Conventional Deep Brain Stimulation Research Articles

A Retrospective Comparison of Multiple Approaches to Anatomically Informed Contact Selection in Subthalamic Deep Brain Stimulation for Parkinson's Disease.

Conventional deep brain stimulation (DBS) programming via trial-and-error warrants improvement to ensure swift achievement of optimal outcomes. The definition of a sweet spot for subthalamic DBS in Parkinson's disease (PD-STN-DBS) may offer such advancement. This investigation examines the association of long-term motor outcomes with contact selection during monopolar review and different strategies for anatomically informed contact selection in a retrospective real-life cohort of PD-STN-DBS. We compared contact selection based on a monopolar review (MPR) to multiple anatomically informed contact selection strategies in a cohort of 28 PD patients with STN-DBS. We employed a commercial software package for contact selection based on visual assessment of individual anatomy following two predefined strategies and two algorithmic approaches with automatic targeting of either the sensorimotor STN or our previously published sweet spot. Similarity indices between chronic stimulation and contact selection strategies were correlated to motor outcomes at 12 months follow-up. Lateralized motor outcomes of chronic DBS were correlated to the similarity between chronic stimulation and visual contact selection targeting the dorsal part of the posterior STN (rho = 0.36, p = 0.007). Similar relationships could not be established for MPR or any of the other investigated strategies. Our data demonstrates that a visual contact selection following a predefined strategy can be linked to beneficial long-term motor outcomes in PD-STN-DBS. Since similar correlations could not be observed for the other approaches to anatomically informed contact selection, we conclude that clear definitions and prospective validation of any approach to imaging-based DBS-programming is warranted.

Real-time field-programmable gate array-based closed-loop deep brain stimulation platform targeting cerebellar circuitry rescues motor deficits in a mouse model of cerebellar ataxia.

The open-loop nature of conventional deep brain stimulation (DBS) produces continuous and excessive stimulation to patients which contributes largely to increased prevalence of adverse side effects. Cerebellar ataxia is characterized by abnormal Purkinje cells (PCs) dendritic arborization, loss of PCs and motor coordination, and muscle weakness with no effective treatment. We aim to develop a real-time field-programmable gate array (FPGA) prototype targeting the deep cerebellar nuclei (DCN) to close the loop for ataxia using conditional double knockout mice with deletion of PC-specific LIM homeobox (Lhx)1 and Lhx5, resulting in abnormal dendritic arborization and motor deficits. We implanted multielectrode array in the DCN and muscles of ataxia mice. The beneficial effect of open-loop DCN-DBS or closed-loop DCN-DBS was compared by motor behavioral assessments, electromyography (EMG), and neural activities (neurospike and electroencephalogram) in freely moving mice. FPGA board, which performed complex real-time computation, was used for closed-loop DCN-DBS system. Closed-loop DCN-DBS was triggered only when symptomatic muscle EMG was detected in a real-time manner, which restored motor activities, electroencephalogram activities and neurospike properties completely in ataxia mice. Closed-loop DCN-DBS was more effective than an open-loop paradigm as it reduced the frequency of DBS. Our real-time FPGA-based DCN-DBS system could be a potential clinical strategy for alleviating cerebellar ataxia and other movement disorders.

Directional deep brain stimulation in the management of Parkinson's disease: efficacy and constraints-an analytical appraisal.

