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Electric field modeling in personalizing TMS interventions

  • Moritz Dannhauer
  • , Luis J Gomez
  • , Pei L. Robins
  • , Dezhi Wang
  • , Nahian I. Hasan
  • , Axel Thielscher
  • , Hartwig R. Siebner
  • , Yong Fan
  • , Zhi-De Deng*
  • *Corresponding author for this work
  • University of Copenhagen
  • National Institutes of Health
  • Purdue University
  • University of Pennsylvania

Research output: Contribution to journal › Journal article › Research › peer-review

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Abstract

The modeling of TMS-induced electric fields (E-fields) serves as 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-field responsible for stimulating and modulating brain function and states, enhancing behavioral task performance, and impacting the clinical outcomes of personalized TMS interventions.
Original languageEnglish
JournalBiological Psychiatry
Volume95
Issue number6
Pages (from-to)494-501
ISSN0006-3223
DOIs
Publication statusPublished - 2024

Keywords

  • Brain stimulation
  • Electric field
  • Individualization
  • Modeling
  • Optimal placement
  • Transcranial magnetic stimulation

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