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Background: 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. Objective: 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. Methods: 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. Results: 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. Conclusions: 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.

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BACKGROUND: Subthalamic nucleus deep brain stimulation (STN-DBS) is an effective treatment for advanced Parkinson's disease (PD). Clinical outcomes after DBS can be limited by poor programming, which remains a clinically driven, lengthy and iterative process. Electrophysiological recordings in PD patients undergoing STN-DBS have shown an association between STN spectral power in the beta frequency band (beta power) and the severity of clinical symptoms. New commercially-available DBS devices now enable the recording of STN beta oscillations in chronically-implanted PD patients, thereby allowing investigation into the use of beta power as a biomarker for DBS programming. OBJECTIVE: To determine the potential advantages of beta-guided DBS programming over clinically and image-guided programming in terms of clinical efficacy and programming time. METHODS: We conducted a randomized, blinded, three-arm, crossover clinical trial in eight Parkinson's patients with STN-DBS who were evaluated three months after DBS surgery. We compared clinical efficacy and time required for each DBS programming paradigm, as well as DBS parameters and total energy delivered between the three strategies (beta-, clinically- and image-guided). RESULTS: All three programming methods showed similar clinical efficacy, but the time needed for programming was significantly shorter for beta- and image-guided programming compared to clinically-guided programming (p < 0.001). CONCLUSION: Beta-guided programming may be a useful and more efficient approach to DBS programming in Parkinson's patients with STN-DBS. It takes significantly less time to program than traditional clinically-based programming, while providing similar symptom control. In addition, it is readily available within the clinical DBS programmer, making it a valuable tool for improving current clinical practice.

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OBJECTIVE: Subthalamic nucleus (STN) beta activity (13-30 Hz) is the most accepted biomarker for adaptive deep brain stimulation (aDBS) for Parkinson's disease (PD). We hypothesize that different frequencies within the beta range may exhibit distinct temporal dynamics and, as a consequence, different relationships to motor slowing and adaptive stimulation patterns. We aim to highlight the need for an objective method to determine the aDBS feedback signal. METHODS: STN LFPs were recorded in 15 PD patients at rest and while performing a cued motor task. The impact of beta bursts on motor performance was assessed for different beta candidate frequencies: the individual frequency strongest associated with motor slowing, the individual beta peak frequency, the frequency most modulated by movement execution, as well as the entire-, low- and high beta band. How these candidate frequencies differed in their bursting dynamics and theoretical aDBS stimulation patterns was further investigated. RESULTS: The individual motor slowing frequency often differs from the individual beta peak or beta-related movement-modulation frequency. Minimal deviations from a selected target frequency as feedback signal for aDBS leads to a substantial drop in the burst overlapping and in the alignment of the theoretical onset of stimulation triggers (to âˆ¼ 75% for 1 Hz, to âˆ¼ 40% for 3 Hz deviation). CONCLUSIONS: Clinical-temporal dynamics within the beta frequency range are highly diverse and deviating from a reference biomarker frequency can result in altered adaptive stimulation patterns. SIGNIFICANCE: A clinical-neurophysiological interrogation could be helpful to determine the patient-specific feedback signal for aDBS.

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BACKGROUND: Suppression of pathologically altered activity in the beta-band has previously been suggested as a biomarker for feedback-based neurostimulation in subthalamic deep brain stimulation (STN-DBS) for Parkinson's Disease (PD). OBJECTIVE: To assess the utility of beta-band suppression as a tool for contact selection in STN-DBS for PD. METHODS: A sample of seven PD patients (13 hemispheres) with newly implanted directional DBS leads of the STN were recorded during a standardized monopolar contact review (MPR). Recordings were received from contact pairs adjacent to the stimulation contact. The degree of beta-band suppression for each investigated contact was then correlated to the respective clinical results. Additionally, we have implemented a cumulative ROC analysis, to test the predictive value of beta-band suppression on the clinical efficacy of the respective contacts. RESULTS: Stimulation ramping led to frequency-specific changes in the beta-band, while lower frequencies remained unaffected. Most importantly, our results showed that the degree of low beta-band suppression from baseline activity (stimulation off) served as a predictor for clinical efficacy of the respective stimulation contact. In contrast suppression of high beta-band activity yielded no predictive power. CONCLUSION: The degree of low beta-band suppression can serve as a time-saving, objective tool for contact selection in STN-DBS.

