Deep Brain Stimulation Parameters Research Articles

Using Theia Markerless Motion capture to measure the impact of adjusting deep brain stimulation parameters on gait in a Parkinson’s Disease case study

The way one swings their arms, turns their torso, and moves their feet, are gait parameters that are commonly disturbed in patients with Parkinson’s Disease (PD).1 Deep brain stimulation in the subthalamic nucleus (STN-DBS) has proven to be an effective long-term therapy for reducing cardinal motor symptoms and improving quality of life, compared to standard medical treatments like levodopa-based therapies.2-4 However, optimizing STN-DBS settings for individual needs can be a complex process, as standard clinical tools for PD progression, such as the Unified Parkinson's Disease Rating Scale, may be subjective and have limitations.5,6 This project aimed to explore markerless motion capture as a novel, objective measurement of gait progression in PD. The study used an n=1 case design to investigate how gait metrics change with adjustments to DBS settings. The participant, a PD patient with STN-DBS, underwent five randomized, double-blind trials in which their DBS frequency (Hz) and current strength (mA) were adjusted. The following combinations of left and right ventral electrode settings were used: Left: 179 Hz, 3.1 mA; Right: 179 Hz, 2.9mA (Baseline) Left: 149 Hz, 3.1 mA; Right: 149 Hz, 2.9mA (149 low) Left: 149 Hz, 3.4 mA; Right: 149 Hz, 3.2 mA (149 high) Left: 104 Hz, 3.1 mA; Right: 104 Hz, 2.9mA (104 low) Left: 104 Hz, 3.3 mA; Right: 104 Hz, 3.2 mA (104 high) During each 5-minute trial, the participant walked on a treadmill at a comfortable speed while being recorded using a Theia3D markerless motion capture system. The recordings were analyzed using Visual3D software, which quantified gait metrics including cadence, step length, stride length, posture, and arm swing. Notably, differences in the participant’s arm swing between the left and right sides were observed across the various DBS settings. This technology provided objective, measurable data on gait, offering a potential tool for future gait assessments in PD. Its applicability extends to long-term tracking of patient progress, with the potential for markerless motion capture to transform how clinicians evaluate the efficacy of DBS in patients with PD.

Utilising activity patterns of a complex biophysical network model to optimise intra-striatal deep brain stimulation

A large-scale biophysical network model for the isolated striatal body is developed to optimise potential intrastriatal deep brain stimulation applied to, e.g. obsessive-compulsive disorder. The model is based on modified Hodgkin–Huxley equations with small-world connectivity, while the spatial information about the positions of the neurons is taken from a detailed human atlas. The model produces neuronal spatiotemporal activity patterns segregating healthy from pathological conditions. Three biomarkers were used for the optimisation of stimulation protocols regarding stimulation frequency, amplitude and localisation: the mean activity of the entire network, the frequency spectrum of the entire network (rhythmicity) and a combination of the above two. By minimising the deviation of the aforementioned biomarkers from the normal state, we compute the optimal deep brain stimulation parameters, regarding position, amplitude and frequency. Our results suggest that in the DBS optimisation process, there is a clear trade-off between frequency synchronisation and overall network activity, which has also been observed during in vivo studies.

SAFE-OPT: a Bayesian optimization algorithm for learning optimal deep brain stimulation parameters with safety constraints

