Optimal Deep Brain Stimulation Research Articles

Smartphone Voice Calls Provide Early Biomarkers of Parkinsonism in Rapid Eye Movement Sleep Behavior Disorder.

Speech dysfunction represents one of the initial motor manifestations to develop in Parkinson's disease (PD) and is measurable through smartphone. The aim was to develop a fully automated and noise-resistant smartphone-based system that can unobtrusively screen for prodromal parkinsonian speech disorder in subjects with isolated rapid eye movement sleep behavior disorder (iRBD) in a real-world scenario. This cross-sectional study assessed regular, everyday voice call data from individuals with iRBD compared to early PD patients and healthy controls via a developed smartphone application. The participants also performed an active, regular reading of a short passage on their smartphone. Smartphone data were continuously collected for up to 3 months after the standard in-person assessments at the clinic. A total of 3525 calls that led to 5990 minutes of preprocessed speech were extracted from 72 participants, comprising 21 iRBD patients, 26 PD patients, and 25 controls. With a high area under the curve of 0.85 between iRBD patients and controls, the combination of passive and active smartphone data provided a comparable or even more sensitive evaluation than laboratory examination using a high-quality microphone. The most sensitive features to induce prodromal neurodegeneration in iRBD included imprecise vowel articulation during phone calls (P = 0.03) and monopitch in reading (P = 0.05). Eighteen minutes of speech corresponding to approximately nine calls was sufficient to obtain the best sensitivity for the screening. We consider the developed tool widely applicable to deep longitudinal digital phenotyping data with future applications in neuroprotective trials, deep brain stimulation optimization, neuropsychiatry, speech therapy, population screening, and beyond. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Precision targeting in the globus pallidus interna: insights from the multicenter, prospective, blinded VA/NINDS CSP 468 study.

Deep brain stimulation (DBS) targeting the globus pallidus interna (GPi) has been shown to significantly improve motor symptoms for the treatment of medication-refractory Parkinson's disease. Yet, heterogeneity in clinical outcomes persists, possibly due to suboptimal target identification within the GPi. By leveraging robust sampling of the GPi and 6-month postsurgical outcomes, this study aims to determine optimal symptom-specific GPi DBS targets. In this study, the authors analyzed the anatomical lead location and 6-month postsurgical, double-blinded outcome measures of 86 patients who underwent bilateral GPi DBS. These patients were selected from the multicenter Veterans Affairs (VA)/National Institutes of Neurological Disorders and Stroke (NINDS) Cooperative Studies Program (CSP) 468 study to identify the optimal target zones ("sweet spots") for the control of overall motor (United Parkinson's Disease Rating Scale [UPDRS]-III), axial, tremor, rigidity, and bradykinesia symptoms. Lead coordinates were normalized to Montreal Neurological Institute space and the optimal target zones were identified and validated using a leave-one-patient-out approach. The authors' findings revealed statistically significant optimal target zones for UPDRS-III (R = 0.37, p < 0.001), axial (R = 0.22, p = 0.042), rigidity (R = 0.20, p = 0.021), and bradykinesia (R = 0.23, p = 0.004) symptoms. These zones were localized within the primary motor and premotor subdivisions of the GPi. Interestingly, these zones extended beyond the GPi lateral border into the GPi-globus pallidus externa (GPe) lamina and into the GPe, but they did not reach the GPi ventral border, challenging traditional surgical approaches based on pallidotomies. Drawing upon a robust dataset, this research effectively delineates specific optimal target zones for not only overall motor improvement but also symptom subscores. These insights hold the potential to enhance the precision of targeting in subsequent bilateral GPi DBS surgical procedures.

