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Ultrasound neuromodulation hypotheses on the directionality of effects and community resource.

Code ↔ Paper

2 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

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  1. [1] § Results › How to use and contribute to the inTUS Resource ↔ Caffaratti_et_al_inTUS_Resource/inTUS_Rscript_1-2025.Rmd, lines 23 to 63 · score 0.65 · Qualtrics form, RStudio, markdown script, inTUS, caffaratti, submit · Report a mismatch
  2. [2] § Results › Meta-analysis limitations ↔ Caffaratti_et_al_inTUS_Resource/inTUS_Rscript_1-2025.Rmd, lines 23 to 63 · score 0.55 · RStudio, Markdown script, inTUS, Qualtrics, welcome, hope · Report a mismatch

Paper

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The authors' code

R Markdown · 268 lines · 8.8 KB · no license · 2 matches

  1. ---
  2. title: "inTUS - Transcranial Ultrasound Resource Output"
  3. author: "Ben Slater, Hugo Caffaratti and Chris Petkov"
  4. date: "`r Sys.Date()`"
  5. output:
  6. pdf_document: default
  7. html_document:
  8. df_print: paged
  9. header-includes:
  10. - \usepackage{helvet}
  11. - \usepackage[T1]{fontenc}
  12. - \renewcommand\familydefault{\sfdefault}
  13. fontsize: 12pt
  14. html_document: html
  15. css: "table-style.css"
  16. institution: Biosciences Institute, Newcastle University, UK; Dept. Neurosurgery, University of Iowa, USA
  17. ---
  19. ```{r document setup, include = FALSE}
  20. knitr::opts_chunk$set(echo = TRUE)
  21. ```
  23. # Citation information
  25. Please cite the original paper: **Caffaratti *et al.*, eLife 2024**
  27. <https://elifesciences.org/reviewed-preprints/100827>
  29. # Instructions on use
  31. 1. Open this R markdown script into RStudio to run it section by section, tested with RStudio 2024 on a Windows PC. The current version only generates the box plots from the paper and the statistical results.
  33. 2. Make sure that the data table (e.g., 'Tables_R_version_01_24.csv') is in the working directory, visible to R.
  35. 3. Run the script section by section to generate the plots and results. Alternatively, if comfortable in using R, click the Knit button in the toolbar above.
  37. 4. Please use the Qualtrics form to submit TUS parameters from a paper not on the datasets. We'll aim to update them semi-annually:
  39. <https://uiowa.qualtrics.com/jfe/form/SV_4VOvb0fdwvACDkO>
  41. 5. We hope you find the inTUS resource useful. We'll be working on evaluating it with multivariate pattern analyses once the sample sizes grow, and welcome your input.
  43. 6. Apologies we are not able to provide more support than making the resource documents available for R and RStudio users to use. Please contacts R team for support in using R.
  45. 7. Email the corresponding authors or contact us via the Qualtrics form with other queries: Hugo Caffaratti, Ben Slater & Chris Petkov.
  47. ```{r load libraries, include = FALSE}
  49. essential_packages <- c("dplyr", "rstatix", "ggplot2", "GGally", "car")
  51. # Function to install missing packages
  52. install_if_missing <- function(pkg) {
  53. if (!requireNamespace(pkg, quietly = TRUE)) {
  54. install.packages(pkg, dependencies = TRUE)
  55. }
  56. }
  58. # Install missing packages
  59. lapply(essential_packages, install_if_missing)
  61. # Load libraries
  62. invisible(lapply(essential_packages, library, character.only = TRUE))
