FMRIPREP generates three broad classes of outcomes:
- Visual QA (quality assessment) reports: one :abbr:`HTML (hypertext markup language)` per subject, that allows the user a thorough visual assessment of the quality of processing and ensures the transparency of fMRIPrep operation.
- Pre-processed imaging data which are derivatives of the original anatomical and functional images after various preparation procedures have been applied. For example, :abbr:`INU (intensity non-uniformity)`-corrected versions of the T1-weighted image (per subject), the brain mask, or :abbr:`BOLD (blood-oxygen level dependent)` images after head-motion correction, slice-timing correction and aligned into the same-subject's T1w space or into MNI space.
- Additional data for subsequent analysis, for instance the transformations between different spaces or the estimated confounds.
In general, FMRIPREP follows the current working draft of the :abbr:`BIDS (brain imaging data structure)`-derivatives extension.
FMRIPREP outputs summary reports, written to <output dir>/fmriprep/sub-<subject_label>.html.
These reports provide a quick way to make visual inspection of the results easy.
Each report is self contained and thus can be easily shared with collaborators (for example via email).
View a sample report.
There are additional files, called "Derivatives", written to
<output dir>/fmriprep/sub-<subject_label>/. See the
BIDS Derivatives
spec for more information.
Derivatives related to T1w files are in the anat subfolder:
*T1w_brainmask.nii.gzBrain mask derived using ANTs'antsBrainExtraction.sh.*T1w_class-CSF_probtissue.nii.gz*T1w_class-GM_probtissue.nii.gz*T1w_class-WM_probtissue.nii.gztissue-probability maps.*T1w_dtissue.nii.gzTissue class map derived using FAST.*T1w_preproc.nii.gzBias field corrected T1w file, using ANTS' N4BiasFieldCorrection*T1w_space-MNI152NLin2009cAsym_brainmask.nii.gzSame as_brainmaskabove, but in MNI space.*T1w_space-MNI152NLin2009cAsym_class-CSF_probtissue.nii.gz*T1w_space-MNI152NLin2009cAsym_class-GM_probtissue.nii.gz*T1w_space-MNI152NLin2009cAsym_class-WM_probtissue.nii.gzProbability tissue maps, transformed into MNI space*T1w_space-MNI152NLin2009cAsym_dtissue.nii.gzSame as_dtissueabove, but in MNI space*T1w_space-MNI152NLin2009cAsym_preproc.nii.gzSame as_preprocabove, but in MNI space*T1w_space-MNI152NLin2009cAsym_target-T1w_warp.h5Composite (warp and affine) transform to map from MNI to T1 space*T1w_target-MNI152NLin2009cAsym_warp.h5Composite (warp and affine) transform to transform T1w into MNI space- (optional)
*T1w_target-fsnative_affine.txtAffine transform to transform T1w intofsnativespace - (optional)
*T1w_smoothwm.[LR].surf.giiSmoothed GrayWhite surfaces - (optional)
*T1w_pial.[LR].surf.giiPial surfaces - (optional)
*T1w_midthickness.[LR].surf.giiMidThickness surfaces - (optional)
*T1w_inflated.[LR].surf.giiFreeSurfer inflated surfaces for visualization
Derivatives related to EPI files are in the func subfolder.
*bold_confounds.tsvA tab-separated value file with one column per calculated confound and one row per timepoint/volume- (optional)
*bold_AROMAnoiseICs.csvA comma-separated value file listing each MELODIC component classified as noise - (optional)
*bold_MELODICmix.tsvA tab-separated value file with one column per MELODIC component
Volumetric output spaces include T1w and MNI152NLin2009cAsym (default).
*bold_space-<space>_brainmask.nii.gzBrain mask for EPI files, calculated by nilearn on the average EPI volume, post-motion correction*bold_space-<space>_preproc.nii.gzHead-motion corrected EPI file- (optional)
*bold_space-<space>_variant-smoothAROMAnonaggr_preproc.nii.gzHead-motion corrected, smoothed (6mm), and non-aggressively denoised (using AROMA) EPI file - currently produced only for theMNI152NLin2009cAsymspace
Surface output spaces include fsnative (full density subject-specific mesh),
fsaverage and the down-sampled meshes fsaverage6 (41k vertices) and
fsaverage5 (10k vertices, default).
- (optional)
*bold_space-<space>.[LR].func.giiMotion-corrected EPI file sampled to surface<space>
EPIs can be saved as a CIFTI dtseries file.