Evoked resonant neural activity long-term dynamics can be reproduced by a computational model with vesicle depletion

Subthalamic deep brain stimulation (DBS) robustly generates high-frequency oscillations known as evoked resonant neural activity (ERNA). Recently the importance of ERNA has been demonstrated through its ability to predict the optimal DBS contact in the subthalamic nucleus in patients with Parkinson's disease. However, the underlying mechanisms of ERNA are not well understood, and previous modelling efforts have not managed to reproduce the wealth of published data describing the dynamics of ERNA. Here, we aim to present a minimal model capable of reproducing the characteristics of the slow ERNA dynamics published to date. We make biophysically-motivated modifications to the Kuramoto model and fit its parameters to the slow dynamics of ERNA obtained from data. Our results demonstrate that it is possible to reproduce the slow dynamics of ERNA (over hundreds of seconds) with a single neuronal population, and, crucially, with vesicle depletion as one of the key mechanisms behind the ERNA frequency decay in our model. We further validate the proposed model against experimental data from Parkinson's disease patients, where it captures the variations in ERNA frequency and amplitude in response to variable stimulation frequency, amplitude, and to stimulation pulse bursting. We provide a series of predictions from the model that could be the subject of future studies for further validation.

Predicting the optimal deep brain stimulation contact-level in Parkinson's disease: Insights from local field potentials recorded by the implanted neurostimulator

Predicting the optimal deep brain stimulation contact-level in Parkinson's disease: Insights from local field potentials recorded by the implanted neurostimulator

Model-based closed-loop control of thalamic deep brain stimulation.

Introduction: Closed-loop control of deep brain stimulation (DBS) is beneficial for effective and automatic treatment of various neurological disorders like Parkinson's disease (PD) and essential tremor (ET). Manual (open-loop) DBS programming solely based on clinical observations relies on neurologists' expertise and patients' experience. Continuous stimulation in open-loop DBS may decrease battery life and cause side effects. On the contrary, a closed-loop DBS system uses a feedback biomarker/signal to track worsening (or improving) of patients' symptoms and offers several advantages compared to the open-loop DBS system. Existing closed-loop DBS control systems do not incorporate physiological mechanisms underlying DBS or symptoms, e.g., how DBS modulates dynamics of synaptic plasticity. Methods: In this work, we propose a computational framework for development of a model-based DBS controller where a neural model can describe the relationship between DBS and neural activity and a polynomial-based approximation can estimate the relationship between neural and behavioral activities. A controller is used in our model in a quasi-real-time manner to find DBS patterns that significantly reduce the worsening of symptoms. By using the proposed computational framework, these DBS patterns can be tested clinically by predicting the effect of DBS before delivering it to the patient. We applied this framework to the problem of finding optimal DBS frequencies for essential tremor given electromyography (EMG) recordings solely. Building on our recent network model of ventral intermediate nuclei (Vim), the main surgical target of the tremor, in response to DBS, we developed neural model simulation in which physiological mechanisms underlying Vim-DBS are linked to symptomatic changes in EMG signals. By using a proportional-integral-derivative (PID) controller, we showed that a closed-loop system can track EMG signals and adjust the stimulation frequency of Vim-DBS so that the power of EMG reaches a desired control target. Results and discussion: We demonstrated that the model-based DBS frequency aligns well with that used in clinical studies. Our model-based closed-loop system is adaptable to different control targets and can potentially be used for different diseases and personalized systems.

Emerging therapies for childhood-onset movement disorders.

We highlight novel and emerging therapies in the treatment of childhood-onset movement disorders. We structured this review by therapeutic entity (small molecule drugs, RNA-targeted therapeutics, gene replacement therapy, and neuromodulation), recognizing that there are two main approaches to treatment: symptomatic (based on phenomenology) and molecular mechanism-based therapy or 'precision medicine' (which is disease-modifying). We highlight reports of new small molecule drugs for Tourette syndrome, Friedreich's ataxia and Rett syndrome. We also discuss developments in gene therapy for aromatic l-amino acid decarboxylase deficiency and hereditary spastic paraplegia, as well as current work exploring optimization of deep brain stimulation and lesioning with focused ultrasound. Childhood-onset movement disorders have traditionally been treated symptomatically based on phenomenology, but focus has recently shifted toward targeted molecular mechanism-based therapeutics. The development of precision therapies is driven by increasing capabilities for genetic testing and a better delineation of the underlying disease mechanisms. We highlight novel and exciting approaches to the treatment of genetic childhood-onset movement disorders while also discussing general challenges in therapy development for rare diseases. We provide a framework for molecular mechanism-based treatment approaches, a summary of specific treatments for various movement disorders, and a clinical trial readiness framework.

