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NeuroData's MRI to Graphs (m2g) - connectome estimation package and pipeline

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m2g

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NeuroData's MR Graphs package, m2g, is a turn-key pipeline which uses structural and diffusion MRI data to estimate multi-resolution connectomes reliably and scalably.

Contents

Overview

The m2g pipeline has been developed as a beginner-friendly solution for human connectome estimation by providing robust and reliable estimates of connectivity across a wide range of datasets. The pipelines are explained and derivatives analyzed in our pre-print, available on BiorXiv.

Documentation

Check out some resources on our website, or our function reference for more information about m2g.

System Requirements

Hardware Requirements

m2g pipelines requires only a standard computer with enough RAM (< 16 GB).

Software Requirements

The m2g pipeline:

  • was developed and tested primarily on Mac OS (10,11), Ubuntu (16, 18, 20), and CentOS (5, 6);
  • made to work on Python 3.7-3.10;
  • is wrapped in a Docker container;
  • has install instructions via a Dockerfile;
  • requires no non-standard hardware to run;
  • has key features built upon FSL, AFNI, INDI, Dipy, Nibabel, Nilearn, Networkx, Numpy, Scipy, Scikit-Learn, and others
    • For Python package version numbers, see requirements.txt
    • For binaries required to install AFNI, FSL, INDI, ICA_AROMA, see the Dockerfile
  • takes approximately 1-core, < 16-GB of RAM, and 1-2 hours to run for most datasets (varies based on data).

Installation

Instructions can be found within our documentation: https://docs.neurodata.io/m2g/install.html

Usage

Instructions can be found within our documentation and a demo can be found here.

License

This project is covered under the Polyform License.

Issues

If you're having trouble, notice a bug, or want to contribute (such as a fix to the bug you may have just found) feel free to open a git issue or pull request. Enjoy!

Citing m2g

If you find m2g useful in your work, please cite the package via the m2g paper

Chung, J., Lawrence, R., Loftus, A., Kiar, G., Bridgeford, E. W., Roncal, W. G., Chandrashekhar, V., ... & Consortium for Reliability and Reproducibility (CoRR). (2024). A low-resource reliable pipeline to democratize multi-modal connectome estimation and analysis. bioRxiv, 2024-04.