Transparent implant takes deep neural recordings from surface of brain

The implant is made up of a polymer strip containing a dense array of graphene electrodes. The technology 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 by researchers at the University of California San Diego has been published in Nature Nanotechnology.

In a statement, senior author Duygu Kuzum said: “We are expanding the spatial reach of neural recordings with this technology. 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.”

Existing surface arrays are minimally invasive but lack the ability to capture information beyond the brain’s outer layers. In contrast, electrode arrays with thin needles that penetrate the brain can probe deeper layers, but can lead to inflammation and scarring that eventually compromises signal quality.

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 circular graphene electrodes measuring 20μm in diameter. Each electrode is connected by a micrometres-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 – simultaneously. 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 up to 250μm 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 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, a professor in the Department of Electrical and Computer Engineering at the UC San Diego Jacobs School of Engineering. “Once it learns that relationship, we can use the model to predict the depth activity from the surface.”

According to UCSD, an advantage of being able to predict calcium activity from electrical signals is that it overcomes the limitations of imaging experiments. When imaging calcium spikes, the subject’s head must be fixed under a microscope. Also, these experiments can only last up to two hours at a time.

“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 behavioural tasks,” said study co-first author Mehrdad Ramezani, an electrical and computer engineering PhD student in Kuzum’s lab. “This can provide a more comprehensive understanding of neural activity in dynamic, real-world scenarios.”

Traditional implants use opaque metal materials for their electrodes and wires, which block the view of neurons beneath the electrodes during imaging experiments. In contrast, an implant made using graphene is transparent, which provides a clear field of view for a microscope during imaging experiments.

To make the implant completely transparent, the researchers used super thin, long graphene wires to connect the electrodes to the circuit board. However, fabricating a single layer of graphene as a thin, long wire is challenging because any defect will render the wire nonfunctional, said Ramezani.

The team addressed this by fabricating the 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,” said Ramezani.

According to the researchers, this study demonstrates the most densely packed transparent electrode array on a surface-sitting neural implant to date.

Achieving high density required fabricating extremely small graphene electrodes. This presented a considerable challenge, as shrinking graphene electrodes in size increases their impedance, thereby hindering the flow of electrical current needed to record neural activity.

To overcome this obstacle, the researchers used a microfabrication technique developed by Kuzum’s lab that involves depositing platinum nanoparticles onto the graphene electrodes. This approach is said to have significantly improved electron flow through the electrodes while keeping them tiny and transparent.

The team will next focus on testing the technology in different animal models, with the ultimate goal of human translation.