Outputs of NiBabies

NiBabies outputs conform to the BIDS Derivatives specification (see BIDS Derivatives, along with the upcoming BEP 011 and BEP 012_). NiBabies 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 NiBabies operations.

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 anatomical space or in some standard space.

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

Warning

Confounds, particularly a/t CompCor, are still in alpha and require additional testing.

Important

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

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

Layout

Assuming NiBabies is invoked with:

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

The outputs will be a BIDS Derivatives dataset of the form:

<output_dir>/
├── logs/
├── sub-<label>/
├── sub-<label>[_ses-<label>].html
├── dataset_description.json
└── .bidsignore

For each participant in the dataset, a directory of derivatives (sub-<label>/) and a visual report (sub-<label>.html) are generated. The log directory contains citation boilerplate_ text. dataset_description.json is a metadata file in which NiBabies records metadata recommended by the BIDS standard.

fMRIPrep deviation - multi-session processing

Due to the potential for significant development across sessions, NiBabies processes at the session level. Each unique session will be nested within a subject’s output directory, and an individual report will be created (i.e. sub-<label>_ses-<label>.html).

Processing level

As of version 24.0.0, NiBabies supports three levels of derivatives:

• --level minimal: This processing mode aims to produce the smallest working directory and output dataset possible, while enabling all further processing results to be deterministically generated. Most components of the visual reports_ can be generated at this level, so the quality of preprocessing can be assessed. Because no resampling is done, confounds and carpetplots will be missing from the reports.

• --level resampling: This processing mode aims to produce additional derivatives that enable third-party resampling, resampling BOLD series in the working directory as needed, but these are not saved to the output directory. The --me-output-echos flag will be enabled at this level, in which case the individual echos will be saved to the working directory after slice-timing correction, head-motion correction, and susceptibility distortion correction.

• --level full: This processing mode aims to produce all derivatives that have previously been a part of the NiBabies output dataset. This is the default processing level.

Visual Reports

NiBabies outputs summary reports, written to <output dir>/nibabies/sub-<subject_label>[_ses-<session_label>].html. These reports provide a quick way to make visual inspection of the results easy. View a sample report. <_static/SampleReport/sample_report.html>_

Derivatives of NiBabies (preprocessed data)

Preprocessed, or derivative, data are written to <output dir>/sub-<subject_label>/. If the data is composed of sessions, each session will have a separate output. The BIDS Derivatives_ specification describes the naming and metadata conventions we follow.

Anatomical derivatives

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

sub-<subject_label>/[ses-<session_label>/]
  anat/
    sub-<subject_label>[_space-<space_label>]_desc-preproc_T1w.nii.gz
    sub-<subject_label>[_space-<space_label>]_desc-preproc_T2w.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.

Changed in version 24.0.0: The anatomical output reference can now be either in T1w or T2w space, and will always be denoted with the space-{T1w/T2w} entity.

Additionally, the following transforms are saved

sub-<subject_label>/[ses-<session_label>/]
  anat/
    sub-<subject_label>_from-MNIInfant+<cohort>_to-<anat>_mode-image_xfm.h5
    sub-<subject_label>_from-<anat>_to-MNIInfant+<cohort>_mode-image_xfm.h5

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

sub-<subject_label>/[ses-<session_label>/]
  anat/
    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

The registration spheres target fsaverage and fsLR spaces.

Warning

Unlike fMRIPrep, MSMSulc support is not available at the moment.

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

sub-<subject_label>/[ses-<session_label>/]
  anat/
    sub-<subject_label>_from-fsnative_to-anat_mode-image_xfm.txt
    sub-<subject_label>_from-anat_to-fsnative_mode-image_xfm.txt

Finally, cortical thickness, curvature, and sulcal depth maps are converted to GIFTI and CIFTI-2::

sub-<subject_label>/[ses-<session_label>/]
  anat/
    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

Warning

GIFTI metric files follow the FreeSurfer conventions and are not modified by NiBabies in any way.

The Human Connectome Project (HCP) inverts the sign of the curvature and sulcal depth maps. For consistency with HCP, NiBabies follows these conventions and masks the medial wall of CIFTI-2 dscalar files.

