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https://github.com/neurodata/m2g
NeuroData's MRI to Graphs (m2g) - connectome estimation package and pipeline
https://github.com/neurodata/m2g
Last synced: 3 days ago
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NeuroData's MRI to Graphs (m2g) - connectome estimation package and pipeline
- Host: GitHub
- URL: https://github.com/neurodata/m2g
- Owner: neurodata
- License: other
- Created: 2016-02-01T14:30:12.000Z (almost 9 years ago)
- Default Branch: deploy
- Last Pushed: 2024-04-13T18:13:37.000Z (7 months ago)
- Last Synced: 2024-04-13T18:15:13.151Z (7 months ago)
- Language: Python
- Homepage: https://docs.neurodata.io/m2g/
- Size: 89 MB
- Stars: 59
- Watchers: 11
- Forks: 35
- Open Issues: 28
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
README
# m2g
[](https://pypi.python.org/pypi/m2g)
[](https://doi.org/10.5281/zenodo.595684)
[](https://codeclimate.com/github/neurodata/ndmg)
[](https://hub.docker.com/r/neurodata/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](#overview)
- [Documentation](#documentation)
- [System Requirements](#system-requirements)
- [Installation Guide](#installation-guide)
- [Usage](#usage)
- [License](#license)
- [Issues](#issues)
- [Citing `m2g`](#citing-m2g)
# 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](https://www.biorxiv.org/content/10.1101/2021.11.01.466686v1.full).
# Documentation
Check out some [resources](http://m2g.io) on our website, or our [function reference](https://ndmg.neurodata.io/) 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](https://hub.docker.com/r/neurodata/m2g);
- 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](requirements.txt)
- For binaries required to install AFNI, FSL, INDI, ICA_AROMA, see the [Dockerfile](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](https://docs.neurodata.io/m2g/usage.html) and a demo can be found [here](https://docs.neurodata.io/m2g/usage.html#demo).
# License
This project is covered under the [Polyform License](https://github.com/neurodata/m2g/blob/deploy/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](https://www.biorxiv.org/content/10.1101/2021.11.01.466686)
> 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.