Detection of the subthalamic nucleus in microelectrographic recordings in Parkinson disease using the high-frequency (> 500 Hz) neuronal background

Classification of DBS microelectrode recordings using a residual neural network with attention in the temporal domain

During the Deep Brain Stimulation (DBS) surgery for Parkinson's disease (PD), the main goal is to place the permanent stimulating electrode into an area of the brain that becomes pathologically hyperactive. This area, called Subthalamic Nucleus (STN), is small and located deep within the brain. Therefore, the main challenge is the precise localization of the STN region, considering various measurement errors and artifacts. In this paper, we have designed and developed a computer-aided decision support system for neurosurgical DBS surgery. The implementation of this system provides a novel method for calculating the expected position of the stimulating electrode based on the recordings of the electrical activity of brain tissue. The artificial neural network with attention is used to classify the microelectrode recordings and determine the final position of the stimulating electrode within the STN area. Experiments have verified the utility and efficiency of our system. The tests were carried out on many recordings collected during DBS surgeries, giving encouraging results. The experimental results demonstrate that deep learning methods extended with self-attention blocks compete with the other solutions. They provide significant robustness to recording artifacts and improve the accuracy of the stimulating electrode placement.

Localization of deep brain stimulation trajectories via automatic mapping of microelectrode recordings to MRI

Objective. Suboptimal electrode placement during subthalamic nucleus deep brain stimulation (STN DBS) surgery may arise from several sources, including frame-based targeting errors and intraoperative brain shift. We present a computer algorithm that can accurately localize intraoperative microelectrode recording (MER) tracks on preoperative magnetic resonance imaging (MRI) in real-time, thereby predicting deviation between the surgical plan and the MER trajectories.Approach. Random forest (RF) modeling was used to derive a statistical relationship between electrophysiological features on intraoperative MER and voxel intensity on preoperative T2-weighted MR imaging. This model was integrated into a larger algorithm that can automatically localize intraoperative MER recording tracks on preoperative MRI in real-time. To verify accuracy, targeting error of both the planned intraoperative trajectory ('planned') and the algorithm-derived trajectory ('calculated') was estimated by measuring deviation from the final DBS lead location on postoperative high-resolution computed tomography ('actual').Main results. MR imaging and MERs were obtained from 24 STN DBS implant trajectories. The cross-validated RF model could accurately distinguish between gray and white matter regions along MER trajectories (AUC 0.84). When applying this model within the localization algorithm, thecalculatedMER trajectory estimate was found to be significantly closer to theactualDBS lead when compared to theplannedtrajectory recorded during surgery (1.04 mm vs 1.52 mm deviation,p< 0.002), with improvement shown in 19/24 cases (79%). When applying the algorithm to simulated DBS trajectory plans with randomized targeting error, up to 4 mm of error could be resolved to <2 mm on average (p< 0.0001).Significance. This work presents an automated system for intraoperative localization of electrodes during STN DBS surgery. This neuroengineering solution may enhance the accuracy of electrode position estimation, particularly in cases where high-resolution intraoperative imaging is not available.

Clinically-derived oscillatory biomarker predicts optimal subthalamic stimulation for Parkinson's disease

OBJECTIVE

Choosing the optimal electrode trajectory, stimulation location, and stimulation amplitude in subthalamic nucleus deep brain stimulation (STN DBS) for Parkinson's disease (PD) remains a time-consuming empirical effort. In this retrospective study, we derive a data-driven electrophysiological biomarker that predicts clinical DBS location and parameters, and we consolidate this information into a quantitative score that may facilitate an objective approach to STN DBS surgery and programming.

APPROACH

Random-forest feature selection was applied to a dataset of 1046 microelectrode recordings sites across 20 DBS implant trajectories to identify features of oscillatory activity that predict clinically programmed volumes of tissue activation (VTA). A cross-validated classifier was used to retrospectively predict VTA regions from these features. Spatial convolution of probabilistic classifier outputs along MER trajectories produced a biomarker score that reflects the probability of localization within a clinically optimized VTA.

MAIN RESULTS

Biomarker scores peaked within the VTA region and were significantly correlated with percent improvement in postoperative motor symptoms (MDS-UPRDS Part III, R = 0.61, p = 0.004). Notably, the length of STN, a common criterion for trajectory selection, did not show similar correlation (R = -0.31, p = 0.18). These findings suggest that biomarker-based trajectory selection and programming may improve motor outcomes by 9 ± 3 percentage points (p = 0.047) in this dataset.