1137 Prospective, Multicenter, International Registry of Deep Brain Stimulation for Dystonia: A Sub-analysis of Cervical Dystonia Patients

INTRODUCTION: Management of dystonia using Deep Brain Stimulation (DBS) is a well-established therapeutic approach. However, optimal DBS target sites in patients with cervical (focal) versus generalized dystonia are thought to diverge and be specific for particular connections (Horn 2022). DBS devices equipped with capabilities such as directionality and Multiple Independent Current Control (MICC) may offer potential for improved clinical outcomes. METHODS: This is a sub-analysis of patients with focal (cervical) dystonia only or cervical dystonia in the context of segmental or generalized dystonia assessed as part of a prospective, multicenter, international dystonia registry (NCT02686125). All patients receive an MICC-based, directional DBS system (Vercise, Boston Scientific). Patients are followed up to 3-years (post-implant). Several study assessments are being collected to evaluate their dystonia symptoms (e.g., TWSTRS), quality of life, and overall satisfaction. Adverse Events are also collected. RESULTS: A total of 43-patients (mean age 56.9-years, 58% females) with focal (cervical) dystonia only and 83 patients (mean 41.95-years, 61% females) with cervical dystonia in context of segmental or generalized dystonia have been evaluated. Both groups reported significant improvement in overall TWSTRS scores – however the extent varied. In the cervical-only cohort, a 19.9-point improvement was noted at 6-months (n = 25) and sustained up to 1-year (23.2-point improvement, n = 20). In those with cervical dystonia within frame of segmental or generalized dystonia, a 9.7-point and 7.3-point improvement in overall TWSTRS scores was noted at 6- (n = 50) and 12-months (n = 38), respectively. Updated data will be reported. CONCLUSIONS: This registry represents the first comprehensive, large-scale collection of real-world outcomes associated with dystonia patients implanted with a directional DBS system capable of MICC. Preliminary results demonstrate significant improvement in patients with cervical dystonia (alone or in context of segmental or generalized dystonia) following DBS.

Multifractal characteristics of neuronal activity of the &lt;i&gt; globus pallidus &lt;/i&gt; in patients with dystonia

Dystonia is a movement disorder characterized by involuntary muscle contractions resulting in anomalous postures, often accompanied by dystonic tremors. Among the indicative dystonic postures is an involuntary tonic tilt or rotation of the head. Currently, the etiology and pathogenesis of the disease remain theories. Currently, only low-frequency (theta-alpha) oscillations in the globus pallidus are regarded as the possible biomarker of pathological activity in dystonia. In many patients with dystonia, the absence of rhythmic neuronal activity in the basal ganglia renders this biomarker unusable for surgical treatment of the disease. Additionally, the mechanisms underlying the rearrangement of temporary patterns of neural activity that result in pathological symptoms remain unclear. We propose that the appearance of these pathological rhythms is associated with the reduction of dynamic complexity in the pattern of neural activity within the globus pallidus. We aim to use multifractal analysis in this study to evaluate changes in the globus pallidus neural activity pattern’s dynamic complexity and their correlation with the clinical manifestations of dystonia. The study examined the electrical activity of the globus pallidus in 23 patients receiving deep brain stimulation (DBS) treatment. We used the microelectrode registration technique and local field potential (LFP) registration during intraoperative neuron monitoring to precisely identify the specific locations of the external and internal segments of the globus pallidus (GPe and GPi). In four patients, an additional postoperative recording of local field potentials (LFP) was conducted while performing DBS and vibrational impact on the neck muscles. A neurologist evaluated the patients to determine the severity of the disease using the Burke-Fahn-Mardsen Dystonia Rating Scale (BFMDRS). The LEAD DBS software package was employed for postoperative reconstruction of the DBS stimulating electrode positions and for simulating the optimal stimulation zone, selected by the neurologist (i.e., volume of tissue activated, VTA). Subsequently, we identified the region that presented the most significant variation in multifractal spectrum parameters following DBS to understand its correlation with the optimal VTA. A total of 745 local potential records were analyzed, with 433 in GPe and 312 in GPi. The severity of dystonia was found to be correlated with the parameters of the multifractal spectrum (rho: –0.5630882, p=0.005149 for GPe and rho: –0.5180609, p=0.01133 for GPi). Moreover, as the dystonia severity increased along the BFMDRS scale, the multifractal spectrum became narrower and more asymmetric. In post-surgery recordings, we observed changes in the shape of the multifractal spectrum during DBS stimulation or vibration. In particular, when exposed, the multifractal spectrum significantly widened, and its symmetry was restored. We identified the region with the greatest alteration in the width of the multifractal spectrum following DBS and discovered that this region considerably intersected or fully matched the neurologist’s chosen zone for optimal DBS stimulation. We demonstrated that multifractal analysis can serve as an ancillary approach to evaluate the extent of proprioceptive feedback impairment and as a biomarker of dystonia. The noteworthy and substantial correlation with dystonia severity underscores the considerable potential of multifractal spectrum features as a biomarker of dystonia. Additionally, the overlapping areas with the most significant DBS impact and the widest variation in the multifractal spectrum in DBS can be used as a tool for identifying the most favorable region for DBS stimulation.

