Aroma

Project title: Aroma

Leader:

• Eneko Uruñuela (@eurunuela)

Collaborators:

• Taylor Salo (@tsalo)
• Vicente Ferrer (@vinferrer)
• Oscar Esteban (@oesteban)

Topic: Data visualization, ICA

Project description: ICA-AROMA (i.e. ‘ICA-based Automatic Removal Of Motion Artifacts’) concerns a data-driven method to identify and remove motion-related independent components from fMRI data. To that end it exploits a small, but robust set of theoretically motivated features, preventing the need for classifier re-training and therefore providing direct and easy applicability. The aim of this project is to modify the original ICA-AROMA code in order to restructure it into a pure Python package; e.g., dropping FSL calls in favor of pure Python functions.

Data to use: https://osf.io/6jda8/files/

Link to project repository: https://github.com/ME-ICA/aroma

Goals for Brainhack Donostia 2021: Our main goal for Brainhack Donostia 2021 is to drop all non-python dependencies, so as to have our first pure-python version. See the list of tasks for more concrete goals.

First tasks: We have a list of issues open that will serve as our list of tasks throughout the project. Some of the are tagged “Good first issue”, which are perfect for people who are new to Brainhack and the project itself.

Communication channels: https://gitter.im/ME-ICA/aroma

Video channel: Zoom

Number of collaborators: 4

Credit to collaborators: We are going to implement the all-contributors bot, so that all contributors get credit for their work on the README file.

Type of project: Coding methods, Documentation, Visualization

Development status: Basic structure

Programming languages: Python

Necessary Programming skills level for the project: Familiar

Necessary git skills level for the project: Familiar

Modality: fMRI

Image

Software suites: fMRIPrep, Jupyter

Email: e.urunuela@bcbl.eu

What will participants learn: Participants will have the opportunity to learn how research collaborate on open source projects. They will also learn how the ICA-AROMA method works, as well as becoming familiar with Python libraries built for neuroimaging. If the participants were to work on the documentation, they would learn how docs are written on an open source project, as well as the common reStructuredText or Sphinx formats. If the participants were to work on the visualization side of the project, they would gain experience with the most commonly used plotting libraries for Python, namely matplotlib, bokeh, seaborn and plotly. Another goal of the project is to make the library accessible for fMRIprep, and so if participants were to take on this goal, they would learn how fMRIprep works, and how to integrate a library into fMRIprep.

Twitter description

The old ICA aroma is a data-driven method to identify and remove motion-related independent components from fMRI data that combines FSL and Python and cannot be used on native-space data.

With the new aroma we want to make aroma Python-only and support native-space data.