Sign-specific stimulation 'hot' and 'cold' spots in Parkinson's disease validated with machine learning

Disentangling Bradykinesia and Rigidity in Parkinson's Disease: Evidence from Short- and Long-Term Subthalamic Nucleus Deep Brain Stimulation

OBJECTIVE

Bradykinesia and rigidity are considered closely related motor signs in Parkinson disease (PD), but recent neurophysiological findings suggest distinct pathophysiological mechanisms. This study aims to examine and compare longitudinal changes in bradykinesia and rigidity in PD patients treated with bilateral subthalamic nucleus deep brain stimulation (STN-DBS).

METHODS

In this retrospective cohort study, the clinical progression of appendicular and axial bradykinesia and rigidity was assessed up to 15 years after STN-DBS in the best treatment conditions (ON medication and ON stimulation). The severity of bradykinesia and rigidity was examined using ad hoc composite scores from specific subitems of the Unified Parkinson's Disease Rating Scale motor part (UPDRS-III). Short- and long-term predictors of bradykinesia and rigidity were analyzed through linear regression analysis, considering various preoperative demographic and clinical data, including disease duration and severity, phenotype, motor and cognitive scores (eg, frontal score), and medication.

RESULTS

A total of 301 patients were examined before and 1 year after surgery. Among them, 101 and 56 individuals were also evaluated at 10-year and 15-year follow-ups, respectively. Bradykinesia significantly worsened after surgery, especially in appendicular segments (p < 0.001). Conversely, rigidity showed sustained benefit, with unchanged clinical scores compared to preoperative assessment (p > 0.05). Preoperative motor disability (eg, composite scores from the UPDRS-III) predicted short- and long-term outcomes for both bradykinesia and rigidity (p < 0.01). Executive dysfunction was specifically linked to bradykinesia but not to rigidity (p < 0.05).

INTERPRETATION

Bradykinesia and rigidity show long-term divergent progression in PD following STN-DBS and are associated with independent clinical factors, supporting the hypothesis of partially distinct pathophysiology. ANN NEUROL 2024;96:234-246.

Differential Responses to Low- and High-Frequency Subthalamic Nucleus Deep Brain Stimulation on Sensor-Measured Components of Bradykinesia in Parkinson's Disease

INTRODUCTION

The current approach to assessing bradykinesia in Parkinson's Disease relies on the Unified Parkinson's Disease Rating Scale (UPDRS), which is a numeric scale. Inertial sensors offer the ability to probe subcomponents of bradykinesia: motor speed, amplitude, and rhythm. Thus, we sought to investigate the differential effects of high-frequency compared to low-frequency subthalamic nucleus (STN) deep brain stimulation (DBS) on these quantified facets of bradykinesia.

METHODS

We recruited advanced Parkinson's Disease subjects with a chronic bilateral subthalamic nucleus (STN) DBS implantation to a single-blind stimulation trial where each combination of medication state (OFF/ON), electrode contacts, and stimulation frequency (60 Hz/180 Hz) was assessed. The Kinesia One sensor system was used to measure upper limb bradykinesia. For each stimulation trial, subjects performed extremity motor tasks. Sensor data were recorded continuously. We identified STN DBS parameters that were associated with improved upper extremity bradykinesia symptoms using a mixed linear regression model.

RESULTS

We recruited 22 subjects (6 females) for this study. The 180 Hz STN DBS (compared to the 60 Hz STN DBS) and dopaminergic medications improved all subcomponents of upper extremity bradykinesia (motor speed, amplitude, and rhythm). For the motor rhythm subcomponent of bradykinesia, ventral contacts yielded improved symptom improvement compared to dorsal contacts.

CONCLUSION

The differential impact of high- and low-frequency STN DBS on the symptoms of bradykinesia may advise programming for these patients but warrants further investigation. Wearable sensors represent a valuable addition to the armamentarium that furthers our ability to conduct objective, quantitative clinical assessments.