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Objective.deep brain stimulation (DBS) of the ventral internal capsule/striatum (VCVS) is a potentially effective treatment for several mental health disorders when conventional therapeutics fail. Its effectiveness, however, depends on correct programming to engage VCVS sub-circuits. VCVS programming is currently an iterative, time-consuming process, with weeks between setting changes and reliance on noisy, subjective self-reports. An objective measure of circuit engagement might allow individual settings to be tested in seconds to minutes, reducing the time to response and increasing patient and clinician confidence in the chosen settings. Here, we present an approach to measuring and optimizing that circuit engagement.Approach.we leverage prior results showing that effective VCVS DBS engages cognitive control circuitry and improves performance on the multi-source interference task, that this engagement depends primarily on which contact(s) are activated, and that circuit engagement can be tracked through a state space modeling framework. We develop a simulation framework based on those empirical results, then combine this framework with an adaptive optimizer to simulate a principled exploration of electrode contacts and identify the contacts that maximally improve cognitive control. We explore multiple optimization options (algorithms, number of inputs, speed of stimulation parameter changes) and compare them on problems of varying difficulty.Main results.we show that an upper confidence bound algorithm outperforms other optimizers, with roughly 80% probability of convergence to a global optimum when used in a majority-vote ensemble.Significance.we show that the optimization can converge even with lag between stimulation and effect, and that a complete optimization can be done in a clinically feasible timespan (a few hours). Further, the approach requires no specialized recording or imaging hardware, and thus could be a scalable path to expand the use of DBS in psychiatric and other non-motor applications.

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BACKGROUND: Electrode reconstruction for postoperative deep brain simulation (DBS) can be achieved manually using a surgical planning system such as Surgiplan, or in a semi-automated manner using software such as the Lead-DBS toolbox. However, the accuracy of Lead-DBS has not been thoroughly addressed. METHODS: In our study, we compared the DBS reconstruction results of Lead-DBS and Surgiplan. We included 26 patients (21 with Parkinson's disease and 5 with dystonia) who underwent subthalamic nucleus (STN)-DBS, and reconstructed the DBS electrodes using the Lead-DBS toolbox and Surgiplan. The electrode contact coordinates were compared between Lead-DBS and Surgiplan with postoperative CT and MRI. The relative positions of the electrode and STN were also compared between the methods. Finally, the optimal contact during follow-up was mapped onto the Lead-DBS reconstruction results to check for overlap between the contacts and the STN. RESULTS: We found significant differences in all axes between Lead-DBS and Surgiplan with postoperative CT, with the mean variance for the X, Y, and Z coordinates being -0.13, -1.16, and 0.59 mm, respectively. Y and Z coordinates showed significant differences between Lead-DBS and Surgiplan with either postoperative CT or MRI. However, no significant difference in the relative distance of the electrode and the STN was found between the methods. All optimal contacts were located in the STN, with 70% of them located within the dorsolateral region of the STN in the Lead-DBS results. CONCLUSIONS: Although significant differences in electrode coordinates existed between Lead-DBS and Surgiplan, our results suggest that the coordinate difference was around 1 mm, and Lead-DBS can capture the relative distance between the electrode and the DBS target, suggesting it is reasonably accurate for postoperative DBS reconstruction.

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BACKGROUND: Deep brain stimulation (DBS) parameter fine-tuning after lead implantation is laborious work because of the almost uncountable possible combinations. Patients and practitioners often gain the perception that assistive devices could be beneficial for adjusting settings effectively. OBJECTIVE: We aimed at a proof-of-principle study to assess the benefits of noninvasive movement recordings as a means to predict best DBS settings. MATERIALS AND METHODS: For this study, 32 patients with idiopathic Parkinson's disease, under chronic subthalamic nucleus stimulation with directional leads, were recorded. During monopolar review, each available contact was activated with currents between 0.5 and 5 mA, and diadochokinesia, rigidity, and tapping ability were rated clinically. Moreover, participants' movements were measured during four simple hand movement tasks while wearing a commercially available armband carrying an inertial measurement unit (IMU). We trained random forest models to learn the relations between clinical ratings, electrode settings, and movement features obtained from the IMU. RESULTS: Firstly, we could show that clinical mobility ratings can be predicted from IMU features with correlations of up to r = 0.68 between true and predicted values. Secondly, these features also enabled a prediction of DBS parameters, which showed correlations of up to approximately r = 0.8 with clinically optimal DBS settings and were associated with congruent volumes of tissue activated. CONCLUSION: Movement recordings from customer-grade mobile IMU carrying devices are promising candidates, not only for remote symptom assessment but also for closed-loop DBS parameter adjustment, and could thus extend the list of available aids for effective programming beyond imaging techniques.

