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Fast computational optimization of TMS coil placement for individualized electric field targeting

BACKGROUND: During transcranial magnetic stimulation (TMS) a coil placed on the scalp is used to non-invasively modulate activity of targeted brain networks via a magnetically induced electric field (E-field). Ideally, the E-field induced during TMS is concentrated on a targeted cortical region of i...

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Autores principales: Gomez, Luis J., Dannhauer, Moritz, Peterchev, Angel V.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7956218/
https://www.ncbi.nlm.nih.gov/pubmed/33385544
http://dx.doi.org/10.1016/j.neuroimage.2020.117696
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author Gomez, Luis J.
Dannhauer, Moritz
Peterchev, Angel V.
author_facet Gomez, Luis J.
Dannhauer, Moritz
Peterchev, Angel V.
author_sort Gomez, Luis J.
collection PubMed
description BACKGROUND: During transcranial magnetic stimulation (TMS) a coil placed on the scalp is used to non-invasively modulate activity of targeted brain networks via a magnetically induced electric field (E-field). Ideally, the E-field induced during TMS is concentrated on a targeted cortical region of interest (ROI). Determination of the coil position and orientation that best achieve this objective presently requires a large computational effort. OBJECTIVE: To improve the accuracy of TMS we have developed a fast computational auxiliary dipole method (ADM) for determining the optimum coil position and orientation. The optimum coil placement maximizes the E-field along a predetermined direction or, alternatively, the overall E-field magnitude in the targeted ROI. Furthermore, ADM can assess E-field uncertainty resulting from precision limitations of TMS coil placement protocols. METHOD: ADM leverages the electromagnetic reciprocity principle to compute rapidly the TMS induced E-field in the ROI by using the E-field generated by a virtual constant current source residing in the ROI. The framework starts by solving for the conduction currents resulting from this ROI current source. Then, it rapidly determines the average E-field induced in the ROI for each coil position by using the conduction currents and a fast-multipole method. To further speed-up the computations, the coil is approximated using auxiliary dipoles enabling it to represent all coil orientations for a given coil position with less than 600 dipoles. RESULTS: Using ADM, the E-fields generated in an MRI-derived head model when the coil is placed at 5900 different scalp positions and 360 coil orientations per position (over 2.1 million unique configurations) can be determined in under 15 min on a standard laptop computer. This enables rapid extraction of the optimum coil position and orientation as well as the E-field variation resulting from coil positioning uncertainty. ADM is implemented in SimNIBS 3.2. CONCLUSION: ADM enables the rapid determination of coil placement that maximizes E-field delivery to a specific brain target. This method can find the optimum coil placement in under 15 min enabling its routine use for TMS. Furthermore, it enables the fast quantification of uncertainty in the induced E-field due to limited precision of TMS coil placement protocols, enabling minimization and statistical analysis of the E-field dose variability.
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spelling pubmed-79562182021-03-14 Fast computational optimization of TMS coil placement for individualized electric field targeting Gomez, Luis J. Dannhauer, Moritz Peterchev, Angel V. Neuroimage Article BACKGROUND: During transcranial magnetic stimulation (TMS) a coil placed on the scalp is used to non-invasively modulate activity of targeted brain networks via a magnetically induced electric field (E-field). Ideally, the E-field induced during TMS is concentrated on a targeted cortical region of interest (ROI). Determination of the coil position and orientation that best achieve this objective presently requires a large computational effort. OBJECTIVE: To improve the accuracy of TMS we have developed a fast computational auxiliary dipole method (ADM) for determining the optimum coil position and orientation. The optimum coil placement maximizes the E-field along a predetermined direction or, alternatively, the overall E-field magnitude in the targeted ROI. Furthermore, ADM can assess E-field uncertainty resulting from precision limitations of TMS coil placement protocols. METHOD: ADM leverages the electromagnetic reciprocity principle to compute rapidly the TMS induced E-field in the ROI by using the E-field generated by a virtual constant current source residing in the ROI. The framework starts by solving for the conduction currents resulting from this ROI current source. Then, it rapidly determines the average E-field induced in the ROI for each coil position by using the conduction currents and a fast-multipole method. To further speed-up the computations, the coil is approximated using auxiliary dipoles enabling it to represent all coil orientations for a given coil position with less than 600 dipoles. RESULTS: Using ADM, the E-fields generated in an MRI-derived head model when the coil is placed at 5900 different scalp positions and 360 coil orientations per position (over 2.1 million unique configurations) can be determined in under 15 min on a standard laptop computer. This enables rapid extraction of the optimum coil position and orientation as well as the E-field variation resulting from coil positioning uncertainty. ADM is implemented in SimNIBS 3.2. CONCLUSION: ADM enables the rapid determination of coil placement that maximizes E-field delivery to a specific brain target. This method can find the optimum coil placement in under 15 min enabling its routine use for TMS. Furthermore, it enables the fast quantification of uncertainty in the induced E-field due to limited precision of TMS coil placement protocols, enabling minimization and statistical analysis of the E-field dose variability. 2020-12-30 2021-03 /pmc/articles/PMC7956218/ /pubmed/33385544 http://dx.doi.org/10.1016/j.neuroimage.2020.117696 Text en This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
spellingShingle Article
Gomez, Luis J.
Dannhauer, Moritz
Peterchev, Angel V.
Fast computational optimization of TMS coil placement for individualized electric field targeting
title Fast computational optimization of TMS coil placement for individualized electric field targeting
title_full Fast computational optimization of TMS coil placement for individualized electric field targeting
title_fullStr Fast computational optimization of TMS coil placement for individualized electric field targeting
title_full_unstemmed Fast computational optimization of TMS coil placement for individualized electric field targeting
title_short Fast computational optimization of TMS coil placement for individualized electric field targeting
title_sort fast computational optimization of tms coil placement for individualized electric field targeting
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7956218/
https://www.ncbi.nlm.nih.gov/pubmed/33385544
http://dx.doi.org/10.1016/j.neuroimage.2020.117696
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