Traditional Trial and Error versus Neuroanatomic 3-Dimensional Image Software-Assisted Deep Brain Stimulation Programming in Patients with Parkinson Disease

Biophysical Principles and Computational Modeling of Deep Brain Stimulation

BACKGROUND

Deep brain stimulation (DBS) has revolutionized the treatment of neurological disorders, yet the mechanisms of DBS are still under investigation. Computational models are important in silico tools for elucidating these underlying principles and potentially for personalizing DBS therapy to individual patients. The basic principles underlying neurostimulation computational models, however, are not well known in the clinical neuromodulation community.

OBJECTIVE

In this study, we present a tutorial on the derivation of computational models of DBS and outline the biophysical contributions of electrodes, stimulation parameters, and tissue substrates to the effects of DBS.

RESULTS

Given that many aspects of DBS are difficult to characterize experimentally, computational models have played an important role in understanding how material, size, shape, and contact segmentation influence device biocompatibility, energy efficiency, the spatial spread of the electric field, and the specificity of neural activation. Neural activation is dictated by stimulation parameters including frequency, current vs voltage control, amplitude, pulse width, polarity configurations, and waveform. These parameters also affect the potential for tissue damage, energy efficiency, the spatial spread of the electric field, and the specificity of neural activation. Activation of the neural substrate also is influenced by the encapsulation layer surrounding the electrode, the conductivity of the surrounding tissue, and the size and orientation of white matter fibers. These properties modulate the effects of the electric field and determine the ultimate therapeutic response.

CONCLUSION

This article describes biophysical principles that are useful for understanding the mechanisms of neurostimulation.

Image-guided programming deep brain stimulation improves clinical outcomes in patients with Parkinson's disease

Deep brain stimulation (DBS) is an effective treatment for patients with Parkinson's disease (PD). However, some patients may not respond optimally to clinical programming adjustments. Advances in DBS technology have led to more complex and time-consuming programming. Image-guided programming (IGP) could optimize and improve programming leading to better clinical outcomes in patients for whom DBS programming is not ideal due to sub-optimal response. We conducted a prospective single-center study including 31 PD patients with subthalamic nucleus (STN) DBS and suboptimal responses refractory to clinical programming. Programming settings were adjusted according to the volumetric reconstruction of the stimulation field using commercial postoperative imaging software. Clinical outcomes were assessed at baseline and at 3-month follow-up after IGP, using motor and quality of life (QoL) scales. Additionally, between these two assessment points, follow-up visits for fine-tuning amplitude intensity and medication were conducted at weeks 2, 4, 6, and 9. After IGP, twenty-six patients (83.9%) experienced motor and QoL improvements, with 25.8% feeling much better and 38.7% feeling moderately better according to the patient global impression scale. Five patients (16.1%) had no clinical or QoL changes after IGP. The MDS-UPDRS III motor scale showed a 21.9% improvement and the DBS-IS global score improved by 41.5%. IGP optimizes STN-DBS therapy for PD patients who are experiencing suboptimal clinical outcomes. These findings support using IGP as a standard tool in clinical practice, which could save programming time and improve patients' QoL.

The Use of Image Guided Programming to Improve Deep Brain Stimulation Workflows with Directional Leads in Parkinson's Disease

BACKGROUND

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is a preferred treatment for parkinsonian patients with severe motor fluctuations. Proper targeting of the STN sensorimotor segment appears to be a crucial factor for success of the procedure. The recent introduction of directional leads theoretically increases stimulation specificity in this challenging area but also requires more precise stimulation parameters.

OBJECTIVE

We investigated whether commercially available software for image guided programming (IGP) could maximize the benefits of DBS by informing the clinical standard care (CSC) and improving programming workflows.

METHODS

We prospectively analyzed 32 consecutive parkinsonian patients implanted with bilateral directional leads in the STN. Double blind stimulation parameters determined by CSC and IGP were assessed and compared at three months post-surgery. IGP was used to adjust stimulation parameters if further clinical refinement was required. Overall clinical efficacy was evaluated one-year post-surgery.

RESULTS

We observed 78% concordance between the two electrode levels selected by the blinded IGP prediction and CSC assessments. In 64% of cases requiring refinement, IGP improved clinical efficacy or reduced mild side effects, predominantly by facilitating the use of directional stimulation (93% of refinements).

