Real-time refinement of subthalamic nucleus targeting using Bayesian decision-making on the root mean square measure

A Systematic Review of Neurophysiology-Based Localization Techniques Used in Deep Brain Stimulation Surgery of the Subthalamic Nucleus

OBJECTIVE

This systematic review is conducted to identify, compare, and analyze neurophysiological feature selection, extraction, and classification to provide a comprehensive reference on neurophysiology-based subthalamic nucleus (STN) localization.

MATERIALS AND METHODS

The review was carried out using the methods and guidelines of the Kitchenham systematic review and provides an in-depth analysis on methods proposed on STN localization discussed in the literature between 2000 and 2021. Three research questions were formulated, and 115 publications were identified to answer the questions.

RESULTS

The three research questions formulated are answered using the literature found on the respective topics. This review discussed the technologies used in past research, and the performance of the state-of-the-art techniques is also reviewed.

CONCLUSION

This systematic review provides a comprehensive reference on neurophysiology-based STN localization by reviewing the research questions other new researchers may also have.

Statistical segmentation model for accurate electrode positioning in Parkinson's deep brain stimulation based on clinical low-resolution image data and electrophysiology

BACKGROUND

Deep Brain Stimulation (DBS), applying chronic electrical stimulation of subcortical structures, is a clinical intervention applied in major neurologic disorders. In order to achieve a good clinical effect, accurate electrode placement is necessary. The primary localisation is typically based on presurgical MRI imaging, often followed by intra-operative electrophysiology recording to increase the accuracy and to compensate for brain shift, especially in cases where the surgical target is small, and there is low contrast: e.g., in Parkinson's disease (PD) and in its common target, the subthalamic nucleus (STN).

METHODS

We propose a novel, fully automatic method for intra-operative surgical navigation. First, the surgical target is segmented in presurgical MRI images using a statistical shape-intensity model. Next, automated alignment with intra-operatively recorded microelectrode recordings is performed using a probabilistic model of STN electrophysiology. We apply the method to a dataset of 120 PD patients with clinical T2 1.5T images, of which 48 also had available microelectrode recordings (MER).

RESULTS

The proposed segmentation method achieved STN segmentation accuracy around dice = 0.60 compared to manual segmentation. This is comparable to the state-of-the-art on low-resolution clinical MRI data. When combined with electrophysiology-based alignment, we achieved an accuracy of 0.85 for correctly including recording sites of STN-labelled MERs in the final STN volume.

CONCLUSION

The proposed method combines image-based segmentation of the subthalamic nucleus with microelectrode recordings to estimate their mutual location during the surgery in a fully automated process. Apart from its potential use in clinical targeting, the method can be used to map electrophysiological properties to specific parts of the basal ganglia structures and their vicinity.

Subthalamic nucleus physiology is correlated with deep brain stimulation motor and non-motor outcomes

Subthalamic nucleus deep brain stimulation is commonly indicated for symptomatic relief of idiopathic Parkinson's disease. Despite the known improvement in motor scores, affective, cognitive, voice and speech functions might deteriorate following this procedure. Recent studies have correlated motor outcomes with intraoperative microelectrode recordings. However, there are no microelectrode recording-based tools with predictive values relating to long-term outcomes of integrative motor and non-motor symptoms. We conducted a retrospective analysis of the outcomes of patients with idiopathic Parkinson's disease who had subthalamic nucleus deep brain stimulation at Tel Aviv Sourasky Medical Centre (Tel Aviv, Israel) during 2015-2016. Forty-eight patients (19 women, 29 men; mean age, 58 ± 8 years) who were implanted with a subthalamic nucleus deep brain stimulation device underwent pre- and postsurgical assessments of motor, neuropsychological, voice and speech symptoms. Significant improvements in all motor symptoms (except axial signs) and levodopa equivalent daily dose were noted in all patients. Mild improvements were observed in more posterior-related neuropsychological functions (verbal memory, visual memory and organization) while mild deterioration was observed in frontal functions (personality changes, executive functioning and verbal fluency). The concomitant decline in speech intelligibility was mild and only partial, probably in accordance with the neuropsychological verbal fluency results. Acoustic characteristics were the least affected and remained within normal values. Dimensionality reduction of motor, neuropsychological and voice scores rendered six principal components that reflect the main clinical aspects: the tremor-dominant versus the rigidity-bradykinesia-dominant motor symptoms, frontal versus posterior neuropsychological deficits and acoustic characteristics versus speech intelligibility abnormalities. Microelectrode recordings of subthalamic nucleus spiking activity were analysed off-line and correlated with the original scores and with the principal component results. Based on 198 microelectrode recording trajectories, we suggest an intraoperative subthalamic nucleus deep brain stimulation score, which is a simple sum of three microelectrode recording properties: normalized neuronal activity, the subthalamic nucleus width and the relative proportion of the subthalamic nucleus dorsolateral oscillatory region. A threshold subthalamic nucleus deep brain stimulation score >2.5 (preferentially composed of normalized root mean square >1.5, subthalamic nucleus width >3 mm and a dorsolateral oscillatory region/subthalamic nucleus width ratio >1/3) predicts better motor and non-motor long-term outcomes. The algorithm presented here optimizes intraoperative decision-making of deep brain stimulation contact localization based on microelectrode recording with the aim of improving long-term (>1 year) motor, neuropsychological and voice symptoms.