Objective.To treat neurological and psychiatric diseases with deep brain stimulation (DBS), a trained clinician must select parameters for each patient by monitoring their symptoms and side-effects in a months-long trial-and-error process, delaying optimal clinical outcomes. Bayesian optimization has been proposed as an efficient method to quickly and automatically search for optimal parameters. However, conventional Bayesian optimization does not account for patient safety and could trigger unwanted or dangerous side-effects.Approach.In this study we develop SAFE-OPT, a Bayesian optimization algorithm designed to learn subject-specific safety constraints to avoid potentially harmful stimulation settings during optimization. We prototype and validate SAFE-OPT using a rodent multielectrode stimulation paradigm which causes subject-specific performance deficits in a spatial memory task. We first use data from an initial cohort of subjects to build a simulation where we design the best SAFE-OPT configuration for safe and accurate searchingin silico. Main results.We then deploy both SAFE-OPT and conventional Bayesian optimization without safety constraints in new subjectsin vivo, showing that SAFE-OPT can find an optimally high stimulation amplitude that does not harm task performance with comparable sample efficiency to Bayesian optimization and without selecting amplitude values that exceed the subject's safety threshold.Significance.The incorporation of safety constraints will provide a key step for adopting Bayesian optimization in real-world applications of DBS.

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

The current approach to assessing bradykinesia in Parkinson's Disease relies on the Unified Parkinson's Disease Rating Scale (UPDRS), which is a numeric scale. Inertial sensors offer the ability to probe subcomponents of bradykinesia: motor speed, amplitude, and rhythm. Thus, we sought to investigate the differential effects of high-frequency compared to low-frequency subthalamic nucleus (STN) deep brain stimulation (DBS) on these quantified facets of bradykinesia. We recruited advanced Parkinson's Disease subjects with a chronic bilateral subthalamic nucleus (STN) DBS implantation to a single-blind stimulation trial where each combination of medication state (OFF/ON), electrode contacts, and stimulation frequency (60 Hz/180 Hz) was assessed. The Kinesia One sensor system was used to measure upper limb bradykinesia. For each stimulation trial, subjects performed extremity motor tasks. Sensor data were recorded continuously. We identified STN DBS parameters that were associated with improved upper extremity bradykinesia symptoms using a mixed linear regression model. We recruited 22 subjects (6 females) for this study. The 180 Hz STN DBS (compared to the 60 Hz STN DBS) and dopaminergic medications improved all subcomponents of upper extremity bradykinesia (motor speed, amplitude, and rhythm). For the motor rhythm subcomponent of bradykinesia, ventral contacts yielded improved symptom improvement compared to dorsal contacts. The differential impact of high- and low-frequency STN DBS on the symptoms of bradykinesia may advise programming for these patients but warrants further investigation. Wearable sensors represent a valuable addition to the armamentarium that furthers our ability to conduct objective, quantitative clinical assessments.

Anti-manic effect of deep brain stimulation of the ventral tegmental area in an animal model of mania induced by methamphetamine.

Treatment of refractory bipolar disorder (BD) is extremely challenging. Deep brain stimulation (DBS) holds promise as an effective treatment intervention. However, we still understand very little about the mechanisms of DBS and its application on BD. The present study aimed to investigate the behavioural and neurochemical effects of ventral tegmental area (VTA) DBS in an animal model of mania induced by methamphetamine (m-amph). Wistar rats were given 14 days of m-amph injections, and on the last day, animals were submitted to 20 min of VTA DBS in two different patterns: intermittent low-frequency stimulation (LFS) or continuous high-frequency stimulation (HFS). Immediately after DBS, manic-like behaviour and nucleus accumbens (NAc) phasic dopamine (DA) release were evaluated in different groups of animals through open-field tests and fast-scan cyclic voltammetry. Levels of NAc dopaminergic markers were evaluated by immunohistochemistry. M-amph induced hyperlocomotion in the animals and both DBS parameters reversed this alteration. M-amph increased DA reuptake time post-sham compared to baseline levels, and both LFS and HFS were able to block this alteration. LFS was also able to reduce phasic DA release when compared to baseline. LFS was able to increase dopamine transporter (DAT) expression in the NAc. These results demonstrate that both VTA LFS and HFS DBS exert anti-manic effects and modulation of DA dynamics in the NAc. More specifically the increase in DA reuptake driven by increased DAT expression may serve as a potential mechanism by which VTA DBS exerts its anti-manic effects.