SIGNIFICANCE

A clinically defined electrophysiological biomarker not only predicts VTA size and location but also correlates well with motor outcomes. Use of this biomarker for trajectory selection and initial stimulation may potentially simplify STN DBS surgery and programming.

Distinct population code for movement kinematics and changes of ongoing movements in human subthalamic nucleus

The subthalamic nucleus (STN) is theorized to globally suppress movement through connections with downstream basal ganglia structures. Current theories are supported by increased STN activity when subjects withhold an uninitiated action plan, but a critical test of these theories requires studying STN responses when an ongoing action is replaced with an alternative. We perform this test in subjects with Parkinson’s disease using an extended reaching task where the movement trajectory changes mid-action. We show that STN activity decreases during action switches, contrary to prevalent theories. Furthermore, beta oscillations in the STN local field potential, which are associated with movement inhibition, do not show increased power or spiking entrainment during switches. We report an inhomogeneous population neural code in STN, with one sub-population encoding movement kinematics and direction and another encoding unexpected action switches. We suggest an elaborate neural code in STN that contributes to planning actions and changing the plans.

Clinical outcome prediction from analysis of microelectrode recordings using deep learning in subthalamic deep brain stimulation for Parkinson`s disease

BACKGROUND

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for improving the motor symptoms of advanced Parkinson's disease (PD). Accurate positioning of the stimulation electrodes is necessary for better clinical outcomes.

OBJECTIVE

We applied deep learning techniques to microelectrode recording (MER) signals to better predict motor function improvement, represented by the UPDRS part III scores, after bilateral STN DBS in patients with advanced PD. If we find the optimal stimulation point with MER by deep learning, we can improve the clinical outcome of STN DBS even under restrictions such as general anesthesia or non-cooperation of the patients.

METHODS

In total, 696 4-second left-side MER segments from 34 patients with advanced PD who underwent bilateral STN DBS surgery under general anesthesia were included. We transformed the original signal into three wavelets of 1-50 Hz, 50-500 Hz, and 500-5,000 Hz. The wavelet-transformed MER was used for input data of the deep learning. The patients were divided into two groups, good response and moderate response groups, according to DBS on to off ratio of UPDRS part III score for the off-medication state, 6 months postoperatively. The ratio were used for output data in deep learning. The Visual Geometry Group (VGG)-16 model with a multitask learning algorithm was used to estimate the bilateral effect of DBS. Different ratios of the loss function in the task-specific layer were applied considering that DBS affects both sides differently.

RESULTS

When we divided the MER signals according to the frequency, the maximal accuracy was higher in the 50-500 Hz group than in the 1-50 Hz and 500-5,000 Hz groups. In addition, when the multitask learning method was applied, the stability of the model was improved in comparison with single task learning. The maximal accuracy (80.21%) occurred when the right-to-left loss ratio was 5:1 or 6:1. The area under the curve (AUC) was 0.88 in the receiver operating characteristic (ROC) curve.

CONCLUSION

Clinical improvements in PD patients who underwent bilateral STN DBS could be predicted based on a multitask deep learning-based MER analysis.

Randomized, Double-Blind Assessment of LFP Versus SUA Guidance in STN-DBS Lead Implantation: A Pilot Study

Background: The efficacy of deep brain stimulation (DBS) therapy in Parkinson's disease (PD) patients is highly dependent on the precise localization of the target structures such as subthalamic nucleus (STN). Most commonly, microelectrode single unit activity (SUA) recordings are performed to refine the target. This process is heavily experience based and can be technically challenging. Local field potentials (LFPs), representing the activity of a population of neurons, can be obtained from the same microelectrodes used for SUA recordings and allow flexible online processing with less computational complexity due to lower sampling rate requirements. Although LFPs have been shown to contain biomarkers capable of predicting patients' symptoms and differentiating various structures, their use in the localization of the STN in the clinical practice is not prevalent. Methods: Here we present, for the first time, a randomized and double-blinded pilot study with intraoperative online LFP processing in which we compare the clinical benefit from SUA- versus LFP-based implantation. Ten PD patients referred for bilateral STN-DBS were randomly implanted using either SUA or LFP guided targeting in each hemisphere. Although both SUA and LFP were recorded for each STN, the electrophysiologist was blinded to one at a time. Three months postoperatively, the patients were evaluated by a neurologist blinded to the intraoperative recordings to assess the performance of each modality. While SUA-based decisions relied on the visual and auditory inspection of the raw traces, LFP-based decisions were given through an online signal processing and machine learning pipeline. Results: We found a dramatic agreement between LFP- and SUA-based localization (16/20 STNs) providing adequate clinical improvement (51.8% decrease in 3-month contralateral motor assessment scores), with LFP-guided implantation resulting in greater average improvement in the discordant cases (74.9%, n = 3 STNs). The selected tracks were characterized by higher activity in beta (11-32 Hz) and high-frequency (200-400 Hz) bands (p < 0.01) of LFPs and stronger non-linear coupling between these bands (p < 0.05). Conclusion: Our pilot study shows equal or better clinical benefit with LFP-based targeting. Given the robustness of the electrode interface and lower computational cost, more centers can utilize LFP as a strategic feedback modality intraoperatively, in conjunction to the SUA-guided targeting.

