Outputs of fMRIPrep

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

  3. Confounds: this is a special family of derivatives that can be utilized to inform subsequent denoising steps.

    Important

    In order to remain agnostic to any possible subsequent analysis, fMRIPrep does not perform any denoising (e.g., spatial smoothing) itself. There are two exceptions to this principle (described in their corresponding sections below):

    • ICA-AROMA’s non-aggressive denoised outputs, and

    • CompCor regressors, which are calculated after temporal high-pass filtering.

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.

Derivatives of fMRIPrep (preprocessed data)

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

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

sub-<subject_label>/
  anat/
    sub-<subject_label>[_space-<space_label>]_desc-preproc_T1w.nii.gz
    sub-<subject_label>[_space-<space_label>]_desc-brain_mask.nii.gz
    sub-<subject_label>[_space-<space_label>]_dseg.nii.gz
    sub-<subject_label>[_space-<space_label>]_label-CSF_probseg.nii.gz
    sub-<subject_label>[_space-<space_label>]_label-GM_probseg.nii.gz
    sub-<subject_label>[_space-<space_label>]_label-WM_probseg.nii.gz

Spatially-standardized derivatives are denoted with a space label, such as MNI152NLin2009cAsym, while derivatives in the original T1w space omit the space- keyword.

Additionally, the following transforms are saved:

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

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

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

And the affine translation (and inverse) between the original T1w sampling and FreeSurfer’s conformed space for surface reconstruction (fsnative) is stored in:

sub-<subject_label>/
  anat/
    sub-<subject_label>_from-fsnative_to-T1w_mode-image_xfm.txt
    sub-<subject_label>_from-T1w_to-fsnative_mode-image_xfm.txt

FreeSurfer derivatives

A FreeSurfer subjects directory is created in <output dir>/freesurfer, or the directory indicated with the --fs-subjects-dir flag.

<output_dir>/
    fmriprep/
        ...
    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.

Functional derivatives

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]:

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

Regularly gridded outputs (images). Volumetric output spaces labels (<space_label> above, and in the following) include T1w and MNI152NLin2009cAsym (default).

Surfaces, segmentations and parcellations from FreeSurfer. 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 mid-thickness surface mesh:

sub-<subject_label>/
  func/
    sub-<subject_label>_[specifiers]_space-T1w_desc-aparcaseg_dseg.nii.gz
    sub-<subject_label>_[specifiers]_space-T1w_desc-aseg_dseg.nii.gz
    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). Additionally, the BOLD can be resampled to fsLR surfaces, a hybrid version of the fsaverage atlas with both hemispheres in precise geographic correspondence, with varying mesh densities: 32k (default), 59k, and 164k.

Grayordinates files. CIFTI is a container format that holds both volumetric (regularly sampled in a grid) and surface (sampled on a triangular mesh) samples. Sub-cortical time series are volumetric (supported spaces: MNI152NLin6Asym), while cortical time series are sampled to surface (supported spaces: fsLR). If CIFTI outputs are requested (with the --cifti-outputs argument), the BOLD series are also saved as dtseries.nii CIFTI2 files:

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

CIFTI output resolution can be specified as an optional parameter after --cifti-output. By default, ‘91k’ outputs are produced and match up to the standard HCP Pipelines CIFTI output (91282 grayordinates @ 2mm). However, ‘170k’ outputs are also possible, and produce higher resolution CIFTI output (170494 grayordinates @ 1.6mm).

Extracted confounding time series. For each BOLD run processed with fMRIPrep, an accompanying confounds file will be generated. Confounds are saved as a TSV file:

sub-<subject_label>/
  func/
    sub-<subject_label>_[specifiers]_desc-confounds_regressors.tsv
    sub-<subject_label>_[specifiers]_desc-confounds_regressors.json

These TSV tables look like the example below, where each row of the file corresponds to one time point found in the corresponding BOLD time series:

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

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

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

Confounds

The BOLD signal measured with fMRI is a mixture of fluctuations of both neuronal and non-neuronal origin. Neuronal signals are measured indirectly as changes in the local concentration of oxygenated hemoglobin. Non-neuronal fluctuations in fMRI data may appear as a result of head motion, scanner noise, or physiological fluctuations (related to cardiac or respiratory effects). For a detailed review of the possible sources of noise in the BOLD signal, refer to [Greve2013].