Deep brain stimulation (DBS) is a widely employed treatment for Parkinson's disease. However, conventional DBS utilizing ring-shaped leads can often result in undesirable side effects by stimulating nearby brain structures, thus limiting its effectiveness. To address this issue, a novel DBS electrode was developed to allow for directional stimulation, avoiding neighboring structures. This literature review aims to analyze the disparities between conventional and directional DBS and discuss the benefits and limitations associated with this innovative electrode design, focusing on the stimulation-induced side effects it can or cannot mitigate. A comprehensive search was conducted in MEDLINE/PubMed, ScienceDirect, and EBSCO databases using the Boolean search criteria: "Deep brain stimulation" AND "Parkinson" AND "Directional." Following the application of inclusion and exclusion criteria, the selected articles were downloaded for full-text reading. Subsequently, the results were organized and analyzed to compose this article. Numerous studies have demonstrated that directional DBS effectively reduces side effects associated with brain stimulation, prevents the stimulation of non-targeted structures, and expands the therapeutic window, among other advantages. However, it has been observed that directional DBS may be more challenging to program and requires higher energy consumption. Furthermore, there is a lack of standardization among different manufacturers of directional DBS electrodes. Various stimulation-induced side effects, including dysarthria, dyskinesia, paresthesias, and symptoms of pyramidal tract activation, have been shown to be mitigated with the use of directional DBS. Moreover, directional electrodes offer a wider therapeutic window and a reduced incidence of undesired effects, requiring the same or lower minimum current for symptom relief compared to conventional DBS. The utilization of directional leads in DBS offers numerous advantages over conventional electrodes without significant drawbacks for patients undergoing directional DBS therapy.

Comparison of subthalamic unilateral and bilateral theta burst deep brain stimulation in Parkinson's disease.

High-frequency, conventional deep brain stimulation (DBS) of the subthalamic nucleus (STN) in Parkinson's disease (PD) is usually applied bilaterally under the assumption of additive effects due to interhemispheric crosstalk. Theta burst stimulation (TBS-DBS) represents a new patterned stimulation mode with 5 Hz interburst and 200 Hz intraburst frequency, whose stimulation effects in a bilateral mode compared to unilateral are unknown. This single-center study evaluated acute motor effects of the most affected, contralateral body side in 17 PD patients with unilateral subthalamic TBS-DBS and 11 PD patients with bilateral TBS-DBS. Compared to therapy absence, both unilateral and bilateral TBS-DBS significantly improved (p < 0.05) lateralized Movement Disorder Society-Unified Parkinson's Disease Rating Scale part III (MDS-UPDRS III) scores. Bilateral TBS-DBS revealed only slight, but not significant additional effects in comparison to unilateral TBS-DBS on total lateralized motor scores, but on the subitem lower limb rigidity. These results indicate that bilateral TBS-DBS has limited additive beneficial effects compared to unilateral TBS-DBS in the short term.

Ethical considerations in closed loop deep brain stimulation

BackgroundClosed-loop deep brain stimulation (DBS) uses feedback to infer a clinical state and adjust stimulation accordingly. This novel mechanism has several potential advantages over conventional DBS including reducing stimulation-induced side effects, improving battery longevity, and alleviating symptoms not optimally treated with standard protocols. However, several ethical challenges may arise with the implementation of this technology, particularly with respect to clinical decision making. ObjectiveTo discuss potential ethical and clinical dilemmas encountered in using closed-loop DBS for neurological and psychiatric disorders. MethodsThe relevant literature is reviewed and supplemented with discussion of ethically challenging clinical scenarios. We outline an ethical framework for addressing these issues and provide practical recommendations for clinicians and researchers. ResultsEthical considerations in closed-loop DBS revolve around five key principles: 1) risk/benefit analysis; 2) inclusion and exclusion criteria; 3) respect for patient autonomy; 4) quality of life and patient benefit; and 5) concerns associated with recording neural activity. Conclusion(s)Developing and implementing a pragmatic framework for ethical considerations in closed-loop DBS will be critical as this technology is utilized in patients with both neurologic and psychiatric indications.

Effects of deep brain stimulation on quantitative sleep electroencephalogram during non-rapid eye movement in Parkinson's disease.

