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Modeling Current Sources for Neural Stimulation in COMSOL

Overview of attention for article published in Frontiers in Computational Neuroscience, June 2018
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (63rd percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

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Title
Modeling Current Sources for Neural Stimulation in COMSOL
Published in
Frontiers in Computational Neuroscience, June 2018
DOI 10.3389/fncom.2018.00040
Pubmed ID
Authors

Nicole A. Pelot, Brandon J. Thio, Warren M. Grill

Abstract

Background: Computational modeling provides an important toolset for designing and analyzing neural stimulation devices to treat neurological disorders and diseases. Modeling enables efficient exploration of large parameter spaces, where preclinical and clinical studies would be infeasible. Current commercial finite element method software packages enable straightforward calculation of the potential distributions, but it is not always clear how to implement boundary conditions to appropriately represent metal stimulating electrodes. By quantifying the effects of different electrode representations on activation thresholds for model axons, we provide recommendations for accurate and efficient modeling of neural stimulating electrodes. Methods: We quantified the effects of different representations of current sources for neural stimulation in COMSOL Multiphysics for monopolar, bipolar, and multipolar electrode designs. Results: We recommend modeling each electrode contact as a thin platinum domain, modeling the electrode substrate with the conductivity of silicone, and either using a point current source in the center of each electrode contact or using a boundary current source. Alternatively, to avoid possible numerical instabilities associated with a large range of conductivity values (i.e., platinum and silicone) and to eliminate the small mesh elements required for thin electrode contacts, the electrode substrate can be assigned the conductivity of platinum by using insulating boundaries between the substrate and surrounding medium, and within the substrate to isolate the contacts from each other. When modeling more than one contact, we recommend using superposition by solving the model once for each contact, leaving inactive contacts floating, and superposing the resulting potentials. We computed comparable errors in activation thresholds across the different implementations in a simplified model (electrode in a homogeneous, isotropic medium), and in realistic models of rat spinal cord stimulation (SCS) and human deep brain stimulation, indicating that the recommended approaches are applicable to different stimulation targets.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 156 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 156 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 26 17%
Researcher 23 15%
Student > Master 21 13%
Student > Bachelor 13 8%
Student > Doctoral Student 7 4%
Other 17 11%
Unknown 49 31%
Readers by discipline Count As %
Engineering 58 37%
Neuroscience 18 12%
Materials Science 4 3%
Physics and Astronomy 3 2%
Agricultural and Biological Sciences 2 1%
Other 11 7%
Unknown 60 38%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 June 2018.
All research outputs
#7,284,788
of 24,226,848 outputs
Outputs from Frontiers in Computational Neuroscience
#363
of 1,406 outputs
Outputs of similar age
#119,180
of 333,203 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#11
of 30 outputs
Altmetric has tracked 24,226,848 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 1,406 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.0. This one has gotten more attention than average, scoring higher than 73% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 333,203 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 63% of its contemporaries.
We're also able to compare this research output to 30 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.