---
title: "Mesh Variability Causes Percent-Level Differences in Temporal Interference Stimulation Simulations"
id: "biorxiv-9-independent-mesh-realizations-introduce-percent-level-variability-in-temporal"
canonical_url: "https://medichelpline.com/clinical-feed/biorxiv-9-independent-mesh-realizations-introduce-percent-level-variability-in-temporal"
content_type: "clinical_feed_article"
specialty: "Neurology"
source_name: "bioRxiv (Biomedical Preprints)"
source_url: "https://www.biorxiv.org/content/10.64898/2026.08.08.743658v1?rss=1"
published_at: "2026-08-10T12:00:00.000Z"
evidence_level: "Verified Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Mesh Variability Causes Percent-Level Differences in Temporal Interference Stimulation Simulations
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/biorxiv-9-independent-mesh-realizations-introduce-percent-level-variability-in-temporal
- **Specialty:** [Neurology](https://medichelpline.com/clinical-feed/neurology.md)
- **Primary Source:** bioRxiv (Biomedical Preprints)
- **Source URL:** [Original Journal Publication](https://www.biorxiv.org/content/10.64898/2026.08.08.743658v1?rss=1)
- **Published At:** 2026-08-10T12:00:00.000Z
- **Evidence Rating:** Verified Feed
## Executive GIST (TL;DR)
- The study assessed how non-deterministic tetrahedral **mesh generation** affects repeatability of computational models for **temporal interference stimulation (TIS)**. - Ten head models were used, targeting left hippocampus and right primary motor cortex (M1). - For each model and target, the authors generated 40 independent mesh realisations and ran a full simulation on each, yielding 400 independent-remesh simulations. - Separately, they selected one mesh per model closest to the median parcel-level field and repeated downstream operations 40 times on that fixed mesh, yielding 1,600 total TIS simulations across both procedures. - The primary outcome was the spatial median of the TIS envelope field within a spherical target region. - Across independent remeshed runs, within-participant coefficients of variation were 1.81–3.65% for the hippocampus and 1.62–2.79% for M1. - Repeated execution on a fixed mesh reduced run-to-run standard deviation by over 99%, indicating variability arises almost entirely from stochastic mesh realisation rather than solver instability, numerical rounding, or post-processing. - Single-run mesh realisations generally preserved cohort ordering (median Kendall’s tau 0.867 for hippocampus, 0.911 for M1) but could invert ranks for participant pairs with similar predicted fields. - A bootstrap analysis showed averaging five to ten independent remesh runs effectively suppressed stochastic noise. - Authors conclude that stochastic mesh variation should be controlled or mitigated through multi-run averaging when conclusions depend on subtle field differences or fixed neuromodulation thresholds.
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Boyan Ivanov 1 The University of Sheffield; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Boyan%2BIvanov%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Ivanov%20B&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3ABoyan%2BIvanov%2B) Mahnaz Arvaneh 1 The University of Sheffield; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Mahnaz%2BArvaneh%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Arvaneh%20M&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AMahnaz%2BArvaneh%2B) * [ORCID record for Mahnaz Arvaneh](http://orcid.org/0000-0002-5124-3497 "Open in new tab") Jake Toth 1 The University of Sheffield; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Jake%2BToth%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Toth%20J&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AJake%2BToth%2B) * [ORCID record for Jake Toth](http://orcid.org/0000-0001-9392-9692 "Open in new tab") Sumientra M Rampersad 2 University of Massachusetts Boston * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Sumientra%2BM%2BRampersad%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Rampersad%20SM&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3ASumientra%2BM%2BRampersad%2B) * [ORCID record for Sumientra M Rampersad](http://orcid.org/0000-0001-9860-4459 "Open in new tab") * For correspondence: sumientra.rampersad@umb.edu * [Abstract](https://www.biorxiv.org/content/10.64898/2026.08.08.743658v1)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_art/node:5692796/1) * [Info/History](https://www.biorxiv.org/content/10.64898/2026.08.08.743658v1.article-info)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_info/node:5692796/1) * [Metrics](https://www.biorxiv.org/content/10.64898/2026.08.08.743658v1.article-metrics)[](https://www.biorxiv.org/panels_ajax_tab/article_tab_metrics/node:5692796/1) * [ Preview PDF](https://www.biorxiv.org/content/10.64898/2026.08.08.743658v1.full.pdf+html)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_pdf/node:5692796/1) ![Loading](https://www.biorxiv.org/sites/all/modules/contrib/panels_ajax_tab/images/loading.gif) ## Abstract Computational models of temporal interference stimulation (TIS) commonly report a single electric-field estimate for a given anatomy and electrode montage. Because non-deterministic tetrahedral mesh generation does not produce a unique discretisation of a fixed tissue-label image, a single mesh realisation may introduce numerical variability. We quantified variation across independent mesh realisations and contrasted it with repeated downstream simulation execution on a single selected mesh. Ten head models were evaluated for stimulation of the left hippocampus and right primary motor cortex (M1). For every model and target, we generated 40 independent meshes and performed one complete simulation on each. Separately, we selected the mesh whose parcel-level field estimate was closest to the median and repeated downstream operations 40 times while holding that geometry fixed, yielding 1,600 TIS simulations in total. The primary outcome was the spatial median of the TIS envelope field within a spherical target region. Across independently remeshed runs, within-participant coefficients of variation were 1.81-3.65% for the hippocampus and 1.62-2.79% for M1. Repeated execution on a fixed mesh reduced run-to-run standard deviation by more than 99%, demonstrating that workflow variability is driven almost entirely by non-deterministic mesh generation rather than solver instability, numerical rounding, or post-processing. Single-run mesh realisations preserved overall cohort ordering (median Kendall's tau of 0.867 for the hippocampus and 0.911 for M1) but frequently inverted the rank order of participant pairs with similar predicted fields. Furthermore, a bootstrap analysis demonstrated that averaging five to ten independent remesh runs effectively suppressed this stochastic noise. These results quantify single-workflow repeatability rather than absolute error. Stochastic mesh variation should therefore be controlled or mitigated through multi-run averaging whenever experimental conclusions depend on subtle field differences or fixed neuromodulation thresholds. ### Competing Interest Statement The authors have declared no competing interest. ## Funder Information Declared Wellcome EPSRC Centre for Medical Engineering, EP/W524360/1 Advanced Research and Invention Agency, SCNI-PR01-P08 National Institute of Neurological Disorders and Stroke, R01NS133229 Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a [CC-BY-ND 4.0 International license](http://creativecommons.org/licenses/by-nd/4.0/). bioRxiv and medRxiv thank the following for their generous financial support: > The Chan Zuckerberg Initiative, Cold Spring Harbor Laboratory, the Sergey Brin Family Foundation, California Institute of Technology, Centre National de la Recherche Scientifique, Fred Hutchinson Cancer Center, Imperial College London, Massachusetts Institute of Technology, Stanford University, The University of Edinburgh, University of Washington, and Vrije Universiteit Amsterdam. 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[ Download PDF](https://www.biorxiv.org/content/10.64898/2026.08.08.743658v1.full.pdf) Print/Save Options [Download PDF](https://www.biorxiv.org/content/biorxiv/early/2026/08/10/2026.08.08.743658.full.pdf)Full Text & In-line FiguresXML [More Info](https://www.biorxiv.org/about/FAQ#PrintOptions "More Information on Print/Save Options") [ Email](https://www.biorxiv.org/ "Email this Article") [ Share](https://www.biorxiv.org/) Independent Mesh Realizations Introduce Percent-Level Variability in Temporal Interference Simulations Boyan Ivanov, Mahnaz Arvaneh, Jake Toth, Sumientra M Rampersad bioRxiv 2026.08.08.743658; doi: https://doi.org/10.64898/2026.08.08.743658 This article is a preprint and has not been certified by peer review [[what does this mean?](https://www.biorxiv.org/about/FAQ#unrefereed)]. 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