TODO: FSDERIVS? .. _fsderivs:

Surface Reconstruction

If any of the surface reconstruction methods are enabled, then a FreeSurfer-like subjects directory is created in <output dir>/sourcedata/freesurfer or the directory indicated with the --fs-subjects-dir flag.

Additionally, FreeSurfer segmentations are resampled into the BOLD space, and lookup tables are provided.

<output_dir>/
  sourcedata/
    freesurfer/
      fsaverage{,5,6}/
          mri/
          surf/
          ...
      sub-<label>/
          mri/
          surf/
          ...
      ...
  desc-aparc_dseg.tsv
  desc-aparcaseg_dseg.tsv

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.

Note that the use of sourcedata/ recognizes FreeSurfer derivatives as an input to the NiBabies workflow. This is strictly true when pre-computed FreeSurfer derivatives are provided either in the sourcedata/ directory or passed via the --fs-subjects-dir flag; if NiBabies runs FreeSurfer, then there is a mutual dependency.

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>/[ses-<session_label>/]
    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

Note

The mask file is part of the minimal processing level. The BOLD series is only generated at the full processing level.

Motion correction outputs

Head-motion correction (HMC) produces a reference image to which all volumes are aligned, and a corresponding transform that maps the original BOLD series to the reference image::

sub-<subject_label>/[ses-<session_label>/]
  func/
    sub-<subject_label>_[specifiers]_desc-hmc_boldref.nii.gz
    sub-<subject_label>_[specifiers]_from-orig_to_boldref_mode-image_desc-hmc_xfm.nii.gz

Note

Motion correction outputs are part of the minimal processing level.

Coregistration outputs

Registration of the BOLD series to the anatomical reference image generates a further reference image and affine transform

sub-<subject_label>/[ses-<session_label>/]
  func/
    sub-<subject_label>_[specifiers]_desc-coreg_boldref.nii.gz
    sub-<subject_label>_[specifiers]_from-boldref_to-<anat>_mode-image_desc-coreg_xfm.txt

Note

Coregistration outputs are part of the minimal processing level.

Fieldmap registration

If a fieldmap is used for the correction of a BOLD series, then a registration is calculated between the BOLD series and the fieldmap. If, for example, the fieldmap is identified with "B0Identifier": "TOPUP", the generated transform will be named

sub-<subject_label>/[ses-<session_label>/]
  func/
    sub-<subject_label>_[specifiers]_from-boldref_to-TOPUP_mode-image_xfm.nii.gz

If the association is discovered through the IntendedFor field of the fieldmap metadata, then the transform will be given an auto-generated name

sub-<subject_label>/[ses-<session_label>/]
  func/
    sub-<subject_label>_[specifiers]_from-boldref_to-auto000XX_mode-image_xfm.txt

Note

Fieldmap registration outputs are part of the minimal processing level.

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 anatomical reference space and resampled to the BOLD grid, and the BOLD series are resampled to the mid-thickness surface mesh

sub-<subject_label>/[ses-<session_label>/]
  func/
    sub-<subject_label>_[specifiers]_space-<anat>_desc-aparcaseg_dseg.nii.gz
    sub-<subject_label>_[specifiers]_space-<anat>_desc-aseg_dseg.nii.gz
    sub-<subject_label>_[specifiers]_hemi-[LR]_space-<space_label>_bold.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).

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 sampled on a regular grid derived from one MNI template, while cortical time series are sampled on surfaces projected from the [1] template. If CIFTI outputs are requested (with the --cifti-outputs argument), the BOLD series are also saved as dtseries.nii CIFTI2 files

sub-<subject_label>/[ses-<session_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 NiBabies, an accompanying confounds file will be generated. Confounds_ are saved as a TSV file::

sub-<subject_label>/[ses-<session_label>/]
  func/
    sub-<subject_label>_[specifiers]_desc-confounds_timeseries.tsv
    sub-<subject_label>_[specifiers]_desc-confounds_timeseries.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
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
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

Multi-echo derivatives

For multi-echo datasets, the output _bold series are “optimally combined” by tedana_ to better estimate the BOLD signal. This process also generates a T2* map, which is resampled into every requested output space.

sub-<subject_label>/[ses-<session_label>/]
  func/
    sub-<subject_label>_[specifiers]_T2starmap.nii.gz