Deep Learning and fMRI-Based Pipeline for Optimization of Deep Brain Stimulation During Parkinson's Disease Treatment: Toward Rapid Semi-Automated Stimulation Optimization.

Optimized deep brain stimulation (DBS) is fast becoming a therapy of choice for the treatment of Parkinson's disease (PD). However, the post-operative optimization (aimed at maximizing patient clinical benefits and minimizing adverse effects) of all possible DBS parameter settings using the standard-of-care clinical protocol requires numerous clinical visits, which substantially increases the time to optimization per patient (TPP), patient cost burden and limit the number of patients who can undergo DBS treatment. The TPP is further elongated in electrodes with stimulation directionality or in diseases with latency in clinical feedback. In this work, we proposed a deep learning and fMRI-based pipeline for DBS optimization that can potentially reduce the TPP from ~1 year to a few hours during a single clinical visit. We developed an unsupervised autoencoder (AE)-based model to extract meaningful features from 122 previously acquired blood oxygenated level dependent (BOLD) fMRI datasets from 39 a priori clinically optimized PD patients undergoing DBS therapy. The extracted features are then fed into multilayer perceptron (MLP)-based parameter classification and prediction models for rapid DBS parameter optimization. The AE-extracted features of optimal and non-optimal DBS were disentangled. The AE-MLP classification model yielded accuracy, precision, recall, F1 score, and combined AUC of 0.96 ± 0.04, 0.95 ± 0.07, 0.92 ± 0.07, 0.93 ± 0.06, and 0.98 respectively. Accuracies of 0.79 ± 0.04, 0.85 ± 0.04, 0.82 ± 0.05, 0.83 ± 0.05, and 0.70 ± 0.07 were obtained in the prediction of voltage, frequency, and x-y-z contact locations, respectively. The proposed AE-MLP models yielded promising results for fMRI-based DBS parameter classification and prediction, potentially facilitating rapid semi-automated DBS parameter optimization. Clinical and Translational Impact Statement-A deep learning-based pipeline for semi-automated DBS parameter optimization is presented, with the potential to significantly decrease the optimization duration per patient and patients' financial burden while increasing patient throughput.

Amplitude Adaptive Modulation of Neural Oscillations Over Long-Term Dynamic Conditions: A Computational Study.

Closed-loop deep brain stimulation (DBS) shows great potential for precise neuromodulation of various neurological disorders, particularly Parkinson's disease (PD). However, substantial challenges remain in clinical translation due to the complex programming procedure of closed-loop DBS parameters. In this study, we proposed an online optimized amplitude adaptive strategy based on the particle swarm optimization (PSO) and proportional-integral-differential (PID) controller for modulation of the beta oscillation in a PD mean field model over long-term dynamic conditions. The strategy aimed to calculate the stimulation amplitude adapting to the fluctuations caused by circadian rhythm, medication rhythm, and stochasticity in the basal ganglia-thalamus-cortical circuit. The PID gains were optimized online using PSO, based on modulation accuracy, mean stimulation amplitude, and stimulation variation. The results showed that the proposed strategy optimized the stimulation amplitude and achieved beta power modulation under the influence of circadian rhythm, medication rhythm, and stochasticity of beta oscillations. This work offers a novel approach for precise neuromodulation with the potential for clinical translation.

Unraveling the mechanisms of deep-brain stimulation of the internal capsule in a mouse model

Deep-brain stimulation (DBS) is an effective treatment for patients suffering from otherwise therapy-resistant psychiatric disorders, including obsessive-compulsive disorder. Modulation of cortico-striatal circuits has been suggested as a mechanism of action. To gain mechanistic insight, we monitored neuronal activity in cortico-striatal regions in a mouse model for compulsive behavior, while systematically varying clinically-relevant parameters of internal-capsule DBS. DBS showed dose-dependent effects on both brain and behavior: An increasing, yet balanced, number of excited and inhibited neurons was recruited, scattered throughout cortico-striatal regions, while excessive grooming decreased. Such neuronal recruitment did not alter basic brain function such as resting-state activity, and only occurred in awake animals, indicating a dependency on network activity. In addition to these widespread effects, we observed specific involvement of the medial orbitofrontal cortex in therapeutic outcomes, which was corroborated by optogenetic stimulation. Together, our findings provide mechanistic insight into how DBS exerts its therapeutic effects on compulsive behaviors.

