Outputs of fMRIPrep

FMRIPrep generates three broad classes of outcomes:

  1. 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.
  2. Pre-processed imaging data which are derivatives of the original anatomical and functional images after 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 into MNI space.
  3. Additional data for subsequent analysis, for instance the transformations between different spaces or the estimated confounds.

fMRIPrep outputs conform to the BIDS Derivatives specification (see BIDS Derivatives RC1).

Visual Reports

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.

Preprocessed data (fMRIPrep derivatives)

Preprocessed, or derivative, data are written to <output dir>/fmriprep/sub-<subject_label>/. The BIDS Derivatives RC1 specification describes the naming and metadata conventions we follow.

Anatomical derivatives are placed in each subject’s anat subfolder:

  • anat/sub-<subject_label>_[space-<space_label>_]desc-preproc_T1w.nii.gz
  • anat/sub-<subject_label>_[space-<space_label>_]desc-brain_mask.nii.gz
  • anat/sub-<subject_label>_[space-<space_label>_]dseg.nii.gz
  • anat/sub-<subject_label>_[space-<space_label>_]label-CSF_probseg.nii.gz
  • anat/sub-<subject_label>_[space-<space_label>_]label-GM_probseg.nii.gz
  • anat/sub-<subject_label>_[space-<space_label>_]label-WM_probseg.nii.gz

Template-normalized derivatives use the space label MNI152NLin2009cAsym, while derivatives in the original T1w space omit the space- keyword.

Additionally, the following transforms are saved:

  • anat/sub-<subject_label>_from-MNI152NLin2009cAsym_to-T1w_mode-image_xfm.h5
  • anat/sub-<subject_label>_from-T1w_to-MNI152NLin2009cAsym_mode-image_xfm.h5

If FreeSurfer reconstructions are used, the following surface files are generated:

  • anat/sub-<subject_label>_hemi-[LR]_smoothwm.surf.gii
  • anat/sub-<subject_label>_hemi-[LR]_pial.surf.gii
  • anat/sub-<subject_label>_hemi-[LR]_midthickness.surf.gii
  • anat/sub-<subject_label>_hemi-[LR]_inflated.surf.gii

And the affine translation between T1w space and FreeSurfer’s reconstruction (fsnative) is stored in:

  • anat/sub-<subject_label>_from-T1w_to-fsnative_mode-image_xfm.txt

Functional derivatives are stored in the func subfolder. All derivatives contain task-<task_label> (mandatory) and run-<run_index> (optional), and these will be indicated with [specifiers].

  • func/sub-<subject_label>_[specifiers]_space-<space_label>_boldref.nii.gz
  • func/sub-<subject_label>_[specifiers]_space-<space_label>_desc-brain_mask.nii.gz
  • func/sub-<subject_label>_[specifiers]_space-<space_label>_desc-preproc_bold.nii.gz

Volumetric output spaces include T1w and MNI152NLin2009cAsym (default).

Confounds are saved as a TSV file:

  • func/sub-<subject_label>_[specifiers]_desc-confounds_regressors.nii.gz

If FreeSurfer reconstructions are used, the (aparc+)aseg segmentations are aligned to the subject’s T1w space and resampled to the BOLD grid, and the BOLD series are resampled to the midthickness surface mesh:

  • func/sub-<subject_label>_[specifiers]_space-T1w_desc-aparcaseg_dseg.nii.gz
  • func/sub-<subject_label>_[specifiers]_space-T1w_desc-aseg_dseg.nii.gz
  • func/sub-<subject_label>_[specifiers]_space-<space_label>_hemi-[LR].func.gii

Surface output spaces include fsnative (full density subject-specific mesh), fsaverage and the down-sampled meshes fsaverage6 (41k vertices) and fsaverage5 (10k vertices, default).

If CIFTI outputs are requested, the BOLD series is also saved as dtseries.nii CIFTI2 files:

  • func/sub-<subject_label>_[specifiers]_bold.dtseries.nii

Sub-cortical time series are volumetric (supported spaces: MNI152NLin2009cAsym), while cortical time series are sampled to surface (supported spaces: fsaverage5, fsaverage6)

Finally, if ICA-AROMA is used, the MELODIC mixing matrix and the components classified as noise are saved:

  • func/sub-<subject_label>_[specifiers]_AROMAnoiseICs.csv
  • func/sub-<subject_label>_[specifiers]_desc-MELODIC_mixing.tsv

FreeSurfer Derivatives

A FreeSurfer subjects directory is created in <output dir>/freesurfer.


