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DeepRetinotopy -- The toolbox

This repository contains (restructured) code for the general use of deepRetinotopy with a command line interface.

Table of Contents

Requirements

Software containers

DeepRetinotopy, pre-trained models, and required software are packaged in software containers available through Dockerhub and Neurodesk.

Docker

If you want to run deepRetinotopy locally, you can install Docker and pull our container from Dockerhub using the following command:

docker pull vnmd/deepretinotopy_1.0.5:latest
docker run -it -v ~:/tmp/ --name deepret -u $(id -u):$(id -g) vnmd/deepretinotopy_1.0.5:latest
# docker exec -it deepret bash

Once in the container (the working directory is deepRetinotopy_TheToolbox), you can run deepRetinotopy.sh:

deepRetinotopy -s $path_freesurfer_dir -t $path_hcp_template_surfaces -d $dataset_name -m $maps

The following arguments are required:

  • -s path to the freesurfer directory
  • -t path to the HCP "fs_LR-deformed_to-fsaverage" surfaces
  • -d dataset name (e.g. "HCP")
  • -m maps to be generated (e.g. "polarAngle,eccentricity,pRFsize")

Singularity

Alternatevely, you can run your analysis on Neurodesk through the following commands:

ml deepretinotopy/1.0.5
deepRetinotopy -s $path_freesurfer_dir -t $path_hcp_template_surfaces -d $dataset_name -m $maps

You can also download the Singularity container using the following command (for Asian/Australian locations) to run it locally or on your HPC:

date_tag=20240609
export container=deepretinotopy_1.0.5_$date_tag
curl -X GET https://objectstorage.ap-sydney-1.oraclecloud.com/n/sd63xuke79z3/b/neurodesk/o/${container}.simg -O

Then, you can execute the container (so long Singularity is already available on your computing environment) using the following command:

singularity exec --nv ./deepretinotopy_1.0.5_$date_tag.simg deepRetinotopy -s $path_freesurfer_dir -t $path_hcp_template_surfaces -d $dataset_name -m $maps

For different locations see the Neurodesk documentation.

Usage

Visual field sign maps

You can also generate visual field sign maps from the predicted maps with the following command:

signMaps -s $path_freesurfer_dir -t $path_hcp_template_surfaces -d $dataset_name

Output

The output of deepRetinotopy is a folder named "deepRetinotopy", in each freesurfer subject folder, containing the following files:

  • sub-id.predicted_eccentricity(polarAngle or pRFsize)_average(model1 to model5).lh(rh).native.func.gii: GIFTI files containing the predicted maps in the native space of the subject.
  • sub-id.fs_predicted_eccentricity(polarAngle or pRFsize)_lh(rh)_curvatureFeat_average(model1 to model5).func.gii: GIFTI files containing the predicted maps in the 32k fsaverage space.

Contributors

If you want to contribute to this repository, please follow the instructions below:

  1. Fork the repository
  2. Create a new branch (e.g. git checkout -b my-new-branch)
  3. Commit your changes (e.g. git commit -am 'Add some feature')
  4. Push the branch (e.g. git push origin my-new-branch)
  5. Create a new Pull Request

Citation

Please cite our work if you used our model:

@article{Ribeiro2021,
	author = {Ribeiro, Fernanda L and Bollmann, Steffen and Puckett, Alexander M},
	doi = {https://doi.org/10.1016/j.neuroimage.2021.118624},
	issn = {1053-8119},
	journal = {NeuroImage},
	keywords = {cortical surface, high-resolution fMRI, machine learning, manifold, visual hierarchy,Vision},
	pages = {118624},
	title = {{Predicting the retinotopic organization of human visual cortex from anatomy using geometric deep learning}},
	url = {https://www.sciencedirect.com/science/article/pii/S1053811921008971},
	year = {2021}
}

Acknowledgements

Docker and Singularity containers were generated and are available through Neurodesk.

Contact

Fernanda Ribeiro <[email protected]>