神经影像学的计算再现性教学
Teaching computational reproducibility for neuroimaging
摘要：
我们描述了一个基于项目的介绍，可重复的和协作的神经影像分析。传统的神经影像学教学通常包括一系列讲座，强调的是大局，而不是技术所基于的基础。讲座通常与实践研讨会配对，在研讨会中，学生使用特定神经成像软件包的图形界面进行成像分析。我们的经验表明，这种结合使学生对基本思想有肤浅的理解，以及非正式的，低效的和不准确的分析方法。为了解决这些问题，我们基于我们的课程围绕一个实质性的开放式小组项目。这使我们能够教：（a）计算工具，以确保计算上可重复的工作，如Unix命令行，结构化代码，版本控制，自动化测试和代码审查和（b）对用于MRI扫描仪中单次运行的基本分析的统计技术的清晰理解。我们对小组项目的重视表明了标准计算工具对准确性、效率和协作的重要性。这些项目广泛成功地吸引学生在真正的科学问题上重复工作。我们建议这个模型的课程应该是未来神经影像学课程的基础。我们相信，它也将作为一个模型，在计算科学的其他领域的教学效率和可重复的研究。
英文摘要：
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.
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