Teaching computational reproducibility for neuroimaging

We describe a project-based introduction to reproducible and collaborative neuroimaging analysis. Traditional teaching on neuroimaging usually consists of a series of lectures that emphasize the big picture rather than the foundations on which the techniques are based. The lectures are often paired with practical workshops in which students run imaging analyses using the graphical interface of specific neu-roimaging software packages. Our experience suggests that this combination leaves the student with asuperficial understanding of the underlying ideas, and an informal, inefficient, and inaccurate approach to analysis. To address these problems, we based our course around a substantial open-ended group project. This allowed us to teach: (a) computational tools to ensure computationally reproducible work,such as the Unix command line, structured code, version control, automated testing, and code reviewand (b) a clear understanding of the statistical techniques used for a basic analysis of a single run in an MRI scanner. The emphasis we put on the group project showed the importance of standard computational tools for accuracy, efficiency, and collaboration. The projects were broadly successful in engagingstudents in working reproducibly on real scientific questions. We propose that a course on this modelshould be the foundation for future programs in neuroimaging. We believe it will also serve as a modelfor teaching efficient and reproducible research in other fields of computational science