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.

Dexmedetomidine depresses neuronal activity in the subthalamic nucleus during deep brain stimulation electrode implantation surgery

Background

Micro-electrode recordings are often necessary during electrode implantation for deep brain stimulation of the subthalamic nucleus. Dexmedetomidine may be a useful sedative for these procedures, but there is limited information regarding its effect on neural activity in the subthalamic nucleus and on micro-electrode recording quality.

Methods

We recorded neural activity in five patients undergoing deep brain stimulation implantation to the subthalamic nucleus. Activity was recorded after subthalamic nucleus identification while patients received dexmedetomidine sedation (loading - 1 μg kg-1 over 10-15 min, maintenance - 0.7 μg kg-1 h-1). We compared the root-mean square (RMS) and beta band (13-30 Hz) oscillation power of multi-unit activity recorded by microelectrode before, during and after recovery from dexmedetomidine sedation. RMS was normalised to values recorded in the white matter.

Results

Multi-unit activity decreased during sedation in all five patients. Mean normalised RMS decreased from 2.8 (1.5) to 1.6 (1.1) during sedation (43% drop, p = 0.056). Beta band power dropped by 48.4%, but this was not significant (p = 0.15). Normalised RMS values failed to return to baseline levels during the time allocated for the study (30 min).

Conclusions

In this small sample, we demonstrate that dexmedetomidine decreases neuronal firing in the subthalamic nucleus as expressed in the RMS of the multi-unit activity. As multi-unit activity is a factor in determining the subthalamic nucleus borders during micro-electrode recordings, dexmedetomidine should be used with caution for sedation during these procedures.

Clinical trial number

NCT01721460.

Dopaminergic Modulation of Spectral and Spatial Characteristics of Parkinsonian Subthalamic Nucleus Beta Bursts

In Parkinson’s disease (PD), subthalamic nucleus (STN) beta burst activity is pathologically elevated. These bursts are reduced by dopamine and deep brain stimulation (DBS). Therefore, these bursts have been tested as a trigger for closed-loop DBS. To provide better targeted parameters for closed-loop stimulation, we investigate the spatial distribution of beta bursts within the STN and if they are specific to a beta sub-band. Local field potentials (LFP) were acquired in the STN of 27 PD patients while resting. Based on the orientation of segmented DBS electrodes, the LFPs were classified as anterior, postero-medial, and postero-lateral. Each recording lasted 30 min with (ON) and without (OFF) dopamine. Bursts were detected in three frequency bands: ±3 Hz around the individual beta peak frequency, low beta band (lBB), and high beta band (hBB). Medication reduced the duration and the number of bursts per minute but not the amplitude of the beta bursts. The burst amplitude was spatially modulated, while the burst duration and rate were frequency dependent. Furthermore, the hBB burst duration was positively correlated with the akinetic-rigid UPDRS III subscore. Overall, these findings on differential dopaminergic modulation of beta burst parameters suggest that hBB burst duration is a promising target for closed-loop stimulation and that burst parameters could guide DBS programming.

Deep Brain Stimulation Can Differentiate Subregions of the Human Subthalamic Nucleus Area by EEG Biomarkers

Introduction: Precise lead localization is crucial for an optimal clinical outcome of subthalamic nucleus (STN) deep brain stimulation (DBS) treatment in patients with Parkinson's disease (PD). Currently, anatomical measures, as well as invasive intraoperative electrophysiological recordings, are used to locate DBS electrodes. The objective of this study was to find an alternative electrophysiology tool for STN DBS lead localization.

Methods: Sixty-one postoperative electrophysiology recording sessions were obtained from 17 DBS-treated patients with PD. An intraoperative physiological method automatically detected STN borders and subregions. Postoperative EEG cortical activity was measured, while STN low frequency stimulation (LFS) was applied to different areas inside and outside the STN. Machine learning models were used to differentiate stimulation locations, based on EEG analysis of engineered features.

Results: A machine learning algorithm identified the top 25 evoked response potentials (ERPs), engineered features that can differentiate inside and outside STN stimulation locations as well as within STN stimulation locations. Evoked responses in the medial and ipsilateral fronto-central areas were found to be most significant for predicting the location of STN stimulation. Two-class linear support vector machine (SVM) predicted the inside (dorso-lateral region, DLR, and ventro-medial region, VMR) vs. outside [zona incerta, ZI, STN stimulation classification with an accuracy of 0.98 and 0.82 for ZI vs. VMR and ZI vs. DLR, respectively, and an accuracy of 0.77 for the within STN (DLR vs. VMR)]. Multiclass linear SVM predicted all areas with an accuracy of 0.82 for the outside and within STN stimulation locations (ZI vs. DLR vs. VMR).