Anatomical substrates and connectivity for bradykinesia motor features in Parkinson's disease after subthalamic nucleus deep brain stimulation

Parkinsonian bradykinesia is rated using a composite scale incorporating the slowed frequency of repetitive movements, decrement amplitude and arrhythmicity. Differential localization of these movement components within the basal ganglia will drive the development of more personalized network-targeted symptomatic therapies. In this study, using an optical motion sensor, we evaluated the amplitude and frequency of hand movements during a grasping task with subthalamic nucleus deep brain stimulation 'on' or 'off' in 15 patients with Parkinson's disease. The severity of bradykinesia was assessed blindly using the Unified Parkinson's Disease Rating Part III scale. The volumes of activated tissue of each subject were estimated where changes in amplitude and frequency were mapped to identify distinct anatomical substrates of each component in the subthalamic nucleus. The volumes of activated tissue were used to seed a normative functional connectome to generate connectivity maps associated with amplitude and frequency changes. Deep brain stimulation-induced change in amplitude was negatively correlated with a change in Unified Parkinson's Disease Rating Part III scale for right (r = -0.65, P < 0.05) and left hand grasping scores (r = -0.63, P < 0.05). The change in frequency was negatively correlated with amplitude for both right (r = -0.63, P < 0.05) and left hands (r = -0.57, P < 0.05). The amplitude and frequency changes were represented as a spatial gradient with overlapping and non-overlapping regions spanning the anteromedial-posterolateral axis of the subthalamic nucleus. Whole-brain correlation maps between functional connectivity and motor changes were also inverted between amplitude and frequency changes. Deep brain stimulation-associated changes in frequency and amplitude were topographically and distinctly represented both locally in the subthalamic nucleus and in whole-brain functional connectivity.

Local neuroanatomical and tract-based proxies of optimal subcallosal cingulate deep brain stimulation

BACKGROUND

Deep brain stimulation of the subcallosal cingulate area (SCC-DBS) is a promising neuromodulatory therapy for treatment-resistant depression (TRD). Biomarkers of optimal target engagement are needed to guide surgical targeting and stimulation parameter selection and to reduce variance in clinical outcome.

OBJECTIVE/HYPOTHESIS

We aimed to characterize the relationship between stimulation location, white matter tract engagement, and clinical outcome in a large (n = 60) TRD cohort treated with SCC-DBS. A smaller cohort (n = 22) of SCC-DBS patients with differing primary indications (bipolar disorder/anorexia nervosa) was utilized as an out-of-sample validation cohort.

METHODS

Volumes of tissue activated (VTAs) were constructed in standard space using high-resolution structural MRI and individual stimulation parameters. VTA-based probabilistic stimulation maps (PSMs) were generated to elucidate voxelwise spatial patterns of efficacious stimulation. A whole-brain tractogram derived from Human Connectome Project diffusion-weighted MRI data was seeded with VTA pairs, and white matter streamlines whose overlap with VTAs related to outcome ('discriminative' streamlines; Puncorrected < 0.05) were identified using t-tests. Linear modelling was used to interrogate the potential clinical relevance of VTA overlap with specific structures.

RESULTS

PSMs varied by hemisphere: high-value left-sided voxels were located more anterosuperiorly and squarely in the lateral white matter, while the equivalent right-sided voxels fell more posteroinferiorly and involved a greater proportion of grey matter. Positive discriminative streamlines localized to the bilateral (but primarily left) cingulum bundle, forceps minor/rostrum of corpus callosum, and bilateral uncinate fasciculus. Conversely, negative discriminative streamlines mostly belonged to the right cingulum bundle and bilateral uncinate fasciculus. The best performing linear model, which utilized information about VTA volume overlap with each of the positive discriminative streamline bundles as well as the negative discriminative elements of the right cingulum bundle, explained significant variance in clinical improvement in the primary TRD cohort (R = 0.46, P < 0.001) and survived repeated 10-fold cross-validation (R = 0.50, P = 0.040). This model was also able to predict outcome in the out-of-sample validation cohort (R = 0.43, P = 0.047).