CONCLUSIONS

The use of image guided programming saves time and assists clinical refinement, which may be beneficial to the clinical standard care for STN-DBS and further improve the outcomes of DBS for PD patients.

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

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.

Surgical Advances in Parkinson's Disease

While symptomatic pharmacological therapy remains the main therapeutic strategy for Parkinson's disease (PD), over the last two decades, surgical approaches have become more commonly used to control levodopa-induced motor complications and dopamine-resistant and non-motor symptoms of PD. In this paper, we discuss old and new surgical treatments for PD and the many technological innovations in this field. We have initially reviewed the relevant surgical anatomy as well as the pathological signaling considered to be the underlying cause of specific symptoms of PD. Subsequently, early attempts at surgical symptom control will be briefly reviewed. As the most well-known surgical intervention for PD is deep brain stimulation, this subject is discussed at length. As deciding on whether a patient stands to benefit from DBS can be quite difficult, the different proposed paradigms for precisely this are covered. Following this, the evidence regarding different targets, especially the subthalamic nucleus and internal globus pallidus, is reviewed as well as the evidence for newer proposed targets for specific symptoms. Due to the rapidly expanding nature of knowledge and technological capabilities, some of these new and potential future capabilities are given consideration in terms of their current and future use. Following this, we have reviewed newer treatment modalities, especially magnetic resonance-guided focused ultrasound and other potential surgical therapies, such as spinal cord stimulation for gait symptoms and others. As mentioned, the field of surgical alleviation of symptoms of PD is undergoing a rapid expansion, and this review provides a general overview of the current status and future directions in the field.

Variations in Determining Actual Orientations of Segmented Deep Brain Stimulation Leads Using the DiODe Algorithm: A Retrospective Study Across Different Lead Designs and Medical Institutions

INTRODUCTION

Directional deep brain stimulation (DBS) leads have become widely used in the past decade. Understanding the asymmetric stimulation provided by directional leads requires precise knowledge of the exact orientation of the lead in respect to its anatomical target. Recently, the DiODe algorithm was developed to automatically determine the orientation angle of leads from the artifact on postoperative computed tomography (CT) images. However, manual DiODe results are user-dependent. This study analyzed the extent of lead rotation as well as the user agreement of DiODe calculations across the two most common DBS systems, namely, Boston Scientific's Vercise and Abbott's Infinity, and two independent medical institutions.

METHODS

Data from 104 patients who underwent an anterior-facing unilateral/bilateral directional DBS implantation at either Northwestern Memorial Hospital (NMH) or Albany Medical Center (AMC) were retrospectively analyzed. Actual orientations of the implanted leads were independently calculated by three individual users using the DiODe algorithm in Lead-DBS and patients' postoperative CT images. The deviation from the intended orientation and user agreement were assessed.

RESULTS

All leads significantly deviated from the intended 0° orientation (p < 0.001), regardless of DBS lead design (p < 0.05) or institution (p < 0.05). However, the Boston Scientific leads showed an implantation bias toward the left at both institutions (p = 0.014 at NMH, p = 0.029 at AMC). A difference of 10° between at least two users occurred in 28% (NMH) and 39% (AMC) of all Boston Scientific and 76% (NMH) and 53% (AMC) of all Abbott leads.

CONCLUSION

Our results show that there is a significant lead rotation from the intended surgical orientation across both DBS systems and both medical institutions; however, a bias toward a single direction was only seen in the Boston Scientific leads. Additionally, these results raise questions into the user error that occurs when manually refining the orientation angles calculated with DiODe.

Electrophysiologic Evidence That Directional Deep Brain Stimulation Activates Distinct Neural Circuits in Patients With Parkinson Disease

OBJECTIVES

Deep brain stimulation (DBS) delivered via multicontact leads implanted in the basal ganglia is an established therapy to treat Parkinson disease (PD). However, the different neural circuits that can be modulated through stimulation on different DBS contacts are poorly understood. Evidence shows that electrically stimulating the subthalamic nucleus (STN) causes a therapeutic effect through antidromic activation of the hyperdirect pathway-a monosynaptic connection from the cortex to the STN. Recent studies suggest that stimulating the substantia nigra pars reticulata (SNr) may improve gait. The advent of directional DBS leads now provides a spatially precise means to probe these neural circuits and better understand how DBS affects distinct neural networks.