Sleep dysfunction is frequently experienced by people with Parkinson's disease (PD) and negatively influences quality of life. Although subthalamic nucleus (STN) deep brain stimulation (DBS) can improve sleep in PD, sleep microstructural features such as sleep spindles provide additional insights about healthy sleep. For example, sleep spindles are important for better cognitive performance and for sleep consolidation in healthy adults. We hypothesized that conventional STN DBS settings would yield a greater enhancement in spindle density compared to OFF and low frequency DBS. In a previous within-subject, cross-sectional study, we evaluated effects of low (60 Hz) and conventional high (≥130 Hz) frequency STN DBS settings on sleep macroarchitectural features in individuals with PD. In this post hoc, exploratory analysis, we conducted polysomnography (PSG)-derived quantitative electroencephalography (qEEG) assessments in a cohort of 15 individuals with PD who had undergone STN DBS treatment a median 13.5 months prior to study participation. Fourteen participants had unilateral DBS and 1 had bilateral DBS. During three nonconsecutive nights of PSG, the participants were assessed under three different DBS conditions: DBS OFF, DBS LOW frequency (60 Hz), and DBS HIGH frequency (≥130 Hz). The primary objective of this study was to investigate the changes in sleep spindle density across the three DBS conditions using repeated-measures analysis of variance. Additionally, we examined various secondary outcomes related to sleep qEEG features. For all participants, PSG-derived EEG data underwent meticulous manual inspection, with the exclusion of any segments affected by movement artifact. Following artifact rejection, sleep qEEG analysis was conducted on frontal and central leads. The measures included slow wave (SW) and spindle density and morphological characteristics, SW-spindle phase-amplitude coupling, and spectral power analysis during non-rapid eye movement (NREM) sleep. The analysis revealed that spindle density was significantly higher in the DBS HIGH condition compared to the DBS LOW condition. Surprisingly, we found that SW amplitude during NREM was significantly higher in the DBS LOW condition compared to DBS OFF and DBS HIGH conditions. However, no significant differences were observed in the other sleep qEEG features during sleep at different DBS conditions. This study presents preliminary evidence suggesting that conventional HIGH frequency DBS settings enhance sleep spindle density in PD. Conversely, LOW frequency settings may have beneficial effects on increasing slow wave amplitude during sleep. These findings may inform mechanisms underlying subjective improvements in sleep quality reported in association with DBS. Moreover, this work supports the need for additional research on the influence of surgical interventions on sleep disorders, which are prevalent and debilitating non-motor symptoms in PD.

Accuracy and safety of deep brain stimulation assisted by a neurosurgical robot attachable to the operating table

Objective: Neurosurgical robots have recently been introduced to assist in guiding trajectories to achieve accurate targeting. This study investigated the accuracy of robot-assisted deep brain stimulation (DBS) compared with conventional DBS using the Leksell arc system.Methods: Between October 2020 and September 2021, 29 patients underwent DBS using the Leksell arc system, while from October 2021 to August 2022, 20 patients underwent DBS using a neurosurgical robot system (KYMERO; Koh Young Technology). Radial errors, total operative time (surgery time, preparation time), and clinical outcomes were compared between both groups through a retrospective analysis. Results: Radial errors were significantly smaller in robot-assisted DBS (1.63±0.39 mm vs. 1.01±0.39 mm, p&lt;0.01). Furthermore, the error of the second implantation was smaller than in the Leksell DBS group (0.216 vs. 0.005 mm, p&lt;0.001). The total operative time of DBS with Leksell frame was 3.47 hours (range, 2.38-5.85 hours) and the surgery time was 2.21 hours (range, 1.08-4.67 hours), while the total operative time in the robot group was 3.92 hours (range, 2.75-5.85 hours) and the surgical time was 2.60 hours (range, 1.02-4.67 hours) (p=0.055, p=0.258, respectively). The clinical outcome did not show a significant difference according to the surgical method.Conclusion: DBS with a robot-assisted system enables the accurate and consistent insertion of DBS electrodes with a lower error rate than the conventional method.

Wireless-Powering Deep Brain Stimulation Platform Based on 1D-Structured Magnetoelectric Nanochains Applied in Antiepilepsy Treatment.

