Neuroimaging visualizations form the centerpiece of the interpretation and communication of scientific results, and are also a cornerstone for data quality control. Often, these images and figures are produced by manually changing settings on Graphical User Interfaces (GUIs). There now exist many well-documented code-based brain visualization tools that allow users to programmatically generate publication-ready figures directly within R, Python and MATLAB environments. Here, we provide a rationale for the wide-spread adoption of code-generated brain visualizations by highlighting corresponding advantages in replicability, flexibility, and integration over GUI based tools. We then provide a practical guide outlining the steps required to generate these code-based brain visualizations. We also present a comprehensive table of tools currently available for programmatic brain visualizations (https://sidchop.shinyapps.io/braincode_selector/) and provide didactic examples of visualizations and associated code as a point of reference. Finally, we provide a web-app (https://sidchop.shinyapps.io/braincode/) which can generate simple code-templates as starting points for these visualizations.
Full paper: A Practical Guide for Generating Reproducible and Programmatic Neuroimaging Visualizations. Chopra, Sidhant., Labache, L., Dhamala, E., Orchard, E., Holmes. A.
To assists researchers in finding a code-based brain visualization tool, we have provided a interactive table: https://sidchop.shinyapps.io/braincode_selector/. If you think that we have missed a code-based package/library or tool box which can generate brain visualization directly witin in R, python or MATLAB environments, please creating an issue on this GitHub page or leave a comment on this google sheet.
Brain-Code: A web-app for generating code templates for brain visualizations
To assist new users transition into generating code-based brain visualizations we have developed a web-app (https://sidchop.shinyapps.io/braincode/) which allows for interactive generation of code-templates for beginner friendly libraries/packages in R and Python. In the web-app, users can select R or Python as their coding environment, and choose between voxel, ROI, vertex, and edge-level visualizations. They can then manually adjust a limited set of visualization setting, such as color-scales and view, and are provided with a reactive code-template which can be copied and then used within their respective programming environment. The provided code templates require users to customize the code, such as alter file-paths. The available settings have been purposefully limited to allow users to explore and fine-tune additional visualization options within their own programming environment. The code-template also contains links and prompts to more detailed documentation, alternate packages/libraries and tutorials which allow for more complex and publication-ready brain visualizations. Note: This web-app is still under development, so some features are a little slow and we are working on the UI. Please feel free to leave feedback by creating an issue on this GitHub page.
Please contact me (Sidhant Chopra) as sidhant.chopra@yale.edu and/or sidhant.chopra4@gmail.com