During Deep Brain Stimulation Research Articles

Monitoring Cognitive Functions During Deep Brain Stimulation Interventions by Real Time Neuropsychological Testing.

We monitored cognition in 14 Parkinson's disease (PD) patients during deep brain stimulation (DBS) surgery when the electrode was positioned at the target subthalamic nucleus (STN) (i.e., the STN motor area). We present the DBS-real-time neuropsychological testing (DBS-RTNT) protocol and our preliminary experience with it; we also compared the intraoperative patient performance with the baseline data. Compared with the baseline data, patients undergoing DBS-RTNT in the target area demonstrated a significantly decreased performance on some tasks belonging to the memory and executive function domains. Patients undergoing right hemisphere DBS-RTNT had significantly lower short-term memory and sequencing scores than did patients undergoing left hemisphere DBS-RTNT. PD patient cognitive performance should be monitored during DBS surgery, as STN-DBS may induce changes. These preliminary data contribute to improving our understanding of the anatomo-functional topography of the STN during DBS surgery, which will enable the identification of the best site for producing positive motor effects without causing negative cognitive and/or emotional changes in individual patients in the future. In principle, medications (i.e., patients who underwent surgery in a levodopa-off state) could have influenced our results; therefore, future studies are needed to address the possible confounding effects of levodopa use.

Characterization of subthalamic nucleus deep brain stimulation evoked resonant neural activity in a large animal model: A pilot study

Recent reports have described stimulation evoked resonant neural activity (ERNA) recorded in the subthalamic nucleus (STN) and globus pallidus internus (GPi) of patients during Deep Brain Stimulation (DBS) surgery. The constraints imposed during intraoperative recordings in patients limit the opportunity for in-depth study of new findings such as ERNA. In this pilot study, we leverage a large animal model to focus on detailed characterization of ERNA. Bilateral DBS leads were implanted in the STN in three ovine subjects and externalized for chronic use with custom stimulation and recording circuitry. ERNA was reliably recorded from the STN region in all three subjects with distinct specificity to recording and stimulation sites/contacts. Basic neural response characteristics such as input/output behavior, frequency response and strength/duration curves were evaluated. ERNA amplitude was highly dependent upon stimulation frequency, due to the interaction of the underlying resonant activity and the evoked response from each stimulus pulse. The results could be predicted by a mathematical model of constructive/destructive phase interference, and importantly, the evoked response latency. Significant time dependent dynamics in these evoked potentials were observed, which will be critically important to understand for future clinical applications. Based upon these recordings from leads in the STN region of healthy ovine subjects, these data confirm that DBS evokes high frequency resonant activity in the basal ganglia network. The clinical utility of ERNA remains to be demonstrated, but its direct association with DBS therapy makes it an interesting biomarker for potential use in contact selection and closed loop therapy.

Spiking Laguerre Volterra networks—predicting neuronal activity from local field potentials

Objective. Understanding the generative mechanism between local field potentials (LFP) and neuronal spiking activity is a crucial step for understanding information processing in the brain. Up to now, most approaches have relied on simply quantifying the coupling between LFP and spikes. However, very few have managed to predict the exact timing of spike occurrence based on LFP variations. Approach. Here, we fill this gap by proposing novel spiking Laguerre–Volterra network (sLVN) models to describe the dynamic LFP-spike relationship. Compared to conventional artificial neural networks, the sLVNs are interpretable models that provide explainable features of the underlying dynamics. Main results. The proposed networks were applied on extracellular microelectrode recordings of Parkinson’s Disease patients during deep brain stimulation (DBS) surgery. Based on the predictability of the LFP-spike pairs, we detected three neuronal populations with unique signal characteristics and sLVN model features. Significance. These clusters were indirectly associated with motor score improvement following DBS surgery, warranting further investigation into the potential of spiking activity predictability as an intraoperative biomarker for optimal DBS lead placement.