Electrical deep brain stimulation (DBS) is a top priority for pharmacoresistant epilepsy treatment, while less-invasive wireless DBS is an urgent priority but challenging. Herein, we developed a conceptual wireless DBS platform to realize local electric stimulation via 1D-structured magnetoelectric Fe3O4@BaTiO3 nanochains (FBC). The FBC was facilely synthesized via magnetic-assisted interface coassembly, possessing a higher electrical output by inducing larger local strain from the anisotropic structure and strain coherence. Subsequently, wireless magnetoelectric neuromodulation in vitro was synergistically achieved by voltage-gated ion channels and to a lesser extent, the mechanosensitive ion channels. Furthermore, FBC less-invasively injected into the anterior nucleus of the thalamus (ANT) obviously inhibited acute and continuous seizures under magnetic loading, exhibiting excellent therapeutic effects in suppressing both high voltage electroencephalogram signals propagation and behavioral seizure stage and neuroprotection of the hippocampus mediated via the Papez circuit similar to conventional wired-in DBS. This work establishes an advanced antiepilepsy strategy and provides a perspective for other neurological disorder treatment.

Closed-Loop Adaptive Deep Brain Stimulation in Parkinson's Disease: Procedures to Achieve It and Future Perspectives.

Parkinson's disease (PD) is a neurodegenerative disease with a heavy burden on patients, families, and society. Deep brain stimulation (DBS) can improve the symptoms of PD patients for whom medication is insufficient. However, current open-loop uninterrupted conventional DBS (cDBS) has inherent limitations, such as adverse effects, rapid battery consumption, and a need for frequent parameter adjustment. To overcome these shortcomings, adaptive DBS (aDBS) was proposed to provide responsive optimized stimulation for PD. This topic has attracted scientific interest, and a growing body of preclinical and clinical evidence has shown its benefits. However, both achievements and challenges have emerged in this novel field. To date, only limited reviews comprehensively analyzed the full framework and procedures for aDBS implementation. Herein, we review current preclinical and clinical data on aDBS for PD to discuss the full procedures for its achievement and to provide future perspectives on this treatment.

On-Off and Proportional Closed-Loop Adaptive Deep Brain Stimulation Reduces Motor Symptoms in Freely Moving Hemiparkinsonian Rats

ObjectivesClosed-loop adaptive deep brain stimulation (aDBS) continuously adjusts stimulation parameters, with the potential to improve efficacy and reduce side effects of deep brain stimulation (DBS) for Parkinson’s disease (PD). Rodent models can provide an effective platform for testing aDBS algorithms and establishing efficacy before clinical investigation. In this study, we compare two aDBS algorithms, on-off and proportional modulation of DBS amplitude, with conventional DBS in hemiparkinsonian rats. Materials and MethodsDBS of the subthalamic nucleus (STN) was delivered wirelessly in freely moving male and female hemiparkinsonian (N = 7) and sham (N = 3) Wistar rats. On-off and proportional aDBS, based on STN local field potential beta power, were compared with conventional DBS and three control stimulation algorithms. Behavior was assessed during cylinder tests (CT) and stepping tests (ST). Successful model creation was confirmed via apomorphine-induced rotation test and Tyrosine Hydroxylase–immunocytochemistry. Electrode location was histologically confirmed. Data were analyzed using linear mixed models. ResultsContralateral paw use in parkinsonian rats was reduced to 20% and 25% in CT and ST, respectively. Conventional, on-off, and proportional aDBS significantly improved motor function, restoring contralateral paw use to approximately 45% in both tests. No improvement in motor behavior was observed with either randomly applied on-off or low-amplitude continuous stimulation. Relative STN beta power was suppressed during DBS. Relative power in the alpha and gamma bands decreased and increased, respectively. Therapeutically effective adaptive DBS used approximately 40% less energy than did conventional DBS. ConclusionsAdaptive DBS, using both on-off and proportional control schemes, is as effective as conventional DBS in reducing motor symptoms of PD in parkinsonian rats. Both aDBS algorithms yield substantial reductions in stimulation power. These findings support using hemiparkinsonian rats as a viable model for testing aDBS based on beta power and provide a path to investigate more complex closed-loop algorithms in freely behaving animals.