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BACKGROUND: Deep brain stimulation (DBS) programming of multicontact DBS leads relies on a very time-consuming manual screening procedure, and strategies to speed up this process are needed. Beta activity in subthalamic nucleus (STN) local field potentials (LFP) has been suggested as a promising marker to index optimal stimulation contacts in patients with Parkinson disease. OBJECTIVE: In this study, we investigate the advantage of algorithmic selection and combination of multiple resting and movement state features from STN LFPs and imaging markers to predict three relevant clinical DBS parameters (clinical efficacy, therapeutic window, side-effect threshold). MATERIALS AND METHODS: STN LFPs were recorded at rest and during voluntary movements from multicontact DBS leads in 27 hemispheres. Resting- and movement-state features from multiple frequency bands (alpha, low beta, high beta, gamma, fast gamma, high frequency oscillations [HFO]) were used to predict the clinical outcome parameters. Subanalyses included an anatomical stimulation sweet spot as an additional feature. RESULTS: Both resting- and movement-state features contributed to the prediction, with resting (fast) gamma activity, resting/movement-modulated beta activity, and movement-modulated HFO being most predictive. With the proposed algorithm, the best stimulation contact for the three clinical outcome parameters can be identified with a probability of almost 90% after considering half of the DBS lead contacts, and it outperforms the use of beta activity as single marker. The combination of electrophysiological and imaging markers can further improve the prediction. CONCLUSION: LFP-guided DBS programming based on algorithmic selection and combination of multiple electrophysiological and imaging markers can be an efficient approach to improve the clinical routine and outcome of DBS patients.

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Intra- and perioperatively recorded local field potential (LFP) activity of the nucleus subthalamicus (STN) has been suggested to guide contact selection in patients undergoing deep brain stimulation (DBS) for Parkinson's disease (PD). Despite the invention of sensing capacities in chronically implanted devices, a comprehensible algorithm that enables contact selection using such recordings is still lacking. We evaluated a fully automated algorithm that uses the weighted average of bipolar recordings to determine effective monopolar contacts based on elevated activity in the beta band. LFPs from 14 hemispheres in seven PD patients with newly implanted directional DBS leads of the STN were recorded. First, the algorithm determined the stimulation level with the highest beta activity. Based on the prior determined level, the directional contact with the highest beta activity was chosen in the second step. The mean clinical efficacy of the contacts chosen using the algorithm did not statistically differ from the mean clinical efficacy of standard contact selection as performed in clinical routine. All recording sites were projected into MNI standard space to investigate the feasibility of the algorithm with respect to the anatomical boundaries of the STN. We conclude that the proposed algorithm is a first step towards LFP-based contact selection in STN-DBS for PD using chronically implanted devices.

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BACKGROUND: Finding the optimal deep brain stimulation (DBS) parameters from a multitude of possible combinations by trial and error is time consuming and requires highly trained medical personnel. OBJECTIVE: We developed an automated algorithm to identify optimal stimulation settings in Parkinson's disease (PD) patients treated with subthalamic nucleus (STN) DBS based on imaging-derived metrics. METHODS: Electrode locations and monopolar review data of 612 stimulation settings acquired from 31 PD patients were used to train a predictive model for therapeutic and adverse stimulation effects. Model performance was then evaluated within the training cohort using cross-validation and on an independent cohort of 19 patients. We inverted the model by applying a brute-force approach to determine the optimal stimulation sites in the target region. Finally, an optimization algorithm was established to identify optimal stimulation parameters. Suggested stimulation parameters were compared to the ones applied in clinical practice. RESULTS: Predicted motor outcome correlated with observed outcome (R = 0.57, P < 10-10 ) across patients within the training cohort. In the test cohort, the model explained 28% of the variance in motor outcome differences between settings. The stimulation site for maximum motor improvement was located at the dorsolateral border of the STN. When compared to two empirical settings, model-based suggestions more closely matched the setting with superior motor improvement. CONCLUSION: We developed and validated a data-driven model that can suggest stimulation parameters leading to optimal motor improvement while minimizing the risk of stimulation-induced side effects. This approach might provide guidance for DBS programming in the future. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

