Sweetspot Mapping in Deep Brain Stimulation: Strengths and Limitations of Current Approaches

Optimal focused ultrasound lesion location in essential tremor

Magnetic resonance-guided focused ultrasound (MRgFUS) thalamotomy is an effective treatment for medically refractory essential tremor. We investigate ablation sites and potential tracts associated with optimal tremor control and side effects based on the analysis of 351 cases from three international hospitals. Lesions were segmented on day 1 thin-cut T2 axial images, mapped to standard Montreal Neurological Institute space, and used to derive probabilistic maps and tracts associated with tremor improvement and side effects. Lesioning of a specific subregion within the ventral intermediate nucleus and the cerebellothalamic tract was associated with optimal tremor improvements. Some lesion locations and tracts were associated with differential side effects. Overlaps with the optimal tremor improvement sites accounted for variance in clinical improvements in out-of-sample cases. Efficacy of this location was further confirmed by test-retest cases that underwent two MRgFUS procedures. We identify and validate a target area for optimal tremor control and sites of avoidance associated with side effects.

Comparison of structural connectomes for modeling deep brain stimulation pathway activation

INTRODUCTION

Structural connectivity models of the brain are commonly employed to identify pathways that are directly activated during deep brain stimulation (DBS). However, various connectomes differ in the technical parameters, parcellation schemes, and methodological approaches used in their construction.

OBJECTIVE

The goal of this study was to compare and quantify variability in DBS pathway activation predictions when using different structural connectomes, while using identical electrode placements and stimulation volumes in the brain.

APPROACH

We analyzed four example structural connectomes: 1) Horn normative connectome (whole brain), 2) Yeh population-averaged tract-to-region pathway atlas (whole brain), 3) Petersen histology-based pathway atlas (subthalamic focused), and 4) Majtanik histology-based pathway atlas (anterior thalamus focused). DBS simulations were performed with each connectome, at four generalized locations for DBS electrode placement: 1) subthalamic nucleus, 2) anterior nucleus of thalamus, 3) ventral capsule, and 4) ventral intermediate nucleus of thalamus.

RESULTS

The choice of connectome used in the simulations resulted in notably distinct pathway activation predictions, and quantitative analysis indicated little congruence in the predicted patterns of brain network connectivity. The Horn and Yeh tractography-based connectomes provided estimates of DBS connectivity for any stimulation location in the brain, but have limitations in their anatomical validity. The Petersen and Majtanik histology-based connectomes are more anatomically realistic, but are only applicable to specific DBS targets because of their limited representation of pathways.

SIGNIFICANCE

The widely varying and inconsistent inferences of DBS network connectivity raises substantial concern regarding the general reliability of connectomic DBS studies, especially those that lack anatomical and/or electrophysiological validation in their analyses.

Exploring the relationship between dystonia and STN-DBS in Parkinson's disease: insights from a single-centre cohort

INTRODUCTION

Motor side effects may emerge after deep brain stimulation (DBS) of the subthalamic nucleus (STN) in Parkinson's disease (PD) patients. Out of 60 PD patients, we observed 16 patients displaying de novo dystonic symptoms after the implantation and 11 dystonic PD patients without benefit from the stimulation. We hypothesized that a common neural pathway may cause dystonia in both conditions. Our study aims to investigate the clinical and connectivity substrates of dystonia after STN-DBS.

METHODS

We divided our cohort into four groups: 16 patients displaying dystonia after STN-DBS, 11 patients with previously known dystonia not improving after surgery, 14 patients with dystonic symptoms relieved by the stimulation and 19 controls who never experienced dystonia. MANOVA was used to compare clinical data and the distance of the active contact center from the STN border among the four groups. Finally, we reconstructed the "sour" spots for dystonic symptoms and the associated structural and functional connectivity using a Parkinsonian normative connectome.

RESULTS

De novo dystonic and not-improved dystonic patients had a statistically significant longer PD duration before surgery (p = 0.001) and a greater active contact-STN distance (p < 0.001). Moreover, the "sour" spots were similar in both groups and structural and functional connectivity profiles were associated with brain areas correlated with dystonia pathophysiology (cerebellum, midbrain, parietal and temporal cortices).

CONCLUSIONS

We formulated a two-hit model for dystonia after STN-DBS: a clinical feature of Parkinsonian patients causes predisposing altered plasticity contributing to dystonic symptoms development when coupled with the stimulation of dystonia-related subcortical and cortical structures.

Evaluation of DBS computational modeling methodologies using in-vivo electrophysiology in Parkinson's disease