Oscillatory beta dynamics inform biomarker-driven treatment optimization for Parkinson's disease.

Parkinson's disease (PD) is a neurodegenerative disorder characterized by loss of dopaminergic neurons and dysregulation of the basal ganglia. Cardinal motor symptoms include bradykinesia, rigidity, and tremor. Deep brain stimulation (DBS) of select subcortical nuclei is standard of care for medication-refractory PD. Conventional open-loop DBS delivers continuous stimulation with fixed parameters that do not account for a patient's dynamic activity state or medication cycle. In comparison, closed-loop DBS, or adaptive DBS (aDBS), adjusts stimulation based on biomarker feedback that correlates with clinical state. Recent work has identified several neurophysiological biomarkers in local field potential recordings from PD patients, the most promising of which are 1) elevated beta (∼13-30 Hz) power in the subthalamic nucleus (STN), 2) increased beta synchrony throughout basal ganglia-thalamocortical circuits, notably observed as coupling between the STN beta phase and cortical broadband gamma (∼50-200 Hz) amplitude, and 3) prolonged beta bursts in the STN and cortex. In this review, we highlight relevant frequency and time domain features of STN beta measured in PD patients and summarize how spectral beta power, oscillatory beta synchrony, phase-amplitude coupling, and temporal beta bursting inform PD pathology, neurosurgical targeting, and DBS therapy. We then review how STN beta dynamics inform predictive, biomarker-driven aDBS approaches for optimizing PD treatment. We therefore provide clinically useful and actionable insight that can be applied toward aDBS implementation for PD.

HH model based smart deep brain stimulator to detect, predict and control epilepsy using machine learning algorithm

BackgroundEpilepsy is the most common neurological disorder in the world. To control epilepsy, deep brain stimulation is one of the widely accepted treatment techniques. However, conventional deep brain stimulation technique provides continuous stimulation without optimizing the stimulation parameters, resulting in adverse side effects and unexpected death. Hence, understanding the dynamic behavior of brain neural networks at a cellular level is required for patient-specific epilepsy treatment. Considering the underlying mechanism of a single neuronal shift in the brain neural network, computational model-based techniques have a new face for healthcare, which aims to develop effective medical devices for preclinical investigations. New methodThis paper discusses the design of a Smart Deep Brain Stimulator (SDBS) using the Hodgkin-Huxley (HH) conductance-based cellular model of brain neurons to automatically detect, predict and regulate epilepsy against patient-specific conditions. Epileptic activity is simulated as a spike train of action potential due to sodium and potassium channel conductance variations in the single-neuron HH model. The proposed SDBS consists of three components:- i) seizure detection using bagging and boosting-based ensemble machine learning classifiers, ii) channel conductance prediction using Long Short Term Memory-Recurrent Neural Network (LSTM-RNN) based Deep Neural Network (DNN) for updating model parameters of brain neuron, and iii) model-based intelligent control of epileptic seizure with Nonlinear Autoregressive Moving Average-L2 (NARMA-L2) Controller and Nonlinear Model Predictive Controller (NMPC). ResultsFor effective treatment, improving the overall accuracy and efficiency of SDBS is essential. For epilepsy detection, the ensemble bagging machine learning algorithm provides better accuracy of 92.7% compared to the ensemble boosting algorithm. LSTM-RNN deep neural network model with four layers predicts the variations in channel conductance with Root Mean Square Error (RMSE) of 0.00568 and 0.009081 for sodium and potassium channel conductance, respectively. From the closed-loop performances of SDBS with an intelligent control scheme, it is observed that SDBS with NMPC provides efficient and accurate stimulation with minimum energy consumption. From a stability point of view, SDBS with NMPC provides better stability than SDBS with NARMA-L2 Controller. Comparison with existing methodThe proposed SDBS is designed to generate accurate stimulation pulses for epilepsy patients with specific conditions depending on the neuronal activity of a single neuron. Moreover, it will also adapt to the dynamic condition of epilepsy patients. The existing deep brain stimulator continuously provides stimulation pulses without adapting to the patient’s conditions. ConclusionThe proposed SDBS could provide patient-specific treatment based on sodium/potassium channel conductance variations of brain neurons. It will help increase the use of deep brain stimulation techniques and reduce sudden death. Furthermore, the proposed technique will be extended to neural network models with larger neuronal populations to improve the practical feasibility.

