ArticleNeuroImage2023
Fast computational E-field dosimetry for transcranial magnetic stimulation using adaptive cross approximation and auxiliary dipole method (ACA-ADM).
Article in NeuroImage, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 22 citations in OpenAlex.
- Real-time computation of brain E-field for enhanced transcranial magnetic stimulation neuronavigation and optimization.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Efficient prospective electric field-informed localization of motor cortical targets of transcranial magnetic stimulation.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- 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
- 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
- Realistic electric field characterization of clinically used deformable large TMS coils in a large cohort.Brain stimulationArticle
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 3 countries.
Funding
Abstract
Transcranial Magnetic Stimulation (TMS) is a non-invasive brain stimulation technique that uses a coil to induce an electric field (E-field) in the brain and modulate its activity. Many applications of TMS call for the repeated execution of E-field solvers to determine the E-field induced in the brain for different coil placements. However, the usage of solvers for these applications remains impractical because each coil placement requires the solution of a large linear system of equations. We develop a fast E-field solver that enables the rapid evaluation of the E-field distribution for a brain region of interest (ROI) for a large number of coil placements, which is achieved in two stages. First, during the pre-processing stage, the mapping between coil placement and brain ROI E-field distribution is approximated from E-field results for a few coil placements. Specifically, we discretize the mapping into a matrix with each column having the ROI E-field samples for a fixed coil placement. This matrix is approximated from a few of its rows and columns using adaptive cross approximation (ACA). The accuracy, efficiency, and applicability of the new ACA approach are determined by comparing its E-field predictions with analytical and standard solvers in spherical and MRI-derived head models. During the second stage, the E-field distribution in the brain ROI from a specific coil placement is determined by the obtained rows and columns in milliseconds. For many applications, only the E-field distribution for a comparatively small ROI is required. For example, the solver can complete the pre-processing stage in approximately 4 hours and determine the ROI E-field in approximately 40 ms for a 100 mm diameter ROI with less than 2% error enabling its use for neuro-navigation and other applications. Highlight: We developed a fast solver for TMS computational E-field dosimetry, which can determine the ROI E-field in approximately 40 ms for a 100 mm diameter ROI with less than 2% error.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.