MATERIALS AND METHODS

We measured cortical evoked potentials (EPs) using electroencephalography (EEG) in response to low-frequency DBS using the different directional DBS contacts in eight patients with PD.

RESULTS

A short-latency EP at 3 milliseconds originating from the primary motor cortex appeared largest in amplitude when stimulating DBS contacts closest to the dorsolateral STN (p < 0.001). A long-latency EP at 10 milliseconds originating from the premotor cortex appeared strongest for DBS contacts closest to the SNr (p < 0.0001).

CONCLUSIONS

Our results show that at the individual patient level, electrical stimulation of different nuclei produces distinct EP signatures. Our approach could be used to identify the functional location of each DBS contact and thus help patient-specific DBS programming.

CLINICAL TRIAL REGISTRATION

The ClinicalTrials.gov registration number for the study is NCT04658641.

DBSegment: Fast and robust segmentation of deep brain structures considering domain generalization

Segmenting deep brain structures from magnetic resonance images is important for patient diagnosis, surgical planning, and research. Most current state-of-the-art solutions follow a segmentation-by-registration approach, where subject magnetic resonance imaging (MRIs) are mapped to a template with well-defined segmentations. However, registration-based pipelines are time-consuming, thus, limiting their clinical use. This paper uses deep learning to provide a one-step, robust, and efficient deep brain segmentation solution directly in the native space. The method consists of a preprocessing step to conform all MRI images to the same orientation, followed by a convolutional neural network using the nnU-Net framework. We use a total of 14 datasets from both research and clinical collections. Of these, seven were used for training and validation and seven were retained for testing. We trained the network to segment 30 deep brain structures, as well as a brain mask, using labels generated from a registration-based approach. We evaluated the generalizability of the network by performing a leave-one-dataset-out cross-validation, and independent testing on unseen datasets. Furthermore, we assessed cross-domain transportability by evaluating the results separately on different domains. We achieved an average dice score similarity of 0.89 ± 0.04 on the test datasets when compared to the registration-based gold standard. On our test system, the computation time decreased from 43 min for a reference registration-based pipeline to 1.3 min. Our proposed method is fast, robust, and generalizes with high reliability. It can be extended to the segmentation of other brain structures. It is publicly available on GitHub, and as a pip package for convenient usage.

Automated deep brain stimulation programming based on electrode location: a randomised, crossover trial using a data-driven algorithm

BACKGROUND

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is highly effective in controlling motor symptoms in patients with Parkinson's disease. However, correct selection of stimulation parameters is pivotal to treatment success and currently follows a time-consuming and demanding trial-and-error process. We aimed to assess treatment effects of stimulation parameters suggested by a recently published algorithm (StimFit) based on neuroimaging data.

METHODS

This double-blind, randomised, crossover, non-inferiority trial was carried out at Charité - Universitätsmedizin, Berlin, Germany, and enrolled patients with Parkinson's disease treated with directional octopolar electrodes targeted at the STN. All patients had undergone DBS programming according to our centre's standard of care (SoC) treatment before study recruitment. Based on perioperative imaging data, DBS electrodes were reconstructed and StimFit was applied to suggest optimal stimulation settings. Patients underwent motor assessments using the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale part III (MDS-UPDRS-III) during OFF-medication and in OFF-stimulation and ON-stimulation states under both conditions, StimFit and SoC parameter settings. Patients were randomly assigned (1:1) to receive either StimFit-programmed DBS first and SoC-programmed DBS second, or SoC-programmed DBS first and StimFit-programmed DBS second. The allocation schedule was generated using a computerised random number generator. Both the rater and patients were masked to the sequence of SoC and StimFit stimulation conditions. All patients who participated in the study were included in the analysis. The primary endpoint of this study was the absolute mean difference between MDS-UPDRS-III scores under StimFit and SoC stimulation, with a non-inferiority margin of 5 points. The study was registered at the German Register for Clinical Trials (DRKS00023115), and is complete.

