Neural selectivity, efficiency, and dose equivalence in deep brain stimulation through pulse width tuning and segmented electrodes

Anderson, Collin J.; Anderson, Daria Nesterovich; Pulst, Stefan M.; Butson, Christopher R.; Dorval, Alan D.

doi:10.1016/j.brs.2020.03.017

Cited by 51 publications

(62 citation statements)

References 43 publications

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“…We modeled as previously described using the finite element method (FEM) 1,2 , implemented in SCIRun 5.0 (Scientific Computing and Imaging (SCI), Institute, University of Utah, Salt Lake City, UT), to solve the bioelectric field problem. We set electrode contacts as ideal conductors and electrode shafts as ideal insulators.…”

Section: Methodsmentioning

confidence: 99%

“…Notably, we repeated experiments with different conductivities, including and excluding an encapsulation layer and found that while activation thresholds changed in response to changes in conductivity layer, the conclusions were similar regardless of conditions. We implemented a Dirichlet boundary condition at the outer edge of the model to serve as a distant ground 1,8,18 (100 mm x 100 mm x 100 mm) and solved for the electric potential solution.…”

Section: Methodsmentioning

confidence: 99%

“…We quantified neuronal response to extracellular stimulation through multicompartment axon modeling in the Neuron 7.7 environment. We used the McIntyre Richardson Grill (MRG) model 19 for 5.7 μm diameter myelinated axons and modified parameters of this model for 2.0 μm and 10 μm myelinated axons 1,2,20,21 . We distributed tangential and vertical neurons evenly around the electrode in 0.2 mm increments from 0 to 10 mm in all directions.…”

Section: Methodsmentioning

confidence: 99%

“…Recently, the deep brain stimulation (DBS) field has advanced in a number of directions with the goal of maximizing therapeutic benefit while minimizing side effects. Numerous reports have been published describing the modulation of DBS pulse width to induce changes in axon size selectivity 1–4 . At the same time, advancements have been made in DBS technology and programming.…”

Section: Introductionmentioning

confidence: 99%

“…Many reports on the modulation of DBS axon size-specific selectivity have focused on changing the pulse width to achieve desired effects in selectivity. However, we recently reported that using directionally segmented contacts may be a more optimal means of focusing the effect of deep brain stimulation on nearby small axons, reducing the inherent preferential selectivity for large axons, which has been a goal of numerous groups in improving side effect avoidance 1 . Further, we recently proposed a novel, multiresolution DBS electrode design with the potential for contacts to vary in size and shape 2 , and the simple modeling studies reported therein provided further evidence that modulation of contact size may be exploitable in order to modulate neural selectivity.…”

Section: Introductionmentioning

confidence: 99%

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Defining the impact of deep brain stimulation contact size and shape on neural selectivity

Anderson

Dorval

Rolston

et al. 2020

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7Background. Understanding neural selectivity is essential for optimizing medical applications of deep brain 8 stimulation (DBS). We previously showed that modulation of the DBS waveform can induce changes in 9 orientation-based selectivity, and that lengthening of DBS pulses or directional segmentation can reduce 10 preferential selectivity for large axons. In this work, we sought to answer a simple, but important question: how 11 do the size and shape of the contact influence neural selectivity? 13Methods. We created multicompartment neuron models for several axon diameters and used finite element 14 modeling with standard-sized cylindrical leads to determine the effects on changing contact size and shape on 15 axon activation profiles and volumes of tissue activated. Contacts ranged in size from 0.04 to 16 mm 2 , 16 compared with a standard size of 6 mm 2 . 18Results. We found that changes in contact size induce substantial changes in orientation-based selectivity in 19 the context of a cylindrical lead, and rectangular shaping of the contact can alter this selectivity. Smaller 20 contact sizes were more effective in constraining neural activation to small, nearby axons representative of 21 grey matter. However, micro-scale contacts enable only limited spread of neural activation before exceeding 22 standard charge density limitations; further, energetic efficiency is optimized by somewhat larger contacts. 24Interpretations. Small-scale contacts are optimal for constraining stimulation in nearby grey matter and 25 avoiding orientation-selective activation. However, given charge density limitations and energy inefficiency of 26 micro-scale contacts, our results suggest that contacts about half the size of those on segmented clinical leads 27 may optimize efficiency and charge density limitation avoidance. 28 29 Introduction 30 Recently, the deep brain stimulation (DBS) field has advanced in a number of directions with the goal of 31 maximizing therapeutic benefit while minimizing side effects. Numerous reports have been published 32 describing the modulation of DBS pulse width to induce changes in axon size selectivity 1-4 . At the same time, 33 advancements have been made in DBS technology and programming. Segmented electrodes have been 34 proposed and built, enabling the directional steering of stimulation to varying degrees of precision 2,5-7 , and 35 optimization algorithms have been created and validated 8-11 , often with the goal of avoidance of maximizing 36 stimulation within a specified target, such as the subthalamic nucleus, or avoidance of other targets, such as 37 the internal capsule. Further, several groups have investigated modifications in charge balancing and leading 38 pulse polarity to optimize neural selectivity 12,13 . Finally, several groups have investigated methods of 39 generating orientation-specific selectivity, whether through the use of multiple electrodes 14,15 , multiple contact 40 cathode configurations 16 , or alterations in the charge balancing pulse or leading pulse polarity ...