  63. ```
  65. ```{r dataframe, echo=FALSE, message=FALSE, warning=FALSE}
  67. # Please replace with the path to where the latest .csv file was downloaded
  68. # setwd("/[YOUR WORKING DIRECTORY WITH DATABASE]")
  70. # Please enter the name of the relevant file
  71. df <- read.csv("inTUS_DATABASE_1-2025.csv")
  73. df_values <- df %>%
  74. select(Effect, Mode, Isppa_brain, DC, SD, PRF, PD)
  76. df_values <- reorder_levels(df_values,
  77. Effect, c("Enhancement", "Disruption"))
  79. df_values <- reorder_levels(df_values,
  80. Mode, c("Online", "Offline"))
  82. ```
  84. \newpage
  86. # Boxplots
  88. Plots to show the distribution of the data for each of the continuous variables.
  90. ## I~sppa~ (brain - W/cm^2^)
  92. ```{r Isppa boxplots, echo=FALSE, message=FALSE, warning=FALSE}
  94. ggplot(df_values, aes(x = Effect, y = Isppa_brain)) +
  95. geom_boxplot() +
  96. geom_jitter(width = .2) +
  97. facet_wrap(Mode ~., scales = "fixed", ncol = 2) +
  98. labs(title = "",
  99. x = "",
  100. y = expression(I[sspa]~Brain~(W/cm^2))) +
  101. theme(axis.line = element_line(linewidth = 1, colour = "black"),
  102. axis.ticks = element_line(linewidth = 1),
  103. axis.ticks.length = unit(2, "mm"),
  104. panel.background = element_blank(),
  105. legend.text = element_text(size = 16, family = "Helvetica", color = "black"),
  106. text = element_text(size = 14, family = "Helvetica", color = "black"),
  107. axis.text.y = element_text(size = 16, family = "Helvetica", color = "black"),
  108. axis.text.x = element_text(size = 12, family = "Helvetica", color = "black", vjust = - 1.5),
  109. axis.title.y = element_text(vjust = + 1.5),
  110. plot.margin=grid::unit(c(2,2,2,2), "mm"))
  111. ```
  113. ## Duty Cycle (%)
  115. ```{r DC boxplot, echo=FALSE, message=FALSE, warning=FALSE}
  117. ggplot(df_values, aes(x = Effect, y = DC)) +
  118. geom_boxplot() +
  119. geom_jitter(width = .2) +
  120. facet_wrap(Mode ~ ., scales = "fixed", ncol = 2) +
  121. labs(title = "",
  122. x = "",
  123. y = "DC (%)") +
  124. theme(axis.line = element_line(linewidth = 1, colour = "black"),
  125. axis.ticks = element_line(linewidth = 1),
  126. axis.ticks.length = unit(2, "mm"),
  127. panel.background = element_blank(),
  128. legend.text = element_text(size = 16, family = "Helvetica", color = "black"),
  129. text = element_text(size = 14, family = "Helvetica", color = "black"),
  130. axis.text.y = element_text(size = 16, family = "Helvetica", color = "black"),
  131. axis.text.x = element_text(size = 12, family = "Helvetica", color = "black", vjust = - 1.5),
  132. axis.title.y = element_text(vjust = + 1.5),
  133. plot.margin=grid::unit(c(2,2,2,2), "mm"))
  135. ```
  137. ## Pulse Repetition Frequency (Hz)
  139. ```{r PRF boxplot, echo=FALSE, message=FALSE, warning=FALSE}
  141. ggplot(df_values, aes(x = Effect, y = PRF)) +
  142. geom_boxplot() +
  143. geom_jitter(width = .2) +
  144. facet_wrap(Mode ~ ., scales = "fixed", ncol = 2) +
  145. labs(title = "",
  146. x = "",
  147. y = "PRF (Hz)") +
  148. theme(axis.line = element_line(linewidth = 1, colour = "black"),
  149. axis.ticks = element_line(linewidth = 1),
  150. axis.ticks.length = unit(2, "mm"),
  151. panel.background = element_blank(),
  152. legend.text = element_text(size = 16, family = "Helvetica", color = "black"),
