DeepRetinotopy

This repository contains all source code necessary to replicate our recent work entitled "Predicting the retinotopic organization of human visual cortex from anatomy using geometric deep learning" available in NeuroImage.

Table of Contents

• Installation and requirements
• Manuscript
• Models
• Retinotopy
• Citation
• Contact

Installation and requirements

Models were generated using Pytorch Geometric. Since this package is under constant updates, we highly recommend that you follow the following steps to run our models locally:

• Create a conda environment (or docker container)
• Install torch first:

$ pip install torch==0.4.1    
$ pip install torchvision==0.2.1

• Install torch-scatter, torch-sparse, torch-cluster, torch-spline-conv and torch-geometric:

$ pip install torch-scatter==1.0.4
$ pip install torch-sparse==0.2.2
$ pip install torch-cluster==1.1.5
$ pip install torch-spline-conv==1.0.4
$ pip install torch-geometric==0.3.1

• Install the remaining required packages that are available at requirements.txt:

$ pip install -r requirements.txt

• Clone DeepRetinotopy:

$ git clone git@github.com:Puckett-Lab/deepRetinotopy.git

Finally, install the following git repository for plots:

$ pip install git+https://github.com/felenitaribeiro/nilearn.git

Manuscript

This folder contains all source code necessary to reproduce all figures and summary statistics in our manuscript.

Models

This folder contains all source code necessary to train a new model and to generate predictions on the test dataset using our pre-trained models.

Retinotopy

This folder contains all source code necessary to replicate datasets generation, in addition to functions and labels used for figures and models' evaluation.

Citation

Please cite our paper if you used our model or if it was somewhat helpful for you 😉

@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}
}

Contact

Fernanda Ribeiro <fernanda.ribeiro@uq.edu.au>

Alex Puckett <a.puckett@uq.edu.au>