Conclusions: Electroencephalogram biomarkers can use low-frequency STN stimulation to localize STN DBS electrodes to ZI, DLR, and VMR STN subregions. These models can be used for both intraoperative electrode localization and postoperative stimulation programming sessions, and have a potential to improve STN DBS clinical outcomes.

Adapting the listening time for micro-electrode recordings in deep brain stimulation interventions

PURPOSE

Deep brain stimulation (DBS) is a common treatment for a variety of neurological disorders which involves the precise placement of electrodes at particular subcortical locations such as the subthalamic nucleus. This placement is often guided by auditory analysis of micro-electrode recordings (MERs) which informs the clinical team as to the anatomic region in which the electrode is currently positioned. Recent automation attempts have lacked flexibility in terms of the amount of signal recorded, not allowing them to collect more signal when higher certainty is needed or less when the anatomy is unambiguous.

METHODS

We have addressed this problem by evaluating a simple algorithm that allows for MER signal collection to terminate once the underlying model has sufficient confidence. We have parameterized this approach and explored its performance using three underlying models composed of one neural network and two Bayesian extensions of said network.

RESULTS

We have shown that one particular configuration, a Bayesian model of the underlying network's certainty, outperforms the others and is relatively insensitive to parameterization. Further investigation shows that this model also allows for signals to be classified earlier without increasing the error rate.

CONCLUSION

We have presented a simple algorithm that records the confidence of an underlying neural network, thus allowing for MER data collection to be terminated early when sufficient confidence is reached. This has the potential to improve the efficiency of DBS electrode implantation by reducing the time required to identify anatomical structures using MERs.

Differential dopaminergic modulation of spontaneous cortico-subthalamic activity in Parkinson's disease

Pathological oscillations including elevated beta activity in the subthalamic nucleus (STN) and between STN and cortical areas are a hallmark of neural activity in Parkinson’s disease (PD). Oscillations also play an important role in normal physiological processes and serve distinct functional roles at different points in time. We characterised the effect of dopaminergic medication on oscillatory whole-brain networks in PD in a time-resolved manner by employing a hidden Markov model on combined STN local field potentials and magnetoencephalography (MEG) recordings from 17 PD patients. Dopaminergic medication led to coherence within the medial and orbitofrontal cortex in the delta/theta frequency range. This is in line with known side effects of dopamine treatment such as deteriorated executive functions in PD. In addition, dopamine caused the beta band activity to switch from an STN-mediated motor network to a frontoparietal-mediated one. In contrast, dopamine did not modify local STN–STN coherence in PD. STN–STN synchrony emerged both on and off medication. By providing electrophysiological evidence for the differential effects of dopaminergic medication on the discovered networks, our findings open further avenues for electrical and pharmacological interventions in PD.

SepaConvNet for Localizing the Subthalamic Nucleus Using One Second Micro-electrode Recordings

Micro-electrode recording (MER) is a powerful way of localizing target structures during neurosurgical procedures such as the implantation of deep brain stimulation electrodes, which is a common treatment for Parkinson's disease and other neurological disorders. While Micro-electrode Recording (MER) provides adjunctive information to guidance assisted by pre-operative imaging, it is not unanimously used in the operating room. The lack of standard use of MER may be in part due to its long duration, which can lead to complications during the operation, or due to high degree of expertise required for their interpretation. Over the past decade, various approaches addressing automating MER analysis for target localization have been proposed, which have mainly focused on feature engineering. While the accuracies obtained are acceptable in certain configurations, one issue with handcrafted MER features is that they do not necessarily capture more subtle differences in MER that could be detected auditorily by an expert neurophysiologist. In this paper, we propose and validate a deep learning-based pipeline for subthalamic nucleus (STN) localization with micro-electrode recordings motivated by the human auditory system. Our proposed Convolutional Neural Network (CNN), referred as SepaConvNet, shows improved accuracy over two comparative networks for locating the STN from one second MER samples.

Independently together: subthalamic theta and beta opposite roles in predicting Parkinson's tremor

