Outputs of fMRIPrep¶
FMRIPrep generates three broad classes of outcomes:
- 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.
- 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.
- 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
.
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.
Confounds¶
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:
References
[Power2016] | Power JD, A simple but useful way to assess fMRI scan qualities. NeuroImage. 2016. doi: 10.1016/j.neuroimage.2016.08.009 |