Welcome to the UNRAVEL's Github repository!

The documentation of the code is available on readthedocs

To unravel has two meanings :

• to disentangle the fibers of
• to resolve the intricacy, complexity, or obscurity of

With the UNRAVEL framework, we utilize tractography to unravel the microstructure of multi-fixel models.

This repository contains the code used to combine macroscopic tractography information with microscopic multi-fixel model estimates in order to improve the accuracy in the estimation of the microstructural properties of neural fibers in a specified tract.

The UNRAVEL package is available through pip install under the name unravel-python. Note that the online version might not always be up to date with the latest changes.

pip install unravel-python

To upgrade the current version : pip install unravel-python --upgrade.

To install a specific version of the package use

pip install unravel-python==1.0.0

All available versions are listed in PyPI. The package names follow the rules of semantic versioning.

If you want to download the latest version directly from GitHub, you can clone this repository

git clone https://github.com/DelinteNicolas/unravel.git

For a more frequent use of the library, you may wish to permanently add the package to your current Python environment. Navigate to the folder where this repository was cloned or downloaded (the folder containing the setup.py file) and install the package as follows

cd UNRAVEL
pip install .

If you have an existing install, and want to ensure package and dependencies are updated use --upgrade

pip install --upgrade .

At the top of your Python scripts, import the library as

import unravel

The version of the UNRAVEL package installed can be displayed by typing the following command in your python environment

unravel.__version__

pip uninstall unravel-python

An example use of the main methods and outputs of UNRAVEL is written in the example.py file. A tractogram of the middle anterior section of the corpus callosum is used as tractography input.

Main publication DOI : 10.3389/fnins.2023.1199568

Cite article as : "Delinte N, Dricot L, Macq B, Gosse C, Van Reybroeck M and Rensonnet G (2023) Unraveling multi-fixel microstructure with tractography and angular weighting. Front. Neurosci. 17:1199568. doi: 10.3389/fnins.2023.1199568"