Confounds (or nuisance regressors) are variables representing fluctuations with a potential non-neuronal origin. Such non-neuronal fluctuations may drive spurious results in fMRI data analysis, including standard activation GLM and functional connectivity analyses. It is possible to minimize confounding effects of non-neuronal signals by including them as nuisance regressors in the GLM design matrix or regressing them out from the fMRI data - a procedure known as denoising. There is currently no consensus on an optimal denoising strategy in the fMRI community. Rather, different strategies have been proposed, which achieve different compromises between how much of the non-neuronal fluctuations are effectively removed, and how much of neuronal fluctuations are damaged in the process. The fMRIPrep pipeline generates a large array of possible confounds.

The most well established confounding variables in neuroimaging are the six head-motion parameters (three rotations and three translations) - the common output of the head-motion correction (also known as realignment) of popular fMRI preprocessing software such as SPM or FSL. Beyond the standard head-motion parameters, the fMRIPrep pipeline generates a large array of possible confounds, which enable researchers to choose the most suitable denoising strategy for their downstream analyses.

Confounding variables calculated in fMRIPrep are stored separately for each subject, session and run in TSV files - one column for each confound variable. Such tabular files may include over 100 columns of potential confound regressors.

Danger

Do not include all columns of ~_desc-confounds_regressors.tsv table into your design matrix or denoising procedure. Filter the table first, to include only the confounds (or components thereof) you want to remove from your fMRI signal. The choice of confounding variables may depend on the analysis you want to perform, and may be not straightforward as no gold standard procedure exists. For a detailed description of various denoising strategies and their performance, see [Parkes2018] and [Ciric2017].

Confound regressors description

Basic confounds. The most commonly used confounding time series:

  • Estimated head-motion parameters: trans_x, trans_y, trans_z, rot_x, rot_y, rot_z - the 6 rigid-body motion parameters (3 translations and 3 rotation), estimated relative to a reference image;

  • Global signals:

    • csf - the average signal within anatomically-derived eroded CSF mask;

    • white_matter - the average signal within the anatomically-derived eroded WM masks;

    • global_signal - the average signal within the brain mask.

Parameter expansion of basic confounds. The standard six-motion parameters may not account for all the variance related to head-motion. [Friston1996] and [Satterthwaite2013] proposed an expansion of the six fundamental head-motion parameters. To make this technique more accessible, fMRIPrep automatically calculates motion parameter expansion [Satterthwaite2013], providing time series corresponding to the first temporal derivatives of the six base motion parameters, together with their quadratic terms, resulting in the total 24 head motion parameters (six base motion parameters + six temporal derivatives of six motion parameters + 12 quadratic terms of six motion parameters and their six temporal derivatives). Additionally, fMRIPrep returns temporal derivatives and quadratic terms for the three global signals (csf, white_matter and global_signal) to enable applying the 36-parameter denoising strategy proposed by [Satterthwaite2013].

Derivatives and quadratic terms are stored under column names with suffixes: _derivative1 and powers _power2. These are calculated for head-motion estimates (trans_ and rot_) and global signals (white_matter, csf, and global_signal).

Outlier detection. These confounds can be used to detect potential outlier time points - frames with sudden and large motion or intensity spikes.

  • framewise_displacement - is a quantification of the estimated bulk-head motion calculated using formula proposed by [Power2012];

  • dvars - the derivative of RMS variance over voxels (or DVARS) [Power2012];

  • std_dvars - standardized DVARS;

  • 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).

Detected outliers can be further removed from time series using methods such as: volume censoring - entirely discarding problematic time points [Power2012], regressing signal from outlier points in denoising procedure, or including outlier points in the subsequent first-level analysis when building the design matrix. Averaged value of confound (for example, mean framewise_displacement) can also be added as regressors in group level analysis [Yan2013]. Spike regressors for outlier censoring can also be generated from within fMRIPrep using the command line options --fd-spike-threshold and --dvars-spike-threshold (default: FD > 0.5 mm or DVARS > 1.5). Spike regressors are stored in separate motion_outlier_XX columns.

Discrete cosine-basis regressors. Physiological and instrumental (scanner) noise sources are generally present in fMRI data, typically taking the form of low-frequency signal drifts. To account for these drifts, temporal high-pass filtering is the immediate option. Alternatively, low-frequency regressors can be included in the statistical model to account for these confounding signals. Using the DCT basis functions, fMRIPrep generates these low-frequency predictors:

  • cosine_XX - DCT-basis regressors.

One characteristic of the cosine regressors is that they are identical for two different datasets with the same TR and the same effective number of time points (effective length). It is relevant to mention effective because initial time points identified as nonsteady states are removed before generating the cosine regressors.

Caution

If your analysis includes separate high-pass filtering, do not include cosine_XX regressors in your design matrix.

