Toth, J., Brosnan, M., King, R.-J. et al. (2 more authors) (2025) Dose standardization for transcranial electrical stimulation: an accessible approach. Scientific Reports, 15. 41791. ISSN: 2045-2322
Abstract
Transcranial electrical stimulation (tES) is a widely used non-invasive brain stimulation technique. However, due to high inter-individual variability in the induced electric fields (E-fields), a fixed stimulation current delivers an inconsistent dose. We developed a dose standardization method without the requirement of participant-specific structural imaging and E-field modeling. Robust multiple linear regression models were trained to predict peak E-field strengths across 10 electrode montages and 418 healthy adults. These regression models predicted peak E-field strengths in unseen participants from accessible demographic and morphological parameters. Estimated peak E-field strength values were subsequently used to standardize tES dosages across our population. Additionally, we developed montage-agnostic models which incorporated inter-electrode distances for each participant. Compared to fixed dosing, our approach significantly reduced peak E-field strength variation for conventional montages, though results were inconsistent for high-definition (HD) montages. Models trained on specific montages accounted for 43% of peak E-field strength variability in conventional montages and 21% in HD montages on average. Our montage-agnostic models accounted for 36% and 13% of the average peak E-field strength variability for conventional and HD montages, respectively. These results have been validated across a large dataset, demonstrating robust performance against unseen data, a significant advancement over current approaches.
Metadata
| Item Type: | Article |
|---|---|
| Authors/Creators: |
|
| Copyright, Publisher and Additional Information: | © The Author(s) 2025. Open Access: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
| Dates: |
|
| Institution: | The University of Sheffield |
| Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
| Funding Information: | Funder Grant number ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL / EPSRC 2884307 |
| Date Deposited: | 07 Nov 2025 09:41 |
| Last Modified: | 02 Dec 2025 17:09 |
| Status: | Published |
| Publisher: | Nature Portfolio |
| Refereed: | Yes |
| Identification Number: | 10.1038/s41598-025-25649-2 |
| Related URLs: | |
| Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:233464 |

CORE (COnnecting REpositories)
CORE (COnnecting REpositories)