Tedana: TE-dependent analysis of multi-echo fMRI
Eneko Uruñuela
BrainHack Donostia
Data visualization, ICA, PCA, Reproducible scientific methods
TE-dependent analysis (tedana) is a Python library for denoising multi-echo functional magnetic resonance imaging (fMRI) data. tedana originally came about as a part of the ME-ICA pipeline, although it has since diverged.
https://github.com/ME-ICA/tedana
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Tasks for all levels
No knowledge needed
We have access to multi-echo data from different sources. A good starting point could be the testing data tedana uses and that is hosted in OSF: https://osf.io/bpe8h/
https://github.com/ME-ICA/tedana
Tedana offers a list of 16 good first issues for folks who are new to the package.At the same time, there are a couple of issues that have long been pending. For instance, joining the Kundu and maPCA methods to obtain a single PCA curves figure.As part of the wider ME-ICA community, there are a couple of possible projects to work on too: Rica needs to be refactored into remix to fix loading issues; aroma still has to be finished; the multi-echo analysis jupyter book could receive some love…
Participants will learn:- how to use git and GitHub in the context of an open source Python library development- how to write and build documentation- how to program for web with React (Remix)- how to write and build Jupyter books- how to perform denoising with multi-echo ICA- how to develop an ICA denoising tool for fMRI data
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Contributions will be acknowledged by listing contributors on the main repository page (README), as well as adding them as authors to future conference abstracts and manuscripts.For mor info on how to contribute, see https://github.com/ME-ICA/tedana/blob/main/CONTRIBUTING.md
Documentation, Tutorial, Visualization
Python, Web
None
fMRI