m2g
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.