Functional connectomics reveals general wiring rule in mouse visual cortex

This repository contains the code for the paper "Functional connectomics reveals general wiring rule in mouse visual cortex".

https://doi.org/10.1038/s41586-025-08840-3.

Installation

You can set up this project in several ways:

Option 1: Using Docker (Recommended)

The easiest way to get started is to use our pre-built Docker container which includes all required dependencies.

Prerequisites

• Docker
• Docker Compose (included in Docker Desktop for Mac/Windows)

Steps

1. Clone the repository:

   git clone https://github.com/cajal/microns-funconn-2025.git
   cd microns-funconn-2025

2. Start the Docker container:

   ./run-docker-container.sh

3. Access Jupyter Lab: Open your browser and navigate to http://localhost:8888 Note: It may take a few seconds for the content to become accessible.

Option 2: Install with pip

Prerequisites

• Python 3.8 or higher
• R 4.0.0 or higher (for statistical analysis)

Steps

1. Clone the repository:

   git clone https://github.com/cajal/microns-funconn-2025.git
   cd microns-funconn-2025

2. Install the package and its Python dependencies:

   # Install in development mode
   pip install -e .

3. Install required R packages:

   # Run the R package setup script
   Rscript setup_r_packages.R

   This script will install the following R packages:

   • glmmTMB
   • tidyverse
   • broom.mixed
   • emmeans
   • performance
   • DHARMa

Reproduce the figures

The intermediate results files are already included in the results folder. Notebooks to load these files and reproduce the figures in the paper are under the figures folder.

1. Navigate to the figures directory
2. Run the Jupyter notebooks:
   • like2like.ipynb - Figures related to the like-to-like connectivity analysis
   • common_input.ipynb - Figures related to the common input analysis

To reproduce the intermediate results, you can run the following scripts:

• funconnect/compute/like2like.py - Script to generate the like-to-like connectivity analysis results
• funconnect/compute/common_inputs.py - Script to generate the common input analysis results

To run the scripts, open a terminal inside the ./funconnect/compute/ directory and run:

python3 ./common_inputs.py
python3 ./like2like.py

Intermediate results are stored in funconnect/results and should match the results in the results folder.

Reproduce methods

Notebooks to demonstrate some of the methods used in the paper are in the methods folder. Currently available methods:

• proximities - compute_proximities.ipynb

Data Availability

To access the datasets analyzed in this study, please see the data availability section of the manuscript.

They are also downloaded inside the Docker container at /data.

Citation

If you find this repository useful, please cite using this BibTeX:

@article{ding2025functional,
  title={Functional connectomics reveals general wiring rule in mouse visual cortex},
  author={Ding, Zhuokun and Fahey, Paul G and Papadopoulos, Stelios and Wang, Eric Y and Celii, Brendan and Papadopoulos, Christos and Chang, Andersen and Kunin, Alexander B and Tran, Dat and Fu, Jiakun and others},
  journal={Nature},
  volume={640},
  number={8058},
  pages={459--469},
  year={2025},
  publisher={Nature Publishing Group UK London}
}