Sub-harmonic entrainment of cortical gamma oscillations to deep brain stimulation in Parkinson's disease: Model based predictions and validation in three human subjects

ObjectivesThe exact mechanisms of deep brain stimulation (DBS) are still an active area of investigation, in spite of its clinical successes. This is due in part to the lack of understanding of the effects of stimulation on neuronal rhythms. Entrainment of brain oscillations has been hypothesised as a potential mechanism of neuromodulation. A better understanding of entrainment might further inform existing methods of continuous DBS, and help refine algorithms for adaptive methods. The purpose of this study is to develop and test a theoretical framework to predict entrainment of cortical rhythms to DBS across a wide range of stimulation parameters. Materials and MethodsWe fit a model of interacting neural populations to selected features characterising PD patients' off-stimulation finely-tuned gamma rhythm recorded through electrocorticography. Using the fitted models, we predict basal ganglia DBS parameters that would result in 1:2 entrainment, a special case of sub-harmonic entrainment observed in patients and predicted by theory. ResultsWe show that the neural circuit models fitted to patient data exhibit 1:2 entrainment when stimulation is provided across a range of stimulation parameters. Furthermore, we verify key features of the region of 1:2 entrainment in the stimulation frequency/amplitude space with follow-up recordings from the same patients, such as the loss of 1:2 entrainment above certain stimulation amplitudes. ConclusionOur results reveal that continuous, constant frequency DBS in patients may lead to nonlinear patterns of neuronal entrainment across stimulation parameters, and that these responses can be predicted by modelling. Should entrainment prove to be an important mechanism of therapeutic stimulation, our modelling framework may reduce the parameter space that clinicians must consider when programming devices for optimal benefit.

Programming Algorithm for the Management of Speech Impairment in Subthalamic Nucleus Deep Brain Stimulation for Parkinson’s Disease

ObjectivesDeep brain stimulation (DBS) of the subthalamic nucleus (STN) for Parkinson’s disease (PD) has an ambiguous relation to speech. Speech impairment can be a stimulation-induced side effect, and parkinsonian dysarthria can improve with STN-DBS. Owing to the lack of an up-to-date and evidence-based approach, DBS reprogramming for speech impairment is largely blind and greatly relies on the physician’s experience. In this study, we aimed to establish an evidence- and experience-based algorithm for managing speech impairment in patients with PD treated with STN-DBS. Materials and MethodsWe performed a single-center retrospective study to identify patients with STN-DBS and speech impairment. Onset of speech impairment, lead localization, and assessment of DBS-induced nature of speech impairment were collected. When DBS settings were adjusted for improving speech, the magnitude and duration of effect were collected. We also performed a systematic literature review to identify studies describing the effects of parameter adjustments aimed at improving speech impairment in patients with PD receiving STN-DBS. ResultsIn the retrospective study, 245 of 631 patients (38.8%) with STN-DBS had significant speech impairment. The probability of sustained marked improvement upon reprogramming was generally low (27.9%). In the systematic review, 23 of 662 identified studies were included. Only two randomized controlled trials have been performed, providing evidence for interleaving-interlink stimulation only. Considerable methodologic heterogeneity precluded the conduction of a meta-analysis. ConclusionsSpeech impairment in STN-DBS for PD is frequent, but high-quality evidence regarding DBS parameter adjustments is scarce, and the probability of sustained improvement is low. To improve this outcome, we propose an evidence- and experience-based approach to address speech impairment in STN-DBS that can be used in clinical practice.

