Optimal Deep Brain Stimulation Research Articles

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.

Clinician vs. imaging-based subthalamic nucleus deep brain stimulation programming

IntroductionWe sought to explore whether electrode visualization tools (EVT) can accurately predict the selection of optimal Deep Brain Stimulation (DBS) electrode contacts. MethodsTwelve patients with Parkinson's disease (PD) undergoing STN-DBS at The Ohio State University were enrolled in a prospective analysis to evaluate the accuracy of EVT-based vs. standard DBS programming. EVTs were generated by the Surgical Information Sciences (SIS) system to develop a 3D model showing the implanted lead location relative to the STN. Then, imaging-based data were compared to the results of a standard monopolar review to evaluate concordance with clinical data and time spent selecting useable, non-useable, and borderline electrode contacts. ResultsA total of 18 DBS leads (n = 68 electrode contacts) were analyzed. The concordance between EVT and standard clinical programming expressed as the kappa coefficient was 0.65 (82.35% raw agreement) for non-useable, 0.52 for useable (64.71% raw agreement), and 0.52 for borderline (58.82% raw agreement). The average time spent determining whether an electrode contact was useable, non-useable, or borderline was 1.46 ± 0.76 min with EVT vs. 61.25 ± 17.47 with standard monopolar review. Eight different categories of side effects were identified, with facial pulling and speech difficulties being observed with the most frequency. The type of side effect observed was accurately predicted using EVT 90% of the time. ConclusionsThis study demonstrates that next-generation EVT-based programming can be implemented into STN-DBS programming workflows with a considerable saving of time and effort spent in testing combinations of stimulation settings, particularly for the identification of non-useable electrode contacts.

PO269 / #622 PROGRAMMING TIME AND THE USE OF A VISUALIZATION TOOL FOR DBS PROGRAMMING OF A MULTIPLE-SOURCE, CONSTANT-CURRENT SYSTEM: E-POSTER VIEWING

Optimization of Deep Brain Stimulation (DBS) programming usually consists of an empirical trial-and-error process involving appraisal of various stimulation parameters to determine optimal settings. This process can be inefficient, lengthy and burdensome for the patient. The use of a visualization tool capable of illustrating the location of the DBS lead within the individual's segmented anatomy and volume of tissue activated (VTA) may have the potential to reach optimal stimulation parameters in a shorter, more efficient fashion.

PO024 / #1057 A NOVEL COMPUTATIONAL TOOLBOX TO ANALYZE ELECTROPHYSIOLOGY DATA FROM PARKINSON’S PATIENTS IMPLANTED WITH DEEP BRAIN STIMULATION DEVICES: E-POSTER VIEWING

Deep brain stimulation (DBS) is a therapy applied in patients with advanced Parkinson’s Disease (PD) [1]. Its benefits and effectiveness are established, improving the quality of life of patients by reducing motor and non-motor symptoms. The mechanisms behind the therapeutic success of DBS remains mostly unknown. There are emergent challenges to achieve the optimal DBS therapy. One innovative field is the adaptive Deep Brain Stimulation (aDBS) with closed-loop feedback, which integrates innovations in the device’s development for DBS and the search for pathological biomarkers [2].

Neural Correlates of Optimal Deep Brain Stimulation for Cervical Dystonia.

A total of 15 individuals with cervical dystonia and good outcome after pallidal deep brain stimulation underwent resting-state functional magnetic resonance imaging under three conditions: stimulation using a priori clinically determined optimal settings (ON-Op), non-optimal settings (ON-NOp), and stimulation off (OFF). ON-Op > OFF and ON-Op > ON-NOp were both associated with significant deactivation within sensorimotor cortex (changes not seen with ON-NOp > OFF). Brain responses to stimulation were related to individual long-term clinical improvement (R = 0.73, R2 = 0.53, p = 0.001). The relationship was consistent when this model included four additional patients with generalized or truncal dystonia. These findings highlight the potential for immediate imaging-based biomarkers of clinical efficacy. ANN NEUROL 2022;92:418-424.

