A Practical Guide for Generating Reproducible and Programmatic Neuroimaging Visualizations

Abstract

Neuroimaging visualizations form the centerpiece of the interpretation and communication of scientific results, and are 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 use code to programmatically generate publication-ready figures directly within programming environments such as R, Python and MATLAB. 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 and provide examples of visualizations and associated code as a point of reference (https://sidchop.shinyapps.io/braincode_selector/ (https://sidchop.shinyapps.io/braincode_selector/)). Finally, we provide a web-app that generates simple code-templates as starting points for these visualizations (https://sidchop.shinyapps.io/braincode/ (https://sidchop.shinyapps.io/braincode/)).