Electrical simulation has the potential to widen treatment possibilities for millions of people with movement disorders, such as Parkinson’s disease, and epilepsy. In the future, this technology may help further treat psychiatric illness or even assist in recovery from brain injuries like stroke.
When exploring how brain networks interact with each other, one can deliver brief pulses of electrical current in a patient’s brain and measure voltage responses. However, this data is complex due to the limited number of measurements that can be made from real-world signals.
In order to make the problem more manageable, Mayo Clinic researchers developed a set of paradigms to simplify comparisons between effects of electrical stimulation on the brain. Because there was no mathematical technique in scientific literature for how assemblies converge from inputs into human brain regions, they collaborated with an international AI expert and created a new type algorithm called “basis profile curve identification.”
This joint research was published in PLOS Computational Biology. It explains how a case where a patient with a brain tumor underwent placement of an electrocorticographic electrode array to locate seizures and map brain function before the tumor was removed. Every time point interaction resulted in hundreds to thousands of places on the grid, which the researchers had to analyze using our new algorithm.
According to Kai Miller, M.D., Ph.D. (lead researcher), the findings of this research explains that the proposed algorithm may help the researchers to understand which brain regions directly interact with one another. This in turn may help guide the placement of electrodes for stimulating devices to treat network brain diseases. With the development of new technologies, the proposed algorithm may help researchers in treating epilepsy patients with movement disorders like Parkinson’s disease and psychiatric illnesses including obsessive compulsive disorder and depression.
To help others use the developed technique, it is available as a downloadable code package. The authors also mention that sharing this information is critical to reproducibility of research efforts.
Code Package: https://purl.stanford.edu/rc201dv0636