Neurolibre is a curated repository of interactive neuroscience notebooks, seamlessly integrating data, text, code and figures. Notebooks can be freely modified and re-executed through the web, offering a fully reproducible, “libre” path from data to figures. Neurolibre is powered by the Binder project, with computational resources provided by CONP, CBRAIN and Compute Canada.

Quantitative T1 Mapping

In-depth interactive tutorial on the fundamentals of measurement techniques for the tissue-specific MRI signal recovery parameter, T1.

This tutorial provides an introduction to quantitative T1 mapping, from an MRI physics perspective. Two widely used techniques are covered in-depth, Inversion Recovery and Variable Flip Angle (VFA), along with some discussions of cutting-edge variants of these techniques. This interactive tutorial is coded in both Octave (an open-source Matlab clone) and Python, and uses qMRLab for the T1 signal simulations and data fitting.

Image processing with Spinal Cord Toolbox (SCT)

A step-by-step walkthrough of an example analysis pipeline using SCT, from raw data to final quantitative metrics.

This notebook presents an example analysis pipeline using the Spinal Cord Toolbox (SCT), a suite of tools specialized for analysis of spinal cord MRI images of the spinal. Topics covered include: segmentation, masking, registration, warping, and quantitative metric computation. This tutorial was generated in a Jupyter Notebook and coded in Python.

Introduction to Machine Learning with Nilearn

An interactive introduction to machine learning with neuroimaging data, using the Nilearn software package.

An introductory tutorial for using the popular Nilearn software package to perform machine learning analyses with neuroimaging data. This material is adapted from the Montreal AI and Neuroscience (MAIN) 2018 workshops.

A highly predictive signature (HPS) of Alzheimer's disease dementia from cognitive and structural brain features

Signature of Alzheimer's disease with simulated cognitive and structural features.

A jupyter notebook containing analyses that give a highly predictive signature (HPS) of Alzheimer's disease dementia from cognitive and structural features using simulated data. Original paper