Tremor is a core feature of Parkinson’s disease and the most easily recognized Parkinsonian sign. Nonetheless, its pathophysiology remains poorly understood. Here, we show that multispectral spiking activity in the posterior-dorso-lateral oscillatory (motor) region of the subthalamic nucleus distinguishes resting tremor from the other Parkinsonian motor signs and strongly correlates with its severity. We evaluated microelectrode-spiking activity from the subthalamic dorsolateral oscillatory region of 70 Parkinson’s disease patients who underwent deep brain stimulation surgery (114 subthalamic nuclei, 166 electrode trajectories). We then investigated the relationship between patients’ clinical Unified Parkinson’s Disease Rating Scale score and their peak theta (4–7 Hz) and beta (13–30 Hz) powers. We found a positive correlation between resting tremor and theta activity (r = 0.41, P < 0.01) and a non-significant negative correlation with beta activity (r = −0.2, P = 0.5). Hypothesizing that the two neuronal frequencies mask each other’s relationship with resting tremor, we created a non-linear model of their proportional spectral powers and investigated its relationship with resting tremor. As hypothesized, patients’ proportional scores correlated better than either theta or beta alone (r = 0.54, P < 0.001). However, theta and beta oscillations were frequently temporally correlated (38/70 patients manifested significant positive temporal correlations and 1/70 exhibited significant negative correlation between the two frequency bands). When comparing theta and beta temporal relationship (r θ β) to patients’ resting tremor scores, we found a significant negative correlation between the two (r = −0.38, P < 0.01). Patients manifesting a positive correlation between the two bands (i.e. theta and beta were likely to appear simultaneously) were found to have lower resting tremor scores than those with near-zero correlation values (i.e. theta and beta were likely to appear separately). We therefore created a new model incorporating patients’ proportional theta–beta power and r θ βscores to obtain an improved neural correlate of resting tremor (r = 0.62, P < 0.001). We then used the Akaike and Bayesian information criteria for model selection and found the multispectral model, incorporating theta–beta proportional power and their correlation, to be the best fitting model, with 0.96 and 0.89 probabilities, respectively. Here we found that as theta increases, beta decreases and the two appear separately—resting tremor is worsened. Our results therefore show that theta and beta convey information about resting tremor in opposite ways. Furthermore, the finding that theta and beta coactivity is negatively correlated with resting tremor suggests that theta–beta non-linear scale may be a valuable biomarker for Parkinson’s resting tremor in future adaptive deep brain stimulation techniques.

Intraoperative Localization of STN During DBS Surgery Using a Data-Driven Model

A new approach is presented for localizing the Subthalamic Nucleus (STN) during Deep Brain Stimulation (DBS) surgery based on microelectrode recordings (MERs). DBS is an accepted treatment for individuals living with Parkinson’s Disease (PD). This surgery involves implantation of a permanent electrode inside the STN to deliver electrical current. Since the STN is a very small region inside the brain, accurate placement of an electrode is a challenging task for the surgical team. Prior to placement of the permanent electrode, microelectrode recordings of brain activity are used intraoperatively to localize the STN. The placement of the electrode and the success of the therapy depend on this location. In this paper, an objective approach is implemented to help the surgical team in localizing the STN. This is achieved by processing the MER signals and extracting features during the surgery to be used in a Machine Learning (ML) algorithm for defining the neurophysiological borders of the STN. For this purpose, a new classification approach is proposed with the goal of detecting both the dorsal and the ventral borders of the STN during the surgical procedure. Results collected from 100 PD patients in this study, show that by calculating and extracting wavelet transformation features from MER signals and using a data-driven computational deep neural network model, it is possible to detect the borders of the STN with an accuracy of 92%. The proposed method can be implemented in real-time during the surgery to model the neurophysiological nonlinearity along the path of the electrode trajectory during insertion.

Real-time machine learning classification of pallidal borders during deep brain stimulation surgery

OBJECTIVE

Deep brain stimulation (DBS) of the internal segment of the globus pallidus (GPi) in patients with Parkinson's disease and dystonia improves motor symptoms and quality of life. Traditionally, pallidal borders have been demarcated by electrophysiological microelectrode recordings (MERs) during DBS surgery. However, detection of pallidal borders can be challenging due to the variability of the firing characteristics of neurons encountered along the trajectory. MER can also be time-consuming and therefore costly. Here we show the feasibility of real-time machine learning classification of striato-pallidal borders to assist neurosurgeons during DBS surgery.

APPROACH

An electrophysiological dataset from 116 trajectories of 42 patients consisting of 11 774 MER segments of background spiking activity in five classes of disease was used to train the classification algorithm. The five classes included awake Parkinson's disease patients, as well as awake and lightly anesthetized genetic and non-genetic dystonia patients. A machine learning algorithm was designed to provide prediction of the striato-pallidal borders, based on hidden Markov models (HMMs) and the L₁-distance measure in normalized root mean square (NRMS) and power spectra of the MER. We tested its performance prospectively against the judgment of three electrophysiologists in the operating rooms of three hospitals using newly collected data.

MAIN RESULTS

The awake and the light anesthesia dystonia classes could be merged. Using MER NRMS and spectra, the machine learning algorithm was on par with the performance of the three electrophysiologists across the striatum-GPe, GPe-GPi, and GPi-exit transitions for all disease classes.

SIGNIFICANCE

Machine learning algorithms enable real-time GPi navigation systems to potentially shorten the duration of electrophysiological mapping of pallidal borders, while ensuring correct pallidal border detection.

Temporal evolution of beta bursts in the parkinsonian cortical and basal ganglia network

Prevalence and temporal dynamics of transient oscillations in the beta frequency band (15 to 35 Hz), referred to as β bursts, are correlated with motor performance. Disturbance of these activities is a candidate mechanism for motor impairment in Parkinson’s disease (PD), where the excessively long bursts correlate with symptom severity and are reduced by pharmacological and surgical treatments. Here we describe the changes in action potential firing that take place across multiple nodes of the cortical and basal ganglia circuit as these transient oscillations evolve. These analyses provide fresh insights into the network dynamics of β bursts that can guide novel strategies to interfere with their generation and maintenance in PD.

