Release 0.12.0 (beta)

This is the first beta release of the load_confound project. Updates include:

• code refactoring. The code (and the tests) are more modular, and each module is kept short. This refactoring impacted the compcor method in particular.
• support for gifti surface data files.
• checks and sensible error messages to match input files with specific strategies. Note that it is not possible anymore to directly specify a tsv confound file, as the target imaging file is necessary to assess that a particular strategy is sensible.
• better documentation for ICA AROMA.
• full redesign of the denoising strategies. Instead of following a particular benchmark, the denoising strategies are now based on the major technical variants used in the literature, and are quite customizable.

Congratulations to all contributors for reaching the first beta release, and special thanks to @htwangtw for all the hard work that went specifically into that new release 🎉 🎉 🎉

The next release will improve support for 'hard' scrubbing, and will likely introduce a new API. The load_confounds project is also set to merge inside nilearn and will be discontinued as a stand-alone repository when the merge is complete.

Release 0.11.1

This major update (0.11.1) is brought to you by Brainhack Global 2020.

The following features have been added to the package:

• Support for ICA AROMA denoising, and corresponding strategies. Contribution by Hao-Ting Wang @htwangtw
• Support for filtering anat compcor noise components based on the type of mask used to calculate the components. Contribution by Steven Meisler @smeisler
• Support for scrubbing. The scrubbing components are computed "on-the-fly" based on mean_fd and std_vars. Contribution by Steven Meisler @smeisler
• Better error messages. fMRIprep does not generate all possible confounds by default, so some denoising strategies may not be possible for a particular preprocessed dataset. Users will now get a detailed list of missing noise components for a requested strategy. Contribution by François Paugam @FrancoisPgm
• A jupyter notebook with a tutorial on load_confounds. Contribution by Michael Weiss @m-w-w and Pierre Bellec @pbellec
• Refactoring of the code for more modularity and readability. More and better tests. Contribution by François Paugam @FrancoisPgm and Pierre Bellec @pbellec

Huge thanks to all contributors 🎉 🎉 🎉

Next release will be beta, with support for all current fMRIprep types of confounds.

release 0.6.5

The load_confounds 0.6.5 minor release extends support for fmriprep version 20.2.0+, and remains compatible with older versions.
Thanks @nuKs for this contribution 🎉 🎉

release 0.6.4

This is a critical bug fix release.

The derivatives confounds had a NaN in the first row, which caused an error when used in nilearn.
The values of the first row are now duplicates of the second row.
This fix impacts all strategies featuring derivatives expansion of confounds.

Fix contributed by P Bellec, with inputs/reviews from H Shamarke, B Pinsard and A Boré.

release 0.6.3

This release fixes the pipy package for load_confounds.

Both the packaging bug and bug fix were contributed by P Bellec.

release 0.6.2

This release includes refactoring of the file structure to comply with Pipy standards.

The package is now located in a folder load_confounds and load_confounds.py was renamed parser.py. A similar behaviour to prior module organization can be achieved using:

import load_confounds.parser as lc

All the strategies are being exposed through the package, so it is still possible to run:

from load_confounds import Confounds

or

from load_confounds import Params6 

This minor release was prepared by Pierre Bellec.