  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 neuroimaging software packages. Our experience suggests that this
combination leaves the student with a superficial 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 review and (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 engaging students in
working reproducibly on real scientific questions. We propose that a course on
this model should be the foundation for future programs in neuroimaging. We
believe it will also serve as a model for teaching efficient and reproducible
research in other fields of computational science.
