Data visualization, ICA, PCA, Reproducible scientific methods
What is the purpose of the project:
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.
Where can participants find key resources to work on this project:
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/
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...
What will participants learn:
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
Number of collaborators:
3
Credit to collaborators:
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
Project title:
Tedana: TE-dependent analysis of multi-echo fMRI
Project leader:
Eneko Uruñuela
Email:
e.urunuela@bcbl.eu
Brainhack Global 2022 Event:
BrainHack Donostia
Topic:
Data visualization, ICA, PCA, Reproducible scientific methods
What is the purpose of the project:
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.
Where can participants find key resources to work on this project:
https://github.com/ME-ICA/tedana
What stage is the project on:
4
Required programming skills for the project:
Tasks for all levels
Background knowledge needed on the topic:
No knowledge needed
Data to use:
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/
Link to project repository:
https://github.com/ME-ICA/tedana
Goals for Brainhack Donostia 2022:
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...
What will participants learn:
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
Number of collaborators:
3
Credit to collaborators:
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
Type of project:
Documentation, Tutorial, Visualization
Programming languages:
Python, Web
Necessary git skills level for the project:
None
Modality:
fMRI