github nipreps/fmriprep 1.4.0
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latest releases: 24.1.1, 24.1.0, 24.1.0rc2...
5 years ago

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Release Notes

The new 1.4 series include several new features, several maintenance patches, and numerous bugfixes. The largest change to fMRIPrep's interface is the new --output-spaces argument that allows running spatial normalization to one or more standard templates, and also to indicate that data preprocessed and resampled to the individual's anatomical space should be generated. The implementation of this option will be completed in future releases to include new nonstandard spaces (e.g., this BOLD run's native space) and custom templates providing a path. For example, the following fMRIPrep options: 

  --template MNI152NLin6Asym --output-space template T1w fsaverage5 ----template-resampling-grid 2mm

now would be accomplished with: 

  --output-spaces MNI152NLin6Asym:res-2 anat fsaverage:den-10k

with the difference that more templates could be specified if needed, e.g., 

  --output-spaces MNI152NLin6Asym:res-2 anat fsaverage:den-10k MNI152NLin2009cAsym:native

Related anatomical preprocessing workflows from sMRIPrep have gone through thorough revisions. In particular, the brain extraction workflow now is implemented in pure Nipype.

Users will notice the addition of two new subsections in the reports generated by fMRIPrep. The first addition describes the cumulative variance explained by successive a/tCompCor components. A second addition shows the correlations between the confounding regressors that fMRIPrep writes to the corresponding file, and their correlation to the global signal.

Series 1.4 increasingly relies on PyBIDS to handle not only inputs, but also outputs and reporting. The reports generation system has been deeply refactored to improve its generalizability across BIDS-Apps and addressing some rendering problems (e.g., when resizing ICA-AROMA components decompositions). Finally, there were several updates to packaging, testing and documentation, which should hopefully improve the experience for new users and contributors.

With thanks to Yaroslav Halchenko, Dan Lurie, Adriana Rivera-Dompenciel, Franklin Feingold, Markus Sneve, Anibal Heinsfeld, and James Kent for contributions.

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