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


Introduction to machine learning with Nilearn

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.


Image processing with Spinal Cord Toolbox (SCT)

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.


Quantitative T1 mapping

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.