Beta frequency oscillations (15 to 35 Hz) in cortical and basal ganglia circuits become abnormally synchronized in Parkinson’s disease (PD). How excessive beta oscillations emerge in these circuits is unclear. We addressed this issue by defining the firing properties of basal ganglia neurons around the emergence of cortical beta bursts (β bursts), transient (50 to 350 ms) increases in the beta amplitude of cortical signals. In PD patients, the phase locking of background spiking activity in the subthalamic nucleus (STN) to frontal electroencephalograms preceded the onset and followed the temporal profile of cortical β bursts, with conditions of synchronization consistent within and across bursts. Neuronal ensemble recordings in multiple basal ganglia structures of parkinsonian rats revealed that these dynamics were recapitulated in STN, but also in external globus pallidus and striatum. The onset of consistent phase-locking conditions was preceded by abrupt phase slips between cortical and basal ganglia ensemble signals. Single-unit recordings demonstrated that ensemble-level properties of synchronization were not underlain by changes in firing rate but, rather, by the timing of action potentials in relation to cortical oscillation phase. Notably, the preferred angle of phase-locked action potential firing in each basal ganglia structure was shifted during burst initiation, then maintained stable phase relations during the burst. Subthalamic, pallidal, and striatal neurons engaged and disengaged with cortical β bursts to different extents and timings. The temporal evolution of cortical and basal ganglia synchronization is cell type-selective, which could be key for the generation/ maintenance of excessive beta oscillations in parkinsonism.

Deep Brain Stimulation for Parkinson Disease

Parkinson disease (PD) is the second most common neurodegenerative disorder and affects more than 1 million individuals in the United States. Deep brain stimulation (DBS) is one form of treatment of PD. DBS treatment is still evolving due to technological innovations that shape how this therapy is used.

Synchronised spiking activity underlies phase amplitude coupling in the subthalamic nucleus of Parkinson's disease patients

Both phase-amplitude coupling (PAC) and beta-bursts in the subthalamic nucleus have been significantly linked to symptom severity in Parkinson's disease (PD) in humans and emerged independently as competing biomarkers for closed-loop deep brain stimulation (DBS). However, the underlying nature of subthalamic PAC is poorly understood and its relationship with transient beta burst-events has not been investigated. To address this, we studied macro- and micro electrode recordings of local field potentials (LFPs) and single unit activity from 15 hemispheres in 10 PD patients undergoing DBS surgery. PAC between beta phase and high frequency oscillation (HFO) amplitude was compared to single unit firing rates, spike triggered averages, power spectral densities, inter spike intervals and phase-spike locking, and was studied in periods of beta-bursting. We found a significant synchronisation of spiking to HFOs and correlation of mean firing rates with HFO-amplitude when the latter was coupled to beta phase (i.e. in the presence of PAC). In the presence of PAC, single unit power spectra displayed peaks in the beta and HFO frequency range and the HFO frequency was correlated with that in the LFP. Furthermore, inter spike interval frequencies peaked in the same frequencies for which PAC was observed. Finally, PAC significantly increased with beta burst-duration. Our findings offer new insight in the pathology of Parkinson's disease by providing evidence that subthalamic PAC reflects the locking of spiking activity to network beta oscillations and that this coupling progressively increases with beta-burst duration. These findings suggest that beta-bursts capture periods of increased subthalamic input/output synchronisation in the beta frequency range and have important implications for therapeutic closed-loop DBS.

Phase-Dependent Suppression of Beta Oscillations in Parkinson's Disease Patients

Synchronized oscillations within and between brain areas facilitate normal processing, but are often amplified in disease. A prominent example is the abnormally sustained beta-frequency (∼20 Hz) oscillations recorded from the cortex and subthalamic nucleus of Parkinson's disease patients. Computational modeling suggests that the amplitude of such oscillations could be modulated by applying stimulation at a specific phase. Such a strategy would allow selective targeting of the oscillation, with relatively little effect on other activity parameters. Here, activity was recorded from 10 awake, parkinsonian patients (6 male, 4 female human subjects) undergoing functional neurosurgery. We demonstrate that stimulation arriving on a particular patient-specific phase of the beta oscillation over consecutive cycles could suppress the amplitude of this pathophysiological activity by up to 40%, while amplification effects were relatively weak. Suppressive effects were accompanied by a reduction in the rhythmic output of subthalamic nucleus (STN) neurons and synchronization with the mesial cortex. While stimulation could alter the spiking pattern of STN neurons, there was no net effect on firing rate, suggesting that reduced beta synchrony was a result of alterations to the relative timing of spiking activity, rather than an overall change in excitability. Together, these results identify a novel intrinsic property of cortico-basal ganglia synchrony that suggests the phase of ongoing neural oscillations could be a viable and effective control signal for the treatment of Parkinson's disease. This work has potential implications for other brain diseases with exaggerated neuronal synchronization and for probing the function of rhythmic activity in the healthy brain.SIGNIFICANCE STATEMENT In Parkinson's disease (PD), movement impairment is correlated with exaggerated beta frequency oscillations in the cerebral cortex and subthalamic nucleus (STN). Using a novel method of stimulation in PD patients undergoing neurosurgery, we demonstrate that STN beta oscillations can be suppressed when consecutive electrical pulses arrive at a specific phase of the oscillation. This effect is likely because of interrupting the timing of neuronal activity rather than excitability, as stimulation altered the firing pattern of STN spiking without changing overall rate. These findings show the potential of oscillation phase as an input for "closed-loop" stimulation, which could provide a valuable neuromodulation strategy for the treatment of brain disorders and for elucidating the role of neuronal oscillations in the healthy brain.