- (optional)
*bold_space-cifti_variant-<variant>_preproc.dtseries.niiMotion-corrected EPI converted to CIFTI filetype. Sub-cortical representations are volumetric (supported spaces:MNI152NLin2009cAsym), while cortical representations are sampled to surface (supported spaces:fsaverage5,fsaverage6)
A FreeSurfer subjects directory is created in <output dir>/freesurfer.
freesurfer/
fsaverage{,5,6}/
mri/
surf/
...
sub-<subject_label>/
mri/
surf/
...
...
Copies of the fsaverage subjects distributed with the running version of
FreeSurfer are copied into this subjects directory, if any functional data are
sampled to those subject spaces.
See implementation on :mod:`~fmriprep.workflows.bold.confounds.init_bold_confs_wf`.
For each :abbr:`BOLD (blood-oxygen level dependent)` run processed with FMRIPREP, a
<output_folder>/fmriprep/sub-<sub_id>/func/sub-<sub_id>_task-<task_id>_run-<run_id>_confounds.tsv
file will be generated.
These are :abbr:`TSV (tab-separated values)` tables, which look like the example below:
WhiteMatter GlobalSignal stdDVARS non-stdDVARS vx-wisestdDVARS FramewiseDisplacement tCompCor00 tCompCor01 tCompCor02 tCompCor03 tCompCor04 tCompCor05 aCompCor00 aCompCor01 aCompCor02 aCompCor03 aCompCor04 aCompCor05 NonSteadyStateOutlier00 X Y Z RotX RotY RotZ AROMAAggrComp01 AROMAAggrComp03 AROMAAggrComp04 AROMAAggrComp05 0.63 2.72 n/a n/a n/a n/a 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 2.62 -1.12 -0.03 3.12 3.14 0.51 1.18 16.05 1.21 0.07 -0.21 -0.36 -0.23 0.29 -0.37 0.04 -0.33 -0.54 -0.36 0.22 -0.07 0.16 0.00 0.00 0.02 0.05 0.00 0.00 0.00 1.66 -1.74 -0.38 -0.99 -1.23 -0.85 1.09 14.86 1.11 0.03 0.02 0.04 -0.22 -0.08 -0.18 0.66 0.11 -0.45 -0.16 -0.28 -0.05 0.26 0.00 0.00 0.00 0.05 0.00 0.00 0.00 0.35 -1.22 0.10 -0.23 -1.61 -1.53 1.01 13.83 1.05 0.03 0.27 0.21 -0.07 0.21 0.30 -0.02 0.24 -0.15 0.24 0.17 0.51 -0.02 0.00 0.01 -0.01 0.04 0.00 0.00 0.00 -0.42 -0.55 0.49 -0.38 -3.43 -1.48 0.98 13.32 1.02 0.03 0.06 0.49 0.24 -0.18 0.06 0.12 0.25 0.11 0.09 -0.10 0.08 0.47 0.00 0.02 -0.01 0.03 0.00 0.00 0.00 -1.12 -0.40 0.21 1.23 0.71 -0.66 0.97 13.26 1.02 0.04 -0.29 0.43 0.14 0.06 -0.20 -0.32 0.40 0.22 -0.07 0.45 -0.02 -0.04 0.00 0.02 -0.02 0.03 0.00 0.00 0.00 -1.00 -0.91 -0.99 0.30 -2.81 0.61 0.95 12.98 1.01 0.08 -0.48 0.24 -0.11 -0.15 -0.16 -0.22 0.38 0.20 -0.35 0.16 -0.31 -0.01 0.00 0.00 0.00 0.05 0.00 0.00 0.00 -0.66 -0.49 -1.89 0.43 2.85 0.35 0.95 12.99 1.01 0.04 -0.22 0.00 -0.50 0.05 0.15 0.14 0.30 -0.20 -0.22 -0.22 0.04 -0.34 0.00 0.00 -0.01 0.03 0.00 0.00 0.00 0.01 0.22 -1.76 -0.39 -2.57 -0.54 1.04 14.22 1.07 0.05 0.45 0.01 -0.43 -0.51 -0.01 -0.20 0.13 -0.02 0.26 -0.62 0.00 -0.30 0.00 0.00 0.00 0.06 0.00 0.00 0.00 0.60 1.59 0.05 -0.46 3.41 -0.72 1.03 14.04 1.05 0.07 0.37 0.06 0.08 0.55 -0.21 -0.14 -0.10 -0.18 0.51 0.17 -0.24 0.05 0.00 0.00 0.02 0.07 0.00 0.00 0.00 0.52 0.71 1.63 -0.95 3.75 -0.54 1.01 13.83 1.04 0.06 0.16 -0.16 0.38 -0.19 -0.01 0.16 -0.11 0.18 0.37 0.00 -0.43 0.20 0.00 0.00 0.00 0.06 0.00 0.00 0.00 -0.53 -0.07 1.85 -0.01 0.41 1.19 1.05 14.28 1.08 0.06 -0.27 -0.38 0.32 -0.11 0.10 0.07 -0.31 0.31 -0.25 -0.24 -0.01 0.27 0.00 0.00 