Intraoperative microelectrode recording during asleep deep brain stimulation of subthalamic nucleus for Parkinson Disease. A case series with systematic review of the literature

The use of microelectrode recording (MER) during deep brain stimulation (DBS) for Parkinson Disease is controversial. Furthermore, in asleep DBS anesthesia can impair the ability to record single-cell electric activity.The purpose of this study was to describe our surgical and anesthesiologic protocol for MER assessment during asleep subthalamic nucleus (STN) DBS and to put our findings in the context of a systematic review of the literature. Sixty-three STN electrodes were implanted in 32 patients under general anesthesia. A frameless technique using O-Arm scanning was adopted in all cases. Total intravenous anesthesia, monitored with bispectral index, was administered using a target controlled infusion of both propofol and remifentanil. A systematic review of the literature with metanalysis on MER in asleep vs awake STN DBS for Parkinson Disease was performed. In our series, MER could be reliably recorded in all cases, impacting profoundly on electrode positioning: the final position was located within 2 mm from the planned target only in 42.9% cases. Depth modification > 2 mm was necessary in 21 cases (33.3%), while in 15 cases (23.8%) a different track was used. At 1-year follow-up we observed a significant reduction in LEDD, UPDRS Part III score off-medications, and UPDRS Part III score on medications, as compared to baseline. The systematic review of the literature yielded 23 papers; adding the cases here reported, overall 1258 asleep DBS cases using MER are described. This technique was safe and effective: metanalysis showed similar, if not better, outcome of asleep vs awake patients operated using MER. MER are a useful and reliable tool during asleep STN DBS, leading to a fine tuning of electrode position in the majority of cases. Collaboration between neurosurgeon, neurophysiologist and neuroanesthesiologist is crucial, since slight modifications of sedation level can impact profoundly on MER reliability.

Generalisation capabilities of machine-learning algorithms for the detection of the subthalamic nucleus in micro-electrode recordings.

Micro-electrode recordings (MERs) are a key intra-operative modality used during deep brain stimulation (DBS) electrode implantation, which allow for a trained neurophysiologist to infer the anatomy in which the electrode is placed. As DBS targets are small, such inference is necessary to confirm that the electrode is correctly positioned. Recently, machine learning techniques have been used to augment the neurophysiologist's capability. The goal of this paper is to investigate the generalisability of these methods with respect to different clinical centres and training paradigms. Five deep learning algorithms for binary classification of MER signals have been implemented. Three databases from two different clinical centres have also been collected with differing size, acquisition hardware, and annotation protocol. Each algorithm has initially been trained on the largest database, then either directly tested or fine-tuned on the smaller databases in order to estimate their generalisability. As a reference, they have also been trained from scratch on the smaller databases as well in order to estimate the effect of the differing database sizes and annotation systems. Each network shows significantly reduced performance (on the order of a 6.5% to 16.0% reduction in balanced accuracy) when applied out-of-distribution. This reduction can be ameliorated through fine-tuning the network on the new database through transfer learning. Although, even for these small databases, it appears that retraining from scratch may still offer equivalent performance as fine-tuning with transfer learning. However, this is at the expense of significantly longer training times. Generalisability is an important criterion for the success of machine learning algorithms in clinic. We have demonstrated that a variety of recent machine learning algorithms for MER classification are negatively affected by domain shift, but that this can be quickly ameliorated through simple transfer learning procedures that can be readily performed for new centres.

Detection of evoked resonant neural activity in Parkinson's disease.

Objective. This study investigated a machine-learning approach to detect the presence of evoked resonant neural activity (ERNA) recorded during deep brain stimulation (DBS) of the subthalamic nucleus (STN) in people with Parkinson's disease.Approach. Seven binary classifiers were trained to distinguish ERNA from the background neural activity using eight different time-domain signal features.Main results. Nested cross-validation revealed a strong classification performance of 99.1% accuracy, with 99.6% specificity and 98.7% sensitivity to detect ERNA. Using a semi-simulated ERNA dataset, the results show that a signal-to-noise ratio of 15 dB is required to maintain a 90% classifier sensitivity. ERNA detection is feasible with an appropriate combination of signal processing, feature extraction and classifier. Future work should consider reducing the computational complexity for use in real-time applications.Significance. The presence of ERNA can be used to indicate the location of a DBS electrode array during implantation surgery. The confidence score of the detector could be useful for assisting clinicians to adjust the position of the DBS electrode array inside/outside the STN.

