An Automated Method for Neuronal Spike Source Identification

Analysis of microelectrode recordings (MER) of extracellular neuronal activity has gained increasing interest due to potential improvements to surgical techniques involving ablation or placement of deep brain stimulators, as is common in the treatment of Parkinson's disease. Critical to these procedures is the identification of different brain structures such as the globus pallidus internus (GPI). Evidence suggests that the spike trains from individual neurons contain enough information to identify the brain structure in which they are located. For the work reported here, spike train data gathered during surgical procedure from multiple patients is used. Using a moving window sampling approach, a novel feature extraction method for spike trains was developed. This method is then used in combination with a support vector classification algorithm. Results strongly indicate that the sampling methods reported here are able to extract the necessary information for highly accurate spike source identification.