0.01 0.09 0.00 0.00 0.00 -0.75 -0.03 0.14 -0.26 -4.14 0.72 0.97 13.20 1.01 0.03 -0.13 -0.28 0.03 -0.16 0.48 -0.28 -0.26 0.40 -0.24 -0.10 0.18 -0.20 0.00 0.00 0.00 0.08 0.00 0.00 0.00 -0.44 1.03 -0.50 -0.15 2.21 -0.02 0.96 13.09 1.00 0.01 0.18 -0.26 -0.04 0.14 -0.05 -0.37 -0.26 -0.10 0.07 0.25 -0.10 -0.54 0.00 0.00 0.00 0.08 0.00 0.00 0.00 0.28 1.54 0.12 -0.77 0.08 -0.06 0.95 12.89 0.99 0.01 0.15 -0.12 0.31 -0.22 -0.37 0.08 -0.22 0.12 -0.02 0.01 -0.15 -0.10 0.00 0.00 0.00 0.08 0.00 0.00 0.00 -0.46 1.00 0.70 0.08 -1.41 0.29 0.96 13.06 0.99 0.01 -0.04 0.07 0.10 0.31 0.47 0.27 -0.22 0.09 0.11 0.12 0.56 0.14 0.00 0.00 0.00 0.07 0.00 0.00 0.00 -0.67 0.44 0.25 -0.57
Each row of the file corresponds to one time point found in the
corresponding :abbr:`BOLD (blood-oxygen level dependent)` time-series
(stored in <output_folder>/fmriprep/sub-<sub_id>/func/sub-<sub_id>_task-<task_id>_run-<run_id>_bold_preproc.nii.gz).
Columns represent the different confounds: CSF and WhiteMatter are the average signal inside
the :abbr:`CSF (cerebro-spinal fluid)` and :abbr:`WM (white matter)` mask across time;
GlobalSignal corresponds to the global-signal within the whole-brain mask; three columns relate to the
derivative of RMS variance over voxels (or :abbr:`DVARS (D referring to difference, )`) that can be
standardized (stdDVARS), non-standardized (non-stdDVARS), and voxel-wise standardized (vx-wisestdDVARS);
the FrameDisplacement is a quantification of the estimated bulk-head motion; X, Y, Z, RotX,
RotY, RotZ are the actual 6 rigid-body transform parameters estimated by FMRIPREP;
the NonSteadyStateOutlierXX columns indicate non-steady state volumes with a single 1 value and 0 elsewhere (there
is one NonSteadyStateOutlierXX column per outlier/volume); and finally six noise components aCompCorXX calculated using
:abbr:`CompCor (Component Based Noise Correction Method)`
and five noise components AROMAaggrCompXX if
:abbr:`ICA (independent components analysis)`-:abbr:`AROMA (Automatic Removal Of Motion Artifacts)` was enabled.
All these confounds can be used to perform scrubbing and censoring of outliers, in the subsequent first-level analysis when building the design matrix, and in group level analysis.
Some of the estimated confounds, as well as a "carpet" visualization of the :abbr:`BOLD (blood-oxygen level-dependant)` time-series (see [Power2016]). This plot is included for each run within the corresponding visual report. An example of these plots follows:
The figure shows on top several confounds estimated for the BOLD series: global signals ('GlobalSignal', 'WM', 'GM'), standardized DVARS ('stdDVARS'), and framewise-displacement ('FramewiseDisplacement'). At the bottom, a 'carpetplot' summarizing the BOLD series. The colormap on the left-side of the carpetplot denotes signals located in cortical gray matter regions (blue), subcortical gray matter (orange), cerebellum (green) and the union of white-matter and CSF compartments (red).
References
| [Power2016] | Power JD, A simple but useful way to assess fMRI scan qualities. NeuroImage. 2016. doi: 10.1016/j.neuroimage.2016.08.009 |