Deep Brain Stimulation Parameters Research Articles

Prediction of Movement Ratings and Deep Brain Stimulation Parameters in Idiopathic Parkinson’s Disease

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

Automated deep brain stimulation programming with safety constraints for tremor suppression in patients with Parkinson’s disease and essential tremor

Objective. Deep brain stimulation (DBS) programming for movement disorders requires systematic fine tuning of stimulation parameters to ameliorate tremor and other symptoms while avoiding side effects. DBS programming can be a time-consuming process and requires clinical expertise to assess response to DBS to optimize therapy for each patient. In this study, we describe and evaluate an automated, closed-loop, and patient-specific framework for DBS programming that measures tremor using a smartwatch and automatically changes DBS parameters based on the recommendations from a closed-loop optimization algorithm thus eliminating the need for an expert clinician. Approach. Bayesian optimization which is a sample-efficient global optimization method was used as the core of this DBS programming framework to adaptively learn each patient’s response to DBS and suggest the next best settings to be evaluated. Input from a clinician was used initially to define a maximum safe amplitude, but we also implemented ‘safe Bayesian optimization’ to automatically discover tolerable exploration boundaries. Main results. We tested the system in 15 patients (nine with Parkinson’s disease and six with essential tremor). Tremor suppression at best automated settings was statistically comparable to previously established clinical settings. The optimization algorithm converged after testing settings when maximum safe exploration boundaries were predefined, and when the algorithm itself determined safe exploration boundaries. Significance. We demonstrate that fully automated DBS programming framework for treatment of tremor is efficient and safe while providing outcomes comparable to that achieved by expert clinicians.

Tremor evaluation using smartphone accelerometry in standardized settings.

Tremor can be highly incapacitating in everyday life and typically fluctuates depending on motor state, medication status as well as external factors. For tremor patients being treated with deep-brain stimulation (DBS), adapting the intensity and pattern of stimulation according the current needs therefore has the potential to generate better symptomatic relief. We here describe a procedure for how patients independently could perform self-tests in their home to generate sensor data for on-line adjustments of DBS parameters. Importantly, the inertia sensor technology needed exists in any standard smartphone, making the procedure widely accessible. Applying this procedure, we have characterized detailed features of tremor patterns displayed by both Parkinson’s disease and essential tremor patients and directly compared measured data against both clinical ratings (Fahn-Tolosa-Marin) and finger-attached inertia sensors. Our results suggest that smartphone accelerometry, when used in a standardized testing procedure, can provide tremor descriptors that are sufficiently detailed and reliable to be used for closed-loop control of DBS.

Automated optimization of deep brain stimulation parameters for modulating neuroimaging-based targets

Objective. Therapeutic efficacy of deep brain stimulation (DBS) in both established and emerging indications, is highly dependent on accurate lead placement and optimized clinical programming. The latter relies on clinicians’ experience to search among available sets of stimulation parameters and can be limited by the time constraints of clinical practice. Recent innovations in device technology have expanded the number of possible electrode configurations and parameter sets available to clinicians, amplifying the challenge of time constraints. We hypothesize that patient specific neuroimaging data can effectively assist the clinical programming using automated algorithms. Approach. This paper introduces the DBS Illumina 3D algorithm as a tool which uses patient-specific imaging to find stimulation settings that optimizes activating a target area while minimizing the stimulation of areas outside the target that could result in unknown or undesired side effects. This approach utilizes preoperative neuroimaging data paired with the postoperative reconstruction of the lead trajectory to search the available stimulation space and identify optimized stimulation parameters. We describe the application of this algorithm in three patients with treatment-resistant depression who underwent bilateral implantation of DBS in subcallosal cingulate cortex and ventral capsule/ventral striatum using tractography optimized targeting with an imaging defined target previously described. Main results. Compared to the stimulation settings selected by the clinicians (informed by anatomy), stimulation settings produced by the algorithm that achieved similar or greater target coverage, produced a significantly smaller stimulation area that spilled outside the target (P = 0.002). Significance. The DBS Illumina 3D algorithm is seamlessly integrated with the clinician programmer software and effectively and rapidly assists clinicians with the analysis of image based anatomy, and provides a starting point to search the highly complex stimulation parameter space and arrive at the stimulation settings that optimize activating a target area.

Deep Brain Stimulation of the Interposed Nucleus Reverses Motor Deficits and Stimulates Production of Anti-inflammatory Cytokines in Ataxia Mice.

