We’re planning to release a large MRI-dataset (containing functional, anatomical, and diffusion MRI, as well as physiology data), containing data from ~1400 subjects in total. As the data is in BIDS-format already, and to make it as easy as possible for people to work with the data, we plan to run the data through FMRIPREP (and MRIQC). We were wondering what the “optimal” preprocessing parameters would be, given that we want to make this dataset as “user-friendly” as possible. At this moment, we were thinking of the following:
- Freesurfer reconstruction;
- No slicetiming correction (TR of functional scans range from 0.75-2.2 sec.);
- Output spaces: native, T1w, fsnative;
- Native resampling grid
- SyN distortion correction for sessions without fieldmap scan
Additionally, we were thinking of running the (preprocessed) anatomical data through the FSL-VBM pipeline and the DWI data through the FSL TBSS pipeline. This, again, to make the data as “ready to-be-analyzed” as possible.
If anyone has ideas/recommendations/suggestions about how to “package” this dataset for publication, let me know!
Lukas (University of Amsterdam, Spinoza Centre for Neuroimaging)