Differences in neuronal activity of subthalamic nuclei in asymmetric manifestation of Parkinson’s disease

Parkinson’s disease (PD) is a widely prevalent condition that has spurred extensive research into identifying its biological markers. Different models posit that various patterns of subthalamic nucleus (STN) activity may constitute such markers, but as yet, none have been established definitively. One major limitation of human studies in this area is the lack of a reliable control group. Therefore, patients with asymmetric motor symptoms in Parkinson’s disease are of great interest. I.I. Koloman and A.Sh. Chimagomedova [1] demonstrated that motor malfunctions’ asymmetry is indicative of asymmetry in the degenerative process within the substantia nigra. Additionally, while the disease normally manifests unilaterally, only a limited number of patients maintain notable distinctions in motor symptom severity as the condition advances. In our study, we analyzed the single-unit activity (SUA) and local field potentials (LFP) recordings obtained duringdeep brain stimulation (DBS) surgeries in 12 patients. Neurologists assessed the severity of bradykinesia, rigidity, and tremor using the UPDRS 3 scale to meet the inclusion criteria, and the difference between hemibodies for each symptom had to be at least 25%. The median disease duration at the time of surgery ranged from 6 to 22 years. The subthalamic nucleus that exhibits more pronounced disturbances on the contralateral side of the body is labeled as “affected”, while the opposite STN, acting as the conditional control, is labeled as “nonaffected”. For analysis, we used a hierarchical clustering method following Ward’s algorithm [2] to classify the recorded neurons into three groups based on their activity type: tonic, burst, and pause cells. We observed no significant differences in neuron activity between hemispheres, as well as the predicted hyperactivity of affected STN neurons outlined by the classical model [3] of basal ganglia functioning. However, the research demonstrates a significant increase in pause neurons and decrease in burst neurons within the affected nucleus. Furthermore, both of these types were found to be situated in the upper half of the STN, which is recognized as the motor region of the nucleus. Our hypothesis is that as the disease advances, some STN neurons (specifically burst neurons in our sample) modify their activity patterns towards a more rhythmic activity (pause neurons in our sample). This supports the notion that rhythmic STN neuron activity is linked to Parkinson’s disease [4]. LFPs were analyzed by estimating oscillation synchrony (o-scores) in multiple spectral bands following 1/f correction. Parkinson’s disease is typically associated with boosted oscillation power in the beta range (13–30 Hz). Our findings established that the power of oscillations in the low-frequency beta range (13–20 Hz) were indeed increased in the affected nucleus, but, in contrast to expectations, the power in the high-frequency part (20–30 Hz) of the band was significantly lower. This is in line with the modern theory of distinct physiological functions of oscillations in these subbands [5]. Additionally, we noticed a rise in frequency spectrum in the alpha range (8–12 Hz) and a decrease in the gamma range (30–60 Hz) in the impacted nucleus. Our findings indicate that a more rhythmic burst neuron activity mode and an increased number of pause neurons in the motor zone of the STN can serve as a marker for PD. Increased power oscillation in alpha and low-frequency beta bands, along with reduced synchronization in high-beta and gamma bands, may also be linked to this disorder. However, further investigation is necessary to determine their association with disease symptoms.

A PCA Based Artifact Removal Algorithm for Neural Signal Acquisition with kS/s Sampling Rate.

The contamination of stimulus artifacts during Deep Brain Stimulation (DBS) brings challenges to the signal processing, especially when the ratio of the kS/s sampling rate to the stimulation frequency is not an integer. In this work we study to deal with this problem. A transfer function is built to describe the relationship between the stimulation signal and the artifact at the acquisition site. A principal component analysis (PCA) based linear regression algorithm for eliminating the artifact is proposed. The algorithm can be used for the artifact removal with low sampling rate of the neural signal. Higher than 60% correlation coefficient of the artifact-free signal and the predetermined self-generated signal is achieved when the artifact is 60dB larger than the predetermined signal. The numerical recipe for the critical algorithm is also proposed, lowering the complexity from cubic degree to square degree.

The Network Systems Underlying Emotions: The Rational Foundation of Deep Brain Stimulation Psychosurgery.

Science and philosophy have tried to understand the origin of emotions for centuries. However, only in the last 150 years have we started to try to understand them in a neuroscientific scope. Emotions include physiological changes involving different systems, such as the endocrine or the musculoskeletal, but they also cause a conscious experience of those changes that are embedded in memory. In addition to the cortico-striato-thalamo-cortical circuit, which is the most important of the basal ganglia, the limbic system and prefrontal circuit are primarily involved in the process of emotion perceptions, thoughts, and memories. The purpose of this review is to describe the anatomy and physiology of the different brain structures involved in circuits that underlie emotions and behaviour, underlying the symptoms of certain psychiatric pathologies. These circuits are targeted during deep brain stimulation (DBS) and knowledge of them is mandatory to understand the clinical-physiological implications for the treatment. We summarize the main outcomes of DBS treatment in several psychiatric illness such as obsessive compulsive disorder, refractory depression, erethism and other conditions, aiming to understand the rationale for selecting these neural systems as targets for DBS.