See also

CompCor confounds. CompCor is a PCA, hence component-based, noise pattern recognition method. In the method, principal components are calculated within an ROI that is unlikely to include signal related to neuronal activity, such as CSF and WM masks. Signals extracted from CompCor components can be further regressed out from the fMRI data with a denoising procedure [Behzadi2007].

  • a_comp_cor_XX - additional noise components are calculated using anatomical CompCor;

  • t_comp_cor_XX - additional noise components are calculated using temporal CompCor.

Four separate CompCor decompositions are performed to compute noise components: one temporal decomposition (t_comp_cor_XX) and three anatomical decompositions (a_comp_cor_XX) across three different noise ROIs: an eroded white matter mask, an eroded CSF mask, and a combined mask derived from the union of these.

Each confounds data file will also have a corresponding metadata file (~desc-confounds_regressors.json). Metadata files contain additional information about columns in the confounds TSV file:

{
  "a_comp_cor_00": {
    "CumulativeVarianceExplained": 0.1081970825,
    "Mask": "combined",
    "Method": "aCompCor",
    "Retained": true,
    "SingularValue": 25.8270209974,
    "VarianceExplained": 0.1081970825
  },
  "dropped_0": {
    "CumulativeVarianceExplained": 0.5965809597,
    "Mask": "combined",
    "Method": "aCompCor",
    "Retained": false,
    "SingularValue": 20.7955177198,
    "VarianceExplained": 0.0701465624
  }
}

For CompCor decompositions, entries include:

  • Method: anatomical or temporal CompCor.

  • Mask: denotes the ROI where the decomposition that generated the component was performed: CSF, WM, or combined for anatomical CompCor.

  • SingularValue: singular value of the component.

  • VarianceExplained: the fraction of variance explained by the component across the decomposition ROI mask.

  • CumulativeVarianceExplained: the total fraction of variance explained by this particular component and all preceding components.

  • Retained: Indicates whether the component was saved in desc-confounds_regressors.tsv for use in denoising. Entries that are not saved in the data file for denoising are still stored in metadata with the dropped prefix.

Caution

Only a subset of these CompCor decompositions should be used for further denoising. The original Behzadi aCompCor implementation [Behzadi2007] can be applied using components from the combined masks, while the more recent Muschelli implementation [Muschelli2014] can be applied using the WM and CSF masks. To determine the provenance of each component, consult the metadata file (described above).

There are many valid ways of selecting CompCor components for further denoising. In general, the components with the largest singular values (i.e., those that explain the largest fraction of variance in the data) should be selected. fMRIPrep outputs components in descending order of singular value. Common approaches include selecting a fixed number of components (e.g., the first 5 or 6), using a quantitative or qualitative criterion (e.g., elbow, broken stick, or condition number), or using sufficiently many components that a minimum cumulative fraction of variance is explained (e.g., 50%).

Caution

Similarly, if you are using anatomical or temporal CompCor it may not make sense to use the csf, or white_matter global regressors - see #1049. Conversely, using the overall global_signal confound in addition to CompCor’s regressors can be beneficial (see [Parkes2018]).

Danger

fMRIPrep does high-pass filtering before running anatomical or temporal CompCor. Therefore, when using CompCor regressors, the corresponding cosine_XX regressors should also be included in the design matrix.

See also

This didactic discussion on NeuroStars.org where Patrick Sadil gets into details about PCA and how that base technique applies to CompCor in general and fMRIPrep’s implementation in particular.

AROMA confounds. AROMA is an ICA based procedure to identify confounding time series related to head-motion [Prium2015]. ICA-AROMA can be enabled with the flag --use-aroma.

  • aroma_motion_XX - the motion-related components identified by ICA-AROMA.

Danger

If you are already using AROMA-cleaned data (~desc-smoothAROMAnonaggr_bold.nii.gz), do not include ICA-AROMA confounds during your design specification or denoising procedure.

Additionally, as per [Hallquist2013] and [Lindquist2019], when using AROMA-cleaned data most of the confound regressors should be recalculated (this feature is a work-in-progress, follow up on #1905). Surprisingly, our simulations (with thanks to JD. Kent) suggest that using the confounds as currently calculated by fMRIPrep –before denoising– would be just fine.

Caution

Nonsteady-states (or dummy scans) in the beginning of every run are dropped before ICA-AROMA is performed. Therefore, any subsequent analysis of ICA-AROMA outputs must drop the same number of nonsteady-states.

Confounds and “carpet”-plot on the visual reports

The visual reports provide several sections per task and run to aid designing a denoising strategy for subsequent analysis. Some of the estimated confounds are plotted with a “carpet” visualization of the BOLD time series [Power2016]. An example of these plots follows:

_images/sub-01_task-mixedgamblestask_run-01_bold_carpetplot.svg

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 color-map 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).