Deep brain stimulation (DBS) is an effective therapy for Parkinson's disease (PD) and other neuropsychiatric disorders, but its outcomes vary due to differences in patient selection, electrode placement, and programming. Optimizing DBS parameter settings requires postoperative adjustments through a trial-and-error process, which is complex and time-consuming. As such, researchers have been developing patient-specific computational models to help guide DBS programming. Despite growing interest in image-guided DBS technology, and recent adoption into clinical practice, the direct validation of the prediction accuracy remains limited. The objective of this study was to establish a comparative framework for validating the accuracy of various DBS computational modeling methodologies in predicting the activation of clinically relevant pathways using in vivo measurements from PD patients undergoing subthalamic (STN) DBS surgery. Our prior work assessed the accuracy of driving force (DF) models in native space by predicting activation of the corticospinal/bulbar tract (CSBT) and cortico-subthalamic hyperdirect pathway (HDP) using very short- (<2 ms) and short-latency (2-4 ms) cortical evoked potentials (cEPs). In this study, we extended our previous work by comparing the accuracy of five computational modeling variations for predicting the activation of HDP and CSBT based on three key factors: modeling method (DF vs. Volume of Tissue Activated [VTA]), imaging space (native vs. normative), and anatomical representation (pathway vs. volume). The model performances were quantified using the coefficient of determination (R2) between the cEP amplitudes and percent pathway activation or percent volume (structure) overlap. We compared model accuracy for 11 PD patients. The DF-Native-Pathway model was the most accurate method for quantitatively predicting experimental subcortical pathway activations. Additionally, our analysis showed that using normative brain space, instead of native (i.e., patient-specific) space, significantly diminished the accuracy of model predictions. Although the DF and VTA modeling methods exhibited comparable accuracy for the hyperdirect pathway, they diverged significantly in their predictions for the corticospinal tract. In conclusion, we believe that the choice of methodology should depend on the specific application and the required level of precision in clinical, surgical, or research settings. These findings offer valuable guidance for developing more accurate models, facilitating reliable DBS outcome predictions, and advancing both clinical practice and research.

Are we ready for automated deep brain stimulation programming?

Deep brain stimulation (DBS) requires individualized programming of stimulation parameters, a time-consuming process performed manually by clinicians with specialized training. This process limits DBS accessibility, delays treatment, and constrains the potential for next-generation technology to improve patient outcomes. This review describes technological advancements that could automate DBS programming, focusing on Parkinson's disease biomarkers that can provide objective outcome measurement and algorithms that can quickly and automatically identify optimal DBS settings. We first define key performance criteria for an automated programming system, including effectiveness, efficiency, and ease of use, and then describe and evaluate each component with respect to these criteria. We conclude that the state of current research provides a strong foundation for developing automated DBS programming. The primary remaining obstacle lies in identifying and deploying the best combination of available techniques that will overcome the high entry barrier needed for wide-scale clinical deployment and adoption.

Deep brain stimulation for chronic refractory cluster headache: A case series about long-term outcomes and connectivity analysis

OBJECTIVE

The aim of this study was to provide long-term clinical results-including "sweet spot" identification and connectomic imaging analysis-in a series of patients treated with deep brain stimulation for refractory chronic cluster headache.

BACKGROUND

Deep brain stimulation is a relatively recent indication for the treatment of refractory chronic cluster headache. This indication has generated substantial debate in recent years due to uncertainty surrounding the mechanism of action and the lack of long-term efficacy data.

METHODS

Case retrospective series of adult patients diagnosed with refractory chronic cluster headache and treated with deep brain stimulation. Demographic and clinical data were registered preoperatively and at 3, 6, 12, and 24 months. The primary endpoint was reduction in headache load, a composite score of frequency, severity, and duration of each attack. Imaging analyses (sweet spot and connectomic analyses) were performed to identify the brain regions most closely correlated with the reduction in headache load and to identify the structural networks involved. Treatment response was categorized according to the reduction in headache load, as follows: poor (<30% reduction), partial (30-50%), or high (>50%).

RESULTS

A total of 14 patients were included, with a mean (standard deviation [SD]) age of 42.4 (10.7) years and mean (SD) headache duration of 8.0 (5.8) years. Headache load scores decreased significantly from baseline to Month 24: mean (SD) 424.2 (325.9) versus 135.9 (155.7) (p = 0.001). In most patients (eight patients [58.0%]), headache load scores decreased by 50% after treatment. The other six patients showed either a partial (three [21.0%]) or poor (three [21.0%]) response. The optimized sweet spot was the lateral ventral tegmental area ((Montreal Neurological Institute) MNI coordinates of the center of mass: x = ± 9.0 mm, y = -10.6 mm, z = -3.5 mm). The connectomic analysis pointed to the probable implication of corticorubral tracts.

CONCLUSION

These findings suggest that a substantial proportion of patients with refractory chronic cluster headache obtain significant long-term clinical benefits from deep brain stimulation. Good responders were characterized by a robust improvement in headache load within 3-6 months after surgery. The lateral ventral tegmental area was identified as the best target for this indication, with the likely participation of corticorubral tracts.

Emerging Techniques for the Personalization of Deep Brain Stimulation Programming

The success of deep brain stimulation (DBS) relies on applying carefully titrated therapeutic stimulation at specific targets. Once implanted, the electrical stimulation parameters at each electrode contact can be modified. Iteratively adjusting the stimulation parameters enables testing for the optimal stimulation settings. Due to the large parameter space, the currently employed empirical testing of individual parameters based on acute clinical response is not sustainable. Within the constraints of short clinical visits, optimization is particularly challenging when clinical features lack immediate feedback, as seen in DBS for dystonia and depression and with the cognitive and axial side effects of DBS for Parkinson's disease. A personalized approach to stimulation parameter selection is desirable as the increasing complexity of modern DBS devices also expands the number of available parameters. This review describes three emerging imaging and electrophysiological methods of personalizing DBS programming. Normative connectome-base stimulation utilizes large datasets of normal or disease-matched connectivity imaging. The stimulation location for an individual patient can then be varied to engage regions associated with optimal connectivity. Electrophysiology-guided open- and closed-loop stimulation capitalizes on the electrophysiological recording capabilities of modern implanted devices to individualize stimulation parameters based on biomarkers of success or symptom onset. Finally, individual functional MRI (fMRI)-based approaches use fMRI during active stimulation to identify parameters resulting in characteristic patterns of functional engagement associated with long-term treatment response. Each method provides different but complementary information, and maximizing treatment efficacy likely requires a combined approach.

