Determination of subthalamic nucleus location by quantitative analysis of despiked background neural activity from microelectrode recordings obtained during deep brain stimulation surgery.

Microelectrode recording during deep brain stimulation surgery is a useful adjunct for subthalamic nucleus (STN) localization. We hypothesize that information in the nonspike background activity can help identify STN boundaries. We present results from a novel quantitative analysis that accomplishes this goal. Thirteen consecutive microelectrode recordings were retrospectively analyzed. Spikes were removed from the recordings with an automated algorithm. The remaining "despiked" signals were converted via root mean square amplitude and curve length calculations into "feature profile" time series. Subthalamic nucleus boundaries determined by inspection, based on sustained deviations from baseline for each feature profile, were compared against those determined intraoperatively by the clinical neurophysiologist. Feature profile activity within STN exhibited a sustained rise in 10 of 13 tracks (77%). The sensitivity of STN entry was 60% and 90% for curve length and root mean square amplitude, respectively, when agreement within 0.5 mm of the neurophysiologist's prediction was used. Sensitivities were 70% and 100% for 1 mm accuracy. Exit point sensitivities were 80% and 90% for both features within 0.5 mm and 1.0 mm, respectively. Reproducible activity patterns in deep brain stimulation microelectrode recordings can allow accurate identification of STN boundaries. Quantitative analyses of this type may provide useful adjunctive information for electrode placement in deep brain stimulation surgery.