In this study we examined whether electrical brain stimulation of the frontal eye field can influence reaction time and accuracy of human eye movements.

• Project title: No evidence that frontal eye field tDCS affects latency or accuracy of prosaccades.
• Project code: sacc-tDCS
• Authors: Reteig, L.C., Knapen T., Roelofs, F.J.F.W., Ridderinkhof, K.R., & Slagter, H.A.
• Affiliation: Department of Psychology, University of Amsterdam
• Year: 2018

Resources

Aside from this page, the following resources are associated with this project:

• Published paper: Reteig LC, Knapen T, Roelofs FJFW, Ridderinkhof KR, Slagter, HA (2018). No evidence that frontal eye field tDCS affects latency or accuracy of prosaccades. Front. Neurosci. 12:617. doi: 10.3389/fnins.2018.00617

• Preprint: bioRxiv

• Data: figshare - (also available as the "Data" component in this project page)

• Code: GitHub - (also available as the "Code" component in this project page)

The sacc-tDCS.nb.html file should be the most interesting, as it contains all the results, figures and statistics reported in the paper, along with the code that produced them. It should be viewable in any browser.

In addition, the Notebooks component contains multiple more such files that contain more in-depth explorations of each different aspect of the project.

All these files were produced using the R notebook functionality in R Studio. See the Github reposity/Code component for the .Rmd source files that generated them.

Reproducing the analyses

Directory structure

After downloading everything, your directory structure should look like the following (N.B. this is just a sketch; does not include every single file. Behind each file/folder, it is indicated where it can be found online).

sacc-tDCS
│   analysis.m [GitHub]
│   sacc-tDCS.md [GitHub]
|   sacc-tDCS.nb.html [OSF storage]
│   sacc-tDCS.Rmd [GitHub]
│   sacc-tDCS.Rproj [GitHub]
│
└───data [figshare, OSF: "Data" component]
│   │   FEF_coords.csv
│   │   FEF.node
│   │   PANAS.csv
│   │   reflection.csv
│   │   sacc-tDCS_data.csv
│   │   sacc-tDCS_quantiles.csv
│   │   session_info.csv   
│   │   subject_info.csv
│   │   tdcs_sensations.csv
│   │
│   └───S01
│   │   │   sacc_tDCS*.asc [24 of these]
│   │   └───raw
│   │       │   sacc_tDCS*.edf [24 of these]
│   │       │   *main.mat [2 of these]
│   │       │   *practice.mat [2 of these]
│   └───S02
│       │  ...
│   
└───doc [OSF: "Documents" component]
│   │   participant_log.csv
│   │
│   └───questionnaires
│   └───tDCS_blinding
│   
└───neuronav [OSF: "Neuronavigation" component]
│   
└───docs [OSF: "Notebooks" component]
│   │   median_latency.Rmd [GitHub, OSF: "Code" component]
│   │   median_latency.nb.html [OSF: "Notebooks" component]
│   │   median_latency.md [GitHub, OSF: "Code" component]
│   │   ...
│   └───median_latency_files [GitHub, OSF: "Code" component]
│   │   ...
│   │   index.html [GitHub]
│   │   sacc-tDCS_paper.html [GitHub]
│   │   sacc-tDCS.nb.html [GitHub]
│   └───figures [GitHub]
│   └───site_libs [GitHub]
│
└───site
│   │   _site.yml [GitHub]
│   │   badges.png [Github]
│   │   index.Rmd [GitHub]
│   │   sacc-tDCS_paper.Rmd [GitHub]
│   │   sacc-tDCS.bib [GitHub]
│   │   ...
│   └───figures
│   
└───src [GitHub, OSF: "Code" component]
│   │
│   └───bin
│   └───func
│   └───lib
│   
└───task [GitHub, OSF: "Code" component]

Running the analyses

Starting from the processed data

Remotely

1. Start a remote RStudio session in your browser using this Binder link:
2. Open the sacc-tDCS.Rmd notebook and run it to reproduce all the figures, results and statistics in the paper. Run any of the other RMarkdown files in the docs folder to see the results of all the additional data exploration / analyses we did.

Locally

1. Make sure you’ve downloaded all the .csv files from figshare/the Data component, and that they’re placed in the data folder (as outlined in the diagram above).
2. Make sure you've downloaded all the code in the src folder from the GitHub reposity/Code component. In addition, you might have to install RStudio and install any packages you're missing (loaded at the beginning of each notebook)

• Open the sacc-tDCS.Rmd RMarkdown-file in RStudio and run it to reproduce all the figures, results and statistics in the paper (if you don't have RStudio, you can still run the R code in the file, but you might have to copy-paste individual code sections, because there is also a lot of text in the file).
• Run any of the other RMarkdown files in the GitHub repository /Code component to see the results of all the additional data exploration / analyses we did. Or, to see the results without having to run the code, simply download and open the .nb.html from the Notebooks component, or view the .md files directly on GitHub.

Starting from the raw data

1. Make sure you've downloaded and unzipped all the individual subject data (i.e. the folders starting with S) , and that they're placed in the data folder (as outlined in the diagram above).
2. Make sure you've downloaded all the code in the src folder from the GitHub reposity/Code component.
3. Make sure the analysis.m file from the GitHub reposity/Code component is placed in the top-level folder (i.e. the one containing data and src). Run it in MATLAB. When the script is finished, you should have generated the sacc-tDCS_data.csv file in the data folder.

Note that this file is already available on figshare/the Data component (and running the analysis.m script should recreate it exactly), so it is not necessary to start from this step.

Exceptions

In general, the code should reproduce the figures, results and statistics exactly as they are in the paper. However, there are at least a few exceptions:

• All the Bayesian analyses involve Markov chain Monte Carlo sampling, so the Bayes factors will change slightly every time. However, this variation should rarely if ever change their interpretation (e.g. from "moderate" to "strong" evidence). The values reported in the paper should be as in sacc-tDCS.nb.html.
• The quantile analysis / shift functions (see Fig. 5 in the paper) involve bootstrapped confidence intervals, so the statistics could change slightly.
• The figures in the paper may differ a little from the version in the notebook, as some were edited afterwards (e.g. to align the axes, get rid of overlapping labels, add annotations, etc.).