One drawback to the existing stimulation systems is that the pattern and amplitude of electrical signals that doctors use to stimulate the brain have been chosen largely by trial and error. “It’s like the early days of cochlear implants,” says Rahul Sarpeshkar, an electrical engineer at MIT who studies neural prosthetics and computational modeling of disease. “At first, people just tried things. But once they approached the problem more rationally and tried to more closely mimic the biology, people could hear better.”
Building models that mimic brain activity has been a challenge because communication in networks of neurons is so complex. “Scientists are just now beginning to investigate how nerve cells interact to produce the phenomenon of seizure,” says Schiff. His team uses an approach that has shown success in modeling weather and other complex nonlinear systems, and it has just recently been applied to neurons.
The idea is that if scientists can accurately model the activity leading up to and during a seizure, they can use that model to test in real time the type of stimulation parameters that are most effective at preventing abnormal activity before it evolves into a full-blown seizure.
So far, Schiff’s team has been able to build models that replicate oscillating neural activity recorded from the cortex of rodents. And in a paper published earlier this year in the Journal of Neural Engineering, the researchers showed that they could control these virtual wave patterns, outlining a potential approach to controlling electrical activity via neural implants. The scientists are now trying to repeat the feat using dynamics of actual seizures recorded in the lab.
Schiff’s team has also built a neural model of Parkinson’s and soon hopes to test his modeling approach in patients. “We hope to sort out over the next year how to proceed with the [Parkinson’s disease] control in order to make it suitable for safe human study.”