FINDINGS

Between July 10, 2020, and Oct 28, 2021, 35 patients were enrolled in the study; 18 received StimFit followed by SoC stimulation, and 17 received SoC followed by StimFit stimulation. Mean MDS-UPDRS-III scores improved from 47·3 (SD 17·1) at OFF-stimulation baseline to 24·7 (SD 12·4) and 26·3 (SD 12·4) under SoC and StimFit stimulation, respectively. Mean difference between motor scores was -1·6 (SD 7·1; 95% CI -4·0 to 0·9; superiority test p_superiority=0·20; n=35), establishing non-inferiority of StimFit stimulation at a margin of -5 points (non-inferiority test p_{non-inferiority}=0·0038). In six patients (17%), initial programming of StimFit settings resulted in acute side-effects and amplitudes were reduced until side-effects disappeared.

INTERPRETATION

Automated data-driven algorithms can predict stimulation parameters that lead to motor symptom control comparable to SoC treatment. This approach could significantly decrease the time necessary to obtain optimal treatment parameters.

FUNDING

Deutsche Forschungsgemeinschaft through NeuroCure Clinical Research Center and TRR 295.

Programming of subthalamic nucleus deep brain stimulation for Parkinson's disease with sweet spot-guided parameter suggestions

Deep Brain Stimulation (DBS) is an effective treatment for advanced Parkinson's disease. However, identifying stimulation parameters, such as contact and current amplitudes, is time-consuming based on trial and error. Directional leads add more stimulation options and render this process more challenging with a higher workload for neurologists and more discomfort for patients. In this study, a sweet spot-guided algorithm was developed that automatically suggested stimulation parameters. These suggestions were retrospectively compared to clinical monopolar reviews. A cohort of 24 Parkinson's disease patients underwent bilateral DBS implantation in the subthalamic nucleus at our center. First, the DBS' leads were reconstructed with the open-source toolbox Lead-DBS. Second, a sweet spot for rigidity reduction was set as the desired stimulation target for programming. This sweet spot and estimations of the volume of tissue activated were used to suggest (i) the best lead level, (ii) the best contact, and (iii) the effect thresholds for full therapeutic effect for each contact. To assess these sweet spot-guided suggestions, the clinical monopolar reviews were considered as ground truth. In addition, the sweet spot-guided suggestions for best lead level and best contact were compared against reconstruction-guided suggestions, which considered the lead location with respect to the subthalamic nucleus. Finally, a graphical user interface was developed as an add-on to Lead-DBS and is publicly available. With the interface, suggestions for all contacts of a lead can be generated in a few seconds. The accuracy for suggesting the best out of four lead levels was 56%. These sweet spot-guided suggestions were not significantly better than reconstruction-guided suggestions (p = 0.3). The accuracy for suggesting the best out of eight contacts was 41%. These sweet spot-guided suggestions were significantly better than reconstruction-guided suggestions (p < 0.001). The sweet spot-guided suggestions of each contact's effect threshold had a mean error of 1.2 mA. On an individual lead level, the suggestions can vary more with mean errors ranging from 0.3 to 4.8 mA. Further analysis is warranted to improve the sweet spot-guided suggestions and to account for more symptoms and stimulation-induced side effects.

Multiple input algorithm-guided Deep Brain stimulation-programming for Parkinson's disease patients

Technological advances of Deep Brain Stimulation (DBS) within the subthalamic nucleus (STN) for Parkinson's disease (PD) provide increased programming options with higher programming burden. Reducing the effort of DBS optimization requires novel programming strategies. The objective of this study was to evaluate the feasibility of a semi-automatic algorithm-guided-programming (AgP) approach to obtain beneficial stimulation settings for PD patients with directional DBS systems. The AgP evaluates iteratively the weighted combination of sensor and clinician assessed responses of multiple PD symptoms to suggested DBS settings until it converges to a final solution. Acute clinical effectiveness of AgP DBS settings and DBS settings that were found following a standard of care (SoC) procedure were compared in a randomized, crossover and double-blind fashion in 10 PD subjects from a single center. Compared to therapy absence, AgP and SoC DBS settings significantly improved (p = 0.002) total Unified Parkinson's Disease Rating Scale III scores (median 69.8 interquartile range (IQR) 64.6|71.9% and 66.2 IQR 58.1|68.2%, respectively). Despite their similar clinical results, AgP and SoC DBS settings differed substantially. Per subject, AgP tested 37.0 IQR 34.0|37 settings before convergence, resulting in 1.7 IQR 1.6|2.0 h, which is comparable to previous reports. Although AgP long-term clinical results still need to be investigated, this approach constitutes an alternative for DBS programming and represents an important step for future closed-loop DBS optimization systems.