ID: 213194 Optimizing Deep Brain Stimulation Parameters in Drug Resistant Epilepsy: An EEG Oriented Approach

Deep brain stimulation (DBS) of anterior thalamic nucleus (ATN) is establishing as an effective adjunctive therapy for patients with drug resistant epilepsy (DRE) not suitable for epilepsy brain surgery and/or vagal nerve stimulation. The success of ATN-DBS is dependent upon exact placement of the DBS electrodes and judicious selection of DBS parameters (DBSPs) and patients; even then, the success has been observed to be limited to mainly one form of intractable seizures (IS), namely complex partial seizures with other forms of IS failing to respond satisfactorily. Furthermore, the conventional mode of selection of DBSPs is by trial and error and the required adjustments in the DBSPs are governed by the clinical response in the seizure profile over the course of multiple sessions and hospital visits, thus, warranting a strong need for optimization of the DBSPs. We present an EEG-guided novel and superior approach to the selection of effective DBSPs that induces EEG-desynchronization which is known to possess potent antiepileptic property with possibly possession of an additional anti-kindling effect that can suppress and even arrest the ongoing process of epileptogenesis in the patients with DRE in addition to its primary role in exercising control over the IS. It is further claimed that the innovative EEG-guided approach can successfully optimize the DBSPs resulting in minimum sessions of DBSP adjustments reducing the frequency of hospital visits, minimum side effects and minimum consumption of the device battery thus prolonging its life. Preliminary results of the clinical application of the novel approach in the selection of the DBSPs in a small case series have been promising and encouraging despite which it is strongly recommended that well designed large sized studies are required for its validation and successful clinical outcome.

ID: 209661 Pilot Study Investigating Sensing Utility to Optimize Deep Brain Stimulation Parameters in Parkinson's Disease

Optimizing deep brain stimulation (DBS) programming for movement disorders requires a trial-and-error process evaluating clinical benefit and adverse effects. Recent advances in sensing technology may enable improvements in selecting optimal settings.1 The use of this technology in programming and comparing clinical outcomes to standard of care has not been fully evaluated. For this study, we assessed whether recorded acute local field potential (LFP) biomarkers can be used in the clinic to optimize DBS parameter programming, and we characterized the reliability of LFP biomarkers, specifically within the theta (4-8Hz), beta (13-30Hz), and gamma (35-90Hz) frequency bands.2

Multi-scale and cross-dimensional TMS mapping: A proof of principle in patients with Parkinson's disease and deep brain stimulation.

Transcranial magnetic stimulation (TMS) mapping has become a critical tool for exploratory studies of the human corticomotor (M1) organization. Here, we propose to gather existing cutting-edge TMS-EMG and TMS-EEG approaches into a combined multi-dimensional TMS mapping that considers local and whole-brain excitability changes as well as state and time-specific changes in cortical activity. We applied this multi-dimensional TMS mapping approach to patients with Parkinson's disease (PD) with Deep brain stimulation (DBS) of the sub-thalamic nucleus (STN) ON and OFF. Our goal was to identifying one or several TMS mapping-derived markers that could provide unprecedent new insights onto the mechanisms of DBS in movement disorders. Six PD patients (1 female, mean age: 62.5 yo [59-65]) implanted with DBS-STN for 1 year, underwent a robotized sulcus-shaped TMS motor mapping to measure changes in muscle-specific corticomotor representations and a movement initiation task to probe state-dependent modulations of corticospinal excitability in the ON (using clinically relevant DBS parameters) and OFF DBS states. Cortical excitability and evoked dynamics of three cortical areas involved in the neural control of voluntary movements (M1, pre-supplementary motor area - preSMA and inferior frontal gyrus - IFG) were then mapped using TMS-EEG coupling in the ON and OFF state. Lastly, we investigated the timing and nature of the STN-to-M1 inputs using a paired pulse DBS-TMS-EEG protocol. In our sample of patients, DBS appeared to induce fast within-area somatotopic re-arrangements of motor finger representations in M1, as revealed by mediolateral shifts of corticomuscle representations. STN-DBS improved reaction times while up-regulating corticospinal excitability, especially during endogenous motor preparation. Evoked dynamics revealed marked increases in inhibitory circuits in the IFG and M1 with DBS ON. Finally, inhibitory conditioning effects of STN single pulses on corticomotor activity were found at timings relevant for the activation of inhibitory GABAergic receptors (4 and 20 ms). Taken together, these results suggest a predominant role of some markers in explaining beneficial DBS effects, such as a context-dependent modulation of corticospinal excitability and the recruitment of distinct inhibitory circuits, involving long-range projections from higher level motor centers and local GABAergic neuronal populations. These combined measures might help to identify discriminative features of DBS mechanisms towards deep clinical phenotyping of DBS effects in Parkinson's Disease and in other pathological conditions.