  153. text = element_text(size = 14, family = "Helvetica", color = "black"),
  154. axis.text.y = element_text(size = 16, family = "Helvetica", color = "black"),
  155. axis.text.x = element_text(size = 12, family = "Helvetica", color = "black", vjust = - 1.5),
  156. axis.title.y = element_text(vjust = + 1.5),
  157. plot.margin=grid::unit(c(2,2,2,2), "mm"))
  158. ```
  160. ## Sonication Duration (SD)
  162. ```{r SD boxplot, echo=FALSE, message=FALSE, warning=FALSE}
  164. ggplot(df_values, aes(x = Effect, y = SD)) +
  165. geom_boxplot() +
  166. geom_jitter(width = .2) +
  167. facet_wrap(Mode ~ ., scales = "fixed", ncol = 2) +
  168. labs(title = "",
  169. x = "",
  170. y = "SD (sec)") +
  171. theme(axis.line = element_line(linewidth = 1, colour = "black"),
  172. axis.ticks = element_line(linewidth = 1),
  173. axis.ticks.length = unit(2, "mm"),
  174. panel.background = element_blank(),
  175. legend.text = element_text(size = 16, family = "Helvetica", color = "black"),
  176. text = element_text(size = 14, family = "Helvetica", color = "black"),
  177. axis.text.y = element_text(size = 16, family = "Helvetica", color = "black"),
  178. axis.text.x = element_text(size = 12, family = "Helvetica", color = "black", vjust = - 1.5),
  179. axis.title.y = element_text(vjust = + 1.5),
  180. plot.margin=grid::unit(c(2,2,2,2), "mm"))
  181. ```
  183. # Summary statistics
  185. Overview of each key variable split by whether the resulting effect was 'disruptive' or 'enhanced'.
  187. ```{r summary stats, echo=FALSE, message=FALSE, warning=FALSE}
  189. df_summary <- df_values %>%
  190. group_by(Effect) %>%
  191. get_summary_stats(Isppa_brain, DC, PRF, SD, type = "mean_sd")
  193. knitr::kable(df_summary)
  194. ```
  196. \newpage
  198. # Correlation Matrix
  200. A simplified correlation matrix, this time the coloured scale refers to strength of correlation:
  202. - Blue refers to a **negative** correlation
  204. - Red refers to a **positive** correlation
  206. ```{r correlation matrix, echo=FALSE, message=FALSE, warning=FALSE}
  208. ggcorr(df_values, method = c("everything", "pearson"), label = TRUE)
  209. ```
  211. \newpage
  213. # Logistic Regression
  215. A binomial logistic regression to understand the relationship between the key variables and the main categorical variables:
  217. - Effect: Disruption vs. Enhancement
  219. - Mode: Online vs. Offline stimulation
  221. ## I~sppa~ (brain - W/cm^2^)
  223. ```{r IsppaRegression, echo=FALSE, message=FALSE, warning=FALSE}
  225. IsspaModel <- glm(data = df_values,
  226. Effect ~ 1 + Mode + Isppa_brain,
  227. family = "binomial")
  229. summary(IsspaModel)
  230. ```
  232. \newpage
  234. ## Sonication Duration (sec)
  236. ```{r SDRegression, echo=FALSE, message=FALSE, warning=FALSE}
  238. SDmodel <- glm(data = df_values,
  239. Effect ~ 1 + Mode + SD, family = "binomial")
  241. summary(SDmodel)
  242. ```
  244. \newpage
  246. ## Duty Cycle (%)
  248. ```{r DCRegression, echo=FALSE, message=FALSE, warning=FALSE}
  250. DCmodel <- glm(data = df_values,
  251. Effect ~ 1 + Mode + DC, family = "binomial")
  253. summary(DCmodel)
  254. ```
  256. \newpage
  258. ## Pulse Repetition Frequency (Hz)
  260. ```{r PRFRegression, echo=FALSE, message=FALSE, warning=FALSE}
  262. PRFmodel <- glm(data = df_values,
  263. Effect ~ 1 + Mode + PRF, family = "binomial")
  265. summary(PRFmodel)
  266. ```
  268. \newpage