β Oscillations of Dorsal STN as a Potential Biomarker in Parkinson's Disease Motor Subtypes: An Exploratory Study.

Parkinson's disease (PD) can be divided into postural instability and difficult gait (PIGD) and tremor dominance (TD) subtypes. However, potential neural markers located in the dorsal ventral side of the subthalamic nucleus (STN) for delineating the two subtypes of PIGD and TD have not been demonstrated. Therefore, this study aimed to investigate the spectral characteristics of PD on the dorsal ventral side. The differences in the β oscillation spectrum of the spike signal on the dorsal and ventral sides of the STN during deep brain stimulation (DBS) were investigated in 23 patients with PD, and coherence analysis was performed for both subtypes. Finally, each feature was associated with the Unified Parkinson's Disease Rating Scale (UPDRS). The β power spectral density (PSD) in the dorsal STN was found to be the best predictor of the PD subtype, with 82.6% accuracy. The PSD of dorsal STN β oscillations was greater in the PIGD group than in the TD group (22.17% vs. 18.22%; p < 0.001). Compared with the PIGD group, the TD group showed greater consistency in the β and γ bands. In conclusion, dorsal STN β oscillations could be used as a biomarker to classify PIGD and TD subtypes, guide STN-DBS treatment, and relate to some motor symptoms.

Targeting Accuracy of the Leksell Vantage Stereotactic System for Deep Brain Stimulation Surgery: A Retrospective Review.

Compared with the Leksell G frame, the new Leksell Vantage stereotactic headframe offers many benefits during deep brain stimulation (DBS) procedures. To define the "real-world" targeting accuracy of the Vantage frame for performing DBS lead implants. Retrospective review of all patients who underwent DBS using the Vantage frame with our surgeon between July 2021 and May 2022. Target and actual coordinates of implanted DBS leads were determined in BrainLab. Thirty-three electrode trajectories (17 left-sided, 16 right-sided) were included. On the right, actual electrode placement was, on average, 0.95 mm more medial, 1.06 mm more posterior, and 0.55 mm more superior compared with planned trajectories. The vector error was 1.93 ± 0.91 mm. On the left, actual electrode placement was 0.49 mm more lateral, 0.66 mm more posterior, and 0.67 mm more superior than planned trajectories. The vector error was 1.68 ± 0.66 mm. After excluding all electrodes that were repositioned after microelectrode recording and/or test stimulation, right-sided electrodes (n = 10) were 0.88 mm more medial, 0.94 mm more posterior, and 0.76 mm more superior than planned trajectories. The vector error was 1.74 ± 1.01 mm. Left-sided electrodes (n = 14) were 0.49 mm more lateral, 0.63 mm more posterior, and 0.49 mm more superior than planned trajectories. The vector error was 1.62 ± 0.68 mm. This study demonstrated the accuracy of the Leksell Vantage frame in comparison with the traditional Leksell G frame.

Excessive α-β Oscillations Mark Enlarged Motor Sign Severity and Parkinson's Disease Duration.

β Oscillations in the subthalamic nucleus (STN) have been proven to contribute to Parkinson's disease (PD), but the exact borders of β subbands vary substantially across the studies, and information regarding heterogeneity of β rhythmic activity is still limited. Recently, α oscillations in the basal ganglia have also become the focus of PD research. The aim was to study rhythmic oscillations in the STN in PD patients to identify different subbands with stable oscillatory peaks within a broad α-β range and to establish their associations with motor symptoms. Local field potentials inside the STN were recorded during deep brain stimulation (DBS) surgeries. After calculating power spectra and extracting an aperiodic component, oscillatory peaks in the 8- to 35-Hz range with amplitude exceeding 90th percentile were clustered into three bands. Peak parameters were estimated for two lower subbands. Clinical features were compared in patients with and without oscillation peaks in the lowest α-β subband. We isolated α-β (8-15 Hz), β (15-25 Hz), and β-γ (25-35 Hz) subbands within the 8- to 35-Hz spectral range using oscillatory parameters and Ward's hierarchical clustering. Additional α-β oscillatory peaks were found in about half of patients with β peaks; they were located more ventrally compared to β. We have found a significant increase in disease duration, bradykinesia, and rigidity scores in the group with additional α-β peaks. Increased α-β oscillations may emerge as additional phenomena complementing β oscillations; they may mark disease progression in PD and affect DBS stimulation setup. © 2023 International Parkinson and Movement Disorder Society.

