BrainIAK tutorials: User-friendly learning materials for advanced fMRI analysis - Kumar, Manoj; Ellis, Cameron T.; Lu, Qihong; Zhang, Hejia; Capotă, Mihai; Willke, Theodore L.; Ramadge, Peter J.; Turk-Browne, Nicholas B.; Norman, Kenneth A. (PLOS Computational Biology, 2020)
Author summary The analysis of brain activity, as measured using functional magnetic resonance imaging (fMRI), has led to significant discoveries about how the brain processes information and how this is affected by disease. However, exhaustive multivariate analyses in space and time, run across a large number of subjects, can be complex and computationally intensive, creating a high barrier for entry into this field. Furthermore, the materials available to learn these methods do not encompass all the methods used, work is often published with no publicly available code, and the analyses are often difficult to run on large datasets without cluster computing. We have created interactive software tutorials that make it easy to understand and execute advanced analyses on fMRI data using the BrainIAK package—an open-source package built in Python. We have released these tutorials freely to the public and have significantly reduced computational roadblocks for users by making it possible to run the tutorials with a web browser and internet connection. We hope that this facilitated access and the usability of the underlying code—a compendium for how to program and optimize the latest fMRI analyses—will accelerate training, reproducibility, and discovery in cognitive neuroscience.