Method for localizing intraoperative recordings from deep brain stimulation surgery using post-operative structural MRI

Background

Implantation of deep brain stimulation (DBS) electrodes for the treatment of involuntary movement disorders, such as Parkinson's disease, routinely relies on the use of intraoperative electrophysiological confirmation to identify the optimal therapeutic target in the brain. However, only a few options exist to visualize the relative anatomic localization of intraoperative electrophysiological recordings with respect to post-operative imaging. We have developed a novel processing pipeline to visualize intraoperative electrophysiological signals registered to post-operative neuroanatomical imaging.

New method

We developed a processing pipeline built on the use of ITK-SNAP and custom MATLAB scripts to visualize the anatomical localization of intraoperative electrophysiological recordings mapped onto the post-operative MRI following implantation of DBS electrodes. This method combines the user-defined relevant electrophysiological parameters measured during the surgery with a manual segmentation of the DBS electrode from post-operative MRI; mapping the microelectrode recording (MER) depths along the DBS lead track.

Results

We demonstrate the use of our processing pipeline on data from Parkinson's disease patients undergoing DBS implantation targeted to the subthalamic nucleus (STN). The primary processing components of the pipeline are: extrapolation of the lead wire and alignment of intraoperative electrophysiology.

Conclusion

We describe the use of a processing pipeline to aid clinicians and researchers engaged in deep brain stimulation work to correlate and visualize the intraoperative recording data with the post-operative DBS trajectory.

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Pipeline that refines a manually segmented DBS wire from post-operative MR imaging.

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MATLAB function library for alignment of intraoperative electrophysiological data with MRI.

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Provides visualization schemes that convey the relative change in magnitude for an electrophysiological parameter

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.

Spatio-temporal dynamics of cortical drive to human subthalamic nucleus neurons in Parkinson's disease

Pathological synchronisation of beta frequency (12–35 Hz) oscillations between the subthalamic nucleus (STN) and cerebral cortex is thought to contribute to motor impairment in Parkinson's disease (PD). For this cortico-subthalamic oscillatory drive to be mechanistically important, it must influence the firing of STN neurons and, consequently, their downstream targets. Here, we examined the dynamics of synchronisation between STN LFPs and units with multiple cortical areas, measured using frontal ECoG, midline EEG and lateral EEG, during rest and movement. STN neurons lagged cortical signals recorded over midline (over premotor cortices) and frontal (over prefrontal cortices) with stable time delays, consistent with strong corticosubthalamic drive, and many neurons maintained these dynamics during movement. In contrast, most STN neurons desynchronised from lateral EEG signals (over primary motor cortices) during movement and those that did not had altered phase relations to the cortical signals. The strength of synchronisation between STN units and midline EEG in the high beta range (25–35 Hz) correlated positively with the severity of akinetic-rigid motor symptoms across patients. Together, these results suggest that sustained synchronisation of STN neurons to premotor-cortical beta oscillations play an important role in disrupting the normal coding of movement in PD.

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Multi-channel EEG with coincident STN single unit and local field potential recordings

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Variable time delays between beta oscillations in different cortical areas and STN neurons.

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Frontal/premotor cortical areas have most stable oscillatory synchronisation with STN neurons.

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Correlation between cortico-subthalamic beta-frequency synchronisation and clinical scores in PD.

Local vs. volume conductance activity of field potentials in the human subthalamic nucleus

Subthalamic nucleus field potentials have attracted growing research and clinical interest over the last few decades. However, it is unclear whether subthalamic field potentials represent locally generated neuronal subthreshold activity or volume conductance of the organized neuronal activity generated in the cortex. This study aimed at understanding of the physiological origin of subthalamic field potentials and determining the most accurate method for recording them. We compared different methods of recordings in the human subthalamic nucleus: spikes (300-9,000 Hz) and field potentials (3-100 Hz) recorded by monopolar micro- and macroelectrodes, as well as by differential-bipolar macroelectrodes. The recordings were done outside and inside the subthalamic nucleus during electrophysiological navigation for deep brain stimulation procedures (150 electrode trajectories) in 41 Parkinson's disease patients. We modeled the signal and estimated the contribution of nearby/independent vs. remote/common activity in each recording configuration and area. Monopolar micro- and macroelectrode recordings detect field potentials that are considerably affected by common (probably cortical) activity. However, bipolar macroelectrode recordings inside the subthalamic nucleus can detect locally generated potentials. These results are confirmed by high correspondence between the model predictions and actual correlation of neuronal activity recorded by electrode pairs. Differential bipolar macroelectrode subthalamic field potentials can overcome volume conductance effects and reflect locally generated neuronal activity. Bipolar macroelectrode local field potential recordings might be used as a biological marker of normal and pathological brain functions for future electrophysiological studies and navigation systems as well as for closed-loop deep brain stimulation paradigms.NEW & NOTEWORTHY Our results integrate a new method for human subthalamic recordings with a development of an advanced mathematical model. We found that while monopolar microelectrode and macroelectrode recordings detect field potentials that are considerably affected by common (probably cortical) activity, bipolar macroelectrode recordings inside the subthalamic nucleus (STN) detect locally generated potentials that are significantly different than those recorded outside the STN. Differential bipolar subthalamic field potentials can be used in navigation and closed-loop deep brain stimulation paradigms.