Unsupervised Detection of Sub-Territories of the Subthalamic Nucleus During DBS Surgery With Manifold Learning.

During Deep Brain Stimulation (DBS) surgery for treating Parkinson's disease, detecting the Subthalamic Nucleus (STN) and its sub-territory called the Dorsolateral Oscillatory Region (DLOR) is crucial for adequate clinical outcomes. Currently, the detection is based on human experts, often guided by supervised machine learning detection algorithms. This procedure depends on the knowledge and experience of particular experts and on the amount and quality of the labeled data used for training the machine learning algorithms. In this paper, to circumvent such dependence and the inevitable bias introduced by the training data, we present a data-driven unsupervised algorithm for detecting the STN and the DLOR during DBS surgery based on an agnostic modeling approach. Given measurements, we extract new features and compute a variant of the Mahalanobis distance between these features. We show theoretically that this distance enhances the differences between measurements with different intrinsic characteristics. Incorporating the new features and distance into a manifold learning method, called Diffusion Maps, gives rise to a representation that is consistent with the underlying factors that govern the measurements. Since this representation does not rely on rigid modeling assumptions and is obtained solely from the measurements, it facilitates a broad range of detection tasks; here, we propose a specification for STN and DLOR detection during DBS surgery. We present detection results on 25 sets of measurements recorded from 16 patients during surgery. Compared to a supervised algorithm, our unsupervised method demonstrates similar results in detecting the STN and superior results in detecting the DLOR.

Subthalamic oscillatory activity during normal and impaired speech

ObjectiveWe studied the relationship between oscillatory activity in the subthalamic nucleus (STN) and speech production in order to better understand the functional role of the STN. MethodsWe simultaneously recorded subthalamic local field potentials and audio recordings from 5 patients with Parkinson's disease while they performed verbal fluency tasks. We then analyzed the oscillatory signals present in the subthalamic nucleus during these tasks. ResultsWe report that normal speech leads to a suppression of subthalamic alpha and beta power. Contrarily, a patient with motor blocks during speech initiation showed a low beta power increase. We also report an increase in error rates in the phonemic non-alternating verbal fluency task during deep brain stimulation (DBS). ConclusionsWe confirm previous findings that intact speech leads to desynchronization in the beta range in the STN. The speech related narrowband beta power increase in a patient with speech problems suggests that exaggerated synchronization in this frequency band is associated with motor blocks during speech initiation. The increased number of errors in verbal fluency tasks during DBS might be caused by an impairment of the response inhibition network caused by stimulation of the STN. SignificanceWe suggest that the inability to attenuate beta activity during motor processes is associated with motor freezing across motor behaviours such as speech and gait, as previously shown for freezing of gait.

Accurate Deep Brain Stimulation Lead Placement Concurrent With Research Electrocorticography.

Using electrocorticography for research (R-ECoG) during deep brain stimulation (DBS) surgery has advanced our understanding of human cortical-basal ganglia neurophysiology and mechanisms of therapeutic circuit modulation. The safety of R-ECoG has been established, but potential effects of temporary ECoG strip placement on targeting accuracy have not been reported. To determine whether temporary subdural electrode strip placement during DBS implantation surgery affects lead implantation accuracy. Twenty-four consecutive patients enrolled in a prospective database who underwent awake DBS surgery were identified. Ten of 24 subjects participated in R-ECoG. Lead locations were determined after fusing postoperative computed tomography scans into the surgical planning software. The effect of brain shift was quantified using Lead-DBS and analyzed in a mixed-effects model controlling for time interval to postoperative computed tomography. Targeting accuracy was reported as radial and Euclidean distance errors and compared with Mann-Whitney tests. Neither radial error nor Euclidean distance error differed significantly between R-ECoG participants and nonparticipants. Pneumocephalus volume did not differ between the 2 groups, but brain shift was slightly greater with R-ECoG. Pneumocephalus volume correlated with brain shift, but neither of these measures significantly correlated with Euclidean distance error. There were no complications in either group. In addition to an excellent general safety profile as has been reported previously, these results suggest that performing R-ECoG during DBS implantation surgery does not affect the accuracy of lead placement.

