Evidence map›Paper›PMID 37928641›Full record

ArticleAPL bioengineering2023

Finite element analysis of electric field distribution during direct current stimulation of the spinal cord: Implications for device design.

Joe G Troughton, Yaw O Ansong, Nida Duobaite, Christopher M Proctor

Erratum issuedAbstract read
In one paragraph

Article in APL bioengineering, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Article
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  4. Review
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Yaw O AnsongDepartment of Engineering, University of Cambridge, Trumpington Street, Cambridge, United Kingdom.ORCID https://orcid.org/0009-0001-4231-5604
Nida DuobaiteDepartment of Engineering, University of Cambridge, Trumpington Street, Cambridge, United Kingdom.ORCID https://orcid.org/0009-0005-9149-9656
Christopher M ProctorDepartment of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, United Kingdom.ORCID https://orcid.org/0000-0002-2066-1354

Funding

Wellcome Trust
6 · The paper itself

Abstract

Spinal cord injury (SCI) arises from damage to the spinal cord, often caused by trauma or disease. The resulting sensorimotor dysfunction is variable and dependent on the extent of the injury. Despite years of research, curative options for SCI remain limited. However, recent advancements in electric field stimulated axonal regrowth have shown promise for neuronal regeneration. One roadblock in the development of therapeutic treatments based on this is a lack of understanding of the exogenous electric field distribution in the injured tissue, and in particular, how this is influenced by electrode geometry and placement. To better understand this electric field, and provide a means by which it can be optimized, we have developed a finite element model of such spinal cord treatment. We investigate the impact of variations in electrode geometry, spinal cord size, and applied current magnitude as well as looking at several injury models in relation to clinically observed outcomes. Through this, we show that electrode shape has little effect on the induced electric field, that the placement of these electrodes has a noticeable influence on the field distribution, and that the magnitude of this field is governed by both the applied current and the spinal cord morphology. We also show that the injury modality influences the induced field distribution and that a stronger understanding of the injury will help decide treatment parameters. This work provides guidance in the design of electrodes for future clinical application in direct current electric field stimulation for axonal regeneration.

Identifiers

PMID37928641
PMCPMC10624505

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.