Towards an optimised deep brain stimulation using a large-scale computational network and realistic volume conductor model

Objective. Constructing a theoretical framework to improve deep brain stimulation (DBS) based on the neuronal spatiotemporal patterns of the stimulation-affected areas constitutes a primary target. Approach. We develop a large-scale biophysical network, paired with a realistic volume conductor model, to estimate theoretically efficacious stimulation protocols. Based on previously published anatomically defined structural connectivity, a biophysical basal ganglia-thalamo-cortical neuronal network is constructed using Hodgkin–Huxley dynamics. We define a new biomarker describing the thalamic spatiotemporal activity as a ratio of spiking vs. burst firing. The per cent activation of the different pathways is adapted in the simulation to minimise the differences of the biomarker with respect to its value under healthy conditions. Main results. This neuronal network reproduces spatiotemporal patterns that emerge in Parkinson’s disease. Simulations of the fibre per cent activation for the defined biomarker propose desensitisation of pallido-thalamic synaptic efficacy, induced by high-frequency signals, as one possible crucial mechanism for DBS action. Based on this activation, we define both an optimal electrode position and stimulation protocol using pathway activation modelling. Significance. A key advantage of this research is that it combines different approaches, i.e. the spatiotemporal pattern with the electric field and axonal response modelling, to compute the optimal DBS protocol. By correlating the inherent network dynamics with the activation of white matter fibres, we obtain new insights into the DBS therapeutic action.

ID: 197800 Image-Guided Programming Reduces Initial DBS Programming Time in Parkinson's Disease Patients

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.

Improvement in Cervical Dystonia – Outcomes of a Prospective, Multicenter, International Registry of Deep Brain Stimulation (DBS) (P11-11.002)