Clinical Application of a Stereotactic Frame–Specific 3D-Printed Attachment for Deep Brain Stimulation Surgery

Mispositioning of microelectrodes during deep brain stimulation (DBS) surgery can incur serious complications for patients. Current practice of creating a burr hole for introduction of the microelectrode is done freehand and can cause trajectory misalignment. Our objective was to create a sterilizable surgical adjunct to minimize error from burr hole placement. We designed and demonstrated the clinical use of a 3D printed surgical jig that can be mounted to the current Cosman-Roberts-Wells stereotactic frame. The jig allowed the accurate placement of the perforating burr for creating of the burr hole. Intraoperative usage of the jig in 11 patients who underwent bilateral DBS microelectrode placement for Parkinson's disease demonstrated high accuracy of microelectrode placement, with an average 1.18 mm deviation (range 0 - 2.7 mm) from intended trajectories. No intraoperative complications were encountered. This proof-of-concept study highlights the utility of 3D-printed surgical adjuncts that are fully customizable and rapidly produced to improve current surgical practice. The jig allows for a reduction in intraoperative surgery duration, need for multiple trajectories and risk of potentially devastating neurological complications. As demonstrated, 3D-printed devices are extremely useful as surgical adjuncts to optimize the safety and efficiency in deep brain stimulation surgeries.

Dexmedetomidine alleviates pain in MPTP-treated mice by activating the AMPK/mTOR/NF-κB pathways in astrocytes

Pain is a major non-motor symptom that contributes to impaired quality of life in Parkinson's disease (PD). However, the mechanisms and treatment of pain in PD have not been well studied. Dexmedetomidine (Dex) is used for analgesia and sedation during deep brain stimulation (DBS) and may reverse the progression of PD. Here, we explored the effect of Dex on Parkinson’s pain and the underlying mechanism. C57BL/6 mice were intraperitoneally injected with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP, 30 mg/kg) to establish a PD model. Then, the mice were treated with Dex (50 µg/kg) or Compound C (CC, 10 mg/kg, AMPK inhibitor). A motor behavioral test was used to validate the PD model, and a plantar test was conducted to assess mechanical and thermal stimulation thresholds. Immunofluorescence and western blotting were used to analyze the level of tyrosine hydroxylase (TH) in the substantia nigra (SN) and the expression of c-Fos, GFAP, p-AMPK, mTOR, NF-κB, TNFα, and IL-6 in the dorsal horn of the spinal cord (DHSC). We found that mice exhibited motor dysfunction and mechanical allodynia and thermal hyperalgesia after MPTP injection, and these changes were partially reversed by Dex. Dex also reduced MPTP-induced astrocyte activation and TNFα and IL-6 expression, increased p-AMPK and reduced mTOR and NF-κB expression in DHSC. Moreover, the effects of Dex were partially reversed by the AMPK inhibitor Compound C. Conclusions: These findings reveal that Dex protects dopaminergic neurons in PD and alleviates pain by reducing the activation of DHSC astrocytes through the AMPK/mTOR/NF-κB pathway. Therefore, Dex may be a potential drug for treating Parkinson’s pain.

Towards a Closed-loop Neuro-Robotic Approach to DBS Electrode Implantation based on Real-Time Wrist Rigidity Evaluation.

The iHandU system is a wearable device that quantitatively evaluates changes in wrist rigidity during Deep Brain Stimulation (DBS) surgery, allowing clinicians to find optimal stimulation settings that reduce patient symptoms. Robotic accuracy is also especially relevant in DBS surgery, as accurate electrode placement is required to increase effectiveness and reduce side effects. The main goal of this work is to integrate the advantages of each system in a closed-loop system between an industrial robot and the iHandU system. For this purpose, a comparative analysis of a Leksell stereotactic frame and neuro-robotic system accuracies was performed using a lab-made phantom. The neuro-robotic system reached 90% of trajectories, while the stereotactic frame reached all trajectories. There are significant differences in accuracy errors between these trajectories (p < 0.0001), which can be explained by the high correlation between the neuro-robotic system errors and the distance from the trajectory to the origin of the Leksell coordinate system (ρ = 0.72). Overall accuracy is comparable to existing neuro-robotic systems, achieving a deviation of (1.0 ± 0.5) mm at the target point. The accuracy of DBS electrode positioning and stimulation parameters choice leads to better long-term clinical outcomes in Parkinson's disease patients. Our neuro-robotic system combines real-time feedback assessment of the patient's symptomatic response and automatic positioning of the DBS electrode in a specific brain area.