Cerebellum is one of the major targets of autoimmunity and cerebellar damage that leads to ataxia characterized by the loss of fine motor coordination and balance, with no treatment available. Deep brain stimulation (DBS) could be a promising treatment for ataxia but has not been extensively investigated. Here, our study aims to investigate the use of interposed nucleus of deep cerebellar nuclei (IN-DCN) for ataxia. We first characterized ataxia-related motor symptom of a Purkinje cell (PC)-specific LIM homeobox (Lhx)1 and Lhx5 conditional double knockout mice by motor coordination tests, and spontaneous electromyogram (EMG) recording. To validate IN-DCN as a target for DBS, in vivo local field potential (LFP) multielectrode array recording of IN-DCN revealed abnormal LFP amplitude surges in PCs. By synchronizing the EMG and IN-DCN recordings (neurospike and LFP) with high-speed video recordings, ataxia mice showed poorly coordinated movements associated with low EMG amplitude and aberrant IN-DCN neural firing. To optimize IN-DCN-DBS for ataxia, we tested DBS parameters from low (30Hz) to high stimulation frequency (130 or 150Hz), and systematically varied pulse width values (60 or 80µs) to maximize motor symptom control in ataxia mice. The optimal IN-DCN-DBS parameter reversed motor deficits in ataxia mice as detected by animal behavioral tests and EMG recording. Mechanistically, cytokine array analysis revealed that anti-inflammatory cytokines such as interleukin (IL)-13 and IL-4 were upregulated after IN-DCN-DBS, which play key roles in neural excitability. As such, we show that IN-DCN-DBS is a promising treatment for ataxia and possibly other movement disorders alike.

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.

P 43 Optimizing beta-burst driven adaptive deep brain stimulation for Parkinson's disease

Introduction Adaptive deep brain stimulation (DBS) aims at improving DBS therapy by adjusting stimulation amplitude to patient specific biomarkers tracked in real-time. In Parkinson’s disease (PD), a promising closed-loop approach exploits fast fluctuations of beta power (beta bursts) in subthalamic local field potentials (LFP). With this method, stimulation is applied as soon as beta bursts of a given magnitude and duration are detected. However, closed-loop algorithms offer clinicians a wide range of possible settings. This will further increase the parameter space and thus the complexity of DBS programming, ultimately limiting ease of clinical usability. To increase the translational potential of adaptive DBS, this constraint must be overcome by narrowing down the parameter space towards a clinically optimal combination of stimulation settings. Objectives To explore the parameter space of a beta burst driven adaptive DBS algorithm and to characterize its neurophysiological and behavioral correlates in order to identify a clinically optimal parameter combination. Patients & methods A research platform for adaptive stimulation has been designed comprising a stimulation artifact suppressing amplifier, a custom-made software interface and an external neurostimulator. On this platform, a configurable LFP beta burst driven closed-loop algorithm has been implemented. This setup was used to conduct closed-loop stimulation in PD patients undergoing two-stage-surgery for DBS. Rest recordings were combined with a standardized motor task to quantify stimulation-induced behavioral effects. By systematically changing the minimum beta burst duration upon which stimulation is triggered and the amount of smoothing applied to real-time beta power, the parameter space of this algorithm was explored. Results Stimulation patterns were governed by parameter combination with higher minimum burst duration leading to less frequent stimulation. Beta dynamics varied depending on parameter choice. Clinically optimal settings are yet to be explored. Conclusion The choice of adaptive DBS parameter combination strongly influences stimulation patterns and stimulation-induced neurophysiological responses. Combined recordings of neurophysiological and behavioral adaptive DBS correlates for different parameter combinations may pave the way for guiding parameter choice based on electrophysiological evaluation. This may facilitate the clinical translation of adaptive DBS.