Online Mapping With the Deep Brain Stimulation Lead: A Novel Targeting Tool in Parkinson's Disease

BACKGROUND

Beta-frequency oscillations (13-30 Hz) are a subthalamic hallmark in patients with Parkinson's disease, and there is increased interest in their utility as an intraoperative marker.

OBJECTIVES

The objectives of this study were to assess whether beta activity measured directly from macrocontacts of deep brain stimulation leads could be used (a) as an intraoperative electrophysiological approach for guiding lead placements and (b) for physiologically informed stimulation delivery.

METHODS

Every millimeter along the surgical trajectory, local field-potential data were collected from each macrocontact, and power spectral densities were calculated and visualized (n = 39 patients). This was done for online intraoperative functional mapping and post hoc statistical analyses using 2 methods: generating distributions of spectral activity along surgical trajectories and direct delineation (presence versus lack) of beta peaks. In a subset of patients, this approach was corroborated by microelectrode recordings. Furthermore, the match rate between beta peaks at the final target position and the clinically determined best stimulation site were assessed.

RESULTS

Subthalamic recording sites were delineated by both methods of reconstructing functional topographies of spectral activity along surgical trajectories at the group level (P < 0.0001). Beta peaks were detected when any portion of the 1.5 mm macrocontact was within the microelectrode-defined subthalamic border. The highest beta peak at the final implantation site corresponded to the site of active stimulation in 73.3% of hemispheres (P < 0.0001). In 93.3% of hemispheres, active stimulation corresponded to the first-highest or second-highest beta peak.

CONCLUSIONS

Online measures of beta activity with the deep brain stimulation macroelectrode can be used to inform surgical lead placement and contribute to optimization of stimulation programming procedures. © 2020 International Parkinson and Movement Disorder Society.

A Computerized Microelectrode Recording to Magnetic Resonance Imaging Mapping System for Subthalamic Nucleus Deep Brain Stimulation Surgery

BACKGROUND

Accurate electrode placement is critical to the success of deep brain stimulation (DBS) surgery. Suboptimal targeting may arise from poor initial target localization, frame-based targeting error, or intraoperative brain shift. These uncertainties can make DBS surgery challenging.

OBJECTIVE

To develop a computerized system to guide subthalamic nucleus (STN) DBS electrode localization and to estimate the trajectory of intraoperative microelectrode recording (MER) on magnetic resonance (MR) images algorithmically during DBS surgery.

METHODS

Our method is based upon the relationship between the high-frequency band (HFB; 500-2000 Hz) signal from MER and voxel intensity on MR images. The HFB profile along an MER trajectory recorded during surgery is compared to voxel intensity profiles along many potential trajectories in the region of the surgically planned trajectory. From these comparisons of HFB recordings and potential trajectories, an estimate of the MER trajectory is calculated. This calculated trajectory is then compared to actual trajectory, as estimated by postoperative high-resolution computed tomography.

RESULTS

We compared 20 planned, calculated, and actual trajectories in 13 patients who underwent STN DBS surgery. Targeting errors for our calculated trajectories (2.33 mm ± 0.2 mm) were significantly less than errors for surgically planned trajectories (2.83 mm ± 0.2 mm; P = .01), improving targeting prediction in 70% of individual cases (14/20). Moreover, in 4 of 4 initial MER trajectories that missed the STN, our method correctly indicated the required direction of targeting adjustment for the DBS lead to intersect the STN.

CONCLUSION

A computer-based algorithm simultaneously utilizing MER and MR information potentially eases electrode localization during STN DBS surgery.

