A software toolkit for TMS electric-field modeling with boundary element fast multipole method: an efficient MATLAB implementation.


Journal

Journal of neural engineering
ISSN: 1741-2552
Titre abrégé: J Neural Eng
Pays: England
ID NLM: 101217933

Informations de publication

Date de publication:
04 08 2020
Historique:
pubmed: 3 4 2020
medline: 29 6 2021
entrez: 3 4 2020
Statut: epublish

Résumé

To present and disseminate our transcranial magnetic stimulation (TMS) modeling software toolkit, including several new algorithmic developments, and to apply this software to realistic TMS modeling scenarios given a high-resolution model of the human head including cortical geometry and an accurate coil model. The recently developed charge-based boundary element fast multipole method (BEM-FMM) is employed as an alternative to the 1st order finite element method (FEM) most commonly used today. The BEM-FMM approach provides high accuracy and unconstrained numerical field resolution close to and across cortical interfaces. Here, the previously proposed BEM-FMM algorithm has been improved in several novel ways. The improvements resulted in a threefold increase in computational speed while maintaining the same solution accuracy. The computational code based on the MATLAB® platform is made available to all interested researchers, along with a coil model repository and examples to create custom coils, head model repository, and supporting documentation. The presented software toolkit may be useful for post-hoc analyses of navigated TMS data using high-resolution subject-specific head models as well as accurate and fast modeling for the purposes of TMS coil/hardware development. TMS is currently the only non-invasive neurostimulation modality that enables painless and safe supra-threshold stimulation by employing electromagnetic induction to efficiently penetrate the skull. Accurate, fast, and high resolution modeling of the electric fields may significantly improve individualized targeting and dosing of TMS and therefore enhance the efficiency of existing clinical protocols as well as help establish new application domains.

Identifiants

pubmed: 32235065
doi: 10.1088/1741-2552/ab85b3
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

046023

Subventions

Organisme : NIBIB NIH HHS
ID : R00 EB015445
Pays : United States
Organisme : NINDS NIH HHS
ID : R44 NS090894
Pays : United States
Organisme : NIMH NIH HHS
ID : R21 MH116484
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH111829
Pays : United States

Auteurs

Sergey N Makarov (SN)

Electrical & Computer Engineering Department, Worcester Polytechnic Institute, Worcester, MA 01609 United States of America. Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115 United States of America. Author to whom any correspondence should be addressed.

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Classifications MeSH