Document Type

Article

Publication Date

6-1-2026

Comments

This article is the author’s final published version in Human Brain Mapping, Volume 47, Issue 8, 2026, Article number e70550.

The published version is available at https://doi.org/10.1002/hbm.70550. Copyright © 2026 The Author(s).

 

Abstract

Quantitative measures for Transcranial Magnetic Stimulation (TMS) intensity are needed to ensure safe and consistent application in therapeutic and research settings. However, resting motor thresholds (rMTs), commonly used to determine stimulation intensity, depend on the coil used. Unless motor mapping and treatment coils are identical, re-thresholding is necessary, increasing patient discomfort and potentially introducing variability across studies. These considerations raise an unresolved fundamental question: does individual rMT reflect a consistent cortical electric field (E-field) magnitude independent of coil geometry? We tested the hypothesis that rMT corresponds to a coil-invariant cortical E-field magnitude and evaluated a computational method for predicting stimulator output across different coils using a reference rMT. Thirteen healthy, right-handed participants were recruited; ten were included in the primary analysis. Participants underwent TMS with two figure-of-eight coils of different sizes. E-field distributions were simulated using a fast multipole boundary element method, in free space and within personalized MRI-based head models. rMT prediction accuracy was compared between a detailed five-layer and a simplified three-layer head model, and both were evaluated against direct rMT scaling using the reference coil. The personalized E-field-based approach significantly improved rMT prediction accuracy over direct scaling (p <  0.001). The root-mean-square error (RMSE) was 1.26% and 1.32% of maximum stimulator output (MSO) for detailed and simplified models, versus 6.1% MSO for direct scaling. Individual rMT corresponds to a constant cortical E-field magnitude ratio across coil types. E-field-based prediction offers a more accurate, coil-independent method for standardizing TMS intensity, reducing the need for repeated thresholding.

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Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License

Additional Files
Supplementary Materials.docx (241 kB)

Language

English

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