DBS in the restoration of motor functional recovery following spinal cord injury

The landscape of therapeutic deep brain stimulation (DBS) for locomotor function recovery is rapidly evolving. This review provides an overview of electrical neuromodulation effects on spinal cord injury (SCI), focusing on DBS for motor functional recovery in human and animal models. We highlight research providing insight into underlying cellular and molecular mechanisms. A literature review via Web of Science and PubMed databases from 1990 to May 29, 2024, reveals a growing body of evidence for therapeutic DBS in SCI recovery. Advances in techniques like optogenetics and whole-brain tractogram have helped elucidate DBS mechanisms. Neuronal targets sites for SCI functional recovery include the mesencephalic locomotor region (MLR), cuneiform nucleus (CNF), and nucleus raphe magnus (NRG), with pedunculopontine nucleus (PPN), periaqueductal gray (PAG), and nucleus ventroposterolateral thalami (VPL) for post-injury functional recovery treatment. Radiologically guided DBS optimization and combination therapy with classical rehabilitation have become an effective therapeutic method, though ongoing interventional trials are needed to enhance understanding and validate DBS efficacy in SCI. On the pre-clinical front, standardization of pre-clinical approaches are essential to enhance the quality of evidence on DBS safety and efficacy. Mapping brain targets and optimizing DBS protocols, aided by combined DBS and medical imaging, are critical endeavors. Overall, DBS holds promise for neurological and functional recovery after SCI, akin to other electrical stimulation approaches.

Functional anatomy of the subthalamic nucleus and the pathophysiology of cardinal features of Parkinson's disease unraveled by focused ultrasound ablation

The subthalamic nucleus (STN) modulates basal ganglia output and plays a fundamental role in the pathophysiology of Parkinson's disease (PD). Blockade/ablation of the STN improves motor signs in PD. We assessed the topography of focused ultrasound subthalamotomy (n = 39) by voxel-based lesion-symptom mapping to identify statistically validated brain voxels with the optimal effect against each cardinal feature and their respective cortical connectivity patterns by diffusion-weighted tractography. Bradykinesia and rigidity amelioration were associated with ablation of the rostral motor STN subregion connected to the supplementary motor and premotor cortices, whereas antitremor effect was explained by lesioning the posterolateral STN projection to the primary motor cortex. These findings were corroborated prospectively in another PD cohort (n = 12). This work concurs with recent deep brain stimulation findings that suggest different corticosubthalamic circuits underlying each PD cardinal feature. Our results provide sound evidence in humans of segregated anatomy of subthalamic-cortical connections and their distinct role in PD pathophysiology and normal motor control.

Evaluating a motor progression connectivity model across Parkinson's disease stages

BACKGROUND

Stimulation of a specific site in the dorsolateral subthalamic nucleus (STN) was recently associated with slower motor progression in Parkinson's Disease (PD), based on the deep brain stimulation (DBS) in early-stage PD pilot clinical trial. Here, subject-level visualizations are presented of this early-stage PD dataset to further describe the relationship between active contacts and motor progression. This study also evaluates whether stimulation of the sweet spot and connectivity model associated with slower motor progression is also associated with improvements in long-term motor outcomes in patients with advanced-stage PD.

METHODS

Active contacts of the early-stage PD cohort (N = 14) were analyzed alongside the degree of two-year motor progression. Sweet spot and connectivity models derived from the early-stage PD cohort were then used to determine how well they can estimate the variance in long-term motor outcomes in an independent STN-DBS cohort of advanced-stage PD patients (N = 29).

RESULTS

In early-stage PD, proximity of stimulation to the dorsolateral STN was associated with slower motor progression. In advanced-stage PD, stimulation proximity to the early PD connectivity model and sweet spot were associated with better long-term motor outcomes (R = 0.60, P < 0.001; R = 0.37, P = 0.046, respectively).

CONCLUSIONS

Results suggest stimulation of a specific site in the dorsolateral STN is associated with both slower motor progression and long-term motor improvements in PD.

The role of KCNQ channel activators in management of major depressive disorder

Depression, a complex disorder with significant treatment challenges, necessitates innovative therapeutic approaches to address its multifaceted nature and enhance treatment outcomes. The modulation of KCNQ potassium (K+) channels, pivotal regulators of neuronal excitability and neurotransmitter release, is a promising innovative therapeutic target in psychiatry. Widely expressed across various tissues, including the nervous and cardiovascular systems, KCNQ channels play a crucial role in modulating membrane potential and regulating neuronal activity. Recent preclinical evidence suggests that KCNQ channels, particularly KCNQ3, contribute to the regulation of neuronal excitability within the reward circuitry, offering a potential target for alleviating depressive symptoms, notably anhedonia. Studies using animal models demonstrate that interventions targeting KCNQ channels can restore dopaminergic firing balance and mitigate depressive symptoms. Human studies investigating the effects of KCNQ channel activators, such as ezogabine, have shown promising results in alleviating depressive symptoms and anhedonia. The aforementioned observations underscore the therapeutic potential of KCNQ channel modulation in depression management and highlight the need and justification for phase 2 and phase 3 dose-finding studies as well as studies prespecifying symptomatic targets in depression including anhedonia.