<h3>Objective:</h3> To report real-world outcomes of Dystonia patients implanted with a Multiple Independent Current Control (MICC)-based Directional DBS system. <h3>Background:</h3> Management of dystonia using Deep Brain Stimulation (DBS) is a well-established therapeutic approach. However, optimal DBS target sites in patients with cervical (focal) versus generalized dystonia are thought to diverge and be specific for particular connections (Horn 2022). DBS devices equipped with capabilities such as directionality and Multiple Independent Current Control (MICC) may offer potential for improved clinical outcomes. Here, we report a sub-analysis of patients with cervical dystonia only or dystonia with cervical involvement from an on-going, multicenter registry. <h3>Design/Methods:</h3> In this prospective, on-label, multi-center, international registry (ClinicalTrials.gov Identifier: NCT02686125) of dystonia, enrolled subjects received MICC-based directional DBS systems (Vercise, Boston Scientific). Subjects were followed up to 3 years (post-implant). Several study assessments evaluating their dystonia symptoms (e.g., TWSTRS), quality of life (GIC) and overall satisfaction were administered. Adverse Events were also collected. <h3>Results:</h3> A total of 43-patients (mean age 56.9-years, 58% females) with focal (cervical) dystonia only and 83-patients (mean 41.95-years, 61% females) with generalized or segmental dystonia with cervical involvement who underwent pallidal DBS were prospectively evaluated. Both groups reported significant improvement in overall TWSTRS scores – however the extent varied. In the cervical only cohort, a 19.9-point improvement was noted at 6-months (n=25) and sustained up to 1-year (23.2-point improvement, n=20). In those with generalized or segmental dystonia (with cervical involvement), a 9.7-point. and 7.3-improvement in overall TWSTRS scores was noted at 6- (n = 50) and 12-months (n = 38), respectively. <h3>Conclusions:</h3> This registry represents the first comprehensive, large-scale collection of real-world outcomes associated with dystonia patients implanted with a directional DBS system capable of MICC. Preliminary results demonstrate significant improvement in patients with cervical dystonia (either alone or with other regions involved) following DBS. <b>Disclosure:</b> Dr. Albanese has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Boston Scientific. Dr. Albanese has received personal compensation in the range of $500-$4,999 for serving as an Editor, Associate Editor, or Editorial Advisory Board Member for Frontiers Publishing Group. Dr. Albanese has a non-compensated relationship as a President-Elect with International Association on Parkinsonism and Related Disorders that is relevant to AAN interests or activities. Roshini Jain has received personal compensation for serving as an employee of Boston Scientific. Roshini Jain has received stock or an ownership interest from Boston Scientific. Lilly Chen has received personal compensation for serving as an employee of Boston Scientific . Joachim Krauss has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Inomed. Joachim Krauss has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for aleva. Joachim Krauss has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Boston.

Precision Mapping of Thalamic Deep Brain Stimulation Lead Positions Associated With the Microlesion Effect in Tourette Syndrome.

The microlesion effect refers to the improvement of clinical symptoms after deep brain stimulation (DBS) lead placement and is suggested to indicate optimal lead placement. Very few studies have reported its implications in neuropsychiatric disorders. To evaluate the magnitude of the microlesion effect in Tourette syndrome and the relationship between the microlesion effect and the anatomic location of implanted DBS leads. Six male patients were included. Their median age at surgery and follow-up period were 25 years (range, 18-47) and 12 months (range, 6-24), respectively. All patients were videotaped pre- and postoperatively, and tic frequencies were counted. We also analyzed the precision of lead placement and evaluated the normative connectome associated with the microlesion area. The microlesion effect was observed as an improvement in tic symptoms in all patients, and the long-term clinical outcomes were favorable. The median motor tic frequency was 20.2 tics/min (range, 9.7-60) at baseline and decreased to 3.2 tics/min (1.2-11.3) in patients on postoperative day 1 ( P = .043) and to 5.7 tics/min (range, 1.9-16.6) in patients on postoperative day 7 ( P = .028). Phonic tic tended to improve immediately after surgery although the changes were not significant. Image analyses revealed that the precise position of the electrode was directed toward the anteromedial centromedian nucleus. Normative connectome analysis demonstrated connections between improvement-related areas and wide areas of the prefrontal cortex. This study shows that the microlesion effect may seem as an immediate improvement after optimal DBS lead placement in patients with Tourette syndrome.