Optimization of the KNN Supervised Classification Algorithm as a Support Tool for the Implantation of Deep Brain Stimulators in Patients with Parkinson's Disease

Deep Brain Stimulation (DBS) of the Subthalamic Nuclei (STN) is the most used surgical treatment to improve motor skills in patients with Parkinson’s Disease (PD) who do not adequately respond to pharmacological treatment, or have related side effects. During surgery for the implantation of a DBS system, signals are obtained through microelectrodes recordings (MER) at different depths of the brain. These signals are analyzed by neurophysiologists to detect the entry and exit of the STN region, as well as the optimal depth for electrode implantation. In the present work, a classification model is developed and supervised by the K-nearest neighbour algorithm (KNN), which is automatically trained from the 18 temporal features of MER registers of 14 patients with PD in order to provide a clinical support tool during DBS surgery. We investigate the effect of different standardizations of the generated database, the optimal definition of KNN configuration parameters, and the selection of features that maximize KNN performance. The results indicated that KNN trained with data that was standardized per cerebral hemisphere and per patient presented the best performance, achieving an accuracy of 94.35% (p < 0.001). By using feature selection algorithms, it was possible to achieve 93.5% in accuracy in selecting a subset of six features, improving computation time while processing in real time.

Microstimulation-induced inhibition of thalamic reticular nucleus in non-human primates

The thalamic reticular nucleus (TRN) modulates activity in the thalamus and controls excitatory input from corticothalamic and thalamocortical glutamatergic projections. It is made up of GABAergic neurons which project topographically to the thalamus. The TRN also receives inhibitory projections from the globus pallidus and the substantia nigra pars reticulata. Photostimulation of the TRN results in local inhibition in rat slice preparations but the effects of local stimulation in vivo are not known. This study aimed to characterize stimulation-evoked responses in the TRN of non-human primates (NHPs). Microelectrodes were inserted into the TRN and neurons were stimulated at 5, 10, 15, and 20 µA using 0.5 s trains at 100 Hz and the subsequent response was recorded from the same electrode. Stimulation in surrounding nuclei and the internal capsule was used for mapping the anatomical borders of the TRN. Stimulation as low as 10 µA resulted in predominantly inhibition, recorded in both single units and background unit activity (BUA). The duration of inhibition (~ 1-3 s) increased with increasing stimulation amplitude and was significantly increased in BUA when single units were present. At 20 µA of current, 93% of the single units (41/44) and 92% of BUA sites (67/73) were inhibited. Therefore, microstimulation of the NHP TRN with low currents results in current-dependent inhibition of single units and BUA. This finding may be useful to further aid in localization of deep thalamic and subthalamic nuclei during brain surgery.

Semi-automated application for estimating subthalamic nucleus boundaries and optimal target selection for deep brain stimulation implantation surgery

OBJECTIVEDeep brain stimulation (DBS) of the subthalamic nucleus (STN) has become standard care for the surgical treatment of Parkinson's disease (PD). Reliable interpretation of microelectrode recording (MER) data, used to guide DBS implantation surgery, requires expert electrophysiological evaluation. Recent efforts have endeavored to use electrophysiological signals for automatic detection of relevant brain structures and optimal implant target location.The authors conducted an observational case-control study to evaluate a software package implemented on an electrophysiological recording system to provide online objective estimates for entry into and exit from the STN. In addition, they evaluated the accuracy of the software in selecting electrode track and depth for DBS implantation into STN, which relied on detecting changes in spectrum activity.METHODSData were retrospectively collected from 105 MER-guided STN-DBS surgeries (4 experienced neurosurgeons; 3 sites), in which estimates for entry into and exit from the STN, DBS track selection, and implant depth were compared post hoc between those determined by the software and those determined by the implanting neurosurgeon/neurophysiologist during surgery.RESULTSThis multicenter study revealed submillimetric agreement between surgeon/neurophysiologist and software for entry into and exit out of the STN as well as optimal DBS implant depth.CONCLUSIONSThe results of this study demonstrate that the software can reliably and accurately estimate entry into and exit from the STN and select the track corresponding to ultimate DBS implantation.

