The University of Southampton
University of Southampton Institutional Repository

Numerical investigation of the electric field distribution induced in the brain by transcranial magnetic stimulation

Numerical investigation of the electric field distribution induced in the brain by transcranial magnetic stimulation
Numerical investigation of the electric field distribution induced in the brain by transcranial magnetic stimulation
Results are presented on the prediction and optimisation of the electric field distribution obtained during transcranial magnetic stimulation (TMS) for deep neuron stimulation by using the finite-element method (FEM) in three dimensions. The effects of the geometrical models of the head on the distribution and penetration of the electric field induced in the brain during TMS are examined. For a magnetic field that can penetrate deeply and safely to activate the brain’s central structures, an iron core is introduced and its core shape is optimised using continuum design sensitivity analysis (CDSA) combined with the FEM. It is revealed that the incorporation of an accurate brain model in terms of shape as well as conductivity values is crucial for improved estimation of the field distribution. The introduction of an optimised iron core is shown to enhance the magnitude and localisation of the electric field induced inside the brain.
TMS, magnetic stimulation, transcranial, modelling, optimisation, design, electromagnetics
1350-2344
479-483
Kim, D.
f41dc51b-2766-4d68-a3ed-7757ee2ae76e
Loukaides, N.
4ce97a28-c4c9-44f6-a6fe-f98f5a862930
Sykulski, J.K.
d6885caf-aaed-4d12-9ef3-46c4c3bbd7fb
Georghiou, G. E.
27c937e2-8024-4c7e-86be-5656ef10cf64
Kim, D.
f41dc51b-2766-4d68-a3ed-7757ee2ae76e
Loukaides, N.
4ce97a28-c4c9-44f6-a6fe-f98f5a862930
Sykulski, J.K.
d6885caf-aaed-4d12-9ef3-46c4c3bbd7fb
Georghiou, G. E.
27c937e2-8024-4c7e-86be-5656ef10cf64

Kim, D., Loukaides, N., Sykulski, J.K. and Georghiou, G. E. (2004) Numerical investigation of the electric field distribution induced in the brain by transcranial magnetic stimulation. IEE Proceedings - Science, Measurement and Technology, 151 (6), 479-483.

Record type: Article

Abstract

Results are presented on the prediction and optimisation of the electric field distribution obtained during transcranial magnetic stimulation (TMS) for deep neuron stimulation by using the finite-element method (FEM) in three dimensions. The effects of the geometrical models of the head on the distribution and penetration of the electric field induced in the brain during TMS are examined. For a magnetic field that can penetrate deeply and safely to activate the brain’s central structures, an iron core is introduced and its core shape is optimised using continuum design sensitivity analysis (CDSA) combined with the FEM. It is revealed that the incorporation of an accurate brain model in terms of shape as well as conductivity values is crucial for improved estimation of the field distribution. The introduction of an optimised iron core is shown to enhance the magnitude and localisation of the electric field induced inside the brain.

Text
IEE-Proc-vol151no6Nov2004page479.pdf - Other
Download (507kB)

More information

Published date: November 2004
Keywords: TMS, magnetic stimulation, transcranial, modelling, optimisation, design, electromagnetics
Organisations: EEE

Identifiers

Local EPrints ID: 259577
URI: http://eprints.soton.ac.uk/id/eprint/259577
ISSN: 1350-2344
PURE UUID: 14e0ed8e-0470-4cbc-bd06-5bff13405932
ORCID for J.K. Sykulski: ORCID iD orcid.org/0000-0001-6392-126X

Catalogue record

Date deposited: 02 Aug 2004
Last modified: 15 Mar 2024 02:34

Export record

Contributors

Author: D. Kim
Author: N. Loukaides
Author: J.K. Sykulski ORCID iD
Author: G. E. Georghiou

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×