Voxelwise modeling tutorials

Welcome to the voxelwise modeling tutorials from the GallantLab.

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.

Getting started

This website contains tutorials describing how to use the voxelwise modeling framework.

To explore these tutorials, one can:

• read the rendered examples in the tutorials gallery of examples (recommended)

• run the Python scripts (tutorials directory)

• run the Jupyter notebooks (tutorials/notebooks directory)

• run the notebooks in Google Colab: all notebooks or only the notebooks about model fitting

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

The project is available on GitHub at gallantlab/voxelwise_tutorials. On top of the tutorials scripts, the GitHub repository contains a Python package called voxelwise_tutorials, which contains useful functions to download the data sets, load the files, process the data, and visualize the results. Install instructions are available here.

Navigation

• Shortclips tutorial
• Vim-2 tutorial

• Voxelwise modeling framework
• Helper Python package
• References

Cite as

If you use one of our packages in your work (voxelwise_tutorials [p1], himalaya [p2], pycortex [p3], or pymoten [p4]), please cite the corresponding publications.

If you use one of our public datasets in your work (vim-2 [3b], shortclips [4b]), please cite the corresponding publications.