Automatic subthalamic nucleus detection from microelectrode recordings based on noise level and neuronal activity

Abstract

Microelectrode recording (MER) along surgical trajectories is commonly applied for refinement of the target location during deep brain stimulation (DBS) surgery. In this study, we utilize automatically detected MER features in order to locate the subthalamic nucleus (STN) employing an unsupervised algorithm. The automated algorithm makes use of background noise level, compound firing rate and power spectral density along the trajectory and applies a threshold-based method to detect the dorsal and the ventral borders of the STN. Depending on the combination of measures used for detection of the borders, the algorithm allocates confidence levels for the annotation made (i.e. high, medium and low). The algorithm has been applied to 258 trajectories obtained from 84 STN DBS implantations. MERs used in this study have not been pre-selected or pre-processed and include all the viable measurements made. Out of 258 trajectories, 239 trajectories were annotated by the surgical team as containing the STN versus 238 trajectories by the automated algorithm. The agreement level between the automatic annotations and the surgical annotations is 88%. Taking the surgical annotations as the golden standard, across all trajectories, the algorithm made true positive annotations in 231 trajectories, true negative annotations in 12 trajectories, false positive annotations in 7 trajectories and false negative annotations in 8 trajectories. We conclude that our algorithm is accurate and reliable in automatically identifying the STN and locating the dorsal and ventral borders of the nucleus, and in a near future could be implemented for on-line intra-operative use.

Neural network dynamics in Parkinson's disease

“…During surgery the entrance and exit of STN were discerned visually by the neurophysiologist as an increase and decrease, respectively, in the background noise amplitude and neuronal firing [18,29,39]. In this way, each MER point was classified to be inside or outside the STN.…”

The Effects of Dexmedetomidine on Microelectrode Recordings of the Subthalamic Nucleus during Deep Brain Stimulation Surgery: A Retrospective Analysis

“…This corresponds with a clinically more alert patient, but it is unknown whether the neural activity remains suppressed. In this study, the MER measures examined here are those typically taken and used intraoperatively, such as RMS power assessment as a marker of background population activity, STN pass length in millimeters (determined by a clear background activity change consistent with STN activity), and the number of passes that yielded STN [9,11,22,23,24,25]. In addition, to help adjunct the results of the study by Krishna et al [21], we also analyze measures of STN unit activity, such as spike frequency (Hz) and the variability of firing activity (i.e., is the pattern or “burstiness” of firing changed by the addition of DEX).…”

“…In addition, to help adjunct the results of the study by Krishna et al [21], we also analyze measures of STN unit activity, such as spike frequency (Hz) and the variability of firing activity (i.e., is the pattern or “burstiness” of firing changed by the addition of DEX). Although these single-neuron electrophysiological measures are not commonly used intraoperatively due to the difficulty of processing online, they are increasingly hypothesized to be of use to help delineate proper targeting within the STN [23,26,27,28]. While this is a retrospective analysis, we feel that these data may provide important qualitative and quantitative measures of the effect of DEX on MER recordings performed during STN-DBS surgery in the real-world Parkinsonian patient.…”

Microelectrode Recording Duration and Spatial Density Constraints for Automatic Targeting of the Subthalamic Nucleus

“…Typically, MER signals are observed both visually and with audio by the physiologist and/or neurosurgeon during surgery to identify the functional targets. Recently, several studies have shown that MER data can be utilized for automatic localization of the STN with reduced variation and better accuracy [25,26,27,28,29,30]. Commonly used signal features include the background noise level, spike count and power spectral density (PSD) in the beta and theta (tremor) frequency bands.…”