CONCLUSION(S)

These findings reinforce prior indications of the importance of white matter engagement to SCC-DBS treatment success while providing new insights that could inform surgical targeting and stimulation parameter selection decisions.

Anatomical Substrates and Connectivity for Parkinson's Disease Bradykinesia Components after STN-DBS

Background

Parkinsonian bradykinesia is rated using a composite scale incorporating slowed frequency of repetitive movements, decrement amplitude, and arrhythmicity. Differential localization of these movement components within basal ganglia would drive the development of more personalized network-targeted symptomatic therapies.

Methods

Using an optical motion sensor, amplitude and frequency of hand movements during grasping task were evaluated with subthalamic nucleus (STN)-Deep Brain Stimulation (DBS) "on" or "off" in 15 patients with Parkinson's disease (PD). The severity of bradykinesia was assessed blindly using the MDS-UPDRS Part-III scale. Volumes of activated tissue (VAT) of each subject were estimated where changes in amplitude and frequency were mapped to identify distinct anatomical substrates of each component in the STN. VATs were used to seed a normative functional connectome to generate connectivity maps associated with amplitude and frequency changes.

Results

STN-DBS-induced change in amplitude was negatively correlated with change in MDS-UPDRS-III right (r = -0.65, p < 0.05) and left hand grasping scores (r = -0.63, p < 0.05). The change in frequency was negatively correlated with amplitude for both right (r = -0.63, p < 0.05) and left hand (r = -0.57, p < 0.05). The amplitude and frequency changes were represented as a spatial gradient with overlapping and non-overlapping regions spanning the dorsolateral-ventromedial axis of the STN. Whole-brain correlation maps between functional connectivity and motor changes were also inverted between amplitude and frequency changes.

Conclusion

DBS-associated changes in frequency and amplitude were topographically and distinctly represented both locally in STN and in whole-brain functional connectivity.

Lead-DBS v3.0: Mapping deep brain stimulation effects to local anatomy and global networks

Following its introduction in 2014 and with support of a broad international community, the open-source toolbox Lead-DBS has evolved into a comprehensive neuroimaging platform dedicated to localizing, reconstructing, and visualizing electrodes implanted in the human brain, in the context of deep brain stimulation (DBS) and epilepsy monitoring. Expanding clinical indications for DBS, increasing availability of related research tools, and a growing community of clinician-scientist researchers, however, have led to an ongoing need to maintain, update, and standardize the codebase of Lead-DBS. Major development efforts of the platform in recent years have now yielded an end-to-end solution for DBS-based neuroimaging analysis allowing comprehensive image preprocessing, lead localization, stimulation volume modeling, and statistical analysis within a single tool. The aim of the present manuscript is to introduce fundamental additions to the Lead-DBS pipeline including a deformation warpfield editor and novel algorithms for electrode localization. Furthermore, we introduce a total of three comprehensive tools to map DBS effects to local, tract- and brain network-levels. These updates are demonstrated using a single patient example (for subject-level analysis), as well as a retrospective cohort of 51 Parkinson's disease patients who underwent DBS of the subthalamic nucleus (for group-level analysis). Their applicability is further demonstrated by comparing the various methodological choices and the amount of explained variance in clinical outcomes across analysis streams. Finally, based on an increasing need to standardize folder and file naming specifications across research groups in neuroscience, we introduce the brain imaging data structure (BIDS) derivative standard for Lead-DBS. Thus, this multi-institutional collaborative effort represents an important stage in the evolution of a comprehensive, open-source pipeline for DBS imaging and connectomics.

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.

Subthalamic high-beta oscillation informs the outcome of deep brain stimulation in patients with Parkinson's disease

Background

The therapeutic effect of deep brain stimulation (DBS) of the subthalamic nucleus (STN) for Parkinson's disease (PD) is related to the modulation of pathological neural activities, particularly the synchronization in the β band (13–35 Hz). However, whether the local β activity in the STN region can directly predict the stimulation outcome remains unclear.