A New Application of Functional Zonal Image Reconstruction in Programming for Parkinson's Disease Treated Using Subthalamic Nucleus-Deep Brain Stimulation

Objective:

Programming plays an important role in the outcome of deep brain stimulation (DBS) for Parkinson's disease (PD). This study introduced a new application for functional zonal image reconstruction in programming.

Methods

Follow-up outcomes were retrospectively compared, including first programming time, number of discomfort episodes during programming, and total number of programming sessions between patients who underwent image-reconstruction-guided programming and those who underwent conventional programming. Data from 142 PD patients who underwent subthalamic nucleus (STN)-DBS between January 2017 and June 2019 were retrospectively analyzed. There were 75 conventional programs and 67 image reconstruction-guided programs.

Results

At 1-year follow-up, there was no significant difference in the rate of stimulus improvement or superposition improvement between the two groups. However, patients who underwent image reconstruction-guided programming were significantly better at the first programming time, number of discomfort episodes during programming, and total number of programming sessions than those who underwent conventional programming.

Conclusion

Imaging-guided programming of directional DBS leads was possible and led to reduced programming time and reduced patient side effects compared with conventional programming.

A Modified Power-on Programming Method after Deep Brain Stimulation for Parkinson Disease

OBJECTIVE

To explore the feasibility of using a modified power-on programming method in deep brain stimulation (DBS) for Parkinson disease (PD).

METHODS

We conducted a retrospective cohort study including 151 PD patients with bilateral robot-assisted DBS surgery from July 2017 to June 2020. Ninety-seven patients were adopted to the modified power-on programming method (Group I) and 54 patients were adopted to the traditional power-on programming method (Group II). In one-year follow-up, power-on programming duration, stimulation parameters, scores of Unified PD Rating Scale (UPDRS) and UPDRS-III of the 2 groups were recorded and compared.

RESULTS

There were no significant differences in the postoperative UPDRS, UPDRS-III improvement rate, and stimulation parameters between the 2 groups. The duration of power-on programming of Group I (1.7 ± 1.1 hours) was significantly less than that of Group II (3.5 ± 1.8 hours, P < 0.0001).

CONCLUSIONS

The modified power-on programming method can achieve a similar clinical effect to the traditional method, with the advantage of more efficiency.

StimFit-A Data-Driven Algorithm for Automated Deep Brain Stimulation Programming

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.

Reduced Programming Time and Strong Symptom Control Even in Chronic Course Through Imaging-Based DBS Programming

Objectives: Deep brain stimulation (DBS) programming is based on clinical response testing. Our clinical pilot trial assessed the feasibility of image-guided programing using software depicting the lead location in a patient-specific anatomical model. Methods: Parkinson's disease patients with subthalamic nucleus-DBS were randomly assigned to standard clinical-based programming (CBP) or anatomical-based (imaging-guided) programming (ABP) in an 8-week crossover trial. Programming characteristics and clinical outcomes were evaluated. Results: In 10 patients, both programs led to similar motor symptom control (MDS-UPDRS III) after 4 weeks (medicationOFF/stimulationON; CPB: 18.27 ± 9.23; ABP: 18.37 ± 6.66). Stimulation settings were not significantly different, apart from higher frequency in the baseline program than CBP (p = 0.01) or ABP (p = 0.003). Time spent in a program was not significantly different (CBP: 86.1 ± 29.82%, ABP: 88.6 ± 29.0%). Programing time was significantly shorter (p = 0.039) with ABP (19.78 ± 5.86 min) than CBP (45.22 ± 18.32). Conclusion: Image-guided DBS programming in PD patients drastically reduces programming time without compromising symptom control and patient satisfaction in this small feasibility trial.