Model-based Quantitative optimization of deep brain stimulation and prediction of Parkinson’s states

Although the exact etiology of Parkinson’s disease (PD) is still unknown, there are a variety of treatments available to alleviate its symptoms according to the development stage of PD. Deep brain stimulation (DBS), the most common surgical treatment for advanced PD, accurately locates and implants stimulating electrodes at specific targets in the brain to deliver high-frequency electrical stimulation that alters the excitability of the corresponding nuclei. However, for different patients and stages of PD development, there exists a choice of the optimal DBS protocol. In this paper, we propose a quantitative method (multi-dimensional feature indexes) to determine the stimulation pattern, stimulation parameters, and target of DBS from the perspective of the network model. On the other hand, based on this method, the development of PD can be predicted so that timely treatment can be given to patients. Simulation results show that, first, different network states can be distinguished by extracting features of the firing activity of neuronal populations within the basal ganglia network system. Secondly, the optimal DBS treatment can be selected by comparing the feature indexes vectors of the pre- and post-state of the network after the action of different modes of DBS. Lastly, the evolution of the network state from normal to pathological is simulated. The critical point of network state transitions is determined. These results provide a quantitative and qualitative method for determining the optimal regimen for DBS for PD, which is helpful for clinical practice.

Deep brain stimulation rectifies the noisy cortex and irresponsive subthalamus to improve parkinsonian locomotor activities

The success of deep brain stimulation (DBS) therapy indicates that Parkinson’s disease is a brain rhythm disorder. However, the manifestations of the erroneous rhythms corrected by DBS remain to be established. We found that augmentation of α rhythms and α coherence between the motor cortex (MC) and the subthalamic nucleus (STN) is characteristically prokinetic and is decreased in parkinsonian rats. In multi-unit recordings, movement is normally associated with increased changes in spatiotemporal activities rather than overall spike rates in MC. In parkinsonian rats, MC shows higher spike rates at rest but less spatiotemporal activity changes upon movement, and STN burst discharges are more prevalent, longer lasting, and less responsive to MC inputs. DBS at STN rectifies the foregoing pathological MC-STN oscillations and consequently locomotor deficits, yet overstimulation may cause behavioral restlessness. These results indicate that delicate electrophysiological considerations at both cortical and subcortical levels should be exercised for optimal DBS therapy.

P 34 StimFit – A data-driven algorithm for automated deep brain stimulation programming

Question: Finding the optimal deep brain stimulation (DBS) parameters out of a multitude of possible combinations by trial-and-error is time-consuming and requires highly trained medical personnel. 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 dorso-lateral 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 which 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.

Optimal deep brain stimulation sites and networks for cervical vs. generalized dystonia

Dystonia is a debilitating disease with few treatment options. One effective option is deep brain stimulation (DBS) to the internal pallidum. While cervical and generalized forms of isolated dystonia have been targeted with a common approach to the posterior third of the nucleus, large-scale investigations regarding optimal stimulation sites and potential network effects have not been carried out. Here, we retrospectively studied clinical results following DBS for cervical and generalized dystonia in a multicenter cohort of 80 patients. We model DBS electrode placement based on pre- and postoperative imaging and introduce an approach to map optimal stimulation sites to anatomical space. Second, we investigate which tracts account for optimal clinical improvements, when modulated. Third, we investigate distributed stimulation effects on a whole-brain functional connectome level. Our results show marked differences of optimal stimulation sites that map to the somatotopic structure of the internal pallidum. While modulation of the striatopallidofugal axis of the basal ganglia accounted for optimal treatment of cervical dystonia, modulation of pallidothalamic bundles did so in generalized dystonia. Finally, we show a common multisynaptic network substrate for both phenotypes in the form of connectivity to the cerebellum and somatomotor cortex. Our results suggest a brief divergence of optimal stimulation networks for cervical vs. generalized dystonia within the pallidothalamic loop that merge again on a thalamo-cortical level and share a common whole-brain network.