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Defining the impact of deep brain stimulation contact size and shape on neural selectivity

Anderson

Dorval

Rolston

et al. 2020

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Home Health Management of Parkinson Disease Deep Brain Stimulation

et al. 2021

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IMPORTANCEThe travel required to receive deep brain stimulation (DBS) programming causes substantial burden on patients and limits who can access DBS therapy.OBJECTIVE To evaluate the efficacy of home health DBS postoperative management in an effort to reduce travel burden and improve access. DESIGN, SETTINGS, AND PARTICIPANTSThis open-label randomized clinical trial was conducted at University of Florida Health from November 2017 to April 2020. Eligible participants had a diagnosis of Parkinson disease (PD) and were scheduled to receive DBS independently of the study. Consenting participants were randomized 1:1 to receive either standard of care or home health postoperative DBS management for 6 months after surgery. Primary caregivers, usually spouses, were also enrolled to assess caregiver strain. INTERVENTIONSThe home health postoperative management was conducted by a home health nurse who chose DBS settings with the aid of the iPad-based Mobile Application for PD DBS system. Prior to the study, the home health nurse had no experience providing DBS care. MAIN OUTCOMES AND MEASURESThe primary outcome was the number of times each patient traveled to the movement disorders clinic during the study period. Secondary outcomes included changes from baseline on the Unified Parkinson's Disease Rating Scale part III. RESULTSApproximately 75 patients per year were scheduled for DBS. Of the patients who met inclusion criteria over the entire study duration, 45 either declined or were excluded for various reasons. Of the 44 patients enrolled, 19 of 21 randomized patients receiving the standard of care (mean [SD] age, 64.1 [10.0] years; 11 men) and 23 of 23 randomized patients receiving home health who underwent a minimum of 1 postoperative management visit (mean [SD] age, 65.0 [10.9] years; 13 men) were included in analysis. The primary outcome revealed that patients randomized to home health had significantly fewer clinic visits than the patients in the standard of care arm (mean [SD], 0.4 [0.8] visits vs 4.8 [0.4] visits; P < .001). We found no significant differences between the groups in the secondary outcomes measuring the efficacy of DBS. No adverse events occurred in association with the study procedure or devices. CONCLUSIONS AND RELEVANCEThis study provides evidence supporting the safety and feasibility of postoperative home health DBS management.

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StimFit—A Data‐Driven Algorithm for Automated Deep Brain Stimulation Programming

et al. 2021

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BackgroundFinding 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.ObjectiveWe 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.MethodsElectrode 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.ResultsPredicted 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.ConclusionWe 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

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Neural selectivity, efficiency, and dose equivalence in deep brain stimulation through pulse width tuning and segmented electrodes

Cited by 51 publications

References 43 publications

Defining the impact of deep brain stimulation contact size and shape on neural selectivity

Defining the impact of deep brain stimulation contact size and shape on neural selectivity

Home Health Management of Parkinson Disease Deep Brain Stimulation

StimFit—A Data‐Driven Algorithm for Automated Deep Brain Stimulation Programming

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