Efficient suppression of parkinsonian beta oscillations in a closed-loop model of deep brain stimulation with amplitude modulation.

Parkinson's disease (PD) is a movement disorder characterized by the pathological beta band (15-30 Hz) neural oscillations within the basal ganglia (BG). It is shown that the suppression of abnormal beta oscillations is correlated with the improvement of PD motor symptoms, which is a goal of standard therapies including deep brain stimulation (DBS). To overcome the stimulation-induced side effects and inefficiencies of conventional DBS (cDBS) and to reduce the administered stimulation current, closed-loop adaptive DBS (aDBS) techniques were developed. In this method, the frequency and/or amplitude of stimulation are modulated based on various disease biomarkers. Here, by computational modeling of a cortico-BG-thalamic network in normal and PD conditions, we show that closed-loop aDBS of the subthalamic nucleus (STN) with amplitude modulation leads to a more effective suppression of pathological beta oscillations within the parkinsonian BG. Our results show that beta band neural oscillations are restored to their normal range and the reliability of the response of the thalamic neurons to motor cortex commands is retained due to aDBS with amplitude modulation. Furthermore, notably less stimulation current is administered during aDBS compared with cDBS due to a closed-loop control of stimulation amplitude based on the STN local field potential (LFP) beta activity. Efficient models of closed-loop stimulation may contribute to the clinical development of optimized aDBS techniques designed to reduce potential stimulation-induced side effects of cDBS in PD patients while leading to a better therapeutic outcome.

Efficacy of short pulse and conventional deep brain stimulation in Parkinson's disease: a systematic review and meta-analysis.

Deep brain stimulation (DBS) is a common treatment for Parkinson's disease. However, the clinical efficacy of short pulse width DBS (spDBS) compared with conventional DBS (cDBS) is still unknown. This meta-analysis investigated the effectiveness of spDBS versus cDBS in patients with PD. Four databases (PubMed, Cochrane, Web of Science, and Embase) were independently searched until October 2021 by two reviewers. We utilized the following scales and items: therapeutic windows (TW), efficacy threshold, side effect threshold, Movement Disorder Society-Sponsored Revision Unified Parkinson's Disease Rating Scale (MDS-UPDRS) part III off-medication score, Speech Intelligence Test (SIT), and Freezing of Gait Questionnaire (FOG-Q). The analysis included seven studies with a total of 87 patients. The results indicated that spDBS significantly widened the therapeutic windows (0.99, 95% CI = 0.61 to 1.38) while increasing the threshold amplitudes of side effects (2.25, 95% CI = 1.69 to 2.81) and threshold amplitudes of effects (1.60, 95% CI = 0.84 to 2.36). There was no statistically significant difference in UPDRS part III, SIT, and FOG-Q scores between spDBS and cDBS groups, suggesting that treatment with both cDBS and spDBS may result in similar effects of improved dysarthria and gait disorders. Compared with cDBS, spDBS is effective in expanding TW. Both types of deep brain stimulation resulted in improved gait disorders and speech intelligibility.

Synaptic reshaping of plastic neuronal networks by periodic multichannel stimulation with single-pulse and burst stimuli.