Subthalamic Deep Brain Stimulation Lead Asymmetry Impacts the Parkinsonian Gait Disorder.

BackgroundThe preferable position of Deep Brain Stimulation (DBS) electrodes is proposed to be located in the dorsolateral subthalamic nucleus (STN) to improve general motor performance. The optimal DBS electrode localization for the post-operative improvement of balance and gait is unknown.MethodsIn this single-center, retrospective analyses, 66 Parkinson’s disease (PD) patients (24 female, age 63 ± 7 years) were assessed pre- and post-operatively (8.45 ± 4.2 months after surgery) by using MDS-UPDRS, freezing of gait (FoG) score, Giladi’s gait and falls questionnaire and Berg balance scale. The clinical outcome was related to the DBS electrode coordinates in x, y, z plane as revealed by image-based reconstruction (SureTune™). Binomial generalized linear mixed models with fixed-effect variables electrode asymmetry, parkinsonian subtype, medication, age class and clinical DBS induced changes were analyzed.ResultsSubthalamic nucleus-deep brain stimulation improved all motor, balance and FoG scores in MED OFF condition, however there were heterogeneous results in MED ON condition. DBS electrode reconstructed coordinates impacted the responsiveness of axial symptoms. FoG and balance responders showed slightly more medially located STN electrode coordinates and less medio-lateral asymmetry of the electrode reconstructed coordinates across hemispheres compared to non-responders.ConclusionDeep brain stimulation electrode reconstructed coordinates, particularly electrode asymmetry on the medio-lateral axis affected the post-operative responsiveness of balance and FoG symptoms in PD patients.

Diving into the subcortex: The potential of chronic subcortical sensing for unravelling basal ganglia function and optimization of deep brain stimulation

Subcortical structures are a relative neurophysiological ‘terra incognita’ owing to their location within the skull. While perioperative subcortical sensing has been performed for more than 20 years, the neurophysiology of the basal ganglia in the home setting has remained almost unexplored. However, with the recent advent of implantable pulse generators (IPG) that are able to record neural activity, the opportunity to chronically record local field potentials (LFPs) directly from electrodes implanted for deep brain stimulation opens up. This allows for a breakthrough of chronic subcortical sensing into fundamental research and clinical practice. In this review an extensive overview of the current state of subcortical sensing is provided. The widespread potential of chronic subcortical sensing for investigational and clinical use is discussed. Finally, status and future perspectives of the most promising application of chronic subcortical sensing —i.e., adaptive deep brain stimulation (aDBS)— are discussed in the context of movement disorders. The development of aDBS based on both chronic subcortical and cortical sensing has the potential to dramatically change clinical practice and the life of patients with movement disorders. However, several barriers still stand in the way of clinical implementation. Advancements regarding IPG and lead technology, physiomarkers, and aDBS algorithms as well as harnessing artificial intelligence, multimodality and sensing in the naturalistic setting are needed to bring aDBS to clinical practice.

Oscillation suppression effects of intermittent noisy deep brain stimulation induced by coordinated reset pattern based on a computational model

Noisy stimulation (NS) has been proposed to alleviate the pathological state in a computational model of Parkinson’s disease. Based on a resonance effect, NS modulates the neural firing pattern so as to suppress the excessive beta (12–35 Hz) synchronization and improve the relay reliability of the thalamus. However, in common with clinically-used pulsed high-frequency stimulation, the use of a continuous stimulation waveform for NS still may require an energy expenditure that is greater than necessary. A coordinated reset (CR) method to optimize NS is explored in this work that considers that the neural nature of the Parkinsonian condition is the enhancement of synchronization. CR methods has an advantage in disturbing the abnormal synchronous rhythm. Thesimulation resultsshow that coordinated reset noisy stimulation (CRNS) can further inhibit the enhanced pathological synchronization in a basal ganglia-thalamus network model with lower energy expenditure, as compared to basic NS. Moreover, two kinds of intermittent CRNS strategies are further derived by adding and adjusting the stimulation-off phases in the CRNS strategy. It is found that m:n ON-OFF CRNS strategies and delayed CRNS may further reduce the stimulation energy cost by 21.2% and 15.3% without compromising control performance, respectively. This work provides a new insight into the optimization of deep brain stimulation and may guide a new approach to treating Parkinson’s disease by optimizing the noise-induced improvement of the basal ganglia dysfunction.

