This is one sub-module within the Neuroimaging cirriculum. 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 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 Quantification | 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 |
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nicely explained in the chapter Contributing to a Project in Pro Git
by Scott Chacon.
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