Researchers at USC and Wake Forest Baptist Medical Center have developed a brain prosthesis designed to help people suffering from memory loss.

The prosthesis, which includes a small array of electrodes implanted into the brain, has performed well in laboratory testing in animals and is currently being evaluated in human patients.

Designed originally at USC and tested at Wake Forest Baptist, the device builds on decades of research by Ted Berger and relies on a new algorithm created by Dong Song, both of the USC Viterbi School of Engineering. The development also builds on more than a decade of collaboration with Sam Deadwyler and Robert Hampson, of the Department of Physiology & Pharmacology of Wake Forest Baptist, who have collected the neural data used to construct the models and algorithms.

Scientists presented results of the work at the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society in Milan in August.

Signals and sensory input

When your brain receives sensory input, it creates a memory in the form of a complex electrical signal that travels through multiple regions of the hippocampus, the memory center of the brain. At each region, the signal is re-encoded until it reaches the final region as a wholly different signal that is sent off for long-term storage.

If there’s damage at any region that prevents this translation, then there is the possibility that long-term memory will not be formed. That’s why someone with hippocampal damage (due to Alzheimer’s disease, for example) can recall events from a long time ago — things that were already translated into long-term memories before the brain damage occurred — but have trouble forming new long-term memories.

Song and Berger found a way to accurately mimic how a memory is translated from short-term memory into long-term memory, using data obtained by Deadwyler and Hampson, first from animals, and then from humans. Their prosthesis is designed to bypass a damaged hippocampal section and provide the next region with the correctly translated memory.

That’s despite the fact that there is currently no way of “reading” a memory just by looking at its electrical signal.

It’s like being able to translate from Spanish to French without being able to understand either language. Ted Berger

“It’s like being able to translate from Spanish to French without being able to understand either language,” Berger said.

Accurate readings

The USC and Wake Forest Baptist teams tested the model’s effectiveness. With the permission of patients who had electrodes implanted in their hippocampi to treat chronic seizures, Hampson and Deadwyler read the electrical signals created during memory formation at two regions of the hippocampus, then sent that information to Song and Berger to construct the model. The team then fed those signals into the model and read how the signals generated from the first region of the hippocampus were translated into signals generated by the second region of the hippocampus.

In hundreds of trials conducted with nine patients, the algorithm accurately predicted how the signals would be translated with about 90 percent accuracy.

“Being able to predict neural signals with the USC model suggests that it can be used to design a device to support or replace the function of a damaged part of the brain,” Hampson said.

In its next step, the team will attempt to send the translated signal back into the brain of a patient with damage at one of the regions to try to bypass the damage and enable the formation of an accurate long-term memory.

The research, which has potential to help wounded soldiers suffering from memory loss, was supported by DARPA (project N66001-14-C-4016 to WFBMC) and USC.

More stories about: Alzheimer's Disease, Convergent Science, Emerging Technology, Engineering, Research