Neurophysiologic Characteristics of the Anterior Nucleus of the Thalamus during Deep Brain Stimulation Surgery for Epilepsy

INTRODUCTION

Anterior nucleus of the thalamus (ANT) deep brain stimulation (DBS) is an increasingly promising treatment option for refractory epilepsy. Optimal therapeutic benefit has been associated with stimulation at the junction of ANT and the mammillothalamic tract (mtt), but electrophysiologic markers of this target are lacking. The present study examined microelectrode recordings (MER) during DBS to identify unique electrophysiologic characteristics of ANT and the ANT-mtt junction.

METHODS

Ten patients with medically refractory epilepsy underwent MER during ANT-DBS implantation under general anesthesia. MER locations were determined based on coregistration of preoperative MRI, postoperative CT, and a stereotactic atlas of the thalamus (Morel atlas). Several neurophysiological parameters including single unit spiking rate, bursting properties, theta and alpha power and cerebrospinal fluid (CSF)-normalized root mean square (NRMS) of multiunit activity were characterized at recording depths and compared to anatomic boundaries.

RESULTS

From sixteen hemispheres, 485 recordings locations were collected from a mean of 30.3 (15.64 ± 5.0 mm) recording spans. Three-hundred and ninety-four of these recording locations were utilized further for analysis of spiking and bursting rates, after excluding recordings that were more than 8 mm above the putative ventral ANT border. The ANT region exhibited discernible features including: (1) mean spiking rate (7.52 Hz ± 6.9 Hz; one-way analysis of variance test, p = 0.014 when compared to mediodorsal nucleus of the thalamus [MD], mtt, and CSF), (2) the presence of bursting activity with 40% of ANT locations (N = 59) exhibited bursting versus 24% the mtt (χ2; p < 0.001), and 32% in the MD (p = 0.38), (3) CSF-NRMS, a proxy for neuronal density, exhibited well demarcated changes near the entry and exit of ANT (linear regression, R = -0.33, p < 0.001). Finally, in the ANT, both theta (4-8 Hz) and alpha band power (9-12 Hz) were negatively correlated with distance to the ventral ANT border (linear regression, p < 0.001 for both). The proportion of recordings with spiking and bursting activity was consistently highest 0-2 mm above the ventral ANT border with the mtt.

CONCLUSION

We observed several electrophysiological markers demarcating the ANT superior and inferior borders including multiple single cell and local field potential features. A local maximum in neural activity just above the ANT-mtt junction was consistent with the previously described optimal target for seizure reduction. These features may be useful for successful targeting of ANT-DBS for epilepsy.

Electrophysiological sweet spot mapping in deep brain stimulation for Parkinson's disease patients

BACKGROUND

Subthalamic deep brain stimulation (STN-DBS) is a well-established therapy to treat Parkinson's disease (PD). However, the STN-DBS sub-target remains debated. Recently, a white matter tract termed the hyperdirect pathway (HDP), directly connecting the motor cortex to STN, has gained interest as HDP stimulation is hypothesized to drive DBS therapeutic effects. Previously, we have investigated EEG-based evoked potentials (EPs) to better understand the neuroanatomical origins of the DBS clinical effect. We found a 3-ms peak (P3) relating to clinical benefit, and a 10-ms peak (P10) suggesting nigral side effects. Here, we aimed to investigate the neuroanatomical origins of DBS EPs using probabilistic mapping.

METHODS

EPs were recorded using EEG whilst low-frequency stimulation was delivered at all DBS-contacts individually. Next, EPs were mapped onto the patients' individual space and then transformed to MNI standard space. Using voxel-wise and fiber-wise probabilistic mapping, we determined hotspots/hottracts and coldspots/coldtracts for P3 and P10. Topography analysis was also performed to determine the spatial distribution of the DBS EPs.

RESULTS

In all 13 patients (18 hemispheres), voxel- and fiber-wise probabilistic mapping resulted in a P3-hotspot/hottract centered on the posterodorsomedial STN border indicative of HDP stimulation, while the P10-hotspot/hottract covered large parts of the substantia nigra.

CONCLUSION

This study investigated EP-based probabilistic mapping in PD patients during STN-DBS, revealing a P3-hotspot/hottract in line with HDP stimulation and P10-hotspot/hottract related to nigral stimulation. Results from this study provide key evidence for an electrophysiological measure of HDP and nigral stimulation.

Asleep versus awake GPi DBS surgery for Parkinson's disease: A systematic review and meta-analysis

BACKGROUND

Patients with Parkinson's Disease (PD) who receive either asleep image-guided subthalamic nucleus deep brain stimulation (DBS) or the traditional awake technique have comparable motor outcomes. However, there are fewer studies regarding which technique should be chosen for globus pallidus internus (GPi) DBS. This systematic review and meta-analysis aims to compare the accuracy of lead placement and motor outcomes of asleep versus awake GPi DBS PD population.

