Transparent brain implant can read deep neural activity from the surface

 Researchers at the University of California San Diego have developed a neural implant that provides information about activity deep inside the brain while sitting on its surface. The implant is made up of a thin, transparent and flexible polymer strip that is packed with a dense array of graphene electrodes. The technology, tested in transgenic mice, brings the researchers a step closer to building a minimally invasive brain-computer interface (BCI) that provides high-resolution data about deep neural activity by using recordings from the brain surface.

The work was published on Jan. 11 in Nature Nanotechnology.

"We are expanding the spatial reach of neural recordings with this technology," said study senior author Duygu Kuzum, a professor in the Department of Electrical and Computer Engineering at the UC San Diego Jacobs School of Engineering. "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."

This work overcomes the limitations of current neural implant technologies. Existing surface arrays, for example, are minimally invasive, but they lack the ability to capture information beyond the brain's outer layers. In contrast, electrode arrays with thin needles that penetrate the brain are capable of probing deeper layers, but they often lead to inflammation and scarring, compromising signal quality over time.

The new neural implant developed at UC San Diego offers the best of both worlds.

The implant is a thin, transparent and flexible polymer strip that conforms to the brain's surface. The strip is embedded with a high-density array of tiny, circular graphene electrodes, each measuring 20 micrometers in diameter. Each electrode is connected by a micrometers-thin graphene wire to a circuit board.

In tests on transgenic mice, the implant enabled the researchers to capture high-resolution information about two types of neural activity-electrical activity and calcium activity-at the same time. When placed on the surface of the brain, the implant recorded electrical signals from neurons in the outer layers. At the same time, the researchers used a two-photon microscope to shine laser light through the implant to image calcium spikes from neurons located as deep as 250 micrometers below the surface. The researchers found a correlation between surface electrical signals and calcium spikes in deeper layers. This correlation enabled the researchers to use surface electrical signals to train neural networks to predict calcium activity -- not only for large populations of neurons, but also individual neurons -- 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," said Kuzum. "Once it learns that relationship, we can use the model to predict the depth activity from the surface."

Source: ScienceDaily