Selection of the Optimal Algorithm for Real-Time Estimation of Beta Band Power during DBS Surgeries in Patients with Parkinson's Disease

Deep Brain Stimulation (DBS) is a surgical procedure for the treatment of motor disorders in patients with Parkinson's Disease (PD). DBS involves the application of controlled electrical stimuli to a given brain structure. The implantation of the electrodes for DBS is performed by a minimally invasive stereotactic surgery where neuroimaging and microelectrode recordings (MER) are used to locate the target brain structure. The Subthalamic Nucleus (STN) is often chosen for the implantation of stimulation electrodes in DBS therapy. During the surgery, an intraoperative validation is performed to locate the dorsolateral region of STN. Patients with PD reveal a high power in the β band (frequencies between 13 Hz and 35 Hz) in MER signal, mainly in the dorsolateral region of STN. In this work, different power spectrum density methods were analyzed with the aim of selecting one that minimizes the calculation time to be used in real time during DBS surgery. In particular, the results of three nonparametric and one parametric methods were compared, each with different sets of parameters. It was concluded that the optimum method to perform the real-time spectral estimation of beta band from MER signal is Welch with Hamming windows of 1.5 seconds and 50% overlap.

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

BACKGROUND

The placement of subthalamic nucleus (STN) deep brain stimulation (DBS) electrodes can be facilitated by intraoperative microelectrode recording (MER) of the STN.

OBJECTIVES

Optimal anesthetic management during surgery remains unclear because of a lack of quantitative data of the effect of anesthetics on MER. Therefore, we measured the effects of dexmedetomidine (DEX) on MER measures of the STN commonly taken intraoperatively.

METHODS

MER from 45 patients was retrospectively compared between patients treated with remifentanil (REMI) alone or both REMI and DEX, which are the 2 main standards of care at our center. The measures examined were population activity, such as root mean square, STN length, and number of passes yielding STN, and the single-neuron measures of firing rate and variability.

RESULTS

The addition of DEX does not affect population measures (number of passes: DEX+REMI, n = 68, REMI only, n = 154), or neuronal firing rates (number of neurons: DEX+REMI, n = 64, REMI only, n = 72), but firing rate variability was reduced.

CONCLUSIONS

In this cohort, population-based measures routinely used for electrode placement in the STN were unaffected by DEX when added to REMI. Neuronal firing rates were also unaffected, but their variability was reduced, even beyond 20 min after cessation.

Stop! border ahead: Automatic detection of subthalamic exit during deep brain stimulation surgery

BACKGROUND

Microelectrode recordings along preplanned trajectories are often used for accurate definition of the subthalamic nucleus (STN) borders during deep brain stimulation (DBS) surgery for Parkinson's disease. Usually, the demarcation of the STN borders is performed manually by a neurophysiologist. The exact detection of the borders is difficult, especially detecting the transition between the STN and the substantia nigra pars reticulata. Consequently, demarcation may be inaccurate, leading to suboptimal location of the DBS lead and inadequate clinical outcomes.

METHODS

We present machine-learning classification procedures that use microelectrode recording power spectra and allow for real-time, high-accuracy discrimination between the STN and substantia nigra pars reticulata.

RESULTS

A support vector machine procedure was tested on microelectrode recordings from 58 trajectories that included both STN and substantia nigra pars reticulata that achieved a 97.6% consistency with human expert classification (evaluated by 10-fold cross-validation). We used the same data set as a training set to find the optimal parameters for a hidden Markov model using both microelectrode recording features and trajectory history to enable real-time classification of the ventral STN border (STN exit). Seventy-three additional trajectories were used to test the reliability of the learned statistical model in identifying the exit from the STN. The hidden Markov model procedure identified the STN exit with an error of 0.04 ± 0.18 mm and detection reliability (error < 1 mm) of 94%.

CONCLUSIONS

The results indicate that robust, accurate, and automatic real-time electrophysiological detection of the ventral STN border is feasible. © 2016 International Parkinson and Movement Disorder Society.

Multi-parametric analysis assists in STN localization in Parkinson's patients

BACKGROUND

Initial subthalamic nucleus (STN) localization is based on MRI and an anatomical atlas and then refined intraoperatively using electrophysiological mapping with microelectrode recordings (IOA - intraoperative multi-unit activity) during deep brain stimulation (DBS) in Parkinson's disease (PD). IOA is time consuming and subjective. The purpose of this study was to assess the value of high frequency multi-unit background activity (MUA, frequency >500Hz), and local field potentials (LFP, frequency 5-500Hz) in detection of the STN borders.

METHODS

This was a retrospective, single center study. 18 leads in ten PD patients that underwent STN DBS surgery were evaluated. IOA, MUA and LFP have been compared in detection of the STN. IOA using single train spikes analysis have been used as a gold standard.

