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 orignially 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.
If you're in need of a pre-processing pipeline, we recommend fmriprep which has been tested for compatibility with multi-echo fMRI data and tedana.
Multi-echo fMRI data is obtained by acquiring multiple TEs (commonly called echo times) for each MRI volume during data collection. While fMRI signal contains important neural information (termed the blood oxygen-level dependent, or BOLD signal), it also contains "noise" (termed non-BOLD signal) caused by things like participant motion and changes in breathing. Because the BOLD signal is known to decay at a set rate, collecting multiple echos allows us to assess whether components of the fMRI signal are BOLD- or non-BOLD. For a comprehensive review, see Kundu et al. (2017), NeuroImage.
In tedana, we take the time series from all the collected TEs, combine them, and decompose the resulting data into components that can be classified as BOLD or non-BOLD. This is performed in a series of steps including:
- Principal components analysis
- Independent components analysis
- Component classification
More information and documentation can be found at https://tedana.readthedocs.io/.
You'll need to set up a working development environment to use tedana.
We provide a Dockerfile for this purpose (check out tips on using Docker), but you can also set up your environment locally.
If you choose the latter, the following packages will need to be installed as dependencies:
mdp
nilearn
nibabel>=2.1.0
numpy
scikit-learn
scipy
tedana will eventually be hosted on PyPi. In the interim, you can still install it with pip using:
pip install https://github.com/ME-ICA/tedana/archive/master.tar.gz
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