3T MRI of rapid brain activity changes driven by subcallosal cingulate deep brain stimulation.

Deep brain stimulation targeting the subcallosal cingulate area, a hub with multiple axonal projections, has shown therapeutic potential for treatment-resistant mood disorders. While subcallosal cingulate deep brain stimulation drives long-term metabolic changes in corticolimbic circuits, the brain areas that are directly modulated by electrical stimulation of this region are not known. We used 3.0 T functional MRI to map the topography of acute brain changes produced by stimulation in an initial cohort of 12 patients with fully implanted deep brain stimulation devices targeting the subcallosal cingulate area. Four additional subcallosal cingulate deep brain stimulation patients were also scanned and employed as a validation cohort. Participants underwent resting state scans (n = 78 acquisitions overall) during (i) inactive deep brain stimulation; (ii) clinically optimal active deep brain stimulation; and (iii) suboptimal active deep brain stimulation. All scans were acquired within a single MRI session, each separated by a 5-min washout period. Analysis of the amplitude of low-frequency fluctuations in each sequence indicated that clinically optimal deep brain stimulation reduced spontaneous brain activity in several areas, including the bilateral dorsal anterior cingulate cortex, the bilateral posterior cingulate cortex, the bilateral precuneus and the left inferior parietal lobule (PBonferroni < 0.0001). Stimulation-induced dorsal anterior cingulate cortex signal reduction correlated with immediate within-session mood fluctuations, was greater at optimal versus suboptimal settings and was related to local cingulum bundle engagement. Moreover, linear modelling showed that immediate changes in dorsal anterior cingulate cortex, posterior cingulate cortex and precuneus activity could predict individual long-term antidepressant improvement. A model derived from the primary cohort that incorporated amplitude of low-frequency fluctuations changes in these three areas (along with preoperative symptom severity) explained 55% of the variance in clinical improvement in that cohort. The same model also explained 93% of the variance in the out-of-sample validation cohort. Additionally, all three brain areas exhibited significant changes in functional connectivity between active and inactive deep brain stimulation states (PBonferroni < 0.01). These results provide insight into the network-level mechanisms of subcallosal cingulate deep brain stimulation and point towards potential acute biomarkers of clinical response that could help to optimize and personalize this therapy.

Deep brain stimulation for movement disorder treatment: exploring frequency-dependent efficacy in a computational network model

A large-scale computational model of the basal ganglia network and thalamus is proposed to describe movement disorders and treatment effects of deep brain stimulation (DBS). The model of this complex network considers three areas of the basal ganglia region: the subthalamic nucleus (STN) as target area of DBS, the globus pallidus, both pars externa and pars interna (GPe-GPi), and the thalamus. Parkinsonian conditions are simulated by assuming reduced dopaminergic input and corresponding pronounced inhibitory or disinhibited projections to GPe and GPi. Macroscopic quantities are derived which correlate closely to thalamic responses and hence motor programme fidelity. It can be demonstrated that depending on different levels of striatal projections to the GPe and GPi, the dynamics of these macroscopic quantities (synchronisation index, mean synaptic activity and response efficacy) switch from normal to Parkinsonian conditions. Simulating DBS of the STN affects the dynamics of the entire network, increasing the thalamic activity to levels close to normal, while differing from both normal and Parkinsonian dynamics. Using the mentioned macroscopic quantities, the model proposes optimal DBS frequency ranges above 130 Hz.

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

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. 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. 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. 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. 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.

