We just finished FMRIPREP 1.0.0, codename “BOLD raccoon”, which is the first official release of FMRIPREP.
FMRIPREP “BOLD” is the outcome of a thorough testing process to assess that FMRIPREP works reliably on any dataset uploaded to OpenfMRI.
At the time of writing, a second pass of FMRIPREP is being run over 243 participants from 62 [editing note: this is ~180 at this moment] datasets of OpenfMRI that contain BOLD images. This accounts for approximately 4 subjects per dataset (with exceptions like My Connectome, which only has one subject but hundreds of BOLD runs). Overall, this release has been tested on more than 320 participants from OpenfMRI, and dozens of other datasets that our users have provided when encountering problems.
Making a thorough pipeline that adapts to many kinds of dataset and associated metadata is a difficult undertaking. Throughout the three testing phases, we have encountered many problems in FMRIPREP (as well as some of the datasets), but the most complicated to deal with were hitting memory limits on high-capacity (>64GB) compute nodes. The effort to profile and limit memory consumption led to many optimizations and bugfixes in FMRIPREP, as well as in Nipype.
This release supports a friendly way of providing FreeSurfer licenses, and comes with FreeSurfer 6.0.1 embedded in the Docker and Singularity containers.
We want to thank all the contributors that have added to FMRIPREP in one way or another (see our credits file). This release also contains modifications to prepare contributions that are in the making, like this pull request for processing only-BOLD datasets or this pull request for Multi-echo BOLD images. We also thank these soon-to-be contributors, and look forward to incorporating their additions.
We would also like to thank the FreeSurfer team for their support when moving to 6.0.1, and particularly Andrew Hoopes, who followed-up closely during this effort.
Finally, we want to thank all our users who posted over 80(!) questions about FMRIPREP on neurostars.org, many of them reporting problems that couldn’t have been diagnosed otherwise. Likewise, many users directly reported to this GitHub repository. We want to express our utmost appreciation to them and their valuable feedback.