Adaptive Stimulations in a Biophysical Network Model of Parkinson’s Disease

Dr. Tom Stojsavljevic
Assistant Professor of Math and Computer Science
Beloit College

Deep brain stimulation (DBS)—through a surgically implanted electrode to the subthalamic nucleus (STN)—has become a widely used therapeutic option for the treatment of Parkinson’s disease and other neurological disorders. At present, the most conventional DBS is done with open-loop methods involving constant high-frequency stimulation (HF). This type of DBS may have adverse effects in the proximate region to the stimulation site. In this computational study, we seek to implement a novel technique of DBS where we stimulate the STN in an adaptive fashion using the interspike time of the neurons to control stimulation. Our results show that our protocol eliminates bursts in the synchronized bursting neuronal activity of the STN, which is hypothesized to cause the failure of thalamocortical neurons (TC) to respond properly to excitatory cortical inputs. Further, we are able to significantly decrease the TC relay errors, representing potential therapeutics for Parkinson’s disease.