ArticleNeuroImage2021
Rapid computation of TMS-induced E-fields using a dipole-based magnetic stimulation profile approach.
Article in NeuroImage, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Charge based boundary element method with residual driven adaptive mesh refinement for high resolution electrical stimulation modeling.Scientific reports · 2026Article
- Standardizing TMS Intensity Across Different Coils Using Individualized Electric Field Modeling.Human brain mapping · 2026Article
- Contrastive Diffusion Alignment: Learning Structured Latents for Controllable Generation.Proceedings of machine learning research · 2026Article
- Real-time computation of brain E-field for enhanced transcranial magnetic stimulation neuronavigation and optimization.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- A survey on integral equations for bioelectric modeling.Physics in medicine and biology · 2024Review
- Real-time estimation of the optimal coil placement in transcranial magnetic stimulation using multi-task deep learning.Scientific reports · 2024Article
- A review of algorithms and software for real-time electric field modeling techniques for transcranial magnetic stimulation.Biomedical engineering letters · 2024Review
- Electric Field Modeling in Personalizing Transcranial Magnetic Stimulation Interventions.Biological psychiatry · 2024Review
- SlicerTMS: Real-Time Visualization of Transcranial Magnetic Stimulation for Mental Health Treatment.ArXiv · 2024Article
- Fast and accurate computational E-field dosimetry for group-level transcranial magnetic stimulation targeting.Computers in biology and medicine · 2023Article
- A fast direct solver for surface-based whole-head modeling of transcranial magnetic stimulation.Scientific reports · 2023Article
- A Review of Formulations, Boundary Value Problems and Solutions for Numerical Computation of Transcranial Magnetic Stimulation Fields.Brain sciences · 2023Review
- Fast computational E-field dosimetry for transcranial magnetic stimulation using adaptive cross approximation and auxiliary dipole method (ACA-ADM).NeuroImage · 2023Article
- Computation of transcranial magnetic stimulation electric fields using self-supervised deep learning.NeuroImage · 2022Article
- Degree of improving TMS focality through a geometrically stable solution of an inverse TMS problem.NeuroImage · 2021Article
- Rapid whole-brain electric field mapping in transcranial magnetic stimulation using deep learning.PloS one · 2021Article
- Together towards more accessible, standardized TMS protocols: Reply to Sorkhabi and Leedham.Brain stimulationArticle
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
backgroundTMS neuronavigation with on-line display of the induced electric field (E-field) has the potential to improve quantitative targeting and dosing of stimulation, but present commercially available solutions are limited by simplified approximations.
objectiveDeveloping a near real-time method for accurate approximation of TMS induced E-fields with subject-specific high-resolution surface-based head models that can be utilized for TMS navigation.
methodsMagnetic dipoles are placed on a closed surface enclosing an MRI-based head model of the subject to define a set of basis functions for the incident and total E-fields that define the subject's Magnetic Stimulation Profile (MSP). The near real-time speed is achieved by recognizing that the total E-field of the coil only depends on the incident E-field and the conductivity boundary geometry. The total E-field for any coil position can be obtained by matching the incident field of the stationary dipole basis set with the incident E-field of the moving coil and applying the same basis coefficients to the total E-field basis functions.
resultsComparison of the MSP-based approximation with an established TMS solver shows great agreement in the E-field amplitude (relative maximum error around 5%) and the spatial distribution patterns (correlation >98%). Computation of the E-field took ~100 ms on a cortical surface mesh with 120k facets.
conclusionThe numerical accuracy and speed of the MSP approximation method make it well suited for a wide range of computational tasks including interactive planning, targeting, dosing, and visualization of the intracranial E-fields for near real-time guidance of coil positioning.
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