fMRIPrep - Neuroanalytics Datasets

fMRIPrep is a NiPreps (NeuroImaging PREProcessing toolS) application ([www.nipreps.org(www.nipreps.org)]) for the preprocessing of task-based and resting-state functional MRI (fMRI).

fMRIPrep is a functional magnetic resonance imaging (fMRI) data preprocessing pipeline that is designed to provide an easily accessible, state-of-the-art interface that is robust to variations in scan acquisition protocols and that requires minimal user input, while providing easily interpretable and comprehensive error and output reporting. It performs basic processing steps (coregistration, normalization, unwarping, noise component extraction, segmentation, skull-stripping, etc.) providing outputs that can be easily submitted to a variety of group level analyses, including task-based or resting-state fMRI, graph theory measures, and surface or volume-based statistics.

fMRIPrep is built around three principles:

Robustness - The pipeline adapts the preprocessing steps depending on the input dataset and should provide results as good as possible independently of scanner make, scanning parameters or presence of additional correction scans (such as fieldmaps).
Ease of use - Thanks to dependence on the BIDS standard, manual parameter input is reduced to a minimum, allowing the pipeline to run in an automatic fashion.
“Glass box” philosophy - Automation should not mean that one should not visually inspect the results or understand the methods. Thus, fMRIPrep provides visual reports for each subject, detailing the accuracy of the most important processing steps. This, combined with the documentation, can help researchers to understand the process and decide which subjects should be kept for the group level analysis.

Usage¶

fMRIPrep should be used through a container. A command like:

fmriprep <input_dir>/ <output_dir>/ participant [OPTIONS]

with [OPTIONS] specific to your dataset is easily invoked with either Docker or Apptainer to use this tool.

You are also able to use Nipoppy!

Output Structure¶

Visual QA (quality assessment) reports: one HTML per subject, that allows the user a thorough visual assessment of the quality of processing and ensures the transparency of fMRIPrep operation.
Derivatives (preprocessed data) the input fMRI data ready for analysis, i.e., after the various preparation procedures have been applied. For example, INU-corrected versions of the T1-weighted image (per subject), the brain mask, or BOLD images after head-motion correction, slice-timing correction and aligned into the same-subject’s T1w space or in some standard space.
Confounds: this is a special family of derivatives that can be utilized to inform subsequent denoising steps.

The output folder where the derivatives is stored will have this structure:

<output_dir>/
  logs/
  sub-<label>/
  sub-<label>.html
  dataset_description.json
  .bidsignore

and within each subject, the following files (generally) will be found. Specifics will vary by version and the options selected, but generlly speaking the typical output will contain:

sub-<subject_label>/
  anat/
	sub-<subject_label>_from-MNI152NLin2009cAsym_to-T1w_mode-image_xfm.h5
	sub-<subject_label>_from-T1w_to-MNI152NLin2009cAsym_mode-image_xfm.h5
	sub-<subject_label>_from-fsnative_to-T1w_mode-image_xfm.txt
	sub-<subject_label>_from-T1w_to-fsnative_mode-image_xfm.txt
	sub-<subject_label>_hemi-[LR]_white.surf.gii
	sub-<subject_label>_hemi-[LR]_midthickness.surf.gii
	sub-<subject_label>_hemi-[LR]_pial.surf.gii
	sub-<subject_label>_hemi-[LR]_desc-reg_sphere.surf.gii
	sub-<subject_label>_hemi-[LR]_space-fsLR_desc-reg_sphere.surf.gii
	sub-<subject_label>_hemi-[LR]_space-fsLR_desc-msmsulc_sphere.surf.gii
	sub-<subject_label>_hemi-[LR]_desc-cortex_mask.label.gii
	sub-<subject_label>_hemi-[LR]_thickness.shape.gii
	sub-<subject_label>_hemi-[LR]_curv.shape.gii
	sub-<subject_label>_hemi-[LR]_sulc.shape.gii
	sub-<subject_label>_space-fsLR_den-32k_thickness.dscalar.nii
	sub-<subject_label>_space-fsLR_den-32k_curv.dscalar.nii
	sub-<subject_label>_space-fsLR_den-32k_sulc.dscalar.nii
  func/
	sub-<subject_label>_[specifiers]_space-<space_label>_desc-brain_mask.nii.gz
	sub-<subject_label>_[specifiers]_space-<space_label>_desc-preproc_bold.nii.gz
	sub-<subject_label>_[specifiers]_desc-hmc_boldref.nii.gz
	sub-<subject_label>_[specifiers]_from-orig_to_boldref_mode-image_desc-hmc_xfm.txt
	sub-<subject_label>_[specifiers]_desc-coreg_boldref.nii.gz
	sub-<subject_label>_[specifiers]_from-boldref_to-T1w_mode-image_desc-coreg_xfm.txt
	sub-<subject_label>_[specifiers]_from-boldref_to-TOPUP_mode-image_xfm.txt
	sub-<subject_label>_[specifiers]_from-boldref_to-auto000XX_mode-image_xfm.txt

Additionally, FreeSurfer derivatives will be written and stored independly in way accessible to typical FreeSurfer analysis functions.

References¶

Esteban, O., Markiewicz, C. J., Blair, R. W., Moodie, C. A., Isik, A. I., Erramuzpe, A., ... & Gorgolewski, K. J. (2019). fMRIPrep: a robust preprocessing pipeline for functional MRI. Nature methods, 16(1), 111-116.

Esteban, O., Ciric, R., Finc, K., Blair, R. W., Markiewicz, C. J., Moodie, C. A., ... & Gorgolewski, K. J. (2020). Analysis of task-based functional MRI data preprocessed with fMRIPrep. Nature protocols, 15(7), 2186-2202.