Local Field Potential-Based Programming: A Proof-of-Concept Pilot Study

OBJECTIVES

Programming deep brain stimulation (DBS) is still based on a trial-and-error approach, often becoming a time-consuming process for both treating physicians and patients. Several strategies have been proposed to streamline DBS programming, most of which are preliminary and mainly address Parkinson's disease, a condition readily responsive to DBS adjustments. In the present proof-of-principle pilot study, we successfully demonstrate that local field potentials (LFP)-based programming can be an effective approach when used for DBS indications that have a delayed temporal onset of benefit.

MATERIALS AND METHODS

A recently commercialized implantable pulse generator (IPG) with the capability to non-invasively and chronically stream live and/or record LFPs from DBS electrode after implantation was used to program one pediatric patient with generalized dystonia and an adult with seizures refractory to multiple medications and vagal nerve stimulation.

RESULTS

The IPG survey function detected a peak in the delta range (1.95 Hz) in the left globus pallidus of first patient. This LFP was detected when recording in the brain area adjacent to contacts 9 and 10 and absent when recording from other areas. The chronic recording of the 1.95 Hz LFP with two sets of stimulation showed a greater power increase with the settings associated with a worsening of dystonia. Broadband LFP home recording of "absence seizure" and "focal/partial seizure" was used in the second patient and reviewer with the IPG "timeline" and "event" functions. The chronic recording of the 2.93 Hz and 8.79 Hz (spit sensing) showed a reduced power with the stimulation setting associated with seizure control.

CONCLUSIONS

The approach presented in this pilot proof-of-concept study may inform and streamline the DBS programming for conditions requiring clinicians and patients to wait weeks before appreciating any clinical benefit. Prospective studies on larger samples of patients are warranted.

Imaging-based programming of subthalamic nucleus deep brain stimulation in Parkinson's disease

BACKGROUND

The need for imaging-guided optimization of Deep Brain Stimulation (DBS) parameters is increasing with recent developments of sophisticated lead designs offering highly individualized, but time-consuming and complex programming.

OBJECTIVE

The objective of this study was to compare changes in motor symptoms of Parkinson's Disease (PD) and the corresponding volume of the electrostatic field (VEsF) achieved by DBS programming using GUIDE XT™, a commercially available software for visualization of DBS leads within the patient-specific anatomy from fusions of preoperative magnetic resonance imaging (MRI) and postoperative computed tomography (CT) scans, versus standard-of-care clinical programming.

METHODS

Clinical evaluation was performed to identify the optimal set of parameters based on clinical effects in 29 patients with PD and bilateral directional leads for Subthalamic Nucleus (STN) DBS. A second DBS program was generated in GUIDE XT™ based on a VEsF optimally located within the dorsolateral STN. Reduction of motor symptoms (Movement Disorders Society Unified Parkinson's Disease Rating Scale, MDS-UPDRS) and the overlap of the corresponding VEsF of both programs were compared.

RESULTS

Clinical and imaging-guided programming resulted in a significant reduction in the MDS-UPDRS scores compared to off-state. Motor symptom control with GUIDE XT™-derived DBS program was non-inferior to standard clinical programming. The overlap of the two VEsF did not correlate with the difference in motor symptom reduction by the programs.

CONCLUSIONS

Imaging-guided programming of directional DBS leads using GUIDE XT™ is possible without computational background and leads to non-inferior motor symptom control compared with clinical programming. DBS programs based on patient-specific imaging data may thus serve as starting point for clinical testing and may promote more efficient DBS programming.

CLOVER-DBS: Algorithm-Guided Deep Brain Stimulation-Programming Based on External Sensor Feedback Evaluated in a Prospective, Randomized, Crossover, Double-Blind, Two-Center Study

BACKGROUND

Recent technological advances in deep brain stimulation (DBS) (e.g., directional leads, multiple independent current sources) lead to increasing DBS-optimization burden. Techniques to streamline and facilitate programming could leverage these innovations.

OBJECTIVE

We evaluated clinical effectiveness of algorithm-guided DBS-programming based on wearable-sensor-feedback compared to standard-of-care DBS-settings in a prospective, randomized, crossover, double-blind study in two German DBS centers.

METHODS

For 23 Parkinson's disease patients with clinically effective DBS, new algorithm-guided DBS-settings were determined and compared to previously established standard-of-care DBS-settings using UPDRS-III and motion-sensor-assessment. Clinical and imaging data with lead-localizations were analyzed to evaluate characteristics of algorithm-derived programming compared to standard-of-care. Six different versions of the algorithm were evaluated during the study and 10 subjects programmed with uniform algorithm-version were analyzed as a subgroup.