Synaptic dysfunction is associated with several brain disorders, including Alzheimer's disease, Parkinson's disease (PD) and obsessive compulsive disorder (OCD). Utilizing synaptic plasticity, brain stimulation is capable of reshaping synaptic connectivity. This may pave the way for novel therapies that specifically counteract pathological synaptic connectivity. For instance, in PD, novel multichannel coordinated reset stimulation (CRS) was designed to counteract neuronal synchrony and down-regulate pathological synaptic connectivity. CRS was shown to entail long-lasting therapeutic aftereffects in PD patients and related animal models. This is in marked contrast to conventional deep brain stimulation (DBS) therapy, where PD symptoms return shortly after stimulation ceases. In the present paper, we study synaptic reshaping by periodic multichannel stimulation (PMCS) in networks of leaky integrate-and-fire (LIF) neurons with spike-timing-dependent plasticity (STDP). During PMCS, phase-shifted periodic stimulus trains are delivered to segregated neuronal subpopulations. Harnessing STDP, PMCS leads to changes of the synaptic network structure. We found that the PMCS-induced changes of the network structure depend on both the phase lags between stimuli and the shape of individual stimuli. Single-pulse stimuli and burst stimuli with low intraburst frequency down-regulate synapses between neurons receiving stimuli simultaneously. In contrast, burst stimuli with high intraburst frequency up-regulate these synapses. We derive theoretical approximations of the stimulation-induced network structure. This enables us to formulate stimulation strategies for inducing a variety of network structures. Our results provide testable hypotheses for future pre-clinical and clinical studies and suggest that periodic multichannel stimulation may be suitable for reshaping plastic neuronal networks to counteract pathological synaptic connectivity. Furthermore, we provide novel insight on how the stimulus type may affect the long-lasting outcome of conventional DBS. This may strongly impact parameter adjustment procedures for clinical DBS, which, so far, primarily focused on acute effects of stimulation.

A systematic review of local field potential physiomarkers in Parkinson’s disease: from clinical correlations to adaptive deep brain stimulation algorithms

Deep brain stimulation (DBS) treatment has proven effective in suppressing symptoms of rigidity, bradykinesia, and tremor in Parkinson’s disease. Still, patients may suffer from disabling fluctuations in motor and non-motor symptom severity during the day. Conventional DBS treatment consists of continuous stimulation but can potentially be further optimised by adapting stimulation settings to the presence or absence of symptoms through closed-loop control. This critically relies on the use of ‘physiomarkers’ extracted from (neuro)physiological signals. Ideal physiomarkers for adaptive DBS (aDBS) are indicative of symptom severity, detectable in every patient, and technically suitable for implementation. In the last decades, much effort has been put into the detection of local field potential (LFP) physiomarkers and in their use in clinical practice. We conducted a research synthesis of the correlations that have been reported between LFP signal features and one or more specific PD motor symptoms. Features based on the spectral beta band (~ 13 to 30 Hz) explained ~ 17% of individual variability in bradykinesia and rigidity symptom severity. Limitations of beta band oscillations as physiomarker are discussed, and strategies for further improvement of aDBS are explored.

Feasibility of epidural temporal interference stimulation for minimally invasive electrical deep brain stimulation: simulation and phantom experimental studies

Objective. Temporal interference stimulation (TIS) has shown the potential as a new method for selective stimulation of deep brain structures in small animal experiments. However, it is challenging to deliver a sufficient temporal interference (TI) current to directly induce an action potential in the deep area of the human brain when electrodes are attached to the scalp because the amount of injection current is generally limited due to safety issues. Thus, we propose a novel method called epidural TIS (eTIS) to address this issue; in this method, the electrodes are attached to the epidural surface under the skull. Approach. We employed finite element method (FEM)-based electric field simulations to demonstrate the feasibility of eTIS. We first optimized the electrode conditions to deliver maximum TI currents to each of the three different targets (anterior hippocampus, subthalamic nucleus, and ventral intermediate nucleus) based on FEM, and compared the stimulation focality between eTIS and transcranial TIS (tTIS). Moreover, we conducted realistic skull-phantom experiments for validating the accuracy of the computational simulation for eTIS. Main results. Our simulation results showed that eTIS has the advantage of avoiding the delivery of TI currents over unwanted neocortical regions compared with tTIS for all three targets. It was shown that the optimized eTIS could induce neural action potentials at each of the three targets when a sufficiently large current equivalent to that for epidural cortical stimulation is injected. Additionally, the simulated results and measured results via the phantom experiments were in good agreement. Significance. We demonstrated the feasibility of eTIS, facilitating more focalized and stronger electrical stimulation of deep brain regions than tTIS, with the relatively less invasive placement of electrodes than conventional deep brain stimulation via computational simulation and realistic skull phantom experiments.