A novel prototype for virtual-reality-based deep brain stimulation trajectory planning using voodoo doll annotation and eye-tracking

ABSTRACT Deep brain stimulation (DBS) is an effective surgical treatment for Parkinson’s disease. The procedure requires precise placement of a stimulation electrode into the therapeutic target while avoiding vital anatomies (e.g. blood vessels) to prevent surgical risks. Therefore, multi-contrast imaging data are often employed to capture full anatomical details for electrode trajectory planning. However, with multiple constraints to consider from several image contrasts, surgical planning with conventional 2D-display-based neuro-navigation software can be time-consuming and challenging. Virtual reality (VR) allows intuitive interaction with 3D data, and thus is an excellent fit to navigate complex anatomy for neurosurgical planning. We present the first VR-based DBS trajectory planning system, where we used a novel voodoo doll interaction strategy to allow precise surgical target selection and a line-of-sight approach with eye-tracking to determine optimal DBS trajectories. With preliminary user studies, the proposed system demonstrates great promises for efficient and intuitive DBS planning.

Short-term changes in cortical physiological arousal measured by electroencephalography during thalamic centromedian deep brain stimulation.

The intralaminar thalamus is well implicated in the processes of arousal and attention. Stimulation of the intralaminar thalamus has been used therapeutically to improve level of alertness in minimally conscious individuals and to reduce seizures in refractory epilepsy, both presumably through modulation of thalamocortical function. Little work exists that directly measures the effects of intralaminar thalamic stimulation on cortical physiological arousal in humans. Therefore, our goal was to quantify cortical physiological arousal in individuals with epilepsy receiving thalamic intralaminar deep brain stimulation. We recorded scalp electroencephalogram (EEG) during thalamic intralaminar centromedian (CM) nucleus stimulation in 11 patients with medically refractory epilepsy. Participants underwent stimulation at 130Hz and 300µs for periods of 5 min alternating with 5 min of rest while stimulus voltage was titrated upward from 1 to 5V. EEG signal power was analyzed in different frequency ranges in relation to stimulus strength and time. We found a progressive increase in broadband gamma (25-100Hz) cortical EEG power (F=7.64, p<.05) and decrease in alpha (8-13Hz) power (F=4.37, p<.05) with thalamic CM stimulation. Topographic maps showed these changes to be widely distributed across the cortical surface rather than localized to one region. Previous work has shown that broadband increases in gamma frequency power and decreases in alpha frequency power are generally associated with states of cortical activation and increased arousal/attention. Our observed changes therefore support the possible role of cortical activation and increased physiological arousal in therapeutic effects of intralaminar thalamic stimulation for improving both epilepsy and attention. Further investigations with this approach may lead to methods for determining optimal deep brain stimulation parameters to improve clinical outcome in these disorders.

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

BackgroundThe 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. ObjectiveThe 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. MethodsClinical 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. ResultsClinical 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. ConclusionsImaging-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.

FV 11. Prediction of mobility ratings and DBS electrode parameters in idiopathic Parkinson's disease