METHODS

We systematically searched PubMed, Embase, and Cochrane for studies comparing asleep vs. awake GPi DBS lead placement in patients with PD. Outcomes were spatial accuracy of lead placement, measured by radial error between intended and actual location, motor improvement measured using (UPDRS III), and postoperative stimulation parameters. Statistical analysis was performed with Review Manager 5.1.7. and OpenMeta [Analyst].

RESULTS

Three studies met inclusion criteria with a total of 247 patients. Asleep DBS was used to treat 192 (77.7 %) patients. Follow-up ranged from 6 to 48 months. Radial error was not statistically different between groups (MD -0.49 mm; 95 % CI -1.0 to 0.02; I2 = 86 %; p = 0.06), with a tendency for higher target accuracy with the asleep technique. There was no significant difference between groups in change on motor function, as measured by UPDRS III, from pre- to postoperative (MD 8.30 %; 95 % CI -4.78 to 21.37; I2 = 67 %, p = 0.2). There was a significant difference in postoperative stimulation voltage, with the asleep group requiring less voltage than the awake group (MD -0.27 V; 95 % CI -0.46 to - 0.08; I2 = 0 %; p = 0.006).

CONCLUSION

Our meta-analysis indicates that asleep image-guided GPi DBS presents a statistical tendency suggesting superior target accuracy when compared with the awake standard technique. Differences in change in motor function were not statistically significant between groups.

Modeling the volume of tissue activated in deep brain stimulation and its clinical influence: a review

Deep brain stimulation (DBS) is a neuromodulatory therapy that has been FDA approved for the treatment of various disorders, including but not limited to, movement disorders (e.g., Parkinson's disease and essential tremor), epilepsy, and obsessive-compulsive disorder. Computational methods for estimating the volume of tissue activated (VTA), coupled with brain imaging techniques, form the basis of models that are being generated from retrospective clinical studies for predicting DBS patient outcomes. For instance, VTA models are used to generate target-and network-based probabilistic stimulation maps that play a crucial role in predicting DBS treatment outcomes. This review defines the methods for calculation of tissue activation (or modulation) including ones that use heuristic and clinically derived estimates and more computationally involved ones that rely on finite-element methods and biophysical axon models. We define model parameters and provide a comparison of commercial, open-source, and academic simulation platforms available for integrated neuroimaging and neural activation prediction. In addition, we review clinical studies that use these modeling methods as a function of disease. By describing the tissue-activation modeling methods and highlighting their application in clinical studies, we provide the neural engineering and clinical neuromodulation communities with perspectives that may influence the adoption of modeling methods for future DBS studies.

The history of deep brain stimulation

Deep brain stimulation (DBS) surgery is an established and effective treatment for several movement disorders (tremor, Parkinson's disease, and dystonia), and is under investigation in numerous other neurological and psychiatric disorders. However, the origins and development of this neurofunctional technique are not always well understood and recognized. In this mini-review, we review the history of DBS, highlighting important milestones and the most remarkable protagonists (neurosurgeons, neurologists, and neurophysiologists) who pioneered and fostered this therapy throughout the 20th and early 21st century. Alongside DBS historical markers, we also briefly discuss newer developments in the field, and the future challenges which accompany such progress.

Advancements in non-invasive microwave brain stimulation: A comprehensive survey

This survey provides a comprehensive insight into the world of non-invasive brain stimulation and focuses on the evolving landscape of deep brain stimulation through microwave research. Non-invasive brain stimulation techniques provide new prospects for comprehending and treating neurological disorders. We investigate the methods shaping the future of deep brain stimulation, emphasizing the role of microwave technology in this transformative journey. Specifically, we explore antenna structures and optimization strategies to enhance the efficiency of high-frequency microwave stimulation. These advancements can potentially revolutionize the field by providing a safer and more precise means of modulating neural activity. Furthermore, we address the challenges that researchers currently face in the realm of microwave brain stimulation. From safety concerns to methodological intricacies, this survey outlines the barriers that must be overcome to fully unlock the potential of this technology. This survey serves as a roadmap for advancing research in microwave brain stimulation, pointing out potential directions and innovations that promise to reshape the field.

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.

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.

Exploring the network effects of deep brain stimulation for rapid eye movement sleep behavior disorder in Parkinson's disease

BACKGROUND

The research findings on the effects of subthalamic nucleus (STN) deep brain stimulation (DBS) in Parkinson's disease (PD) with Rapid Eye Movement Sleep Behavior Disorder (RBD) are inconsistent, and there is a lack of research on DBS electrode sites and their network effects for the explanation of the differences. Our objective is to explore the optimal stimulation sites (that is the sweet spot) and the brain network effects of STN-DBS for RBD in PD.

METHODS

In this study, among the 50 PD patients who underwent STN-DBS treatment, 24 PD patients with RBD were screened. According to clinical scores and imaging data, the sweet spot of STN-DBS was analyzed in PD patients with RBD, and the optimal structure and functional network models of subthalamic stimulation were constructed.

