Voxelwise Encoding Model (VEM) tutorials#
Welcome to the tutorials on the Voxelwise Encoding Model framework from the Gallant Lab.
If you use these tutorials for your work, consider citing the corresponding paper:
T. Dupré la Tour, M. Visconti di Oleggio Castello, and J. L. Gallant. The Voxelwise Encoding Model framework: a tutorial introduction to fitting encoding models to fMRI data. PsyArXiv, 2024. doi:10.31234/osf.io/t975e.
You can find a copy of the paper here.
How to use the tutorials#
To explore the VEM tutorials, one can:
Read the tutorials on this website (recommended)
Run the notebooks in Google Colab (clicking on the following links opens Colab): all notebooks or only the notebooks about model fitting
Use the provided Dockerfiles to run the notebooks locally (recommended for Windows users, as some of the packages used do not support Windows)
The code of this project is available on GitHub at gallantlab/voxelwise_tutorials .
The GitHub repository also 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
Cite as#
Please cite the corresponding publications if you use the code or data in your work:
voxelwise_tutorials[Dupré la Tour et al., 2024]himalaya[Dupré la Tour et al., 2022]pycortex[Gao et al., 2015]pymoten[Nunez-Elizalde et al., 2021]shortclipsdataset [Huth et al., 2022]vim-2dataset [Nishimoto et al., 2014]
References#
T. Dupré la Tour, M. Eickenberg, A.O. Nunez-Elizalde, and J. L. Gallant. Feature-space selection with banded ridge regression. NeuroImage, 267:119728, 2022. doi:10.1016/j.neuroimage.2022.119728.
T. Dupré la Tour, M. Visconti di Oleggio Castello, and J. L. Gallant. The voxelwise encoding model framework: a tutorial introduction to fitting encoding models to fMRI data. PsyArXiv, 2024. doi:10.31234/osf.io/t975e.
J. S. Gao, A. G. Huth, M. D. Lescroart, and J. L. Gallant. Pycortex: an interactive surface visualizer for fMRI. Frontiers in Neuroinformatics, 2015. doi:10.3389/fninf.2015.00023.
A. G. Huth, S. Nishimoto, A. T. Vu, T. Dupré la Tour, and J. L. Gallant. Gallant lab natural short clips 3t fMRI data. 2022. doi:10.12751/g-node.vy1zjd.
S. Nishimoto, A. T. Vu, T. Naselaris, Y. Benjamini, B. Yu, and J. L. Gallant. Gallant lab natural movie 4t fMRI data. 2014. doi:10.6080/K00Z715X.
A.O. Nunez-Elizalde, F. Deniz, T. Dupré la Tour, M. Visconti di Oleggio Castello, and J.L. Gallant. Pymoten: scientific python package for computing motion energy features from video. 2021. doi:10.5281/zenodo.6349625.