RESULTS

Both LFP in beta range (20-35Hz) and MUA increased as the microelectrode entered the STN and their increase correlated with dorsal/ventral STN borders. The differences (mean±sd) were: between IOA and MUA of the dorsal/ventral border 0.20±0.76/0.28±0.30mm; between IOA and LFP of the dorsal/ventral border 0.08±0.94/0.05±0.53mm. Using Bland-Altman statistics, only 2/36 (5.6%) differences between IOA and MUA and also 2/36 differences between IOA and LFP (one for the dorsal border and one for the ventral border) were out of ±1.96 SD line of measurement differences. Correlation between dorsal border/ventral border positions obtained by IOA and MUA was 0.86, p<0.000005/0.97, p<10(-11); by IOA and LFP was 0.78, p<0.00015/0.88, p<0.000001.

CONCLUSIONS

Both MUA and LFP are characteristically elevated in the STN compared to neighboring structures. They may provide fast, real-time, objective and reliable markers of STN borders.

Sparse representation of MER signals for localizing the Subthalamic Nucleus in Parkinson's disease surgery

Deep brain stimulation (DBS) of Subthalamic Nucleus (STN) is the best method for treating advanced Parkinson's disease (PD), leading to striking improvements in motor function and quality of life of PD patients. During DBS, online analysis of microelectrode recording (MER) signals is a powerful tool to locate the STN. Therapeutic outcomes depend of a precise positioning of a stimulator device in the target area. In this paper, we show how a sparse representation of MER signals allows to extract discriminant features, improving the accuracy in identification of STN. We apply three techniques for over-complete representation of signals: Method of Frames (MOF), Best Orthogonal Basis (BOB) and Basis Pursuit (BP). All the techniques are compared to classical methods for signal processing like Wavelet Transform (WT), and a more sophisticated method known as adaptive Wavelet with lifting schemes (AW-LS). We apply each processing method in two real databases and we evaluate its performance with simple supervised classifiers. Classification outcomes for MOF, BOB and BP clearly outperform WT and AW-LF in all classifiers for both databases, reaching accuracy values over 98%.

[Deep brain recording and length of surgery in stereotactic and functional neurosurgery for movement disorders]

OBJECTIVE

Our objectives were to study the length of multi-unit recordings (MURs) of brain activity in 20 years of movement disorder neurosurgeries and to determine the number of times in which it was necessary for the teams using single-unit recording (SUR) to explore all the electrode tracks in the simultaneously recorded sites (SRS).

MATERIAL AND METHOD

This was a retrospective descriptive statistical analysis of MUR length on 4,296 tracks in 952 surgeries. The exclusion criteria were: tracks with fewer than 5 recorded signals, tracks that had a signal length different from the habitual 2s, or there being unusual situations not related to the MUR, as well as the first 20 surgeries of each surgical target. This yielded a total of 3,448 tracks in 805 surgeries. We also determined the number of the total 952 surgeries in which all the tracks in the SURs of the SRS were explored.

RESULTS

The mean and its confidence interval (P=.05) of time per MUR track were 5.49±0.16min in subthalamic nucleus surgery, 8.82±0.24min in the medial or internal globus pallidus) and 18.51±1.31min in the ventral intermediate nucleus of the thalamus. For the total sum of tracks per surgery, in 75% of cases the total time was less than 39min in subthalamic nucleus, almost 42min in the medial or internal globus pallidus and less than 1h and 17min in ventral intermediate nucleus of the thalamus. All the tracks in the SUR SRS were explored in only 4.2% of the surgeries.

CONCLUSIONS

The impact of MUR on surgical time is acceptable for this guide in objective localization for surgical targets, without having to use several simultaneous electrodes (not all indispensable in most of the cases). Consequently, there is less risk for the patient.

Microelectrode recording duration and spatial density constraints for automatic targeting of the subthalamic nucleus

BACKGROUND

Accurate detection of the boundaries of the subthalamic nucleus (STN) in deep brain stimulation (DBS) surgery using microelectrode recording (MER) is considered to refine localization and may therefore improve clinical outcome. However, MER tends to extend operation time and its cost-utility balance has been debated.

OBJECTIVES

To quantify the tradeoff between accuracy of STN localization and the spatial and temporal parameters of MER that effect the operation time using an automated detection method.

METHODS

We retrospectively estimated the accuracy of STN detection on data from 100 microelectrode trajectories. Our dense (average step = 0.12 mm) and long (average duration = 22.5 s) MER data was downsampled in the spatial and temporal domains. Then, the STN borders were detected automatically on both the downsampled and original data and compared to each other.