Objective

We tested the hypothesis that low-β (13–20 Hz) and/or high-β (20–35 Hz) band activities recorded from the STN region can predict DBS efficacy.

Methods

Local field potentials (LFPs) were recorded in 26 patients undergoing deep brain stimulation surgery in the subthalamic nucleus area. Recordings were made after the implantation of the DBS electrode prior to its connection to a stimulator. The maximum normalized powers in the theta (4–7 Hz), alpha (7–13 Hz), low-β (13–20 Hz), high-β (20–35 Hz), and low-γ (40–55 Hz) subbands in the postoperatively recorded LFP were correlated with the stimulation-induced improvement in contralateral tremor or bradykinesia–rigidity. The distance between the contact selected for stimulation and the contact with the maximum subband power was correlated with the stimulation efficacy. Following the identification of the potential predictors by the significant correlations, a multiple regression analysis was performed to evaluate their effect on the outcome.

Results

The maximum high-β power was positively correlated with bradykinesia–rigidity improvement (rs = 0.549, p &lt; 0.0001). The distance to the contact with maximum high-β power was negatively correlated with bradykinesia–rigidity improvement (rs = −0.452, p &lt; 0.001). No significant correlation was observed with low-β power. The maximum high-β power and the distance to the contact with maximum high-β power were both significant predictors for bradykinesia–rigidity improvement in the multiple regression analysis, explaining 37.4% of the variance altogether. Tremor improvement was not significantly correlated with any frequency.

Conclusion

High-β oscillations, but not low-β oscillations, recorded from the STN region with the DBS lead can inform stimulation-induced improvement in contralateral bradykinesia–rigidity in patients with PD. High-β oscillations can help refine electrode targeting and inform contact selection for DBS therapy.

Lead-OR: A multimodal platform for deep brain stimulation surgery

Background

Deep brain stimulation (DBS) electrode implant trajectories are stereotactically defined using preoperative neuroimaging. To validate the correct trajectory, microelectrode recordings (MERs) or local field potential recordings can be used to extend neuroanatomical information (defined by MRI) with neurophysiological activity patterns recorded from micro- and macroelectrodes probing the surgical target site. Currently, these two sources of information (imaging vs. electrophysiology) are analyzed separately, while means to fuse both data streams have not been introduced.

Methods

Here, we present a tool that integrates resources from stereotactic planning, neuroimaging, MER, and high-resolution atlas data to create a real-time visualization of the implant trajectory. We validate the tool based on a retrospective cohort of DBS patients (N = 52) offline and present single-use cases of the real-time platform.

Results

We establish an open-source software tool for multimodal data visualization and analysis during DBS surgery. We show a general correspondence between features derived from neuroimaging and electrophysiological recordings and present examples that demonstrate the functionality of the tool.

Conclusions

This novel software platform for multimodal data visualization and analysis bears translational potential to improve accuracy of DBS surgery. The toolbox is made openly available and is extendable to integrate with additional software packages.

Funding

Deutsche Forschungsgesellschaft (410169619, 424778381), Deutsches Zentrum für Luft- und Raumfahrt (DynaSti), National Institutes of Health (2R01 MH113929), and Foundation for OCD Research (FFOR).

Neuronal oscillations predict deep brain stimulation outcome in Parkinson's disease

BACKGROUND

Neuronal oscillations are linked to symptoms of Parkinson's disease. This relation can be exploited for optimizing deep brain stimulation (DBS), e.g. by informing a device or human about the optimal location, time and intensity of stimulation. Whether oscillations predict individual DBS outcome is not clear so far.

OBJECTIVE

To predict motor symptom improvement from subthalamic power and subthalamo-cortical coherence.

