Python is on its way to become the most used programming language in neuroscience. It is easy to understand, can be learned rather quickly and has a very strong and helpful community behind it. There exist many amazing neuroimaging software packages, such as Nipype, Nilearn, fMRIPrep and more which facilitate the everyday life of a neuroscientist.
The goal of this 2-day workshop is to introduce participants to many different neuroimaging toolboxes, the Python framework around it and everything you need to know about Nipype to start creating your own pipelines and optimizing your workflows.
# Save the workshop and nipype_tutorial content into your output folder
!cp -R /home/neuro/workshop/* /output/
We will try to get back to any questions as quickly as possible. Either during the event or afterwards. And feel free to upvote any questions you find urgend or of higher priority.
09:30-10:00
Workshop overview10:00-10:45
Quick recap on Jupyter, Python and moreThis section is meant as a quick introduction and recap to Jupyter Notebooks, Python and its core packages.
Note: Notebooks will not be covered during the workshop but are included for individual learning.
10:45-12:00
Explore MRI data with Nibabel and NilearnHaving direct access to your neuroimaging data can be quite liberating. Nibabel
and Nilearn
allow exactly that. With those two neuroimaging packages, you can consider any brain image, a simple 3D/4D matrix of voxel values, which you can transform and move around like you would do with any other toolbox.
This section involves exercises and individual exploration of notebooks.
12:00-13:00
Lunch13:00-13:30
How to set up your system, using Conda and DockerThere are many ways to create the right computational environment for your research. But if you want to use the newest technologies you will not get around using Docker or Conda.
13:30-15:00
Functional connectivity and machine learningIn this section we will focus on resting-state fMRI analysis, in particular functionali connectivity analysis and how these kind of information can be used to predict the age of a participants. The goal of this section is to show you some capabilities of Nilearn and Scikit-Learn.
This section involves exercises and individual exploration of notebooks.
15:00-15:30
Innovations in neuroimaging tools (Part I)There are many new and innovative neuroimaging resources & softwares, such as BIDS, fMRIPrep, MRIQC, OpenNeuro, etc. And many of them wouldn't be possible without Nipype and the open-source neuroimaging community. In this section, we want to introduce you to some toolboxes. If you don't already use them yet yourself, you certainly want to be aware about them!
15:30-...
Open ended for questions09:30-10:00
Introduction to NipypeIn this short introduction, we will show you what Nipype is and why you should use it. It contains a powerful short example that shows the strength behind Nipype.
10:00-11:00
Exploration of Nipype's building blocksNipype can be learned very quickly, but it's nonetheless important that you know about some of the main building blocks.
This section involves exercises and individual exploration of notebooks.
11:00-12:00
Creating a Nipype Pipeline from A-ZThis section involves exercises and individual exploration of notebooks.
12:00-13:00
Lunch13:00-14:00
PyBIDS and statistical analysis of fMRI dataThis section involves exercises and individual exploration of notebooks.
14:00-15:00
Multivariate pattern analysis using Searchlight and Deep LearningThis section involves exercises and individual exploration of notebooks.
15:00-15:30
Innovations in neuroimaging tools (Part 2)This continues the presentations from the first day by introducing some more great open source neuroimaging tools!
15:30-...
Open ended for questions