If the --me-output-echos flag is specified, then the distortion-corrected (STC, HMC, SDC) per-echo time series are output. For example, if the inputs are of the form::

sub-01/
  func/
    sub-01_task-rest_echo-1_bold.nii.gz
    sub-01_task-rest_echo-2_bold.nii.gz
    sub-01_task-rest_echo-3_bold.nii.gz

Then the output will include::

sub-01/
  func/
    sub-01_task-rest_boldref.nii.gz
    sub-01_task-rest_desc-brain_mask.nii.gz
    sub-01_task-rest_echo-1_desc-preproc_bold.nii.gz
    sub-01_task-rest_echo-2_desc-preproc_bold.nii.gz
    sub-01_task-rest_echo-3_desc-preproc_bold.nii.gz

These may then be used independently with multi-echo tools, such as tedana_, to perform more advanced denoising or alternative combination strategies.

Danger

Slice timing correction in NiBabies is referenced to the middle slice by default, which leads to a time shift in the volume onsets by 0.5 TR (repetition time). For example, assuming a TR of 2s, original onsets of 0, 2, and 4s would be shifted to 1, 3, and 5s, respectively. In case you did execute slice timing correction, you must check that subsequent analyses (e.g., general linear modeling) consider the right onset shifts. For example, when specifying a first-level model, you should set parameters in your software package or first-level model function accordingly (e.g., select the middle slice as reference). Alternatively, you could manually adjust the volume onsets (e.g. as mentioned in the example above from [0, 2, 4] to [1, 3, 5]) or the event onsets accordingly. In contrast to volume onsets, event onsets need to be shifted backward by half a TR, for example, from [5, 10, 15] to [4, 9, 14].

Further information on this issue is found at this blog post (with thanks to Russell Poldrack and Jeanette Mumford).

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 [2].

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 NiBabies 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 NiBabies 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 NiBabies 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_timeseries.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, NiBabies 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, NiBabies 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 [3];

• rmsd - is a quantification of the estimated relative (frame-to-frame) bulk head motion calculated using the RMS approach of [4];

• dvars - the derivative of RMS variance over voxels (or DVARS) [3];

• 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 [3], 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 [5]. Regressors of motion spikes for outlier censoring are generated from within NiBabies, and their calculation may be adjusted with the command line options --fd-spike-threshold and --dvars-spike-threshold (defaults are FD > 0.5 mm or DVARS > 1.5). Regressors of motion spikes 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, NiBabies 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

• A detailed explanation about temporal high-pass filtering is provided with the BrainVoyager User Guide.

• This comment on an issue regarding CompCor regressors.

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 [6].

• 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_timeseries.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 [6] can be applied using components from the combined masks, while the more recent Muschelli implementation [7] 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. NiBabies 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 fmriprep#1049. Conversely, using the overall global_signal confound in addition to CompCor’s regressors can be beneficial (see [8]).

Danger

NiBabies 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.

Confounds estimated from the brain’s outer edge

Reusing the implementation of aCompCor, NiBabies generates regressors corresponding to the 24 first principal components extracted with PCA using the voxel time-series delineated by the brain’s outer edge (crown) mask. The procedure essentially follows the initial proposal of the approach by Patriat et al. [9] and is described in our ISMRM abstract [10].

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 [11].

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 NiBabies 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. NiBabies can be configured to save all components instead using the command line option --return-all-components. NiBabies reports include a plot of the cumulative variance explained by each component, ordered by descending singular value.

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.

See implementation on <python#nibabies.workflows.bold.confounds.init_bold_confs_wf>.

References

---

[1]

Glasser MF, Coalson TS Robinson EC, Hacker CD, Harwell J, Yacoub E, Ugurbil K, Andersson J, Beckmann CF, Jenkinson M, Smith SM, Van Essen DC. A multi-modal parcellation of human cerebral cortex. Nature. 2016. doi:10.1038/nature18933

[2]

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

[3] (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](https://doi.org/10.1016/j.neuroimage.2011.10.018=[]

[4]

Jenkinson M, Bannister P, Brady M, Smith S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage. 2002. doi:10.1016/s1053-8119(02)91132-8

[5]

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

[6] (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

[7]

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

[8]

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