Overview

This repository contains code and data pertaining to High-level cognition is supported by information-rich but compressible brain activity patterns by Lucy L. W. Owen and Jeremy R. Manning.

The repository is organized as follows:

root
├── code: analysis code used in the paper
│   └── notebooks: run these to reproduce all figures and statistical tests
├── data: all data analyzed in the paper
│   ├── images: example images used/analyzed in Figure 1
│   ├── networks: brain parcellation-related files
│   ├── neurosynth: topic decoding-related files
│   └── scratch: pre-computed results and temporary (*.pkl) files
└── paper: all files needed to generate a PDF of the paper and supplement
    ├── compile.sh: run this to generate PDFs (requires a latex distribution with pdflatex and bibtex support)
    └── figs: PDF copies of all figures

Our project uses davos to improve shareability and compatability across systems.

Setup instructions

Note: we have tested these instructions on MacOS and Ubuntu (Linux) systems. We think they are likely to work on Windows systems too, but we haven't explicitly verified Windows compatability.

We recommend running all of the analyses in a fresh Python 3.10 conda environment. To set up your environment:

1. Install Anaconda
2. Clone this repository by running the following in a terminal: git clone https://github.com/ContextLab/pca_paper.git
3. Create a new (empty) virtual environment by running the following (in the terminal): conda create --name pca-paper python=3.10 (follow the prompts)
4. Navigate (in terminal) to the activate the virtual environment (conda activate pca-paper)
5. Install support for jupyter notebooks (conda install -c anaconda ipykernel jupyter) and then add the new kernel to your notebooks (python -m ipykernel install --user --name=pca-paper). Follow any prompts that come up (accepting the default options should work).
6. Navigate to the code directory (cd code) in terminal
7. Start a notebook server (jupyter notebook) and click on the notebook you want to run in the browser window that comes up. The pie_images.ipynb notebook is a good place to start. Selecting "Restart & Run All" from the "Kernel" menu will automatically run all cells.
8. When you're running the notebooks, always make sure the notebook kernel is set to pca-paper (indicated in the top right). If not, in the Kernel menu at the top of the notebook, select "Change kernel" and then "pca-paper".
9. To stop the server, send the "kill" command in terminal (e.g., ctrl + c on a Mac or Linux system).
10. To "exit" the virtual environment, type conda deactivate.

Notes:

• After setting up your environment for the first time, you can skip steps 1, 2, 3, and 5 when you wish to re-enter the analysis environment in the future.
• To run any notebook:
  • Select the desired notebook from the Jupyter "Home Page" menu to open it in a new browser tab
  • Verify that the notebook is using the pca-paper kernel, using the above instructions to adjust the kernel if needed.
  • Select "Kernel" $\rightarrow$ "Restart & Run All" to execute all of the code in the notebook.

To remove the pca-paper environment from your system, run conda remove --name pca-paper --all in the terminal and follow the prompts. (If you remove the pca-paper environment, you will need to repeat the initial setup steps if you want to re-run any of the code in the repository.)

Each notebook contains embedded documentation that describes what the various code blocks do. Any figures you generate will end up in paper/figures/source. Statistical results are printed directly inside each notebook when you run it.