Teaching computational reproducibility for neuroimaging

TL;DR

This paper presents a project-based neuroimaging course emphasizing reproducibility, computational tools, and statistical understanding, aiming to improve student engagement and accuracy in analysis over traditional lecture-based methods.

Contribution

It introduces a novel course model centered on open-ended projects that teach reproducibility tools and statistical concepts, enhancing learning outcomes in neuroimaging education.

Findings

Students engaged effectively with real scientific questions.

The project-based approach improved understanding of reproducibility.

The course model is recommended for future neuroimaging programs.

Abstract

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.