Information

The estimated time to complete this training module is 4h.

The prerequisites to take this module are:

• the installation module.
• the introduction to python for data analysis module.

Contact Désirée Lussier for questions on this module and once you have completed the exercises to receive credit for the module.

Resources

This module was presented by Pierre Bellec during the QLSC 612 course in 2020. The video of the presentation is available below:

The slides are available here.

You can find the Jupyter notebook for this module here

Exercise

• Watch the video presentation by Pierre Bellec and go over the slides.
• Download the jupyter notebook using the link above or the following command

wget https://raw.githubusercontent.com/brainhackorg/school/master/content/en/modules/fmri_connectivity/BHS_fMRI_connectivity.ipynb

• Run the notebook and complete the 3 exercises at the end.
• Follow up with Désirée Lussier to validate you completed the exercise correctly.
• 🎉 🎉 🎉 you completed this training module! 🎉 🎉 🎉

More resources

Here are Pierre Bellec's slides for a course on brain parcellation. They contain snippets of examples of nilearn code to load datasets, plot brains, compute and plot connectomes.

Chapter on Functional Connectivity from Méthods en neurosciences cognitives here

The video on resting state mentioned by Pierre in his presentation is here

Additional Nilearn tutorials on functional connectivity can be found here

If you want to know more about fMRIprep, Basile Pinsard made a presentation on this topic for BrainHack school 2019: