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 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.

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