gaozhy / tedana

TE-dependent analysis of multi-echo fMRI

tedana: TE Dependent ANAlysis

The tedana package is part of the ME-ICA pipeline, performing TE-dependent analysis of multi-echo functional magnetic resonance imaging (fMRI) data. TE-dependent analysis (tedana) is a Python module for denoising multi-echo functional magnetic resonance imaging (fMRI) data.

About

tedana originally came about as a part of the ME-ICA pipeline. The ME-ICA pipeline originally performed both pre-processing and TE-dependent analysis of multi-echo fMRI data; however, tedana now assumes that you're working with data which has been previously preprocessed.

More information and documentation can be found at https://tedana.readthedocs.io/.

Installation

You'll need to set up a working development environment to use tedana. To set up a local environment, you will need Python >=3.6 and the following packages will need to be installed:

numpy scipy scikit-learn nilearn nibabel>=2.1.0

You can then install tedana with

pip install tedana

Creating a miniconda environment for use with tedana

In using tedana, you can optionally configure a conda environment.

We recommend using miniconda3. After installation, you can use the following commands to create an environment for tedana:

conda create -n ENVIRONMENT_NAME python=3 pip mdp numpy scikit-learn scipy nilearn nibabel
source activate ENVIRONMENT_NAME
pip install tedana

tedana will then be available in your path. This will also allow any previously existing tedana installations to remain untouched.

To exit this conda environment, use

source deactivate

Getting involved

We 💛 new contributors! To get started, check out our contributing guidelines.

Want to learn more about our plans for developing tedana? Have a question, comment, or suggestion? Open or comment on one of our issues!

We ask that all contributions to tedana respect our code of conduct.