Mentor(s): Kirstie Whitaker and Taylor Salo
Context and motivation: Traditional fMRI denoising makes a priori assumptions about the shape of noise fluctuations across time. Multi-echo fMRI (ME-fMRI) enables data-driven denoising by collecting multiple echoes in a single fMRI volume, offering a significant improvement over standard approaches. Supporting this, previous ME-fMRI denoising methods such as ME-ICA (multi-echo independent component analysis) have been shown to improve data quality. However, existing implementations lack provenance for data inclusion criteria and are difficult to extend or improve.
The tedana Python package  is designed to serve as both a canonical multi-echo denoising pipeline with robust default settings and a toolbox into which researchers can integrate new methods for denoising. In creating tedana as a Python package, we have remained committed to best-practice principles in open-source development, including extensive documentation for both users and contributors as well as an open governance structure .
The proposed project aims to further develop tedana’s testing suite in order to improve test coverage and to make the codebase more robust to improvements and additions. Given the development team’s current emphasis on integrated workflows, appropriate unit tests have not been written for a large portion of the core functionality of the package. The GSOC project goal is to improve test coverage of tedana by modularizing existing workflows and writing unit tests of existing code.
The GSoC student will develop their skills working with Python, employing modern software testing suites to improve reproducibility and robustness of code and will have explicit mentorship in ways of open and collaborative working using git and GitHub.
Tool description: The tedana test suite is implemented as a collection of pytest-compatible testing functions. Tests will be evaluated on the continuous integration platforms CircleCI  and Travis , and will employ coverage profilers like CodeCov .
Improved modularization of existing workflows will be done in conjunction with members of the tedana developer community, leveraging the community’s distributed expertise both in Python programming knowledge and familiarity with tedana.
Project description and aims: This project is aimed towards students seeking to develop their coding skills and to gain familiarity with collaborative development. The successful candidate will gain 1) real world experience engaging with a wide range of researchers and developers and 2) experience with test-driven development.
Measurable outcomes include increased test coverage of the tedana package and implemented checksums  in regression testing.
Skills needed/desired: Interested students should be comfortable with Python and GitHub, with a desire to learn the continuous integration platforms CircleCI  and Travis , and coverage profilers like CodeCov . A basic familiarity with neuroimaging data formats and preprocessing is also desirable. A commitment to open and collaborative working is essential. All contributors to tedana are expected to comply with the tedana code of conduct at all times .
Key words: Python; usability; brain imaging; reproducible research
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