Eltoprazine modulated gamma oscillations on ameliorating L-dopa-induced dyskinesia in rats.

Parkinson's disease (PD) is a pervasive neurodegenerative disease, and levodopa (L-dopa) is its preferred treatment. The pathophysiological mechanism of levodopa-induced dyskinesia (LID), the most common complication of long-term L-dopa administration, remains obscure. Accumulated evidence suggests that the dopaminergic as well as non-dopaminergic systems contribute to LID development. As a 5-hydroxytryptamine 1A/1B receptor agonist, eltoprazine ameliorates dyskinesia, although little is known about its electrophysiological mechanism. The aim of this study was to investigate the cumulative effects of chronic L-dopa administration and the potential mechanism of eltoprazine's amelioration of dyskinesia at the electrophysiological level in rats. Neural electrophysiological analysis techniques were conducted on the acquired local field potential (LFP) data from primary motor cortex (M1) and dorsolateral striatum (DLS) during different pathological states to obtain the information of power spectrum density, theta-gamma phase-amplitude coupling (PAC), and functional connectivity. Behavior tests and AIMs scoring were performed to verify PD model establishment and evaluate LID severity. We detected exaggerated gamma activities in the dyskinetic state, with different features and impacts in distinct regions. Gamma oscillations in M1 were narrowband manner, whereas that in DLS had a broadband appearance. Striatal exaggerated theta-gamma PAC in the LID state contributed to broadband gamma oscillation, and aperiodic-corrected cortical beta power correlated robustly with aperiodic-corrected gamma power in M1. M1-DLS coherence and phase-locking values (PLVs) in the gamma band were enhanced following L-dopa administration. Eltoprazine intervention reduced gamma oscillations, theta-gamma PAC in the DLS, and coherence and PLVs in the gamma band to alleviate dyskinesia. Excessive cortical gamma oscillation is a compelling clinical indicator of dyskinesia. The detection of enhanced PAC and functional connectivity of gamma-band oscillation can be used to guide and optimize deep brain stimulation parameters. Eltoprazine has potential clinical application for dyskinesia.

Assess stimulation-induced dyskinesia and its potential physiological mechanism using phase-amplitude coupling

With the increasing use of deep brain stimulation (DBS) on clinical treatment of Parkinsons diseases, stimulation-induced dyskinesia (SID) becomes more and more common. SID was often detected shortly after DBS treatment on patients. However, the pathogenesis of SID and exact region of SID remains unclear. The aim of this paper is to review the studies of stimulation-induced dyskinesia and try to propose a new method to study and quantify SID, which is phase-amplitude coupling. phase-amplitude coupling (PAC) has been widely used in many other studies of brain-related illness and therapies. It is believed to have a profound relationship with our brain activities. Because of the features of PAC itself, the anatomical regions related to SID after pallidal DBS in Parkinson's disease (PD) patients can be possibly found, and the value of PAC can be served as a biomarker for us to assess the stimulation-induced dyskinesia. However, further researches on patients should be done to verify this method. It is very important to understand how SID is formed and its pathogenesis because it may help us find the appropriate parameters of deep brain stimulation and reduce the damage caused by the implant of electrode

