Voxelwise modeling tutorials

Welcome to the voxelwise modeling tutorial from the GallantLab.

Paper

If you use these tutorials for your work, consider citing the corresponding paper:

Dupré La Tour, T., Visconti di Oleggio Castello, M., & Gallant, J. L. (2024). The Voxelwise Modeling framework: a tutorial introduction to fitting encoding models to fMRI data. https://doi.org/10.31234/osf.io/t975e

You can find a copy of the paper here.

Tutorials

This repository contains tutorials describing how to use the voxelwise modeling framework. Voxelwise modeling is a framework to perform functional magnetic resonance imaging (fMRI) data analysis, fitting encoding models at the voxel level.

To explore these tutorials, one can:

• read the rendered examples in the tutorials website (recommended)
• run the Python scripts (tutorials directory)
• run the Jupyter notebooks (tutorials/notebooks directory)
• run the merged notebook in Colab.

The tutorials are best explored in order, starting with the "Shortclips" tutorial.

Helper Python package

To run the tutorials, this repository contains a small Python package called voxelwise_tutorials, with useful functions to download the data sets, load the files, process the data, and visualize the results.

Installation

To install the voxelwise_tutorials package, run:

pip install voxelwise_tutorials

To also download the tutorial scripts and notebooks, clone the repository via:

git clone https://github.com/gallantlab/voxelwise_tutorials.git
cd voxelwise_tutorials
pip install .

Developers can also install the package in editable mode via:

pip install --editable .

Requirements

The package voxelwise_tutorials has the following dependencies: numpy, scipy, h5py, scikit-learn, matplotlib, networkx, nltk, pycortex, himalaya, pymoten, datalad.

Cite as

If you use one of our packages in your work (voxelwise_tutorials [1], himalaya [2], pycortex [3], or pymoten [4]), please cite the corresponding publications:

[1]	Dupré La Tour, T., Visconti di Oleggio Castello, M., & Gallant, J. L. (2024). The Voxelwise Modeling framework: a tutorial introduction to fitting encoding models to fMRI data. https://doi.org/10.31234/osf.io/t975e

[2]	Dupré La Tour, T., Eickenberg, M., Nunez-Elizalde, A.O., & Gallant, J. L. (2022). Feature-space selection with banded ridge regression. NeuroImage. https://doi.org/10.1016/j.neuroimage.2022.119728

[3]	Gao, J. S., Huth, A. G., Lescroart, M. D., & Gallant, J. L. (2015). Pycortex: an interactive surface visualizer for fMRI. Frontiers in neuroinformatics, 23. https://doi.org/10.3389/fninf.2015.00023

[4]	Nunez-Elizalde, A.O., Deniz, F., Dupré la Tour, T., Visconti di Oleggio Castello, M., and Gallant, J.L. (2021). pymoten: scientific python package for computing motion energy features from video. Zenodo. https://doi.org/10.5281/zenodo.6349625