inTUS_Rscript_1-2025.Rmd, no license · at the source

Overview

Authors: Hugo Caffaratti1, Ben Slater2, Nour Shaheen1, Ariane E Rhone1, Ryan Calmus1,2, Michael Kritikos1, Sukhbinder Kumar1, Brian Dlouhy1, Hiroyuki Oya1, Timothy D Griffiths1,2, Aaron Boes3, Nicholas Trapp3, Marcus Kaiser4,5, Jerome Sallet6,7, Matthew I Banks8, Matthew A Howard III1, Mario Zanaty1, Chris Petkov1,2,9
ORCID iDs: Hugo Caffaratti, Ariane E Rhone, Hiroyuki Oya, Timothy D Griffiths, Chris Petkov
  1. Department of Neurosurgery, University of Iowa Iowa City, Iowa United States
  2. Biosciences Institute, Newcastle University Medical School Newcastle upon Tyne United Kingdom
  3. Department of Psychiatry, University of Iowa Iowa City United States
  4. NIHR Biomedical Research Centre, School of Medicine, University of Nottingham Nottingham United Kingdom
  5. Rui Jin Hospital, Shanghai Jiao Tong University Shanghai China
  6. Stem Cell and Brain Research Institute, INSERM U1208, University of Lyon Lyon France
  7. Department of Experimental Psychology, University of Oxford Oxford United Kingdom
  8. Department of Anesthesiology, University of Wisconsin at Madison Madison United States
  9. Iowa Neuroscience Institute, University of Iowa Iowa City United States
Institutions: University of Iowa (United States); Newcastle University (United Kingdom); University of Nottingham (United Kingdom); Shanghai Jiao Tong University (China); Institut Cellule Souche et Cerveau (France); University of Oxford (United Kingdom); University of Wisconsin–Madison (United States)
Journal: eLife, volume 13, article RP100827
Dates: published online 28 October 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.7554/elife.100827 · PMID 42950002 · PMCID PMC13716386 · OpenAlex W4405105674
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: other (modality), human (organism)
Methods: Statistics, Machine learning, Evoked potentials, Physiology & signal measures, fMRI & imaging
Keywords: brain stimulation, Human, neuromodulation, non-invasive, transcranial ultrasound, low intensity focused ultrasound
MeSH: Brain*, Humans (* major topic)
Journal subjects: Neuroscience
Topic: Ultrasound and Hyperthermia Applications (Biomedical Engineering, Engineering), according to OpenAlex
Funding: National Institutes of Health (U01NS137991, R01DC004290); National Science Foundation (SBE-2342847); Biotechnology and Biological Sciences Research Council (PhD studentship); Engineering and Physical Sciences Research Council (EP/W004488/1, EP/X01925X/1); Medical Research Council (UKRI527); Ruijin Hospital (Guangci Professorship Program)
Citations: not cited yet (Europe PMC); 177 references in the paper

Abstract

Low-intensity transcranial ultrasound stimulation (TUS) is a promising non-invasive technique for brain stimulation and focal neuromodulation. Research with humans and animal models has raised the possibility that TUS can be biased towards enhancing or suppressing neural function. Here, we first collate a set of hypotheses on the directionality of TUS effects and conduct an initial meta-analysis on available healthy human participant TUS studies reporting stimulation parameters and outcomes (n=47 studies, 52 experiments). In initial exploratory analyses with univariate tests, we find that parameters, such as the intensity and continuity of stimulation (duty cycle, DC) show statistical trends towards likely TUS neural enhancement or suppression of function. Machine-learning analyses were limited by the currently small sample size. Given that human TUS sample sizes are expected to increase, predictability on the directionality of TUS effects could improve if a database is available. Therefore, we establish an inTUS database and resource for the systematic reporting of TUS parameters and outcomes to assist in greater precision in TUS use for brain stimulation and neuromodulation. The paper concludes with a selective review of human clinical TUS studies illustrating how hypotheses on the directionality of TUS effects could be developed for empirical testing in the intended clinical application, not limited to the examples provided.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