TU-197. Eight-hours conventional versus adaptive Deep Brain Stimulation of the Subthalamic Nucleus (STN) in Parkinson’s Disease

TU-197. Eight-hours conventional versus adaptive Deep Brain Stimulation of the Subthalamic Nucleus (STN) in Parkinson’s Disease

The modulatory effect of self-paced and cued motor execution on subthalamic beta-bursts in Parkinson's disease: Evidence from deep brain recordings in humans

Deep brain stimulation (DBS) electrodes provide an unparalleled window to record and investigate neuronal activity right at the core of pathological brain circuits. In Parkinson's disease (PD), basal ganglia beta-oscillatory activity (13–35 Hz) seems to play an outstanding role. Conventional DBS, which globally suppresses beta-activity, does not meet the requirements of a targeted treatment approach given the intricate interplay of physiological and pathological effects of beta-frequencies. Here, we wanted to characterise the local field potential (LFP) in the subthalamic nucleus (STN) in terms of beta-burst prevalence, amplitude and length between movement and rest as well as during self-paced as compared to goal-directed motor control. Our electrophysiological recordings from externalised DBS-electrodes in nine patients with PD showed a marked decrease in beta-burst durations and prevalence during movement as compared to rest as well as shorter and less frequent beta-bursts during cued as compared to self-paced movements. These results underline the importance of beta-burst modulation in movement generation and are in line with the clinical observation that cued motor control is better preserved than self-paced movements. Furthermore, our findings motivate the use of adaptive DBS based on beta-bursts, which selectively trim longer beta-bursts, as it is more suitable and efficient over a range of motor behaviours than conventional DBS.

Techniques of Frameless Robot-Assisted Deep Brain Stimulation and Accuracy Compared with the Frame-Based Technique.

sBackground: Frameless robot-assisted deep brain stimulation (DBS) is an innovative technique for leads implantation. This study aimed to evaluate the accuracy and precision of this technique using the Sinovation SR1 robot. Methods: 35 patients with Parkinson’s disease who accepted conventional frame-based DBS surgery (n = 18) and frameless robot-assisted DBS surgery (n = 17) by the same group of neurosurgeons were analyzed. The coordinate of the tip of the intended trajectory was recorded as xi, yi, and zi. The actual position of lead implantation was recorded as xa, ya, and za. The vector error was calculated by the formula of √(xi − xa)2 + (yi − ya)2 + (zi − za)2 to evaluate the accuracy. Results: The vector error was 1.52 ± 0.53 mm (range: 0.20–2.39 mm) in the robot-assisted group and was 1.77 ± 0.67 mm (0.59–2.98 mm) in the frame-based group with no significant difference between two groups (p = 0.1301). In 10.7% (n = 3) frameless robot-assisted implanted leads, the vector error was greater than 2.00 mm with a maximum offset of 2.39 mm, and in 35.5% (n = 11) frame-based implanted leads, the vector error was larger than 2.00 mm with a maximum offset of 2.98 mm. Leads were more posterior than planned trajectories in the robot-assisted group and more medial and posterior in the conventional frame-based group. Conclusions: Awake frameless robot-assisted DBS surgery was comparable to the conventional frame-based technique in the accuracy and precision for leads implantation.