HP08: Deep Brain Stimulation Evoked Potentials in Children with dystonia

Pallidal Deep Brain Stimulation (DBS) is an established therapy for dystonia. Stimulation parameters are currently determined empirically. Recording the cortical response to the pallidal stimulus, known as Deep Brain Stimulation Evoked Potentials (DBSEPs) could provide a more objective measure of optimal contact selection (Tisch et al, MovDisord 2008;23:265-73; Bhanpuri et al, BrainStim 2014;7:718-26). Methods: Fifteen children with dystonia participated (age 7-19years), all with bilateral pallidal DBS implanted at least 6 months previously. Scalp EEG (10-20 system) was sampled at 5KHz. Contacts from each stimulating electrode were tested separately, using 6Hz bipolar stimulation. Pairs of electrode contacts were tested sequentially using combinations of cathodal stimulation (2 volts) with an adjacent anode, and pulse width at the patient’s therapeutic level. Each combination was recorded for 3 minutes, giving >1000 stimuli per contact pair. Therapeutic settings were restored on study completion. Offline, the DBS stimulus artefact was used to segment EEG data into epochs (10ms pre-stimulus, 150ms post-stimulus). After artefact rejection, 900-1100 epochs were averaged to obtain the DBSEP for each contact pair. Results: Clear DBSEPs were obtained in 12/15 patients on one or both sides, with peak latency of 20ms, and largest amplitude over the ipsilateral central/centro-parietal region, concordant with adult literature. In 3/15 patients no convincing DBSEP was obtained. Conclusion: To our knowledge this is the largest study of DBSEPs in dystonia and confirms the feasibility and tolerability of this technique in children. Future work will investigate potential clinical applications of DBSEPs to inform an individualised approach to DBS parameter selection.

Periodic Artifact Removal With Applications to Deep Brain Stimulation

Deep brain stimulation (DBS) therapies have shown clinical success in the treatment of a number of neurological illnesses, including obsessive-compulsive disorder, epilepsy, and Parkinson’s disease. An emerging strategy for increasing the efficacy of DBS therapies is to develop closed-loop, adaptive DBS systems that can sense biomarkers associated with particular symptoms and in response, adjust DBS parameters in real-time. The development of such systems requires extensive analysis of the underlying neural signals while DBS is on, so that candidate biomarkers can be identified and the effects of varying the DBS parameters can be better understood. However, DBS creates high amplitude, high frequency stimulation artifacts that prevent the underlying neural signals and thus the biological mechanisms underlying DBS from being analyzed. Additionally, DBS devices often require low sampling rates, which alias the artifact frequency, and rely on wireless data transmission methods that can create signal recordings with missing data of unknown length. Thus, traditional artifact removal methods cannot be applied to this setting. We present a novel periodic artifact removal algorithm for DBS applications that can accurately remove stimulation artifacts in the presence of missing data and in some cases where the stimulation frequency exceeds the Nyquist frequency. The numerical examples suggest that, if implemented on dedicated hardware, this algorithm has the potential to be used in embedded closed-loop DBS therapies to remove DBS stimulation artifacts and hence, to aid in the discovery of candidate biomarkers in real-time. Code for our proposed algorithm is publicly available on Github.

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.

Controlling Alzheimer's Disease Through the Deep Brain Stimulation to Thalamic Relay Cells.

Experimental and clinical studies have shown that the technique of deep brain stimulation (DBS) plays a potential role in the regulation of Alzheimer’s disease (AD), yet it still desires for ongoing studies including clinical trials, theoretical approach and action mechanism. In this work, we develop a modified thalamo-cortico-thalamic (TCT) model associated with AD to explore the therapeutic effects of DBS on AD from the perspective of neurocomputation. First, the neuropathological state of AD resulting from synapse loss is mimicked by decreasing the synaptic connectivity strength from the Inter-Neurons (IN) neuron population to the Thalamic Relay Cells (TRC) neuron population. Under such AD condition, a specific deep brain stimulation voltage is then implanted into the neural nucleus of TRC in this TCT model. The symptom of AD is found significantly relieved by means of power spectrum analysis and nonlinear dynamical analysis. Furthermore, the therapeutic effects of DBS on AD are systematically examined in different parameter space of DBS. The results demonstrate that the controlling effect of DBS on AD can be efficient by appropriately tuning the key parameters of DBS including amplitude A, period P and duration D. This work highlights the critical role of thalamus stimulation for brain disease, and provides a theoretical basis for future experimental and clinical studies in treating AD.

Therapeutic maps for a sensor-based evaluation of deep brain stimulation programming.