METHODS

We applied machine learning techniques to simultaneously recorded magnetoencephalography and local field potential data from 36 patients with Parkinson's disease. Gradient-boosted tree learning was applied in combination with feature importance analysis to generate and understand out-of-sample predictions.

RESULTS

A few features sufficed for making accurate predictions. A model operating on five coherence features, for example, achieved correlations of r > 0.8 between actual and predicted outcomes. Coherence comprised more information in less features than subthalamic power, although in general their information content was comparable. Both signals predicted akinesia/rigidity reduction best. The most important local feature was subthalamic high-beta power (20-35 Hz). The most important connectivity features were subthalamo-parietal coherence in the very high frequency band (>200 Hz) and subthalamo-parietal coherence in low-gamma band (36-60 Hz). Successful prediction was not due to the model inferring distance to target or symptom severity from neuronal oscillations.

CONCLUSION

This study demonstrates for the first time that neuronal oscillations are predictive of DBS outcome. Coherence between subthalamic and parietal oscillations are particularly informative. These results highlight the clinical relevance of inter-areal synchrony in basal ganglia-cortex loops and might facilitate further improvements of DBS in the future.

Deep Brain Stimulation: Emerging Tools for Simulation, Data Analysis, and Visualization

Deep brain stimulation (DBS) is a well-established neurosurgical procedure for movement disorders that is also being explored for treatment-resistant psychiatric conditions. This review highlights important consideration for DBS simulation and data analysis. The literature on DBS has expanded considerably in recent years, and this article aims to identify important trends in the field. During DBS planning, surgery, and follow up sessions, several large data sets are created for each patient, and it becomes clear that any group analysis of such data is a big data analysis problem and has to be handled with care. The aim of this review is to provide an update and overview from a neuroengineering perspective of the current DBS techniques, technical aids, and emerging tools with the focus on patient-specific electric field (EF) simulations, group analysis, and visualization in the DBS domain. Examples are given from the state-of-the-art literature including our own research. This work reviews different analysis methods for EF simulations, tractography, deep brain anatomical templates, and group analysis. Our analysis highlights that group analysis in DBS is a complex multi-level problem and selected parameters will highly influence the result. DBS analysis can only provide clinically relevant information if the EF simulations, tractography results, and derived brain atlases are based on as much patient-specific data as possible. A trend in DBS research is creation of more advanced and intuitive visualization of the complex analysis results suitable for the clinical environment.

Toward personalized medicine in connectomic deep brain stimulation

At the group-level, deep brain stimulation leads to significant therapeutic benefit in a multitude of neurological and neuropsychiatric disorders. At the single-patient level, however, symptoms may sometimes persist despite "optimal" electrode placement at established treatment coordinates. This may be partly explained by limitations of disease-centric strategies that are unable to account for heterogeneous phenotypes and comorbidities observed in clinical practice. Instead, tailoring electrode placement and programming to individual patients' symptom profiles may increase the fraction of top-responding patients. Here, we propose a three-step, circuit-based framework with the aim of developing patient-specific treatment targets that address the unique symptom constellation prevalent in each patient. First, we describe how a symptom network target library could be established by mapping beneficial or undesirable DBS effects to distinct circuits based on (retrospective) group-level data. Second, we suggest ways of matching the resulting symptom networks to circuits defined in the individual patient (template matching). Third, we introduce network blending as a strategy to calculate optimal stimulation targets and parameters by selecting and weighting a set of symptom-specific networks based on the symptom profile and subjective priorities of the individual patient. We integrate the approach with published literature and conclude by discussing limitations and future challenges.

Structural Connectivity of Subthalamic Nucleus Stimulation for Improving Freezing of Gait

BACKGROUND

Freezing of gait (FOG) is among the most common and disabling symptoms of Parkinson's disease (PD). Studies show that deep brain stimulation (DBS) of the subthalamic nucleus (STN) can reduce FOG severity. However, there is uncertainty about pathways that need to be modulated to improve FOG.

