============================ Voxelwise modeling tutorials

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Welcome to the voxelwise modeling tutorial from the GallantLab <https://gallantlab.org>_.

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 <paper/voxelwise_tutorials_paper.pdf>_.

Tutorials

This repository contains tutorials describing how to use the voxelwise modeling framework. Voxelwise modeling <https://gallantlab.github.io/voxelwise_tutorials/voxelwise_modeling.html>_ 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 <https://gallantlab.github.io/voxelwise_tutorials/>_ (recommended)
• run the Python scripts (tutorials <tutorials>_ directory)
• run the Jupyter notebooks (tutorials/notebooks <tutorials/notebooks>_ directory)
• run the merged notebook in Colab <https://colab.research.google.com/github/gallantlab/voxelwise_tutorials/blob/main/tutorials/notebooks/shortclips/merged_for_colab.ipynb>_.

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:

.. code-block:: bash

pip install voxelwise_tutorials

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

.. code-block:: bash

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

Developers can also install the package in editable mode via:

.. code-block:: bash

pip install --editable .

Requirements

The package voxelwise_tutorials has the following dependencies: numpy <https://github.com/numpy/numpy>, scipy <https://github.com/scipy/scipy>, h5py <https://github.com/h5py/h5py>, scikit-learn <https://github.com/scikit-learn/scikit-learn>, matplotlib <https://github.com/matplotlib/matplotlib>, networkx <https://github.com/networkx/networkx>, nltk <https://github.com/nltk/nltk>, pycortex <https://github.com/gallantlab/pycortex>, himalaya <https://github.com/gallantlab/himalaya>, pymoten <https://github.com/gallantlab/pymoten>, datalad <https://github.com/datalad/datalad>_.

.. |Github| image:: https://img.shields.io/badge/github-voxelwise_tutorials-blue :target: https://github.com/gallantlab/voxelwise_tutorials

.. |Python| image:: https://img.shields.io/badge/python-3.7%2B-blue :target: https://www.python.org/downloads/release/python-370

.. |License| image:: https://img.shields.io/badge/License-BSD%203--Clause-blue.svg :target: https://opensource.org/licenses/BSD-3-Clause

.. |Build| image:: https://github.com/gallantlab/voxelwise_tutorials/actions/workflows/run_tests.yml/badge.svg :target: https://github.com/gallantlab/voxelwise_tutorials/actions/workflows/run_tests.yml

.. |Downloads| image:: https://pepy.tech/badge/voxelwise_tutorials :target: https://pepy.tech/project/voxelwise_tutorials

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