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.
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.
tedana with your local Python environment
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:
You can then install
pip install tedana
Creating a miniconda environment for use with
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
conda create -n ENVIRONMENT_NAME python=3 pip mdp numpy scikit-learn scipy conda activate ENVIRONMENT_NAME pip install nilearn nibabel 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
NOTE: Conda < 4.6 users will need to use the soon-to-be-deprecated option
source rather than
conda for the activation and deactivation steps. You can read more about managing conda environments and this discrepancy here.
Use and contribute to
tedana as a developer:
If you aim to contribute to the
tedana codebase and/or documentation, please first read the developer installation instructions in our contributing section. You can then continue to set up your preferred development environment.