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.

Supervised segmentation of microelectrode recording artifacts using power spectral density

Appropriate detection of clean signal segments in extracellular microelectrode recordings (MER) is vital for maintaining high signal-to-noise ratio in MER studies. Existing alternatives to manual signal inspection are based on unsupervised change-point detection. We present a method of supervised MER artifact classification, based on power spectral density (PSD) and evaluate its performance on a database of 95 labelled MER signals. The proposed method yielded test-set accuracy of 90%, which was close to the accuracy of annotation (94%). The unsupervised methods achieved accuracy of about 77% on both training and testing data.

Higher neuronal discharge rate in the motor area of the subthalamic nucleus of Parkinsonian patients

In Parkinson's disease, pathological synchronous oscillations divide the subthalamic nucleus (STN) of patients into a dorsolateral oscillatory region and ventromedial nonoscillatory region. This bipartite division reflects the motor vs. the nonmotor (associative/limbic) subthalamic areas, respectively. However, significant topographic differences in the neuronal discharge rate between these two STN subregions in Parkinsonian patients is still controversial. In this study, 119 STN microelectrode trajectories (STN length > 2 mm, mean = 5.32 mm) with discernible oscillatory and nonoscillatory regions were carried on 60 patients undergoing deep brain stimulation surgery for Parkinson's disease. 2,137 and 2,152 multiunit stable signals were recorded (recording duration > 10 s, mean = 21.25 s) within the oscillatory and nonoscillatory STN regions, respectively. Spike detection and sorting were applied offline on every multiunit stable signal using an automatic method with systematic quantification of the isolation quality (range = 0–1) of the identified units. In all, 3,094 and 3,130 units were identified in the oscillatory and nonoscillatory regions, respectively. On average, the discharge rate of better-isolated neurons (isolation score > 0.70) was higher in the oscillatory region than the nonoscillatory region (44.55 ± 0.87 vs. 39.97 ± 0.77 spikes/s, N = 665 and 761, respectively). The discharge rate of the STN neurons was positively correlated to the strength of their own and their surrounding 13- to 30-Hz beta oscillatory activity. Therefore, in the Parkinsonian STN, beta oscillations and higher neuronal discharge rate are correlated and coexist in the motor area of the STN compared with its associative/limbic area.

Activity parameters of subthalamic nucleus neurons selectively predict motor symptom severity in Parkinson's disease

Parkinson's disease (PD) is a heterogeneous disorder that leads to variable expression of several different motor symptoms. While changes in firing rate, pattern, and oscillation of basal ganglia neurons have been observed in PD patients and experimental animals, there is limited evidence linking them to specific motor symptoms. Here we examined this relationship using extracellular recordings of subthalamic nucleus neurons from 19 PD patients undergoing surgery for deep brain stimulation. For each patient, ≥ 10 single units and/or multi-units were recorded in the OFF medication state. We correlated the proportion of neurons displaying different activities with preoperative Unified Parkinson's Disease Rating Scale subscores (OFF medication). The mean spectral power at sub-beta frequencies and percentage of units oscillating at beta frequencies were positively correlated with the axial and limb rigidity scores, respectively. The percentage of units oscillating at gamma frequency was negatively correlated with the bradykinesia scores. The mean intraburst rate was positively correlated with both bradykinesia and axial scores, while the related ratio of interspike intervals below/above 10 ms was positively correlated with these symptoms and limb rigidity. None of the activity parameters correlated with tremor. The grand average of all the significantly correlated subthalamic nucleus activities accounted for >60% of the variance of the combined bradykinetic-rigid and axial scores. Our results demonstrate that the occurrence of alterations in the rate and pattern of basal ganglia neurons could partly underlie the variability in parkinsonian phenotype.

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.