Programming in deep brain stimulation (DBS) is a labour-intensive process for treating advanced motor symptoms. Specifically for patients with medication-refractory tremor in multiple sclerosis (MS). Wearable sensors are able to detect some manifestations of pathological signs, such as intention tremor in MS. However, methods are needed to visualise the response of tremor to DBS parameter changes in a clinical setting while patients perform the motor task finger-to-nose. To this end, we attended DBS programming sessions of a MS patient and intention tremor was effectively quantified by acceleration amplitude and frequency. A new method is introduced which results in the generation of therapeutic maps for a systematic review of the programming procedure in DBS. The maps visualise the combination of tremor acceleration power, clinical rating scores, total electrical energy delivered to the brain and possible side effects. Therapeutic maps have not yet been employed and could lead to a certain degree of standardisation for more objective decisions about DBS settings. The maps provide a base for future research on visualisation tools to assist physicians who frequently encounter patients for DBS therapy.

Multiple Sessions of Entorhinal Cortex Deep Brain Stimulation in C57BL/6J Mice Increases Exploratory Behavior and Hippocampal Neurogenesis.

Deep brain stimulation (DBS) has been a medical intervention for a variety of nervous system diseases and mental diseases. The input of DBS in the entorhinal cortex (EC) regulates the neurophysiological activities in its downstream regions, such as the dentate gyrus (DG) area. EC DBS may play a role in the treatment of diseases through hippocampal neurogenesis. This study we examined the effect of multiple sessions of EC DBS on the regulation of hippocampal neurogenesis. 4-month-old male C57BL/6J mice received bilateral multiple sessions of EC DBS (130 Hz, 90 μs, 100 μA, 1 h/d, 21 days), and the DBS parameters used are close to the high-frequency DBS parameters in clinical studies. The open field test (OFT) was used to test the exploratory behavior of mice, and hippocampal neurogenesis was detected by immunofluorescence staining with anti-doublecortin (DCX). We found that multiple sessions of EC DBS were tolerated in C57BL/6J mice, significantly increased exploratory behavior and the number of DCX-positive neurons in the DG area.Clinical Relevance- Hippocampal neurogenesis may be part of the reason for DBS to improve memory, and the results of this study show that multiple sessions of EC DBS increases exploratory behavior and hippocampal neurogenesis, which is conducive to the application of DBS in nervous system diseases and mental diseases related to memory impairment.

Basal Ganglia Local Field Potentials as a Potential Biomarker for Sleep Disturbance in Parkinson's Disease.

Sleep disturbances, specifically decreases in total sleep time and sleep efficiency as well as increased sleep onset latency and wakefulness after sleep onset, are highly prevalent in patients with Parkinson's disease (PD). Impairment of sleep significantly and adversely impacts several comorbidities in this patient population, including cognition, mood, and quality of life. Sleep disturbances and other non-motor symptoms of PD have come to the fore as the effectiveness of advanced therapies such as deep brain stimulation (DBS) optimally manage the motor symptoms. Although some studies have suggested that DBS provides benefit for sleep disturbances in PD, the mechanisms by which this might occur, as well as the optimal stimulation parameters for treating sleep dysfunction, remain unknown. In patients treated with DBS, electrophysiologic recording from the stimulating electrode, in the form of local field potentials (LFPs), has led to the identification of several findings associated with both motor and non-motor symptoms including sleep. For example, beta frequency (13–30 Hz) oscillations are associated with worsened bradykinesia while awake and decrease during non-rapid eye movement sleep. LFP investigation of sleep has largely focused on the subthalamic nucleus (STN), though corresponding oscillatory activity has been found in the globus pallidus internus (GPi) and thalamus as well. LFPs are increasingly being recognized as a potential biomarker for sleep states in PD, which may allow for closed-loop optimization of DBS parameters to treat sleep disturbances in this population. In this review, we discuss the relationship between LFP oscillations in STN and the sleep architecture of PD patients, current trends in utilizing DBS to treat sleep disturbance, and future directions for research. In particular, we highlight the capability of novel technologies to capture and record LFP data in vivo, while patients continue therapeutic stimulation for motor symptoms. These technological advances may soon allow for real-time adaptive stimulation to treat sleep disturbances.

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.

A Novel Framework for Network-Targeted Neuropsychiatric Deep Brain Stimulation.

Deep brain stimulation (DBS) has emerged as a promising therapy for neuropsychiatric illnesses, including depression and obsessive-compulsive disorder, but has shown inconsistent results in prior clinical trials. We propose a shift away from the empirical paradigm for developing new DBS applications, traditionally based on testing brain targets with conventional stimulation paradigms. Instead, we propose a multimodal approach centered on an individualized intracranial investigation adapted from the epilepsy monitoring experience, which integrates comprehensive behavioral assessment, such as the Research Domain Criteria proposed by the National Institutes of Mental Health. In this paradigm-shifting approach, we combine readouts obtained from neurophysiology, behavioral assessments, and self-report during broad exploration of stimulation parameters and behavioral tasks to inform the selection of ideal DBS parameters. Such an approach not only provides a foundational understanding of dysfunctional circuits underlying symptom domains in neuropsychiatric conditions but also aims to identify generalizable principles that can ultimately enable individualization and optimization of therapy without intracranial monitoring.