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 init_bold_confs_wf.

For each BOLD run processed with fMRIPrep, a <output_folder>/fmriprep/sub-<sub_id>/func/sub-<sub_id>_task-<task_id>_run-<run_id>_desc-confounds_regressors.tsv file will be generated. These are TSV tables, which look like the example below:

csf   white_matter    global_signal   std_dvars       dvars   framewise_displacement  t_comp_cor_00   t_comp_cor_01   t_comp_cor_02   t_comp_cor_03   t_comp_cor_04   t_comp_cor_05   a_comp_cor_00   a_comp_cor_01   a_comp_cor_02   a_comp_cor_03   a_comp_cor_04   a_comp_cor_05   non_steady_state_outlier00      trans_x trans_y trans_z rot_x   rot_y   rot_z   aroma_motion_02 aroma_motion_04
682.75275     0.0     491.64752000000004      n/a     n/a     n/a     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     1.0     0.0     0.0     0.0     -0.00017029     -0.0    0.0     0.0     0.0
669.14166     0.0     489.4421        1.168398        17.575331       0.07211929999999998     -0.4506846719   0.1191909139    -0.0945884724   0.1542023065    -0.2302324641   0.0838194238    -0.032426848599999995   0.4284323184    -0.5809158299   0.1382414008    -0.1203486637   0.3783661265    0.0     0.0     0.0207752       0.0463124       -0.000270924    -0.0    0.0     -2.402958171    -0.7574011893
665.3969      0.0     488.03  1.085204        16.323903999999995      0.0348966       0.010819676200000001    0.0651895837    -0.09556632150000001    -0.033148835    -0.4768871111   0.20641088559999998     0.2818768463    0.4303863764    0.41323714850000004     -0.2115232212   -0.0037154909000000004  0.10636180070000001     0.0     0.0     0.0     0.0457372       0.0     -0.0    0.0     -1.341359143    0.1636017242
662.82715     0.0     487.37302       1.01591 15.281561       0.0333937       0.3328022893    -0.2220965269   -0.0912891436   0.2326688125    0.279138129     -0.111878887    0.16901660629999998     0.0550480212    0.1798747037    -0.25383302620000003    0.1646403629    0.3953613889    0.0     0.010164        -0.0103568      0.0424513       0.0     -0.0    0.00019174      -0.1554834655   0.6451987913

Each row of the file corresponds to one time point found in the corresponding BOLD time-series (stored in <output_folder>/fmriprep/sub-<sub_id>/func/sub-<sub_id>_task-<task_id>_run-<run_id>_desc-preproc_bold.nii.gz).

Columns represent the different confounds: csf and white_matter are the average signal inside the anatomically-derived CSF and WM masks across time; global_signal corresponds to the mean time series within the brain mask; two columns relate to the derivative of RMS variance over voxels (or DVARS), and both the original (dvars) and standardized (std_dvars) are provided; framewise_displacement is a quantification of the estimated bulk-head motion; trans_x, trans_y, trans_z, rot_x, rot_y, rot_z are the 6 rigid-body motion-correction parameters estimated by fMRIPrep; if present, non_steady_state_outlier_XX columns indicate non-steady state volumes with a single 1 value and 0 elsewhere (i.e., there is one non_steady_state_outlier_XX column per outlier/volume); six noise components are calculated using CompCor, according to both the anatomical (a_comp_cor_XX) and temporal (t_comp_cor_XX) variants; and the motion-related components identified by ICA-AROMA (if enabled) are indicated with aroma_motioon_XX.

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.

Confounds and “carpet”-plot on the visual reports

Some of the estimated confounds, as well as a “carpet” visualization of the BOLD 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).


[Power2016]Power JD, A simple but useful way to assess fMRI scan qualities. NeuroImage. 2016. doi: 10.1016/j.neuroimage.2016.08.009