Evaluation of high-perimeter electrode designs for deep brain stimulation

Howell, Bryan; Grill, Warren M.

doi:10.1088/1741-2560/11/4/046026

Cited by 34 publications

(20 citation statements)

References 40 publications

(54 reference statements)

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“…Finally, device lifetime is related to the capacity (volume) of the battery, and as the energy efficiency of stimulation is improved, devices with the same lifetime can be made smaller. There are a number of approaches for improving stimulation efficiency including altering the material properties (Cogan, 2008, Merrill et al, 2005) or geometry (Golestanirad et al, 2013, Howell and Grill, 2014, Wei and Grill, 2005) of the electrode. Additionally, and the focus here, efficiency may be improved by modifying the stimulation waveform.…”

Section: The Importance Of Energy Efficient Neural Stimulationmentioning

confidence: 99%

Model-based analysis and design of waveforms for efficient neural stimulation

Grill

2015

Progress in Brain Research

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The design space for electrical stimulation of the nervous system is extremely large, and because the response to stimulation is highly non-linear, the selection of stimulation parameters to achieve a desired response is a challenging problem. Computational models of the response of neurons to extracellular stimulation allow analysis of the effects of stimulation parameters on neural excitation and provide an approach to select or design optimal parameters of stimulation. Here, I review the use of computational models to understand the effects of stimulation waveform on the energy efficiency of neural excitation and to design novel stimulation waveforms to increase the efficiency of neural stimulation.

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Section: The Importance Of Energy Efficient Neural Stimulationmentioning

confidence: 99%

Model-based analysis and design of waveforms for efficient neural stimulation

Grill

2015

Progress in Brain Research

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“…It should be noted that in a few cases the clinical and 12-electrode arrays performed marginally better than the optimal designs. Using more than three contacts increased the total perimeter of the configurations, which decreased the electrode impedance (Howell and Grill, 2014, Wei and Grill, 2005), leading to slight improvements in performance.…”

Section: Resultsmentioning

confidence: 99%

“…Examples of latter include surface roughening (Norlin et al, 2002) and electrode coatings (Cogan, 2008). Additionally, efficiency may be improved by increasing the area and/or perimeter of the electrode (Golestanirad et al, 2013, Howell and Grill, 2014, Wei and Grill, 2005). …”

Section: Introductionmentioning

confidence: 99%

Design andin vivoevaluation of more efficient and selective deep brain stimulation electrodes

2015

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Objective Deep brain stimulation (DBS) is an effective treatment for movement disorders and a promising therapy for treating epilepsy and psychiatric disorders. Despite its clinical success, the efficiency and selectivity of DBS can be improved. Our objective was to design electrode geometries that increased the efficiency and selectivity of DBS. Approach We coupled computational models of electrodes in brain tissue with cable models of axons of passage (AOPs), terminating axons (TAs), and local neurons (LNs); we used engineering optimization to design electrodes for stimulating these neural elements; and the model predictions were tested in vivo. Main results Compared with the standard electrode used in the Medtronic Model 3387 and 3389 arrays, model-optimized electrodes consumed 45–84 % less power. Similar gains in selectivity were evident with the optimized electrodes: 50 % of parallel AOPs could be activated while reducing activation of perpendicular AOPs from 44–48 % with the standard electrode to 0–14 % with bipolar designs; 50 % of perpendicular AOPs could be activated while reducing activation of parallel AOPs from 53–55 % with the standard electrode to 1–5 % with an array of cathodes; and, 50 % of TAs could be activated while reducing activation of AOPs from 43–100 % with the standard electrode to 2–15 % with a distal anode. In vivo, both the geometry and polarity of the electrode had a profound impact on the efficiency and selectivity of stimulation. Significance Model-based design is a powerful tool that can be used to improve the efficiency and selectivity of DBS electrodes.

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“…Thus, while increasing the number of electrodes may provide more spatially focused stimulation, generating a therapeutic effect through DBS arrays is likely to require grouping electrodes together for high-density DBS arrays. This grouping approach would be complicated by radial diffusion properties that result in higher charge densities near the edges of each electrode in a group (Wei and Grill, 2005 ; Howell and Grill, 2014 ).…”

Section: Discussionmentioning

confidence: 99%

Model-Based Comparison of Deep Brain Stimulation Array Functionality with Varying Number of Radial Electrodes and Machine Learning Feature Sets

Teplitzky

Zitella

Xiao

et al. 2016

Front. Comput. Neurosci.

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Deep brain stimulation (DBS) leads with radially distributed electrodes have potential to improve clinical outcomes through more selective targeting of pathways and networks within the brain. However, increasing the number of electrodes on clinical DBS leads by replacing conventional cylindrical shell electrodes with radially distributed electrodes raises practical design and stimulation programming challenges. We used computational modeling to investigate: (1) how the number of radial electrodes impact the ability to steer, shift, and sculpt a region of neural activation (RoA), and (2) which RoA features are best used in combination with machine learning classifiers to predict programming settings to target a particular area near the lead. Stimulation configurations were modeled using 27 lead designs with one to nine radially distributed electrodes. The computational modeling framework consisted of a three-dimensional finite element tissue conductance model in combination with a multi-compartment biophysical axon model. For each lead design, two-dimensional threshold-dependent RoAs were calculated from the computational modeling results. The models showed more radial electrodes enabled finer resolution RoA steering; however, stimulation amplitude, and therefore spatial extent of the RoA, was limited by charge injection and charge storage capacity constraints due to the small electrode surface area for leads with more than four radially distributed electrodes. RoA shifting resolution was improved by the addition of radial electrodes when using uniform multi-cathode stimulation, but non-uniform multi-cathode stimulation produced equivalent or better resolution shifting without increasing the number of radial electrodes. Robust machine learning classification of 15 monopolar stimulation configurations was achieved using as few as three geometric features describing a RoA. The results of this study indicate that, for a clinical-scale DBS lead, more than four radial electrodes minimally improved in the ability to steer, shift, and sculpt axonal activation around a DBS lead and a simple feature set consisting of the RoA center of mass and orientation enabled robust machine learning classification. These results provide important design constraints for future development of high-density DBS arrays.

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Evaluation of high-perimeter electrode designs for deep brain stimulation

Cited by 34 publications

References 40 publications

Model-based analysis and design of waveforms for efficient neural stimulation

Model-based analysis and design of waveforms for efficient neural stimulation

Design andin vivoevaluation of more efficient and selective deep brain stimulation electrodes

Model-Based Comparison of Deep Brain Stimulation Array Functionality with Varying Number of Radial Electrodes and Machine Learning Feature Sets

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