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.

Blood oxygen level-dependent (BOLD) response patterns with thalamic deep brain stimulation in patients with medically refractory epilepsy

ObjectiveAnterior nucleus of thalamus (ANT) deep brain stimulation (DBS) has shown promise as a treatment for medically refractory epilepsy. To better understand the mechanism of this intervention, we used functional magnetic resonance imaging (fMRI) to map the acute blood oxygen level-dependent (BOLD) response pattern to thalamic DBS in fully implanted patients with epilepsy. MethodsTwo patients with epilepsy implanted with bilateral ANT-DBS devices underwent four fMRI acquisitions each, during which active left-sided monopolar stimulation was delivered in a 30-s DBS-ON/OFF cycling paradigm. Each fMRI acquisition featured left-sided stimulation of a different electrode contact to vary the locus of stimulation within the thalamus and to map the brain regions modulated as a function of different contact selection. To determine the extent of peri-electrode stimulation and the engagement of local structures during each fMRI acquisition, volume of tissue activated (VTA) modeling was also performed. ResultsMarked changes in the pattern of BOLD response were produced with thalamic stimulation, which varied with the locus of the active contact in each patient. BOLD response patterns to stimulation that directly engaged at least 5% of the anterior nuclear group by volume were characterized by changes in the bilateral putamen, thalamus, and posterior cingulate cortex, ipsilateral middle cingulate cortex and precuneus, and contralateral medial prefrontal and anterior cingulate. SignificanceThe differential BOLD response patterns associated with varying thalamic DBS parameters provide mechanistic insights and highlight the possibilities of fMRI biomarkers of optimizing stimulation in patients with epilepsy.

Four-six year clinical follow-up of deep brain stimulation for obsessive compulsive disorder

BackgroundOver the past 20 years a number of robust studies have established the clinical effectiveness and safety of Deep brain stimulation (DBS) in adults with profound multi-treatment-refractory obsessive-compulsive disorder (OCD). However long term (&gt;12 months) outcomes with this novel neurosurgical intervention are still inadequately reported. Our group conducted the first UK study of DBS in OCD between 2013-2017. All participants in our trial achieved a responder status at 15 month endpoint and the main results were reported in 2019. A specialist multidisciplinary clinic was established after the trial to provide life-long aftercare in the form of scheduled clinical and hardware reviews. Here we are reporting a preliminary analysis of the long-term clinical, functional and social outcomes from this cohort.MethodLong term follow-up clinical data (15–75 months, 2015 onwards) were prospectively collected from the participants who were enrolled in the original MRC-UCL pilot study of DBS for OCD. DBS parameters, battery health and status, social circumstances, mental state and medication adjustments were noted alongside the outcome measures of YBOCS at clinical follow-up encounters. Additional ratings of GAF, SDS and certain qualitative measures were recorded at least once every year since initial study completion.ResultFive out of six participants continued with DBS treatment and kept responder status. One participant had his DBS switched off and hardware removed. One participant had multiple hospital admissions to manage comorbidity progression to primary condition. One participant had OCD severity scores revised upwards despite continuing gains in QoL. Secondary outcomes generally matched the 15 month end point of initial trial. All participants experienced minor to major changes in their relationships with partners or family. Qualitative feedback indicated that DBS was well tolerated by 5/6 subjects but the burden of specialist follow-up remained significant.ConclusionOur long term follow-up data indicate that DBS is safe and conferred a sustained long-term benefit in reduction of obsessive-compulsive symptoms. A non-trivial burden of checking and maintenance of implanted hardware, comorbidity-unmasking following successful OCD treatment, perceived ‘burden of normality’ by the participant, need for life-long follow-ups with specialist multidisciplinary team including DBS nurses, highly specialist psychiatrists from National OCD service, neuropsychiatrists, neurologists and neurosurgeons partially counterbalances the gains offered by this treatment. Overall DBS offers a safe, effective and enduring alternative to participants who do not respond to any other form of OCD treatment and do not wish to undergo ablation surgery.