Fusing the Virtual and Physical Worlds to Identify Declines in Instrumental Activities of Daily Living Associated with Parkinson’s Disease (S37.008)

<h3>Objective:</h3> The objective of this study was to utilize a novel virtual reality paradigm, Cleveland Clinic Virtual Reality Shopping (CC-VRS), to identify changes in instrumental activities of daily living (IADL) associated with Parkinson’s disease (PD). <h3>Background:</h3> Understanding the effects of medication or surgical interventions on PD IADL performance is necessary to ensure optimized function and maximize independence. The CC-VRS combines virtual reality content with an omnidirectional treadmill to create an immersive full-scale grocery shopping environment that presents cognitive and physical challenges, objectively and quantitatively evaluating cognitive and motor function. <h3>Design/Methods:</h3> Fourteen individuals with PD (UPDRS-III 25.9 ± 13.9, ON antiparkinsonian medication), and 14 age- and gender-matched healthy adults completed Basic and Complex shopping scenarios; the latter includes additional cognitive and physical demands while shopping. During the scenario, participants physically navigated through the virtual grocery store on the omnidirectional treadmill that allowed them to walk in a straight line and perform 90° and 180° turns. The store route, shopping items, and distractors were standardized across participants. <h3>Results:</h3> Motor and cognitive performance differed across PD and control groups. From a motor perspective, PD patients exhibited significantly shorter step length (<i>p</i>&lt;0.05) and experienced multiple freezing episodes while turning. Cognitive function was evaluated through list observation behaviors; PD patients spent significantly more time looking at their virtual shopping list (<i>p</i>&lt;0.01) and time for each list activation was nearly twice that of controls (<i>p</i>&lt;0.01). Both groups tolerated the virtual environment well, as Simulator Sickness Questionnaire scores for both groups were minimal following completion of CC-VRS. <h3>Conclusions:</h3> The CC-VRS platform successfully quantifies PD motor symptoms and subtle cognitive declines in mild PD versus healthy controls. The CC-VRS platform was well-tolerated across groups and offers an innovative approach to objectively quantifying IADL performance, with potential for use in titrating medication and tuning deep brain stimulation parameters. <b>Disclosure:</b> Morgan McGrath has nothing to disclose. Anson Rosenfeldt has received personal compensation for serving as an employee of Cleveland Clinic. Anson Rosenfeldt has received personal compensation in the range of $10,000-$49,999 for serving as a Consultant for Enspire DBS. The institution of Anson Rosenfeldt has received research support from Michael J. Fox Foundation. The institution of Anson Rosenfeldt has received research support from National Institute of Health. The institution of Anson Rosenfeldt has received research support from Department of Defense. Anson Rosenfeldt has received intellectual property interests from a discovery or technology relating to health care. Mr. Waltz has nothing to disclose. Mandy Koop has nothing to disclose. Dr. Fernandez has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Abbvie. Dr. Fernandez has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Cerevel. Dr. Fernandez has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Amneal. Dr. Fernandez has received personal compensation in the range of $10,000-$49,999 for serving as an Editor, Associate Editor, or Editorial Advisory Board Member for Elsevier. The institution of Dr. Fernandez has received research support from Biogen. The institution of Dr. Fernandez has received research support from Michael J Fox Founda. The institution of Dr. Fernandez has received research support from Roche. The institution of Dr. Fernandez has received research support from Parkinson Foundation. The institution of Dr. Fernandez has received research support from UCB. Dr. Fernandez has received publishing royalties from a publication relating to health care. Dr. Fernandez has received personal compensation in the range of $10,000-$49,999 for serving as a Steering Committee/Advisory Committee Member with Parkinson Study Group. The institution of Dr. Alberts has received research support from NIH. Dr. Alberts has received intellectual property interests from a discovery or technology relating to health care. Dr. Alberts has received personal compensation in the range of $500-$4,999 for serving as a Member, Health and Wellness Council with Peloton Interactive.