Introduction. Idiopathic Parkinson Syndrome (iPS) is a chronic neurodegenerative disorder with rising prevalence. Initially satisfactory drug treatment often gets insufficient later on giving rise to hypokinetic motoric states (OFF) several times a day. Deep Brain stimulation (DBS) is a treatment alternative especially in later stages of the disease. Yet, DBS settings require adjustment by specialized physicians which is time consuming and can be exhausting for patients. Mobility measurements from mobile devices with an inertial measurement unit (IMU) could enable more efficient detection of DBS parameters resulting in optimal symptom control. Method. We measured 23 patients suffering from iPS with chronic DBS lead placement in the subthalamicus nucleus. During consequent monopolar testing, patients were rated at every setting for tremor, bradykinesia and rigidity and performed four hand motor tasks: i) tapping, ii) diadochokinesia, iii) posture holding and iv) rest. Linear acceleration and angular velocity of the forearm were measured with a commercially available armband carrying an IMU. Finally DBS settings were set to optimal values by an experienced physician. We used random forest models to predict 1. clinical mobility ratings and 2. The importance of each electrode in the optimal DBS setting. Models were trained on 80% of the data while 20% served for validation. Results. We used correlations between true and predicted values for model assessment. IMU measurements from the diadochokinesia task were associated with the best prediction of clinical mobility ratings with a maximum correlation of .678 ( p < .001, r 2 = .46) for the prediction of clinical diadochokinesia ratings. Correlations between true and predicted stimulation strength of electrodes in the optimal settings were lower but still substantial. Here IMU data from the tapping task had the best predictive value ( r = .57, p < .001, r 2 = .32). Discussion. We present an automated approach to mobility rating and optimal DBS parameter estimation in patients suffering from iPS. We show that clinical mobility ratings can be predicted with substantial validity using inexpensive IMUs. We also show that one can estimate the role of a particular electrode in an optimal DBS setting according to a specialized physicians adjustment. Our results may pave the way for implementation of machine learning algorithms enabling self-adjusting DBS devices in the future. Fig. 1. Correlations between true and predicted ratings of diadochokinesia, tapping and rigor. Colors show from which task IMU data was used for prediction (dd: diadochokinesia, hold: holding, rest: resting tap: tapping). Errorbars are 95% confidence intervals. Fig. 2. Correlations between true and predicted optimal current (mA) of a particular electrode contact. Groups show from which task IMU data was used for prediction (dd: diadochokinesia, hold: holding, rest: resting tap: tapping). Errorbars are 95% confidence intervals.

Comparative connectivity correlates of dystonic and essential tremor deep brain stimulation

The pathophysiology of dystonic tremor and essential tremor remains partially understood. In patients with medication-refractory dystonic tremor or essential tremor, deep brain stimulation (DBS) targeting the thalamus or posterior subthalamic area has evolved into a promising treatment option. However, the optimal DBS targets for these disorders remains unknown. This retrospective study explored the optimal targets for DBS in essential tremor and dystonic tremor using a combination of volumes of tissue activated estimation and functional and structural connectivity analyses. We included 20 patients with dystonic tremor who underwent unilateral thalamic DBS, along with a matched cohort of 20 patients with essential tremor DBS. Tremor severity was assessed preoperatively and approximately 6 months after DBS implantation using the Fahn-Tolosa-Marin Tremor Rating Scale. The tremor-suppressing effects of DBS were estimated using the percentage improvement in the unilateral tremor-rating scale score contralateral to the side of implantation. The optimal stimulation region, based on the cluster centre of gravity for peak contralateral motor score improvement, for essential tremor was located in the ventral intermediate nucleus region and for dystonic tremor in the ventralis oralis posterior nucleus region along the ventral intermediate nucleus/ventralis oralis posterior nucleus border (4 mm anterior and 3 mm superior to that for essential tremor). Both disorders showed similar functional connectivity patterns: a positive correlation between tremor improvement and involvement of the primary sensorimotor, secondary motor and associative prefrontal regions. Tremor improvement, however, was tightly correlated with the primary sensorimotor regions in essential tremor, whereas in dystonic tremor, the correlation was tighter with the premotor and prefrontal regions. The dentato-rubro-thalamic tract, comprising the decussating and non-decussating fibres, significantly correlated with tremor improvement in both dystonic and essential tremor. In contrast, the pallidothalamic tracts, which primarily project to the ventralis oralis posterior nucleus region, significantly correlated with tremor improvement only in dystonic tremor. Our findings support the hypothesis that the pathophysiology underpinning dystonic tremor involves both the cerebello-thalamo-cortical network and the basal ganglia-thalamo-cortical network. Further our data suggest that the pathophysiology of essential tremor is primarily attributable to the abnormalities within the cerebello-thalamo-cortical network. We conclude that the ventral intermediate nucleus/ventralis oralis posterior nucleus border and ventral intermediate nucleus region may be a reasonable DBS target for patients with medication-refractory dystonic tremor and essential tremor, respectively. Uncovering the pathophysiology of these disorders may in the future aid in further improving DBS outcomes.