Brain stimulation, where targeted electrical impulses are directly applied to a patient’s brain, is already an effective therapy for depression, epilepsy, Parkinson’s and other neurological disorders, but many more applications are on the horizon. Clinicians and researchers believe the technique could be used to restore or improve memory and motor function after an injury, for example, but progress is hampered by how difficult it is to predict how the entire brain will respond to stimulation at a given region.
In an effort to better personalize and optimize this type of therapy, researchers from the University of Pennsylvania’s School of Engineering and Applied Science and Perelman School of Medicine, as well as Thomas Jefferson University Hospital and the University of California, Riverside, have developed a way to model how a given patient’s brain activity will change in response to targeted stimulation.
To test the accuracy of their model, they recruited a group of study participants who were undergoing an unrelated treatment for severe epilepsy, and thus had a series of electrodes already implanted in their brains. Using each individual’s brain activity data as inputs for their model, the researchers made predictions about how to best stimulate that participant’s brain to improve their performance on a basic memory test.
The participants’ brain activity before and after stimulation suggest the researchers’ models have meaningful predictive power and offer a first step towards a more generalizable approach to specific stimulation therapies.
The study, published in the journal Cell Reports, was led by Danielle Bassett, J. Peter Skirkanich Professor in Penn Engineering’s Department of Bioengineering, and Jennifer Stiso, a neuroscience graduate student in Penn Medicine and a member of Bassett’s Complex Systems Lab.