<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Dannhauer M</submitter><funding>Intramural NIH HHS</funding><funding>NIBIB NIH HHS</funding><funding>NIMH NIH HHS</funding><funding>NINDS NIH HHS</funding><funding>National Institute of Mental Health</funding><pagination>494-501</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC10922371</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>95(6)</volume><pubmed_abstract>The modeling of transcranial magnetic stimulation (TMS)-induced electric fields (E-fields) is a versatile technique for evaluating and refining brain targeting and dosing strategies, while also providing insights into dose-response relationships in the brain. This review outlines the methodologies employed to derive E-field estimations, covering TMS physics, modeling assumptions, and aspects of subject-specific head tissue and coil modeling. We also summarize various numerical methods for solving the E-field and their suitability for various applications. Modeling methodologies have been optimized to efficiently execute numerous TMS simulations across diverse scalp coil configurations, facilitating the identification of optimal setups or rapid cortical E-field visualization for specific brain targets. These brain targets are extrapolated from neurophysiological measurements and neuroimaging, enabling precise and individualized E-field dosing in experimental and clinical applications. This necessitates the quantification of E-field estimates using metrics that enable the comparison of brain target engagement, functional localization, and TMS intensity adjustments across subjects. The integration of E-field modeling with empirical data has the potential to uncover pivotal insights into the aspects of E-fields responsible for stimulating and modulating brain function and states, enhancing behavioral task performance, and impacting the clinical outcomes of personalized TMS interventions.</pubmed_abstract><journal>Biological psychiatry</journal><pubmed_title>Electric Field Modeling in Personalizing Transcranial Magnetic Stimulation Interventions.</pubmed_title><pmcid>PMC10922371</pmcid><funding_grant_id>R01 MH120811</funding_grant_id><funding_grant_id>U24 NS130411</funding_grant_id><funding_grant_id>R00 MH120046</funding_grant_id><funding_grant_id>R01 EB022573</funding_grant_id><funding_grant_id>ZIAMH002955</funding_grant_id><funding_grant_id>ZIA MH002955</funding_grant_id><pubmed_authors>Siebner HR</pubmed_authors><pubmed_authors>Dannhauer M</pubmed_authors><pubmed_authors>Robins PL</pubmed_authors><pubmed_authors>Hasan NI</pubmed_authors><pubmed_authors>Fan Y</pubmed_authors><pubmed_authors>Wang D</pubmed_authors><pubmed_authors>Gomez LJ</pubmed_authors><pubmed_authors>Thielscher A</pubmed_authors><pubmed_authors>Deng ZD</pubmed_authors></additional><is_claimable>false</is_claimable><name>Electric Field Modeling in Personalizing Transcranial Magnetic Stimulation Interventions.</name><description>The modeling of transcranial magnetic stimulation (TMS)-induced electric fields (E-fields) is a versatile technique for evaluating and refining brain targeting and dosing strategies, while also providing insights into dose-response relationships in the brain. This review outlines the methodologies employed to derive E-field estimations, covering TMS physics, modeling assumptions, and aspects of subject-specific head tissue and coil modeling. We also summarize various numerical methods for solving the E-field and their suitability for various applications. Modeling methodologies have been optimized to efficiently execute numerous TMS simulations across diverse scalp coil configurations, facilitating the identification of optimal setups or rapid cortical E-field visualization for specific brain targets. These brain targets are extrapolated from neurophysiological measurements and neuroimaging, enabling precise and individualized E-field dosing in experimental and clinical applications. This necessitates the quantification of E-field estimates using metrics that enable the comparison of brain target engagement, functional localization, and TMS intensity adjustments across subjects. The integration of E-field modeling with empirical data has the potential to uncover pivotal insights into the aspects of E-fields responsible for stimulating and modulating brain function and states, enhancing behavioral task performance, and impacting the clinical outcomes of personalized TMS interventions.</description><dates><release>2024-01-01T00:00:00Z</release><publication>2024 Mar</publication><modification>2026-06-02T21:04:56.47Z</modification><creation>2025-04-04T01:29:00.56Z</creation></dates><accession>S-EPMC10922371</accession><cross_references><pubmed>38061463</pubmed><doi>10.1016/j.biopsych.2023.11.022</doi></cross_references></HashMap>