RESULTS

With a recording duration of 16 s, average accuracy for detecting STN entry ranged from 0.06 mm for a 0.1-mm step to 0.51 mm for a 1.0-mm step. Smaller effects were found along the temporal axis. For example, a 0.1-mm recording step yielded an STN entry average accuracy ranging from 0.06 mm for a 16-second recording duration to 0.16 mm for 0.1 s.

CONCLUSIONS

STN entry detection error was about half of the step size. Sampling duration of STN activity can be minimized to 1 s/record without compromising accuracy. We conclude that bilateral DBS surgery time utilizing MER may be significantly shortened without compromising targeting accuracy.

Localization of the subthalamic nucleus in Parkinson disease using multiunit activity

BACKGROUND

Refinement of the subthalamic nucleus (STN) coordinates using intraoperative microelectrode recordings (MER) is routinely performed during deep brain stimulation (DBS) surgeries in Parkinson disease (PD). The commonly used criteria for electrophysiological localization of the STN are qualitative. The goal of this study was to validate quantitative STN detection algorithm (QD) derived from the multi-unit activity in a prospective setting.

METHODS

Ten PD patients underwent STN DBS surgery. The MUA was obtained by removing large spikes close to microelectrode using wavelet method and integrating the 500-2000Hz band in the power spectral density. The qualitative intraoperative mapping of the STN using MER (IOM) versus QD was compared using Bland-Altman and Pearson's correlation analysis.

RESULTS

The clinical efficacy was confirmed in all subjects. The mean difference between IOM and QD of the dorsal/ventral border was 0.31±0.84/0.44±0.47mm. Using Bland-Altman statistic, only 2/36 (5.6%) differences (one for the dorsal border and one for the ventral border) were out of ±2 sd line of measurement differences. Correlation between dorsal border/ventral border positions obtained by IOM and QD was 0.79, p<0.0001/0.91, p<0.0001.

CONCLUSION

Both methods are in reasonable agreement and are strongly correlated. The QD gives objective coordinates of the STN borders at high precision and may be more accurate than IOM. Prospective blinded comparative studies where the DBS leads will be placed using either QD or IOM are warranted.

Depth-time interpolation of feature trends extracted from mobile microelectrode data with kernel functions

BACKGROUND/AIMS

Microelectrode recording (MER) is necessary for precision localization of target structures such as the subthalamic nucleus during deep brain stimulation (DBS) surgery. Attempts to automate this process have produced quantitative temporal trends (feature activity vs. time) extracted from mobile MER data. Our goal was to evaluate computational methods of generating spatial profiles (feature activity vs. depth) from temporal trends that would decouple automated MER localization from the clinical procedure and enhance functional localization in DBS surgery.

METHODS

We evaluated two methods of interpolation (standard vs. kernel) that generated spatial profiles from temporal trends. We compared interpolated spatial profiles to true spatial profiles that were calculated with depth windows, using correlation coefficient analysis.

RESULTS

Excellent approximation of true spatial profiles is achieved by interpolation. Kernel-interpolated spatial profiles produced superior correlation coefficient values at optimal kernel widths (r = 0.932-0.940) compared to standard interpolation (r = 0.891). The choice of kernel function and kernel width resulted in trade-offs in smoothing and resolution.

CONCLUSIONS

Interpolation of feature activity to create spatial profiles from temporal trends is accurate and can standardize and facilitate MER functional localization of subcortical structures. The methods are computationally efficient, enhancing localization without imposing additional constraints on the MER clinical procedure during DBS surgery.

Towards high accuracy classification of MER signals for target localization in Parkinson's disease

In recent years Microelectrode recording (MER) analysis has proved to be a powerful localization tool of basal ganglia for Parkinson disease's treatment, especially the Subthalamic Nucleus (STN). In this paper, a signal-dependent method is presented for identification of the STN and other brain zones in Parkinsonian patients. The proposed method, refereed as optimal wavelet feature extraction method (OWFE), is constructed by lifting schemes (LS), which are a flexible and fast implementation of the wavelet transform (WT). The operators in the LS are optimized by means of Genetic Algorithms and Lagrange multipliers considering information contained in MER signals. Then a basic Bayesian classifier (LDC) is used to identify STN and other types of basal ganglia nuclei. The proposed method introduced several advantages from similar works reported in literature. First, the method is signal-dependent and non a priori information is required to decompose the MER signal. Second, the classification accuracy is mostly depended on the feature selection stage because it is not enhanced by elaborated classifiers such as support vector machines or hidden Markov models. Finally, the generalization property of the OWFE has been validated with two databases and different types of classifiers such as k-NN classifier and quadratic Bayesian classifier (QDC). Results have shown that proposed method is able to identify the STN with average accuracy superior than 97%.