Stimulation-induced changes at the electrode–tissue interface and their influence on deep brain stimulation

Objective. During deep brain stimulation (DBS) the electrode–tissue interface (ETI) forms a critical path between device and brain tissue. Although changes in the electrical double layer (EDL) and glial scar can impact stimulation efficacy, the effects of chronic DBS on the ETI have not yet been established. Approach. In this study, we characterised the ETI surrounding chronically implanted DBS electrodes in rats and compared the impedance and histological properties at the electrode interface in animals that received daily stimulation and in those where no stimulation was applied, up to 8 weeks post-surgery. A computational model was developed based on the experimental data, which allowed the dispersive electrical properties of the surrounding encapsulation tissue to be estimated. The model was then used to study the effect of stimulation-induced changes in the ETI on the electric field and neural activation during voltage- and current-controlled stimulation. Main results. Incorporating the observed changes in simulations in silico, we estimated the frequency-dependent dielectric properties of the EDL and surrounding encapsulation tissue. Through simulations we show how stimulation-induced changes in the properties of the ETI influence the electric field and alter neural activation during voltage-controlled stimulation. A substantial increase in the number of stimulated collaterals, and their distance from the electrode, was observed during voltage-controlled stimulation with stimulated ETI properties. In vitro examination of stimulated electrodes confirmed that high frequency stimulation leads to desorption of proteins at the electrode interface, with a concomitant reduction in impedance. Significance. The demonstration of stimulation-induced changes in the ETI has important implications for future DBS systems including closed-loop systems where the applied stimulation may change over time. Understanding these changes is particularly important for systems incorporating simultaneous stimulation and sensing, which interact dynamically with brain networks.

Electrophysiological confrontation of Lead-DBS-based electrode localizations in patients with Parkinson’s disease undergoing deep brain stimulation

Microelectrode recordings (MERs) are often used during deep brain stimulation (DBS) surgeries to confirm the position of electrodes in patients with advanced Parkinson’s disease.The present study focused on 32 patients who had undergone DBS surgery for advanced Parkinson’s disease. The first objective was to confront the anatomical locations of intraoperative individual MERs as determined electrophysiologically with those determined postoperatively by image reconstructions. The second aim was to search for differences in cell characteristics among the three subthalamic nucleus (STN) subdivisions and between the STN and other identified subcortical structures.Using the DISTAL atlas implemented in the Lead-DBS image reconstruction toolbox, each MER location was determined postoperatively and attributed to specific anatomical structures (sensorimotor, associative or limbic STN; substantia nigra [SN], thalamus, nucleus reticularis polaris, zona incerta [ZI]). The STN dorsal borders determined intraoperatively from electrophysiology were then compared with the STN dorsal borders determined by the reconstructed images. Parameters of spike clusters (firing rates, amplitudes – with minimum amplitude of 60 μV -, spike durations, amplitude spectral density of β-oscillations) were compared between structures (ANOVAs on ranks).Two hundred and thirty one MERs were analyzed (144 in 34 STNs, 7 in 4 thalami, 5 in 4 ZIs, 34 in 10 SNs, 41 others). The average difference in depth of the electrophysiological dorsal STN entry in comparison with the STN entry obtained with Lead-DBS was found to be of 0.1 mm (standard deviation: 0.8 mm). All 12 analyzed MERs recorded above the electrophysiologically-determined STN entry were confirmed to be in the thalamus or zona incerta. All MERs electrophysiologically attributed to the SN were confirmed to belong to this nucleus. However, 6/34 MERs that were electrophysiologically attributed to the ventral STN were postoperatively reattributed to the SN. Furthermore, 44 MERs of 3 trajectories, which were intraoperatively attributed to the STN, were postoperatively reattributed to the pallidum or thalamus.MER parameters seemed to differ across the STN, with higher spike amplitudes (H = 10.64, p < 0.01) and less prevalent β-oscillations (H = 9.81, p < 0.01) in the limbic STN than in the sensorimotor and associative subdivisions. Some cells, especially in the SN, showed longer spikes with lower firing rates, in agreement with described characteristics of dopamine cells. However, these probabilistic electrophysiological signatures might become clinically less relevant with the development of image reconstruction tools, which deserve to be applied intraoperatively.