RESULTS

Bilateral STN-DBS can effectively improve the symptoms of RBD and other non-motor symptoms in 24 PD patients with RBD. RBD Questionnaire-Hong Kong (RBDQ-HK) score was 41.33 ± 17.45 at baseline and 30.83 ± 15.83 at 1-year follow-up, with statistical significance between them (P < 0.01). However, the MoCA score was an exception with a baseline of 22.04 ± 4.28 and a 1-year follow-up of 21.58 ± 4.33, showing no statistical significance (P = 0.12). The sweet spot and optimal network connectivity models for RBD improvement have been validated as effective.

CONCLUSIONS

Bilateral STN-DBS can improve the symptoms of RBD in PD. There exist the sweet spot and brain network effects of bilateral STN-DBS in the treatment of PD with RBD. Our study also demonstrates that RBD is a brain network disease.

Quantifying the Variability Associated with Postoperative Localization of Deep Brain Stimulation Electrodes

INTRODUCTION

Computational models of deep brain stimulation (DBS) have become common tools in clinical research studies that attempt to establish correlations between stimulation locations in the brain and behavioral outcome measures. However, the accuracy of any patient-specific DBS model depends heavily upon accurate localization of the DBS electrodes within the anatomy, which is typically defined via co-registration of clinical CT and MRI datasets. Several different approaches exist for this challenging registration problem, and each approach will result in a slightly different electrode localization. The goal of this study was to better understand how different processing steps (e.g., cost-function masking, brain extraction, intensity remapping) affect the estimate of the DBS electrode location in the brain.

METHODS

No "gold standard" exists for this kind of analysis, as the exact location of the electrode in the living human brain cannot be determined with existing clinical imaging approaches. However, we can estimate the uncertainty associated with the electrode position, which can be used to guide statistical analyses in DBS mapping studies. Therefore, we used high-quality clinical datasets from 10 subthalamic DBS subjects and co-registered their long-term postoperative CT with their preoperative surgical targeting MRI using 9 different approaches. The distances separating all of the electrode location estimates were calculated for each subject.

RESULTS

On average, electrodes were located within a median distance of 0.57 mm (0.49-0.74) of one another across the different registration approaches. However, when considering electrode location estimates from short-term postoperative CTs, the median distance increased to 2.01 mm (1.55-2.78).

CONCLUSIONS

The results of this study suggest that electrode location uncertainty needs to be factored into statistical analyses that attempt to define correlations between stimulation locations and clinical outcomes.

Neuroimaging-based analysis of DBS outcomes in pediatric dystonia: Insights from the GEPESTIM registry

INTRODUCTION

Deep brain stimulation (DBS) is an established treatment in patients of various ages with pharmaco-resistant neurological disorders. Surgical targeting and postoperative programming of DBS depend on the spatial location of the stimulating electrodes in relation to the surrounding anatomical structures, and on electrode connectivity to a specific distribution pattern within brain networks. Such information is usually collected using group-level analysis, which relies on the availability of normative imaging resources (atlases and connectomes). Analysis of DBS data in children with debilitating neurological disorders such as dystonia would benefit from such resources, especially given the developmental differences in neuroimaging data between adults and children. We assembled pediatric normative neuroimaging resources from open-access datasets in order to comply with age-related anatomical and functional differences in pediatric DBS populations. We illustrated their utility in a cohort of children with dystonia treated with pallidal DBS. We aimed to derive a local pallidal sweetspot and explore a connectivity fingerprint associated with pallidal stimulation to exemplify the utility of the assembled imaging resources.

METHODS

An average pediatric brain template (the MNI brain template 4.5-18.5 years) was implemented and used to localize the DBS electrodes in 20 patients from the GEPESTIM registry cohort. A pediatric subcortical atlas, analogous to the DISTAL atlas known in DBS research, was also employed to highlight the anatomical structures of interest. A local pallidal sweetspot was modeled, and its degree of overlap with stimulation volumes was calculated as a correlate of individual clinical outcomes. Additionally, a pediatric functional connectome of 100 neurotypical subjects from the Consortium for Reliability and Reproducibility was built to allow network-based analyses and decipher a connectivity fingerprint responsible for the clinical improvements in our cohort.

RESULTS

We successfully implemented a pediatric neuroimaging dataset that will be made available for public use as a tool for DBS analyses. Overlap of stimulation volumes with the identified DBS-sweetspot model correlated significantly with improvement on a local spatial level (R = 0.46, permuted p = 0.019). The functional connectivity fingerprint of DBS outcomes was determined to be a network correlate of therapeutic pallidal stimulation in children with dystonia (R = 0.30, permuted p = 0.003).

CONCLUSIONS

Local sweetspot and distributed network models provide neuroanatomical substrates for DBS-associated clinical outcomes in dystonia using pediatric neuroimaging surrogate data. Implementation of this pediatric neuroimaging dataset might help to improve the practice and pave the road towards a personalized DBS-neuroimaging analyses in pediatric patients.

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.

Probabilistic Subthalamic Nucleus Stimulation Sweet Spot Integration Into a Commercial Deep Brain Stimulation Programming Software Can Predict Effective Stimulation Parameters

OBJECTIVES

Subthalamic nucleus (STN) deep brain stimulation (DBS) programming in patients with Parkinson disease (PD) may be challenging, especially when using segmented leads. In this study, we integrated a previously validated probabilistic STN sweet spot into a commercially available software to evaluate its predictive value for clinically effective DBS programming.