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INTRODUCTION: Deep brain stimulation of the internal globus pallidus is an effective treatment for dystonia. However, there is a large variability in clinical outcome with up to 25% non-responders even in highly selected primary dystonia patients. In a large cohort of patients we recently demonstrated that the variable clinical outcomes of pallidal DBS for dystonia may result to a large degree by the exact location and stimulation volume within the pallidal region. Here we test a novel approach of programing based on these insights: we first defined probabilistic maps of anti-dystonic effects by aggregating individual electrode locations and volumes of tissue activated of > 80 patients collected in a multicentre effort. We subsequently modified the algorithms to be able to test all possible stimulation settings of de novo patients in silico based on the expected clinical outcome and thus potentially predict the best possible stimulation parameters for the individual patients. METHODS: Within the framework of a BMBF-funded study, this concept of a computer-based prediction of optimal stimulation parameters for patients with dystonia will be tested in a randomized, controlled crossover study. The main parameter for clinical efficacy and primary endpoint is based on the blinded physician rating of dystonia severity reflected by Clinical Dystonia Rating Scales for both interventions (best clinical settings and model predicted settings) after 4 weeks of continuous stimulation. The primary endpoint is defined as "successful treatment with model predicted settings" (yes or no). The value is "yes" if the motor symptoms with model predicted settings are equal or better (tolerance 5% of absolute difference in percentages) to clinical settings. Secondary endpoints will include measures of quality of life, calculated energy consumption of the neurostimulation system and physician time for programming. PERSPECTIVE: We envision, that computer-guided deep brain stimulation programming in silico might provide optimal stimulation settings for patients with dystonia without the burden of months of programming sessions. The study protocol is designed to evaluate which programming method is more effective in controlling motor symptom severity and improving quality of life in dystonia (best clinical settings and model predicted settings). Trial registration Registered with ClinicalTrials.gov on Oct 27, 2021 (NCT05097001).

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The use of telemedicine in the management of chronic neurological conditions including movement disorders has expanded over time. In addition to enabling remote access to specialized care, telemedicine has also been shown to reduce caregiver burden and to improve patient satisfaction. With the COVID-19 pandemic, implementation of telehealth for patients with movement disorders, particularly those with more severe mobility issues, has increased rapidly. Although telemedicine care has been shown to be effective for patients with various movement disorders, its utilization for patients with device aided therapies such as deep brain stimulation (DBS) is limited due to challenges related to adjusting these devices remotely and to the lack of consensus recommendations for using telemedicine in this patient population. Thus, guidelines for telemedicine and DBS will assist clinicians on the appropriate implementation of telemedicine to provide care to DBS patients. Optimizing the use of telemedicine for DBS will expand this type of therapy to remote locations with limited access to programming expertise, and also reduce the need for patient travel. Telemedicine is particularly important during the ongoing pandemic due to infection risk and limited access to clinic visits. In this article we review the currently available and emerging strategies for telemedicine and remote care for DBS. We then outline common principles and recommendations for telemedicine care in patients with DBS, review patient selection and best practices. Finally, we briefly discuss the current state of reimbursement for DBS telemedicine visits.

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Background: While case series have established the efficacy of deep brain stimulation (DBS) in treating obsessive-compulsive disorder (OCD), it has been our experience that few OCD patients present without comorbidities that affect outcomes associated with DBS treatment. Here we present our experience with DBS therapy for OCD in patients who all have comorbid disease, together with the results of our programming strategies. Methods: For this case series, we assessed five patients who underwent ventral capsule/ventral striatum (VC/VS) DBS for OCD between 2015 and 2019 at the University of Colorado Hospital. Every patient in this cohort exhibited comorbidities, including substance use disorders, eating disorder, tic disorder, and autism spectrum disorder. We conducted an IRB-approved, retrospective study of programming modifications and treatment response over the course of DBS therapy. Results: In addition to patients' subjective reports of improvement, we observed significant improvement in the Yale-Brown Obsessive-Compulsive Scale (44%), the Montgomery-Asberg Depression Rating Scale (53%), the Quality of Life Enjoyment and Satisfaction Questionnaire (27%), and the Hamilton Anxiety Rating scales (34.9%) following DBS. With respect to co-morbid disease, there was a significant improvement in a patient with tic disorder's Total Tic Severity Score (TTSS) (p = 0.005). Conclusions: DBS remains an efficacious tool for the treatment of OCD, even in patients with significant comorbidities in whom DBS has not previously been investigated. Efficacious treatment results not only from the accurate placement of the electrodes by the surgeon but also from programming by the psychiatrist.

