Evidence map›Paper›PMID 42216705›Full record

ArticleHuman brain mapping2026

Standardizing TMS Intensity Across Different Coils Using Individualized Electric Field Modeling.

Evgenii Kim, Mohammad Daneshzand, Keren Zhu, Danyal Fareed Bhutto, Teresa Jacobson Kimberley, Dylan Edwards, Netri Pajankar, Parker Kotlarz, Tommi Raij, Sergey N Makaroff and 1 more

Abstract read
In one paragraph

Article in Human brain mapping, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Evgenii KimAthinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.ORCID https://orcid.org/0009-0007-7773-2727
Mohammad DaneshzandAthinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.
Keren ZhuAthinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.ORCID https://orcid.org/0000-0002-1655-7183
Danyal Fareed BhuttoAthinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.ORCID https://orcid.org/0000-0002-7388-1852
Teresa Jacobson KimberleyAthinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.ORCID https://orcid.org/0000-0001-6397-4054
Dylan EdwardsJefferson Moss Rehabilitation Research Institute, Philadelphia, Pennsylvania, USA.
Netri PajankarAthinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.ORCID https://orcid.org/0009-0002-9423-6630
Parker KotlarzAthinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.ORCID https://orcid.org/0000-0003-3667-8908
Tommi RaijAthinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.
Sergey N MakaroffWorcester Polytechnic Institute, Worcester, Massachusetts, USA.ORCID https://orcid.org/0000-0003-0478-8248
Aapo NummenmaaAthinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.

Funding

Training and Dissemination CoreP41EB030006 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI Susie Yi Huang · 2020 to 2026
$10.9M
Near real-time system for high-resolution computationalTMS navigationR01MH128421 · NIMH · MASSACHUSETTS GENERAL HOSPITAL · PI Aapo Nummenmaa · 2022 to 2026
$3.8M
Re-Wiring the Human BrainR01NS126337 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI Tommi Raij · 2022 to 2026
$3.4M
Charge-Based Brain Modeling Engine with Boundary Element Fast Multipole MethodR01MH130490 · NIMH · WORCESTER POLYTECHNIC INSTITUTE · PI Sergey N Makaroff · 2023 to 2026
$3.3M
The effects of neural modulation on phonatory function in laryngeal dystoniaK24DC018603 · NIDCD · MGH INSTITUTE OF HEALTH PROFESSIONS · PI KIMBERLEY, TERESA JACOBSON · 2021 to 2025
$978k
CRSNS: Development of EEG/MEG Source Reconstruction with Fast Multipole MethodR01EB035484 · NIBIB · WORCESTER POLYTECHNIC INSTITUTE · PI Sergey N Makaroff · 2023 to 2026
$777k
CRCNS: Multifocal causal mapping of brain networks supporting human cognitionR01DC020891 · NIDCD · MASSACHUSETTS GENERAL HOSPITAL · PI NUMMENMAA, AAPO · 2022 to 2024
$756k
Collaborative robot (cobot) controlled system for transcranial magnetic stimulationS10OD028668 · OD · MASSACHUSETTS GENERAL HOSPITAL · PI NUMMENMAA, AAPO · 2021 to 2021
$558k
Probing network-specific cortical connectivity by combining multifocal transcranial magnetic stimulation with electroencephalographyK01MH138823 · NIMH · MASSACHUSETTS GENERAL HOSPITAL · PI Mohammad Daneshzand · 2024 to 2026
$549k
Chernowitz Medical Research FoundationNIBIB NIH HHS P41 EB030006NIBIB NIH HHS R01 EB035484NIDCD NIH HHS K24 DC018603NIDCD NIH HHS R01 DC020891NIH HHS K01MH138823NIH HHS K24DC018603NIH HHS P41EB030006NIH HHS R01DC020891NIH HHS R01EB035484NIH HHS R01MH128421NIH HHS R01MH130490NIH HHS R01NS126337NIH HHS S10 OD028668NIH HHS S10OD028668NIMH NIH HHS K01 MH138823NIMH NIH HHS R01 MH128421NIMH NIH HHS R01 MH130490NINDS NIH HHS R01 NS126337
6 · The paper itself

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.

Indexed as

Electromagnetic FieldsMotor CortexTranscranial Magnetic StimulationAdultEvoked Potentials, MotorFemaleHumansMagnetic Resonance ImagingMaleYoung Adultdosingelectrical field stimulationneuronavigationtranscranial magnetic stimulation

Identifiers

PMID42216705
PMCPMC13238680

What OpenQuestion holds

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Registered trials

None linked

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.