RESULTS

Algorithm-guided and standard-of-care DBS-settings effectively reduced motor symptoms compared to off-stimulation-state. UPDRS-III scores were reduced significantly more with standard-of-care settings as compared to algorithm-guided programming with heterogenous algorithm versions in the entire cohort. A subgroup with the latest algorithm version showed no significant differences in UPDRS-III achieved by the two programming-methods. Comparing active contacts in standard-of-care and algorithm-guided DBS-settings, contacts in the latter had larger location variability and were farther away from a literature-based optimal stimulation target.

CONCLUSION

Algorithm-guided programming may be a reasonable approach to replace monopolar review, enable less trained health-professionals to achieve satisfactory DBS-programming results, or potentially reduce time needed for programming. Larger studies and further improvements of algorithm-guided programming are needed to confirm these results.

Stimulation Sweet Spot in Subthalamic Deep Brain Stimulation - Myth or Reality? A Critical Review of Literature

INTRODUCTION

While deep brain stimulation (DBS) of the subthalamic nucleus (STN) has been extensively used for more than 20 years in Parkinson's disease (PD), the optimal area of stimulation to relieve motor symptoms remains elusive.

OBJECTIVE

We aimed at localizing the sweet spot within the subthalamic region by performing a systematic review of the literature.

METHOD

PubMed database was searched for published studies exploring optimal stimulation location for STN DBS in PD, published between 2000 and 2019. A standardized assessment procedure based on methodological features was applied to select high-quality publications. Studies conducted more than 3 months after the DBS procedure, employing lateralized scores and/or stimulation condition, and reporting the volume of tissue activated or the position of the stimulating contact within the subthalamic region were considered in the final analysis.

RESULTS

Out of 439 references, 24 were finally retained, including 21 studies based on contact location and 3 studies based on volume of tissue activated (VTA). Most studies (all VTA-based studies and 13 of the 21 contact-based studies) suggest the superior-lateral STN and the adjacent white matter as the optimal sites for stimulation. Remaining contact-based studies were either inconclusive (5/21), favoured the caudal zona incerta (1/21), or suggested a better outcome of STN stimulation than adjacent white matter stimulation (2/21).

CONCLUSION

Using a standardized methodological approach, our review supports the presence of a sweet spot located within the supero-lateral STN and extending to the adjacent white matter.

Predicting optimal deep brain stimulation parameters for Parkinson's disease using functional MRI and machine learning

Commonly used for Parkinson’s disease (PD), deep brain stimulation (DBS) produces marked clinical benefits when optimized. However, assessing the large number of possible stimulation settings (i.e., programming) requires numerous clinic visits. Here, we examine whether functional magnetic resonance imaging (fMRI) can be used to predict optimal stimulation settings for individual patients. We analyze 3 T fMRI data prospectively acquired as part of an observational trial in 67 PD patients using optimal and non-optimal stimulation settings. Clinically optimal stimulation produces a characteristic fMRI brain response pattern marked by preferential engagement of the motor circuit. Then, we build a machine learning model predicting optimal vs. non-optimal settings using the fMRI patterns of 39 PD patients with a priori clinically optimized DBS (88% accuracy). The model predicts optimal stimulation settings in unseen datasets: a priori clinically optimized and stimulation-naïve PD patients. We propose that fMRI brain responses to DBS stimulation in PD patients could represent an objective biomarker of clinical response. Upon further validation with additional studies, these findings may open the door to functional imaging-assisted DBS programming.

Deep brain stimulation programming for Parkinson’s disease entails the assessment of a large number of possible simulation settings, requiring numerous clinic visits after surgery. Here, the authors show that patterns of functional MRI can predict the optimal stimulation settings.

In silico Accuracy and Energy Efficiency of Two Steering Paradigms in Directional Deep Brain Stimulation

Background: In Deep Brain Stimulation (DBS), stimulation field steering is used to achieve stimulation spatial specificity, which is critical to obtain clinical benefits and avoid side effects. Multiple Independent Current Control (MICC) and Interleaving/Multi Stim Set (Interleaving/MSS) are two stimulation field steering paradigms in commercially available DBS systems. This work investigates the stimulation field steering accuracy and energy efficiency of these two paradigms in directional DBS.