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The programming of deep brain stimulation (DBS) parameters for tremor is laborious and empirical. Despite extensive efforts, the end-result is often suboptimal. One reason for this is the poorly understood relationship between the stimulation parameters' voltage, pulse width, and frequency. In this study, we aim to improve DBS programming for essential tremor (ET) by exploring a new strategy. At first, the role of the individual DBS parameters in tremor control was characterized using a meta-analysis documenting all the available parameters and tremor outcomes. In our novel programming strategy, we applied 10 random combinations of stimulation parameters in eight ET-DBS patients with suboptimal tremor control. Tremor severity was assessed using accelerometers and immediate and sustained patient-reported outcomes (PRO's), including the occurrence of side-effects. The meta-analysis showed no substantial relationship between individual DBS parameters and tremor suppression. Nevertheless, with our novel programming strategy, a significantly improved (accelerometer p = 0.02, PRO p = 0.02) and sustained (p = 0.01) tremor suppression compared to baseline was achieved. Less side-effects were encountered compared to baseline. Our pilot data show that with this novel approach, tremor control can be improved in ET patients with suboptimal tremor control on DBS. In addition, this approach proved to have a beneficial effect on stimulation-related complications.

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The clinical benefit of deep brain stimulation (DBS) for Parkinson's disease (PD) is relevant to the tracts adjacent to the stimulation site, but it remains unclear what connectivity pattern is associated with effective DBS. The aim of this study was to identify clinically effective electrode contacts on the basis of brain connectivity markers derived from diffusion tensor tractography. We reviewed 77 PD patients who underwent bilateral subthalamic nucleus DBS surgery. The patients were assigned into the training (n = 58) and validation (n = 19) groups. According to the therapeutic window size, all contacts were classified into effective and ineffective groups. The whole-brain connectivity of each contact's volume of tissue activated was estimated using tractography with preoperative diffusion tensor data. Extracted connectivity features were put into an all-relevant feature selection procedure within cross-validation loops, to identify features with significant discriminative power for contact classification. A total of 616 contacts on 154 DBS leads were discriminated, with 388 and 228 contacts being classified as effective and ineffective ones, respectively. After the feature selection, the connectivity of contacts with the thalamus, pallidum, hippocampus, primary motor area, supplementary motor area and superior frontal gyrus was identified to significantly contribute to contact classification. Based on these relevant features, the random forest model constructed from the training group achieved an accuracy of 84.9% in the validation group, to discriminate effective contacts from the ineffective. Our findings advanced the understanding of the specific brain connectivity patterns associated with clinical effective electrode contacts, which potentially guided postoperative DBS programming.

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Deep brain stimulation (DBS) has become the treatment of choice for advanced stages of Parkinson's disease, medically intractable essential tremor, and complicated segmental and generalized dystonia. In addition to accurate electrode placement in the target area, effective programming of DBS devices is considered the most important factor for the individual outcome after DBS. Programming of the implanted pulse generator (IPG) is the only modifiable factor once DBS leads have been implanted and it becomes even more relevant in cases in which the electrodes are located at the border of the intended target structure and when side effects become challenging. At present, adjusting stimulation parameters depends to a large extent on personal experience. Based on a comprehensive literature search, we here summarize previous studies that examined the significance of distinct stimulation strategies for ameliorating disease signs and symptoms. We assess the effect of adjusting the stimulus amplitude (A), frequency (f), and pulse width (pw) on clinical symptoms and examine more recent techniques for modulating neuronal elements by electrical stimulation, such as interleaving (Medtronic®) or directional current steering (Boston Scientific®, Abbott®). We thus provide an evidence-based strategy for achieving the best clinical effect with different disorders and avoiding adverse effects in DBS of the subthalamic nucleus (STN), the ventro-intermedius nucleus (VIM), and the globus pallidus internus (GPi).

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BACKGROUND: Although recently introduced directional DBS leads provide control of the stimulation field, programing is time-consuming. OBJECTIVES: Here, we validate local field potentials recorded from directional contacts as a predictor of the most efficient contacts for stimulation in patients with PD. METHODS: Intraoperative local field potentials were recorded from directional contacts in the STN of 12 patients and beta activity compared with the results of the clinical contact review performed after 4 to 7 months. RESULTS: Normalized beta activity was positively correlated with the contact's clinical efficacy. The two contacts with the highest beta activity included the most efficient stimulation contact in up to 92% and that with the widest therapeutic window in 74% of cases. CONCLUSION: Local field potentials predict the most efficient stimulation contacts and may provide a useful tool to expedite the selection of the optimal contact for directional DBS. © 2017 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

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