MRI analysis in Python using Nipype, Nilearn and more
Welcome!
Diversity, Equity and Inclusion
Workshop overview
Setup for the workshop
Outline
Course prerequisites
Introduction to the (unix) command line: bash
Introduction to git and github
Introduction to the jupyter ecosystem & notebooks
Introduction to Python
Introduction to NumPy
Introduction to SciPy
Introduction to Visualization in python
Introduction to Statistics in python
Introduction to scikit-learn & scikit-image
Basics in data handling
Using Python for neuroimaging data - NiBabel
Using Python for neuroimaging data - Nilearn
Nipype
Introduction to Nipype
Nipype Showcase
Nipype Quickstart
Nipype Quickstart - non-imaging
Tutorial Dataset
Basic concepts
Interfaces
Nodes
Workflows
Graph Visualization
Data Input
Data input for BIDS datasets
Data Output
Using Nipype Plugins
Function Interface
Iterables
MapNode
JoinNode, synchronize and itersource
Debugging Nipype Workflows
Execution Configuration Options
Example workflows
Example 1: Preprocessing Workflow
Example 2: 1st-level Analysis
Example 3: Normalize data to MNI template
Example 4: 2nd-level Analysis
Hands-on 1: How to create a fMRI preprocessing workflow
Hands-on 2: How to create a fMRI analysis workflow
Advanced concepts
Create interfaces
Interface caching
Nipype Command Line Interface
Using Nipype with Amazon Web Services (AWS)
Sphinx extensions
Using SPM with MATLAB Common Runtime (MCR)
Using MIPAV, JIST, and CBS Tools
Resources
Download and install
Where to find help
Advanced and specialized analyses
Introduction to
pybids
Nilearn GLM: statistical analyses of MRI in Python
Functional connectivity and resting state
Preparation Machine Learning
MVPA and Searchlight with
nilearn
TensorFlow
/
Keras
Machine learning to predict age from rs-fmri
Structural connectivity and diffusion imaging
Yoga and/or dance break
Code of Conduct
repository
open issue
Index