MATERIALS AND METHODS

A total of 14 patients with PD undergoing bilateral STN DBS with segmented leads were included. A nonlinear co-registration of a previously defined probabilistic sweet spot onto the manually segmented STN was performed together with lead reconstruction and tractography of the corticospinal tract (CST) in each patient. Contacts were ranked (level and direction), and corresponding effect and side-effect thresholds were predicted based on the overlap of the volume of activated tissue (VTA) with the sweet spot and CST. Image-based findings were correlated with postoperative clinical testing results during monopolar contact review and chronic stimulation parameter settings used after 12 months.

RESULTS

Image-based contact prediction showed high interrater reliability (Cohen kappa 0.851-0.91). Image-based and clinical ranking of the most efficient ring level and direction of stimulation were matched in 72% (95% CI 57.0-83.3) and 65% (95% CI 44.9-81.2), respectively, across the whole cohort. The mean difference between the predicted and clinically observed effect thresholds was 0.79 ± 0.69 mA (p = 0.72). The median difference between the predicted and clinically observed side-effect thresholds was -0.5 mA (p < 0.001, Wilcoxon paired signed rank test).

CONCLUSIONS

Integration of a probabilistic STN functional sweet spot into a surgical programming software shows a promising capability to predict the best level and directional contact(s) as well as stimulation settings in DBS for PD and could be used to optimize programming with segmented lead technology. This integrated image-based programming approach still needs to be evaluated on a bigger data set and in a future prospective multicenter cohort.

Optimal deep brain stimulation sites and networks for stimulation of the fornix in Alzheimer's disease

Deep brain stimulation (DBS) to the fornix is an investigational treatment for patients with mild Alzheimer's Disease. Outcomes from randomized clinical trials have shown that cognitive function improved in some patients but deteriorated in others. This could be explained by variance in electrode placement leading to differential engagement of neural circuits. To investigate this, we performed a post-hoc analysis on a multi-center cohort of 46 patients with DBS to the fornix (NCT00658125, NCT01608061). Using normative structural and functional connectivity data, we found that stimulation of the circuit of Papez and stria terminalis robustly associated with cognitive improvement (R = 0.53, p < 0.001). On a local level, the optimal stimulation site resided at the direct interface between these structures (R = 0.48, p < 0.001). Finally, modulating specific distributed brain networks related to memory accounted for optimal outcomes (R = 0.48, p < 0.001). Findings were robust to multiple cross-validation designs and may define an optimal network target that could refine DBS surgery and programming.

The Contribution of Subthalamic Nucleus Deep Brain Stimulation to the Improvement in Motor Functions and Quality of Life

BACKGROUND

Subthalamic nucleus deep brain stimulation (STN-DBS) effectively treats motor symptoms and quality of life (QoL) of advanced and fluctuating early Parkinson's disease. Little is known about the relation between electrode position and changes in symptom control and ultimately QoL.

OBJECTIVES

The relation between the stimulated part of the STN and clinical outcomes, including the motor score of the Unified Parkinson's Disease Rating Scale (UPDRS) and the quality-of-life questionnaire, was assessed in a subcohort of the EARLYSTIM study.

METHODS

Sixty-nine patients from the EARLYSTIM cohort who underwent DBS, with a comprehensive clinical characterization before and 24 months after surgery, were included. Intercorrelations of clinical outcome changes, correlation between the affected functional parts of the STN, and changes in clinical outcomes were investigated. We further calculated sweet spots for different clinical parameters.

RESULTS

Improvements in the UPDRS III and Parkinson's Disease Questionnaire (PDQ-39) correlated positively with the extent of the overlap with the sensorimotor STN. The sweet spots for the UPDRS III (x = 11.6, y = -13.1, z = -6.3) and the PDQ-39 differed (x = 14.8, y = -12.4, z = -4.3) ~3.8 mm.

CONCLUSIONS

The main influence of DBS on QoL is likely mediated through the sensory-motor basal ganglia loop. The PDQ sweet spot is located in a posteroventral spatial location in the STN territory. For aspects of QoL, however, there was also evidence of improvement through stimulation of the other STN subnuclei. More research is necessary to customize the DBS target to individual symptoms of each patient. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

A Randomized, Double-Blinded Crossover Trial of Short Versus Conventional Pulse Width Subthalamic Deep Brain Stimulation in Parkinson's Disease

BACKGROUND

Subthalamic nucleus deep brain stimulation (STN-DBS) is a well-established treatment for patients with Parkinson's disease. Previous acute challenge studies suggested that short pulse widths might increase the therapeutic window while maintaining motor symptom control with a decrease in energy consumption. However, only little is known about the effect of short pulse width stimulation beyond the setting of an acute challenge.

OBJECTIVE

To compare 4 weeks of STN-DBS with conventional pulse width stimulation (60 μs) to 4 weeks of STN-DBS with short pulse width stimulation (30 μs) regarding motor symptom control.

