Grouping Neuronal Spiking Patterns in the Subthalamic Nucleus of Parkinsonian Patients

Subthalamic Nucleus (STN) is currently used as a promising target in deep brain stimulation to control the motor symptoms of Parkisnon’s Disease (PD). Identification of the spiking patterns in Subthalamic Nucleus is important in order to understand the neuropathophysiology of PD and can also assist in electrophysiological mapping of the structure. This study aims to provide a tool for classifying these firing patterns based on several extracted features from the spiking data. Single neuronal activity from the STN of PD subjects were detected and sorted, after which features like Location Variation, Firing Rate, Coefficient of Variation, Bursting Index, Peak Power and Peak Prominence were extracted and further scrutinized based on how well they describe the data. A subset of these features that described the data best were selected. Clustering was performed based on these features in 3D space and the results show that this approach could be used for automatizing the grouping of stereotypic firing patterns in STN.