Identification of the subthalamic nucleus in deep brain stimulation surgery with a novel wavelet-derived measure of neural background activity

OBJECT

The authors developed a wavelet-based measure for quantitative assessment of neural background activity during intraoperative neurophysiological recordings so that the boundaries of the subthalamic nucleus (STN) can be more easily localized for electrode implantation.

METHODS

Neural electrophysiological data were recorded in 14 patients (20 tracks and 275 individual recording sites) with dopamine-sensitive idiopathic Parkinson disease during the target localization portion of deep brain stimulator implantation surgery. During intraoperative recording, the STN was identified based on audio and visual monitoring of neural firing patterns, kinesthetic tests, and comparisons between neural behavior and the known characteristics of the target nucleus. The quantitative wavelet-based measure was applied offline using commercially available software to measure the magnitude of the neural background activity, and the results of this analysis were compared with the intraoperative conclusions. Wavelet-derived estimates were also compared with power spectral density measurements.

RESULTS

The wavelet-derived background levels were significantly higher in regions encompassed by the clinically estimated boundaries of the STN than in the surrounding regions (STN, 225 +/- 61 microV; ventral to the STN, 112 +/- 32 microV; and dorsal to the STN, 136 +/- 66 microV). In every track, the absolute maximum magnitude was found within the clinically identified STN. The wavelet-derived background levels provided a more consistent index with less variability than measurements with power spectral density.

CONCLUSIONS

Wavelet-derived background activity can be calculated quickly, does not require spike sorting, and can be used to identify the STN reliably with very little subjective interpretation required. This method may facilitate the rapid intraoperative identification of STN borders.

Microstimulation-induced inhibition as a tool to aid targeting the ventral border of the subthalamic nucleus

OBJECT

The aim of the current study was to examine and compare the aftereffects of local high-frequency microstimulation through the recording electrode on the firing of neurons in the subthalamic nucleus (STN) and the substantia nigra pars reticulata (SNr) in patients undergoing surgery for deep brain stimulation. Deep brain stimulation has been playing an increasing role in the treatment of Parkinson disease, with the subthalamic nucleus (STN) being the preferred implantation target. Changes in cellular activity indicative of the borders of the STN are typically used during surgery to determine the extent of the STN and locate the optimal target, but in some cases borders may be difficult to identify. In this study the authors compared the effects of microstimulation in the SNr and STN. In previous studies they have shown that microstimulation in the internal globus pallidus, which is functionally similar to the SNr, inhibits firing, whereas similar microstimulation in the STN has minimal effect. The presence of inhibition in the SNr but not in the STN could be used as an additional criterion to help identify the location of the border between the STN and SNr.

METHODS

Dual microelectrode recordings were performed during stereotactic surgery in 4 patients. Well-isolated high-amplitude units were stimulated extracellularly through the recording microelectrode with 0.5-second trains of high frequency (200 Hz) and low current (<or= 5 microA).

RESULTS

In the majority (92%) of SNr neurons, this type of stimulation led to a period of inhibition lasting several hundreds of milliseconds following the end of the train. In contrast, only 1 neuron of 70 judged to be in the STN by other criteria was inhibited by this type of microstimulation, and this neuron was located at the ventral border of the STN.

CONCLUSIONS

These findings indicate that prolonged inhibition of firing following low-amplitude high-frequency microstimulation via the recording electrode is a consistent feature of almost all SNr neurons and rarely if ever occurs in STN neurons. This feature therefore provides a useful additional finding that can be used to help identify the border between the STN and SNr.

Automatic noise-level detection for extra-cellular micro-electrode recordings

Extra-cellular neuro-recording signals used for functional mapping in deep brain stimulation (DBS) surgery and invasive brain computer interfaces, may suffer from poor signal to noise ratio. Therefore, a reliable automatic noise estimate is essential to extract spikes from recordings. We show that current methods are biased toward overestimation of noise-levels with increasing neuronal activity or artifacts. An improved and novel method is proposed that is based on an estimate of the mode of the distribution of the signal envelope. Our method makes use of the inherent characteristics of the noise distribution. For band-limited Gaussian noise the envelope of the signal is known to follow the Rayleigh distribution. The location of the peak of this distribution provides a reliable noise-level estimate. It is demonstrated that this new 'envelope' method gives superior performance both on simulated data, and on actual micro-electrode recordings made during the implantation surgery of DBS electrodes for the treatment of Parkinson's disease.