DBS-evoked cortical responses index optimal contact orientations and motor outcomes in Parkinson’s disease

Although subthalamic deep brain stimulation (DBS) is a highly-effective treatment for alleviating motor dysfunction in patients with Parkinson’s disease (PD), clinicians currently lack reliable neurophysiological correlates of clinical outcomes for optimizing DBS parameter settings, which may contribute to treatment inefficacies. One parameter that could aid DBS efficacy is the orientation of current administered, albeit the precise mechanisms underlying optimal contact orientations and associated clinical benefits are not well understood. Herein, 24 PD patients received monopolar stimulation of the left STN during magnetoencephalography and standardized movement protocols to interrogate the directional specificity of STN-DBS current administration on accelerometer metrics of fine hand movements. Our findings demonstrate that optimal contact orientations elicit larger DBS-evoked cortical responses in the ipsilateral sensorimotor cortex, and importantly, are differentially predictive of smoother movement profiles in a contact-dependent manner. Moreover, we summarize traditional evaluations of clinical efficacy (e.g., therapeutic windows, side effects) for a comprehensive review of optimal/non-optimal STN-DBS contact settings. Together, these data suggest that DBS-evoked cortical responses and quantitative movement outcomes may provide clinical insight for characterizing the optimal DBS parameters necessary for alleviating motor symptoms in patients with PD in the future.

Artificial neural network-based rapid predictor of biological nerve fiber activation for DBS applications

Objective. Computational models are powerful tools that can enable the optimization of deep brain stimulation (DBS). To enhance the clinical practicality of these models, their computational expense and required technical expertise must be minimized. An important aspect of DBS models is the prediction of neural activation in response to electrical stimulation. Existing rapid predictors of activation simplify implementation and reduce prediction runtime, but at the expense of accuracy. We sought to address this issue by leveraging the speed and generalization abilities of artificial neural networks (ANNs) to create a novel predictor of neural fiber activation in response to DBS. Approach. We developed six variations of an ANN-based predictor to predict the response of individual, myelinated axons to extracellular electrical stimulation. ANNs were trained using datasets generated from a finite-element model of an implanted DBS system together with multi-compartment cable models of axons. We evaluated the ANN-based predictors using three white matter pathways derived from group-averaged connectome data within a patient-specific tissue conductivity field, comparing both predicted stimulus activation thresholds and pathway recruitment across a clinically relevant range of stimulus amplitudes and pulse widths. Main results. The top-performing ANN could predict the thresholds of axons with a mean absolute error (MAE) of 0.037 V, and pathway recruitment with an MAE of 0.079%, across all parameters. The ANNs reduced the time required to predict the thresholds of 288 axons by four to five orders of magnitude when compared to multi-compartment cable models. Significance. We demonstrated that ANNs can be fast, accurate, and robust predictors of neural activation in response to DBS.

Towards biomarker-based optimization of deep brain stimulation in Parkinson's disease patients.

Subthalamic deep brain stimulation (DBS) is an established therapy to treat Parkinson's disease (PD). To maximize therapeutic outcome, optimal DBS settings must be carefully selected for each patient. Unfortunately, this is not always achieved because of: (1) increased technological complexity of DBS devices, (2) time restraints, or lack of expertise, and (3) delayed therapeutic response of some symptoms. Biomarkers to accurately predict the most effective stimulation settings for each patient could streamline this process and improve DBS outcomes. To investigate the use of evoked potentials (EPs) to predict clinical outcomes in PD patients with DBS. In ten patients (12 hemispheres), a monopolar review was performed by systematically stimulating on each DBS contact and measuring the therapeutic window. Standard imaging data were collected. EEG-based EPs were then recorded in response to stimulation at 10 Hz for 50 s on each DBS-contact. Linear mixed models were used to assess how well both EPs and image-derived information predicted the clinical data. Evoked potential peaks at 3 ms (P3) and at 10 ms (P10) were observed in nine and eleven hemispheres, respectively. Clinical data were well predicted using either P3 or P10. A separate model showed that the image-derived information also predicted clinical data with similar accuracy. Combining both EPs and image-derived information in one model yielded the highest predictive value. Evoked potentials can accurately predict clinical DBS responses. Combining EPs with imaging data further improves this prediction. Future refinement of this approach may streamline DBS programming, thereby improving therapeutic outcomes. ClinicalTrials.gov, identifier NCT04658641.

Optimal deep brain stimulation connectivity to treat Holmes tremor

Optimal deep brain stimulation connectivity to treat Holmes tremor

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].