded electrical signals from neurons in the outer layers. When at the same time the researchers used a two-photon microscope to shine laser light through the implant, it imaged calcium spikes from neurons located as deep as 250 μm below the brain’s surface.

The team discovered a correlation between surface electrical signals and calcium spikes in deeper layers. Based on this finding, the researchers used surface electrical signals to train neural networks to predict calcium activity for individual neurons, as well as large neuron populations, at various depths.

“The neural network model is trained to learn the relationship between the surface electrical recordings and the calcium ion activity of the neurons at depth,” Kuzum said. “Once it learns that relationship, we can use the model to predict the depth activity from the surface.”
Closeup of the transparent graphene electrode array. Courtesy of David Baillot/UC San Diego Jacobs School of Engineering.
Closeup of the transparent graphene electrode array developed by a team led by Duygo Kuzum at the University of California San Diego. Courtesy of David Baillot/UC San Diego Jacobs School of Engineering.

Typically, a subject’s head is fixed under a microscope when imaging calcium spikes, and the experiment can only last for an hour or two. The ability to predict calcium activity from electrical signals will eliminate these restrictions.

“Since electrical recordings do not have these limitations, our technology makes it possible to conduct longer duration experiments in which the subject is free to move around and perform complex behavioral tasks,” Ramezani said. “This can provide a more comprehensive understanding of neural activity in dynamic, real-world scenarios.”

Next, the team plans to focus on testing the technology in different animal models with the future goal of translating the technology to humans. To advance neuroscience research, the team is sharing the technology with labs across the U.S. and Europe, where it is being used in diverse studies. To make the technology more widely available, the team has applied for a National Institutes of Health (NIH) grant to fund efforts in scaling up production and facilitating its adoption by researchers worldwide.

“This technology can be used for so many different fundamental neuroscience investigations, and we are eager to do our part to accelerate progress in better understanding the human brain,” Kuzum said.

“We are expanding the spatial reach of neural recordings with this technology,” she said. “Even though our implant resides on the brain’s surface, its design goes beyond the limits of physical sensing in that it can infer neural activity from deeper layers.”

The research was published in Nature Nanotechnology (https://www.nature.com/articles/s41565-023-01576-z).

Published: January 2024
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