An introduction to magnetic resonance imaging analysis in Python.

Python is rapidly becoming the standard language for data analysis, visualization and automated workflow building. It is a free and open-source software that is relatively easy to pick up by new programmers. In addition, with Python packages such as Jupyter one can keep an interactive code journal of analysis - this is what we'll be using in the workshop. Using Jupyter notebooks allows you to keep a record of all the steps in your analysis, enabling transparency and ease of code sharing.

Another advantage of Python is that it is maintained by a large user-base. Anyone can easily make their own Python packages for others to use. Therefore, there exists a very large codebase for you to take advantage of for your neuroimaging analysis; from basic statistical analysis, to brain visualization tools, to advanced machine learning and multivariate methods!

This lesson teaches:

• a (re?) introduction to MR nomenclature - with BIDS
• "converting" your data to BIDS
• BIDS apps
• queueing up neuroimaging pipelines
• how neuroimaging data is stored

We welcome all contributions to improve the lesson! Maintainers will do their best to help you if you have any questions, concerns, or experience any difficulties along the way.

We'd like to ask you to familiarize yourself with our Contribution Guide and have a look at the more detailed guidelines on proper formatting, ways to render the lesson locally, and even how to write new episodes.

Please see the current list of issues for ideas for contributing to this repository. For making your contribution, we use the GitHub flow, which is nicely explained in the chapter Contributing to a Project in Pro Git by Scott Chacon. Look for the tag . This indicates that the maintainers will welcome a pull request fixing this issue.

Current maintainers of this lesson are

• Michael Joseph
• Jerrold Jeyachandra

A list of contributors to the lesson can be found in AUTHORS

Instructional material from this lesson is made available under the Creative Commons Attribution (CC BY 4.0) license. Except where otherwise noted, example programs and software included as part of this lesson are made available under the MIT license. For more information, see LICENSE.

To cite this lesson, please consult with CITATION