Noise components computed during each CompCor decomposition are evaluated according to the fraction of variance that they explain across the nuisance ROI. This is used by fMRIPrep to determine whether each component should be saved for use in denoising operations: a component is saved if it contributes to explaining the top 50 percent of variance in the nuisance ROI. fMRIPrep can be configured to save all components instead using the command line option --return-all-components. fMRIPrep reports include a plot of the cumulative variance explained by each component, ordered by descending singular value.

_images/sub-01_task-rest_compcor.svg

The figure displays the cumulative variance explained by components for each of four CompCor decompositions (left to right: anatomical CSF mask, anatomical white matter mask, anatomical combined mask, temporal). The number of components is plotted on the abscissa and the cumulative variance explained on the ordinate. Dotted lines indicate the minimum number of components necessary to explain 50%, 70%, and 90% of the variance in the nuisance mask. By default, only the components that explain the top 50% of the variance are saved.

Also included is a plot of correlations among confound regressors. This can be used to guide selection of a confound model or to assess the extent to which tissue-specific regressors correlate with global signal.

_images/sub-01_task-mixedgamblestask_run-01_confounds_correlation.svg

The left-hand panel shows the matrix of correlations among selected confound time series as a heat-map. Note the zero-correlation blocks near the diagonal; these correspond to each CompCor decomposition. The right-hand panel displays the correlation of selected confound time series with the mean global signal computed across the whole brain; the regressors shown are those with greatest correlation with the global signal. This information can be used to diagnose partial volume effects.

See implementation on init_bold_confs_wf.

References

Behzadi2007(1,2)

Behzadi Y, Restom K, Liau J, Liu TT, A component-based noise correction method (CompCor) for BOLD and perfusion-based fMRI. NeuroImage. 2007. doi:10.1016/j.neuroimage.2007.04.042

Ciric2017

Ciric R, Wolf DH, Power JD, Roalf DR, Baum GL, Ruparel K, Shinohara RT, Elliott MA, Eickhoff SB, Davatzikos C., Gur RC, Gur RE, Bassett DS, Satterthwaite TD. Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity. Neuroimage. 2017. doi:10.1016/j.neuroimage.2017.03.020

Greve2013

Greve DN, Brown GG, Mueller BA, Glover G, Liu TT, A Survey of the Sources of Noise in fMRI. Psychometrika. 2013. doi:10.1007/s11336-013-9344-2

Friston1996

Friston KJ1, Williams S, Howard R, Frackowiak RS, Turner R, Movement‐Related effects in fMRI time‐series. Magnetic Resonance in Medicine. 1996. doi:10.1002/mrm.191035031

Hallquist2013

Hallquist MN, Hwang K, Luna B. The Nuisance of Nuisance Regression. NeuroImage. 2013. doi:10.1016/j.neuroimage.2013.05.116

Lindquist2019

Lindquist, MA, Geuter, S, and Wager, TD, Caffo, BS, Modular preprocessing pipelines can reintroduce artifacts into fMRI data. Human Brain Mapping. 2019. doi: 10.1002/hbm.24528

Muschelli2014

Muschelli J, Nebel MB, Caffo BS, Barber AD, Pekar JJ, Mostofsky SH, Reduction of motion-related artifacts in resting state fMRI using aCompCor. NeuroImage. 2014. doi:10.1016/j.neuroimage.2014.03.028

Parkes2018(1,2)

Parkes L, Fulcher B, Yücel M, Fornito A, An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI. NeuroImage. 2018. doi:10.1016/j.neuroimage.2017.12.073

Power2012(1,2,3)

Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen, SA, Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage. 2012. doi:10.1016/j.neuroimage.2011.10.018

Power2016

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

Prium2015

Pruim RHR, Mennes M, van Rooij D, Llera A, Buitelaar JK, Beckmann CF. ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data. Neuroimage. 2015 May 15;112:267–77. doi:10.1016/j.neuroimage.2015.02.064.

Satterthwaite2013(1,2,3)

Satterthwaite TD, Elliott MA, Gerraty RT, Ruparel K, Loughead J, Calkins ME, Eickhoff SB, Hakonarson H, Gur RC, Gur RE, Wolf DH, An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data. NeuroImage. 2013. doi:10.1016/j.neuroimage.2012.08.052

Yan2013

Yan CG, Cheung B, Kelly C, Colcombe S, Craddock RC, Di Martino A, Li Q, Zuo XN, Castellanos FX, Milham MP, A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics. NeuroImage. 2013. doi:10.1016/j.neuroimage.2013.03.004