Neurons controlling voluntary vocalization in the macaque ventral premotor cortex

The voluntary control of phonation is a crucial achievement in the evolution of speech. In humans, ventral premotor cortex (PMv) and Broca's area are known to be involved in voluntary phonation. In contrast, no neurophysiological data are available about the role of the oro-facial sector of nonhuman primates PMv in this function. In order to address this issue, we recorded PMv neurons from two monkeys trained to emit coo-calls. Results showed that a population of motor neurons specifically fire during vocalization. About two thirds of them discharged before sound onset, while the remaining were time-locked with it. The response of vocalization-selective neurons was present only during conditioned (voluntary) but not spontaneous (emotional) sound emission. These data suggest that the control of vocal production exerted by PMv neurons constitutes a newly emerging property in the monkey lineage, shedding light on the evolution of phonation-based communication from a nonhuman primate species.

The effects of chronic levodopa treatments on the neuronal firing properties of the subthalamic nucleus and substantia nigra reticulata in hemiparkinsonian rhesus monkeys

Dopamine replacement therapy with levodopa (LD) is currently the most effective pharmacological treatment for Parkinson's disease (PD), a neurodegenerative disorder characterized by dysfunction of basal ganglia electrophysiology. The effects of chronic LD treatments on the electrophysiological activity of the subthalamic nucleus (STN) and the substantia nigra reticulata (SNR) in parkinsonism are not clear. In the present study we examined the effects of chronic LD treatments on the firing rate and firing pattern of STN and SNR neurons in the stable hemiparkinsonian monkey model of PD. We also evaluated local field potentials of both nuclei before and after LD treatments. In a stable hemiparkinsonian state, STN and SNR had a mean firing rate of 42.6 ± 3.5H z (mean ± SEM) and 52.1 ± 5.7 Hz, respectively. Chronic intermittent LD exposure induced marked amelioration of parkinsonism with no apparent drug-induced motor complications. LD treatments did not significantly change the mean firing rate of STN neurons (41.3 ± 3.3 Hz) or bursting neuronal firing patterns. However, LD treatments induced a significant reduction of the mean firing rates of SNR neurons to 36.2 ± 3.3 Hz (p<0.05) and a trend toward increased burstiness. The entropy of the spike sequences from STN and SNR was unchanged by LD treatment, while there was a shift of spectral power into higher frequency bands in the LFPs. The inability of chronic LD treatments to reduce the bursty firing patterns in the STN and SNR should be further examined as a potential pathophysiological mechanism for PD symptoms that are refractory to LD treatments.

Propofol decreases neuronal population spiking activity in the subthalamic nucleus of Parkinsonian patients

BACKGROUND

Implantation of deep brain stimulation (DBS) electrodes in the subthalamic nucleus (STN) for the treatment of Parkinson disease is often performed using microelectrode recording (MER) of STN population spike activity. The extent to which sedative drugs interfere with MER is unknown. We recorded the population activity of STN neurons during propofol sedation and examined its effect on neuronal activity.

METHODS

The procedure was performed during DBS surgery for Parkinson disease. We administered propofol (50 μg/kg/min) at a constant electrode location in the STN until stable sedation was achieved. We recorded the electrical activity, and calculated its root mean square (RMS) before, during, and after the propofol infusions.

RESULTS

The activity of 24 electrode trajectories was recorded in 16 patients. The RMS of STN activity decreased significantly after propofol administration in 18 of the 24 trajectories. The average normalized RMS decreased by 23.2%± 9.1% (mean ± SD) during propofol administration (P < 0.001), and returned to baseline 9.3 ± 4.0 minutes after it was stopped.

CONCLUSIONS

Propofol administration leads to a significant decrease of STN neuronal activity. Thus, it may interfere with MER identification of the STN borders. However, activity returns to baseline shortly after administration stops. Therefore, propofol can be safely used until shortly before MER for DBS.

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.

Automated 3-dimensional brain atlas fitting to microelectrode recordings from deep brain stimulation surgeries

OBJECTIVE

Deep brain stimulation (DBS) surgeries commonly rely on brain atlases and microelectrode recordings (MER) to help identify the target location for electrode implantation. We present an automated method for optimally fitting a 3-dimensional brain atlas to intraoperative MER and predicting a target DBS electrode location in stereotactic coordinates for the patient.

METHODS

We retrospectively fit a 3-dimensional brain atlas to MER points from 10 DBS surgeries targeting the subthalamic nucleus (STN). We used a constrained optimization algorithm to maximize the MER points correctly fitted (i.e., contained) within the appropriate atlas nuclei. We compared our optimization approach to conventional anterior commissure-posterior commissure (AC/PC) scaling, and to manual fits performed by four experts. A theoretical DBS electrode target location in the dorsal STN was customized to each patient as part of the fitting process and compared to the location of the clinically defined therapeutic stimulation contact.

RESULTS

The human expert and computer optimization fits achieved significantly better fits than the AC/PC scaling (80, 81, and 41% of correctly fitted MER, respectively). However, the optimization fits were performed in less time than the expert fits and converged to a single solution for each patient, eliminating interexpert variance.

CONCLUSIONS AND SIGNIFICANCE

DBS therapeutic outcomes are directly related to electrode implantation accuracy. Our automated fitting techniques may aid in the surgical decision-making process by optimally integrating brain atlas and intraoperative neurophysiological data to provide a visual guide for target identification.