Methods: Volumes of Tissue Activated (VTAs) were generated in silico using pulse widths of 60 μs and five pulse amplitude fractionalizations intended to steer the VTAs radially in 12° steps. For each fractionalization, VTAs were generated with nine pre-defined target radii. Stimulation field steering accuracy was assessed based on the VTAs rotation angle. Energy efficiency was inferred from current draw from battery values, which were calculated based on the pulse amplitudes needed to generate and steer the VTAs, as well as electrode impedance measurements of clinically implanted directional leads.

Results: For radial steering, MICC needed a single VTA. In contrast, Interleaving/MSS required the generation of two VTAs, whose union and intersection created an Interleaving/MSS VTA and an Intersection VTA, respectively. MICC VTAs were 6.8 (−3.2–11.8)% larger than Interleaving/MSS VTAs. The Intersection VTAs accounted for 26.2 (16.0–32.8)% of Interleaving/MSS VTAs and were exposed to a higher stimulation frequency. For all VTA radius-fractionalization combinations, steering accuracy was 7.0 (4.5–10.5)° for MICC and 24.0 (9.0–25.3)° for Interleaving/MSS. Pulse amplitudes were 16.1 (9.2–28.6)% lower for MICC than for Interleaving/MSS, leading to a 45.9 (18.8–72.6)% lower current draw from battery for MICC.

Conclusions: The results of this work show that in silico, MICC achieves a significantly better stimulation field steering accuracy and has a significantly higher energy efficiency than Interleaving/MSS. Although direct evidence still needs to be generated to translate the results of this work to clinical practice, clinical outcomes may profit from the better stimulation field steering accuracy of MICC and longevity of DBS systems may profit from its higher energy efficiency.

Longitudinal Assessment of Rotation Angles after Implantation of Directional Deep Brain Stimulation Leads

PURPOSE

The risk/benefit-ratio of deep brain stimulation (DBS) depends on focusing the electrical field onto the target volume, excluding side-effect eliciting structures. Directional leads limiting radial current diffusion can target stimulation but add a spatial degree of freedom that requires control to align multimodal imaging datasets and for anatomical interpretation of stimulation. Unpredictable postoperative lead rotations have been reported. The extent and timing of rotation from the surgically intended alignment remain uncertain, as does the time point at which directional stimulation can be safely initiated without risking unexpected shifts in stimulation volume. We present a retrospective analysis of clinically indicated, repeated neuroimaging controls postimplantation in patients with directional DBS systems, which allow estimation of the amount and timing of postoperative lead rotation.

METHODS

Data from 67 patients with directional leads and multiple cranial computer tomographies (CCT) and/or rotation fluoroscopies at different postoperative time points were included. Rotation angles were detected based on CCT artifacts (n = 56) or direct visualization of lead segments on rotation fluoroscopies (n = 52). Cross-validation of both methods was conducted in patients who received both imaging modalities (n = 51).

RESULTS

Rotation angles deviated significantly (∼30°) from their intended 0° anterior/posterior orientation. Rotation was firmly established within the first postoperative day, with no additional torque in subsequent scans. The two methods highly correlated (right hemisphere: R2 = 0.94, left hemisphere: R2 = 0.91).

CONCLUSION

Both methods for measuring rotation angles led to comparable results and can be used interchangeably. Directional stimulation settings can safely be initiated after the first postoperative day, without risking subsequent lead rotation-related anatomical shifts.

Update on Current Technologies for Deep Brain Stimulation in Parkinson's Disease

Deep brain stimulation (DBS) is becoming increasingly central in the treatment of patients with Parkinson's disease and other movement disorders. Recent developments in DBS lead and implantable pulse generator design provide increased flexibility for programming, potentially improving the therapeutic benefit of stimulation. Directional DBS leads may increase the therapeutic window of stimulation by providing a means of avoiding current spread to structures that might give rise to stimulation-related side effects. Similarly, control of current to individual contacts on a DBS lead allows for shaping of the electric field produced between multiple active contacts. The following review aims to describe the recent developments in DBS system technology and the features of each commercially available DBS system. The advantages of each system are reviewed, and general considerations for choosing the most appropriate system are discussed.