EP 63. Spectral analysis and visualization of multi-unit activity in subthalamic nucleus in Parkinson’s as a tool for automated electrophysiological classification of basal ganglia structures during deep brain stimulation procedures

Abstract

Introduction In deep brain stimulation (DBS) of the subthalamic nucleus (STN) intraoperative microelectrode recordings (MER) are a well-established method for electrophysiological stereotactic target identification. Despite the main electrophysiological properties of STN are already well described ( [Cagnan et al., 2011] , [Seifried et al., 2012] ), mapping of STN rely on visual and acoustic inspection of raw recordings of MER trajectories. In this study we analyzed spectral decomposition and spike train patterns retrospectively of MER in STN-DBS to proposing a simple and reliable electrophysiological classification system. Secondly we suggest visualization techniques to present electrophysiological results in a user-friendly way. Patients and methods We analyzed MER in 7 patients with bilateral STN-DBS including 26 MER trajectories retrospectively. All recordings were carried out on multiple trajectories 10 mm prior and 4–5 mm below MR-tomographic STN target along planed DBS lead trajectory. Data was recorded intraoperative by Medtronic Leadpoint system using sharp microTargeting electrodes (FHC, USA). Offline Multi-Unit activity analysis was realized with Fieldtrip-Toolbox and Matlab, Mathworks. Statistical analysis of power spectral density, noise level and spike interstimulus interval were obtained through non-parametric student’s t-test. In purpose of visualization we integrated the stereotactic surgical plan to the electrophysiological results. Results Spectral density analysis showed a strong increase in frequency bands of 300–1000 Hz within STN borders. Spike train patterns within STN were heterogeneously and differed in firing rate and interstimulus interval to the key anatomical structures, thalamus and substantia nigra. Conclusion Through combination of spectral density analysis and spike train analysis we propose a simple and reliable MER signal classification method, which can be used for automated electrophysiological mapping of STN. Three-dimensional visualization of mapping results can provide help for decision of final DBS electrode implantation site.