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Modelling of the Current Density Distributions during Cortical Electric Stimulation for Neuropathic Pain Treatment.

Authors :
Fiocchi, S.
Chiaramello, E.
Ravazzani, P.
Parazzini, M.
Source :
Computational & Mathematical Methods in Medicine. 4/23/2018, p1-12. 12p.
Publication Year :
2018

Abstract

In the last two decades, motor cortex stimulation has been recognized as a valuable alternative to pharmacological therapy for the treatment of neuropathic pain. Although this technique started to be used in clinical studies, the debate about the optimal settings that enhance its effectiveness without inducing tissue damage is still open. To this purpose, computational approaches applied to realistic human models aimed to assess the current density distribution within the cortex can be a powerful tool to provide a basic understanding of that technique and could help the design of clinical experimental protocols. This study aims to evaluate, by computational techniques, the current density distributions induced in the brain by a realistic electrode array for cortical stimulation. The simulation outcomes, summarized by specific metrics quantifying the efficacy of the stimulation (i.e., the effective volume and the effective depth of penetration) over two cortical targets, were evaluated by varying the interelectrode distance, the stimulus characteristics (amplitude and frequency), and the anatomical human model. The results suggest that all these parameters somehow affect the current density distributions and have to be therefore taken into account during the planning of effective electrical cortical stimulation strategies. In particular, our calculations show that (1) the most effective interelectrode distance equals 2 cm; (2) increasing voltage amplitudes increases the effective volume; (3) increasing frequencies allow enlarging the effective volume; and (4) the effective depth of penetration is strictly linked to both the anatomy of the subject and the electrode placement. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
1748670X
Database :
Academic Search Index
Journal :
Computational & Mathematical Methods in Medicine
Publication Type :
Academic Journal
Accession number :
129243267
Full Text :
https://doi.org/10.1155/2018/1056132