OBJECTIVE

To investigate whether STN-DBS effectively reduces FOG postoperatively and whether structural connectivity of the stimulated tissue explains variance of outcomes.

METHODS

We investigated 47 patients with PD and preoperative FOG. Freezing prevalence and severity was primarily assessed using the Freezing of Gait Questionnaire (FOG-Q). In a subset of 18 patients, provoked FOG during a standardized walking course was assessed. Using a publicly available model of basal-ganglia pathways we determined stimulation-dependent connectivity associated with postoperative changes in FOG. A region-of-interest analysis to a priori defined mesencephalic regions was performed using a disease-specific normative connectome.

RESULTS

Freezing of gait significantly improved six months postoperatively, marked by reduced frequency and duration of freezing episodes. Optimal stimulation volumes for improving FOG structurally connected to motor areas, the prefrontal cortex and to the globus pallidus. Stimulation of the lenticular fasciculus was associated with worsening of FOG. This connectivity profile was robust in a leave-one-out cross-validation. Subcortically, stimulation of fibers crossing the pedunculopontine nucleus and the substantia nigra correlated with postoperative improvement.

CONCLUSION

STN-DBS can alleviate FOG severity by modulating specific pathways structurally connected to prefrontal and motor cortices. More differentiated FOG assessments may allow to differentiate pathways for specific FOG subtypes in the future.

Deep brain stimulation effect on anterior pallidum reduces motor impulsivity in Parkinson's disease

BACKGROUND

Deep Brain Stimulation (DBS) of the subthalamic nucleus or globus pallidus internus is used to treat the motor symptoms of Parkinson's disease. The former can worsen impulsive and compulsive behaviors after controlling for the reduction of dopaminergic medications. However, the effect of pallidal DBS on such behaviors in PD patients is less clear.

OBJECTIVE/HYPOTHESIS

We hypothesized that greater stimulation spread to the pallidum with prefrontal connectivity would reduce motor impulsivity.

METHODS

Seven Parkinson's patients with stable globus pallidus internus DBS settings for 3 months, disease duration of 13 ± 1.3 years, and Montreal Cognitive Assessment of 26.8 ± 1.1 each had two stimulation settings defined based on reconstructions of lead placement and volume of tissue activation targeting either a dorsal or ventral position along the DBS electrode but still within the globus pallidus internus. Subjects performed a stop signal reaction time task with the DBS turned off vs. on in each of the defined stimulation settings, which was correlated with the degree of stimulation effect on pallidal subregions.

RESULTS

A shorter distance between the volume of tissue activation and the right prefrontally-connected GPi correlated with less impulsivity on the stop signal reaction time task (r = 0.69, p < 0.05). Greater volume of tissue activation overlap with the non-prefrontally-connected globus pallidus internus was associated with increased impulsivity.

CONCLUSION

These data can be leveraged to optimize DBS programming in PD patients with problematic impulsivity or in other disorders involving impulsive behaviors such as substance use disorders.

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.

Deep Brain Stimulation for Tremor: Update on Long-Term Outcomes, Target Considerations and Future Directions

Deep brain stimulation (DBS) of the thalamic ventral intermediate nucleus is one of the main advanced neurosurgical treatments for drug-resistant tremor. However, not every patient may be eligible for this procedure. Nowadays, various other functional neurosurgical procedures are available. In particular cases, radiofrequency thalamotomy, focused ultrasound and radiosurgery are proven alternatives to DBS. Besides, other DBS targets, such as the posterior subthalamic area (PSA) or the dentato-rubro-thalamic tract (DRT), may be appraised as well. In this review, the clinical characteristics and pathophysiology of tremor syndromes, as well as long-term outcomes of DBS in different targets, will be summarized. The effectiveness and safety of lesioning procedures will be discussed, and an evidence-based clinical treatment approach for patients with drug-resistant tremor will be presented. Lastly, the future directions in the treatment of severe tremor syndromes will be elaborated.