OSF arqp8

License: none: the authors keep all their rights
State: the link answers, verified on 1 October 2026
Evidence: files inventoried
Languages: MATLAB (2), R (1)
Size: 27 files, 3 scripts
Software Heritage: not checked
Found in: “Data availability”
Holds: 2 notebooks
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: car (1 file), ggplot2 (1 file), rstatix (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 1 October 2026: the link answers (HTTP 200)
  • 1 October 2026: the link answers (HTTP 200)
3 files
At the source: osf.io/arqp8/

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 3 scripts, each with its path and the digest of its content;
  • 2 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability

The datasets and R script generated in this study have been deposited in the Open Science Framework https://osf.io/arqp8/ in the Caffaratti_et_al_inTUS_Resource' folder.

The following dataset was generated:

PetkovC KikuchiY CalmusRM WikmanP BalezeauF OyaH RocchiF CaffarattiH 2026Laboratory of Comparative Neuropsychology Share - Neurophysiology, Neuroimaging, System PerturbationOpen Science Framework10.17605/OSF.IO/ARQP8

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 30 October 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 18 authors, 6 keywords, 2 MeSH terms, 6 funders, 175 references.

Cite

This paper

Caffaratti, H., Slater, B., Shaheen, N., Rhone, A. E., Calmus, R., Kritikos, M., Kumar, S., Dlouhy, B., Oya, H., Griffiths, T. D., Boes, A., Trapp, N., Kaiser, M., Sallet, J., Banks, M. I., Howard, M. A., Zanaty, M., & Petkov, C. (2026). Ultrasound neuromodulation hypotheses on the directionality of effects and community resource. eLife, 13, RP100827. https://doi.org/10.7554/elife.100827

BibTeX

@article{caffaratti2026ultrasound,
author = {Caffaratti, Hugo and Slater, Ben and Shaheen, Nour and Rhone, Ariane E and Calmus, Ryan and Kritikos, Michael and Kumar, Sukhbinder and Dlouhy, Brian and Oya, Hiroyuki and Griffiths, Timothy D and Boes, Aaron and Trapp, Nicholas and Kaiser, Marcus and Sallet, Jerome and Banks, Matthew I and Howard, Matthew A and Zanaty, Mario and Petkov, Chris},
title = {{Ultrasound neuromodulation hypotheses on the directionality of effects and community resource}},
journal = {eLife},
year = {2026},
month = oct,
volume = {13},
pages = {RP100827},
publisher = {eLife Sciences Publications, Ltd},
issn = {2050-084X},
doi = {10.7554/elife.100827},
url = {https://doi.org/10.7554/elife.100827},
pmid = {42950002},
pmcid = {PMC13716386}
}

RIS

TY - JOUR
AU - Caffaratti, Hugo
AU - Slater, Ben
AU - Shaheen, Nour
AU - Rhone, Ariane E
AU - Calmus, Ryan
AU - Kritikos, Michael
AU - Kumar, Sukhbinder
AU - Dlouhy, Brian
AU - Oya, Hiroyuki
AU - Griffiths, Timothy D
AU - Boes, Aaron
AU - Trapp, Nicholas
AU - Kaiser, Marcus
AU - Sallet, Jerome
AU - Banks, Matthew I
AU - Howard, Matthew A
AU - Zanaty, Mario
AU - Petkov, Chris
TI - Ultrasound neuromodulation hypotheses on the directionality of effects and community resource
T2 - eLife
J2 - eLife
PY - 2026
DA - 2026/10/28
VL - 13
SP - RP100827
SN - 2050-084X
PB - eLife Sciences Publications, Ltd
DO - 10.7554/elife.100827
UR - https://doi.org/10.7554/elife.100827
LA - en
ER -

CSL-JSON

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