NeuroGen: activation optimized image synthesis for discovery neuroscience

NeuroGen is a framework for synthesizing images that control brain activations. Details can be found here: https://www.sciencedirect.com/science/article/pii/S1053811921010831. Supplementary Material can be found here: https://drive.google.com/drive/folders/1333yhTqTro6UgRS4sr6WAiR6a-J50PHK?usp=sharing

Requirements

• Python 3.7
• Pytorch 1.4.0
• Other basic computing modules

Instructions

1. output directory contains the trained encoding model for 8 subjects in the NSD dataset.
2. encoding.py is called when loading the encoding model to NeuroGen.
3. getROImask.py is used to get the ROI mask for the 24 used ROIs.
4. getmaskedROI.py is used to get the voxel response within certain ROI.
5. getmaskedROImean.py is used to get the mean voxel response within certain ROI.
6. neurogen.py is the main script for NeuroGen, and can be called by

python neurogen.py --roi 1 --steps 1000 --gpu 0 --lr 0.01 --subj 1 --reptime 1 --truncation 1

7. visualize.py contains some useful functions to save images and visualize them.
8. pytorch_pretrained_biggan is available here: https://github.com/huggingface/pytorch-pretrained-BigGAN

Note: getROImask.py, getmaskedROI.py and getmaskedROImean.py deal with the NSD data which has not been released yet and are not necessary to run NeuroGen at this time. Paths in all scripts may need to change according to needs.

Citation

@article{gu2022neurogen,
title={NeuroGen: activation optimized image synthesis for discovery neuroscience},
author={Gu, Zijin and Jamison, Keith Wakefield and Khosla, Meenakshi and Allen, Emily J and Wu, Yihan and Naselaris, Thomas and Kay, Kendrick and Sabuncu, Mert R and Kuceyeski, Amy},
journal={NeuroImage},
volume={247},
pages={118812},
year={2022},
publisher={Elsevier}
}