GLMsingle is a toolbox for obtaining accurate single-trial estimates in fMRI time-series data. We provide both MATLAB and Python implementations.

GLMsingle can be viewed as a wholesale replacement of its predecessor, GLMdenoise (http://github.com/kendrickkay/GLMdenoise).

For additional information, please see the Wiki page of the GLMsingle repository (https://github.com/kendrickkay/GLMsingle/wiki).

If you have questions or discussion points, please use the Discussions feature of this github repository, or alternatively, e-mail Kendrick ([email protected]). If you find a bug, please let us know by raising a Github issue.

To use the GLMsingle toolbox, add it to your MATLAB path: addpath(genpath('GLMsingle/matlab'));

You will also need to download and add fracridge to your path. It is available here: https://github.com/nrdg/fracridge

We provide a number of example scripts that demonstrate usage of GLMsingle. You can browse these example scripts here:

(Python Example 1 - event-related design) https://htmlpreview.github.io/?https://github.com/kendrickkay/GLMsingle/blob/main/examples/example1.html

(Python Example 2 - block design) https://htmlpreview.github.io/?https://github.com/kendrickkay/GLMsingle/blob/main/examples/example2.html

(MATLAB Example 1 - event-related design) https://htmlpreview.github.io/?https://github.com/kendrickkay/GLMsingle/blob/main/matlab/examples/example1preview/example1.html

(MATLAB Example 2 - block design) https://htmlpreview.github.io/?https://github.com/kendrickkay/GLMsingle/blob/main/matlab/examples/example2preview/example2.html

If you would like to run these example scripts, the Python versions are available in /GLMsingle/examples, and the MATLAB versions are available in /GLMsingle/matlab/examples. Each notebook contains a full walkthrough of the process of loading an example dataset and design matrix, estimating neural responses using GLMsingle, estimating the reliability of responses at each voxel, and comparing those achieved via GLMsingle to those achieved using a baseline GLM.

To install:

pip install -r requirements.txt
pip install .

Code dependencies: see requirements.txt

Notes:

• Please note that GLMsingle is not (yet) compatible with Python 3.9 (due to an incompatibility between scikit-learn and Python 3.9). Please use Python 3.8 or earlier.
• Currently, numpy has a 4GB limit for the pickle files it writes; thus, GLMsingle will crash if the file outputs exceed that size. One workaround is to turn off "disk saving" and instead get the outputs of GLMsingle in your workspace and save the outputs yourself to HDF5 format.

For additional information, please visit the Wiki page associated with this repository: https://github.com/kendrickkay/GLMsingle/wiki

Terms of use: This content is licensed under a BSD 3-Clause License.

If you use GLMsingle in your research, please cite the following paper:

• Allen, E.J., St-Yves, G., Wu, Y., Breedlove, J.L., Dowdle, L.T., Caron, B., Pestilli, F., Charest, I., Hutchinson, J.B., Naselaris, T.*, Kay, K.* A massive 7T fMRI dataset to bridge cognitive and computational neuroscience. bioRxiv (2021).

• 2021/10/12 - Version 1.0 of GLMsingle is now released. A git tag has been added to the repo.
• 2021/05/21 - The core code is complete, but is in "beta" and we are generating tutorial examples of usage. The initial 1.0 release should be forthcoming.