This is one sub-module within the [Neuroimaging curriculum][neuro_curriculum]. Visit the link to view all the modules associated with the Neuroimaging Carpentries program.
sMRI Analysis in Python is a programme developed to facilitate reproducibility in structural neuroimaging analyses. Python is emerging as a standard language of data analysis, visualization, and workflow building. More recently, it has rapidly been adopted by the neuroimaging community as a means of developing powerful open-source tools in favour of historically used opaque software such as AFNI, FSL and SPM. In addition, the barrier to entry to Python is low - meaning that you as the user can easily develop your own packages and contribute to the open-source codebase of neuroimaging!
The sMRI Analysis in Python is a workshop series started up via a collaboration between researchers and staff at the Centre for Addiction and Mental Health (Toronto, ON), the University of Western Ontario (London, Ontario), and McGill University (Montreal, Quebec).
This lesson covers a typrical sMRI imaging pipeline by introducing 1) image modalities, 2) image preprocessing, 3) phoenotype quantification, and 4) statistical analyses.
The primary goals of this workshop are:
- Understand the basics of strcutural MR image acquisition
- Familiarize with structural MR image (pre)processing pipeline
- Perform and visualize group-level neuroanatomical analyses
| Time | Episode | Question(s) Answered |
|---|---|---|
| Setup | Download files required for the lesson | |
| 00:00 | 1. sMRI Acquisition and Modalities | How is MR image acquired? What anatomical features do different modalities capture? |
| 00:30 | 2. sMRI Clean-up | How do we remove intensity artifacts and extract brains? |
| 01:15 | 3. sMRI Spatial Normalization | What are "coordinate spaces", "templates", "atlases"? What is image registration? |
| 02:00 | 4. sMRI Segmentation and Parcellation | How do we delineate brain anatomy and quantify phenotypes? |
| 02:45 | 5. sMRI Quality-control | How do we identify image preprocessing failures? |
| 03:15 | 6. Statistical Analysis | How to compare regional anatomical differences in case-control groups? |
| 04:00 | 7. Reproducibility Considerations | How sensitive are the findings to your MR pipeline parameters? |
| 04:30 | Finish |
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 
A list of contributors to the lesson can be found in AUTHORS
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