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Cited by 442

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Localized stimulation of neural tissues in the brain by means of a paired configuration of time-varying magnetic fields

Ueno, S., Tashiro, T., Harada, K.·1988·Journal of Applied Physics

Abstract

A method of localized stimulation of the human brain is proposed. The basic idea is to concentrate induced eddy currents locally in the vicinity of a target in the cortex by a pair of coils which are positioned outside the head so that time-varying magnetic fields pass through the head in the opposite directions around a target. The eddy currents induced at the target are expected to flow together, which results in an increased current flow at the target. Spatial distributions of induced eddy currents are calculated in cubical and spherical volume conductor models by a finite element method. The results show that the current vectors make themselves two vortexes which flow together at the target. The current density at the target makes a peak which is higher by 2–3 times than current densities at nontarget regions. The validity of the proposed method is demonstrated by experiments using frog nerve-muscle preparations.

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Metadata

Authors
Ueno, S., Tashiro, T., & Harada, K.
Venue
Journal of Applied Physics
Published
1988-11-15
Volume
64
Issue
10
Pages
5862-5864
ISSN
  • 0021-8979 (ISSN-L)
  • 1089-7550
OpenAlex
W2021018218
Cited by
442
References
0
FWCI
5.35
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Localized stimulation of neural tissues in the brain by means of a paired configuration of time-varying magnetic fields. https://app.cochat.ai/papers/10.1063/1.342181
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Ueno, S., Tashiro, T., & Harada, K. (1988). Localized stimulation of neural tissues in the brain by means of a paired configuration of time-varying magnetic fields. Journal of Applied Physics. https://doi.org/10.1063/1.342181

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.