Deep brain stimulation normalizes amygdala responsivity in treatment-resistant depression.

Deep brain stimulation (DBS) of the ventral anterior limb of the internal capsule (vALIC) is a promising intervention for treatment-resistant depression (TRD). However, the working mechanisms of vALIC DBS in TRD remain largely unexplored. As major depressive disorder has been associated with aberrant amygdala functioning, we investigated whether vALIC DBS affects amygdala responsivity and functional connectivity. To investigate the long-term effects of DBS, eleven patients with TRD performed an implicit emotional face-viewing paradigm during functional magnetic resonance imaging (fMRI) before DBS surgery and after DBS parameter optimization. Sixteen matched healthy controls performed the fMRI paradigm at two-time points to control for test-retest effects. To investigate the short-term effects of DBS de-activation after parameter optimization, thirteen patients additionally performed the fMRI paradigm after double-blind periods of active and sham stimulation. Results showed that TRD patients had decreased right amygdala responsivity compared to healthy controls at baseline. Long-term vALIC DBS normalized right amygdala responsivity, which was associated with faster reaction times. This effect was not dependent on emotional valence. Furthermore, active compared to sham DBS increased amygdala connectivity with sensorimotor and cingulate cortices, which was not significantly different between responders and non-responders. These results suggest that vALIC DBS restores amygdala responsivity and behavioral vigilance in TRD, which may contribute to the DBS-induced antidepressant effect.

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.

An active learning framework for personalized deep brain stimulation

Abstract Background: To personalize deep brain stimulation (DBS), we need to identify a link between DBS parameters and the neural response of an individual. The existing approach based on random or empirical sampling (RS) is time-consuming, costly, and practically impossible in a clinical setting. This limits the discovery of novel settings under challenging situations. To address this problem, we have developed a new algorithmic framework based on active learning (AL) that can obtain the best model between the DBS parameters and the brain response while minimizing the number of experiments. Methods: We used a computational Parkinson’s disease model to generate synthetic data. We swept the subthalamic nucleus DBS amplitude, frequency, and pulse width while estimating the globus pallidus internus (GPi) beta (13-30 Hz) power for each DBS parameter. This resulted in 200 different samples. We randomly selected 80% of this data for pool training and reserved the remaining 20% as unseen test data. Using three initial training samples, we trained two linear regression models (based on RS and AL) to obtain a link between DBS parameters and GPi beta power. We iteratively added one training sample at a time to both models based on AL and RS approaches until we had 20 training samples, then tested both models on unseen test data at each iteration and calculated the root mean squared error (RMSE). This process was repeated 1000 times. Results: The mean RMSE of the AL and RS approach was 0.043 and 0.039. Results showed the AL-based model outperformed the RS-based model by showing significantly less errors on the unseen dataset based on a two-sample t-test (p= 2.33e-07, N=1000). Conclusion: We validated that our AL approach outperforms the existing approach in identifying an individualized link between DBS parameters and brain response while minimizing the duration of the experimental procedure. Research Category and Technology and Methods Translational Research: 1. Deep Brain Stimulation (DBS) Keywords: Personalized Deep Brain Stimulation, Active Learning, Machine Learning

Implementing automation in deep brain stimulation: has the time come?

In The Lancet Digital Health, Jan Roediger and colleagues1 present the results of a prospective, randomised, double-blind, crossover clinical trial assessing the therapeutic benefit of deep brain stimulation (DBS) settings derived from individual neuroimaging metrics. This evaluates a data-driven model of automated DBS parameter setting, maximising the predicted motor symptom control while accounting for potential stimulation-induced side-effects. Compared with current standard of care, the longer monopolar review based on existing clinical algorithms, the authors established non-inferiority of motor symptom control in a cohort of 35 patients with Parkinson's disease treated with DBS of the subthalamic nucleus.