METHODS

This study was a monocentric, double-blinded, randomized crossover non-inferiority trial investigating whether short pulse width stimulation with 30 μs maintains equal motor control as conventional 60 μs stimulation over a period of 4 weeks (German Clinical Trials Register No. DRKS00017528). Primary outcome was the difference in motor symptom control as assessed by a motor diary. Secondary outcomes included energy consumption measures, non-motor effects, side-effects, and quality of life.

RESULTS

Due to a high dropout rate, the calculated sample size of 27 patients was not met and 24 patients with Parkinson's disease and STN-DBS were included in the final analysis. However, there were no differences in any investigated outcome parameter between the two treatment conditions.

CONCLUSION

This study demonstrates that short pulse width settings (30 μs) provide non-inferior motor symptom control as conventional (60 μs) stimulation without significant differences in energy consumption. Future studies are warranted to evaluate a potential benefit of short pulse width settings in patients with pronounced dyskinesia.

A Randomized, Double-Blinded Crossover Trial of Short Versus Conventional Pulse Width Subthalamic Deep Brain Stimulation in Parkinson's Disease

BACKGROUND

Subthalamic nucleus deep brain stimulation (STN-DBS) is a well-established treatment for patients with Parkinson's disease. Previous acute challenge studies suggested that short pulse widths might increase the therapeutic window while maintaining motor symptom control with a decrease in energy consumption. However, only little is known about the effect of short pulse width stimulation beyond the setting of an acute challenge.

OBJECTIVE

To compare 4 weeks of STN-DBS with conventional pulse width stimulation (60 μs) to 4 weeks of STN-DBS with short pulse width stimulation (30 μs) regarding motor symptom control.

METHODS

This study was a monocentric, double-blinded, randomized crossover non-inferiority trial investigating whether short pulse width stimulation with 30 μs maintains equal motor control as conventional 60 μs stimulation over a period of 4 weeks (German Clinical Trials Register No. DRKS00017528). Primary outcome was the difference in motor symptom control as assessed by a motor diary. Secondary outcomes included energy consumption measures, non-motor effects, side-effects, and quality of life.

RESULTS

Due to a high dropout rate, the calculated sample size of 27 patients was not met and 24 patients with Parkinson's disease and STN-DBS were included in the final analysis. However, there were no differences in any investigated outcome parameter between the two treatment conditions.

CONCLUSION

This study demonstrates that short pulse width settings (30 μs) provide non-inferior motor symptom control as conventional (60 μs) stimulation without significant differences in energy consumption. Future studies are warranted to evaluate a potential benefit of short pulse width settings in patients with pronounced dyskinesia.

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.

Predicting Outcome in a Cohort of Isolated and Combined Dystonia within Probabilistic Brain Mapping

Background

Probabilistic brain mapping is a promising tool to estimate the expected benefit of pallidal deep brain stimulation (GPi‐DBS) in patients with isolated dystonia (IsoD).

Objectives

To investigate the role of probabilistic mapping in combined dystonia (ComD).

Methods

We rendered the pallidal atlas and the volume of tissue activated (VTA) for a cohort of patients with IsoD (n = 20) and ComD (n = 10) that underwent GPi‐DBS. The VTA was correlated with clinical improvement. Afterwards, each VTA was applied on the previously published probabilistic model (Reich et al., 2019). The correlation between predicted and observed clinical benefit was studied in a linear regression model.

Results

A good correlation between observed and predicted outcome was found for both patients with IsoD (n = 14) and ComD (n = 7) (r² = 0.32; P < 0.05). In ComD, 42% of the variance in DBS response is explained by VTA‐based outcome map.

Conclusion

A probabilistic model would be helpful in clinical practice to circumvent unpredictable and less impressive motor results often found in ComD.

Thalamic deep brain stimulation for Tourette Syndrome: A naturalistic trial with brief randomized, double-blinded sham-controlled periods

BACKGROUND

There is still a lack of controlled studies to prove efficacy of thalamic deep brain stimulation for Tourette's Syndrome.

OBJECTIVES

In this controlled trial, we investigated the course of tic severity, comorbidities and quality of life during thalamic stimulation and whether changes in tic severity can be assigned to ongoing compared to sham stimulation.

METHODS

We included eight adult patients with medically refractory Tourette's syndrome. Bilateral electrodes were implanted in the centromedian-parafascicular-complex and the nucleus ventro-oralis internus. Tic severity, quality of life and comorbidities were assessed before surgery as well as six and twelve months after. Short randomized, double-blinded sham-controlled crossover sequences with either active or sham stimulation were implemented at both six- and twelve-months' assessments. The primary outcome measurement was the difference in the Yale Global Tic Severity Scale tic score between active and sham stimulation. Adverse events were systematically surveyed for all patients to evaluate safety.

RESULTS

Active stimulation resulted in significantly higher tic reductions than sham stimulation (F = 79.5; p = 0.001). Overall quality of life and comorbidities improved significantly in the open-label-phase. Over the course of the trial two severe adverse events occurred that were resolved without sequelae.

CONCLUSION

Our results provide evidence that thalamic stimulation is effective in improving tic severity and overall quality of life. Crucially, the reduction of tic severity was primarily driven by active stimulation. Further research may focus on improving stimulation protocols and refining patient selection to improve efficacy and safety of deep brain stimulation for Tourette's Syndrome.