confound2 models artifactual signals present in a 4D time series image. The confound model created by this module can be used to mitigate effects of subject motion and other artifactual processes by residualizing the 4D image with respect to the confound model. (The regression/residualization procedure is managed separately in the regress module.) Several types of artifact can be modeled: physiological sources, including white matter and CSF signals; global signal; realignment parameters; and signals derived from principal component analysis (PCA, CompCor). Derivatives and squares can also be added to the confound model, as can signal during prior time points. It is a rewrite of the original confound module to use outputs from FMRIPREP.

Model construction order

Currently, the confound model is assembled in the following order:

  1. Add realignment parameters and mean time series from GM, WM, CSF, and global
  2. Add temporal derivatives of any time series in the model
  3. Add powers of any time series in the model (e.g., quadratic terms)
  4. Add component-based time series (CompCor)
  5. Add custom time series

So, for instance, including the second power will also include not only the squared time series, but also the squares of derivatives.

In the future, a control sequence will probably be implemented to support greater flexibility in confound models.

Module configuration


Realignment parameters

Early models that attempted to correct for the introduction of spurious variance by the movement of subjects in the scanner did so by regressing out the 6 parameters (3 translational, 3 rotational) used to realign each volume in the time series to a reference volume. Later work has demonstrated that a model consisting of realignment parameters alone is ineffective at removing motion artifact from functional MR time series.:

# Use realignment parameters

# No realignment parameters


Relative RMS displacement.

The relative root-mean-square displacement is estimated by FSL’s MCFLIRT. This is equivalent to the Jenkinson formulation of framewise displacement and is approximately double the Power formulation of framewise displacement. Using the relative RMS displacement as a confound time series is not recommended; this is an uncommon denoising strategy and is not likely to be effective.:

# Use RMS displacement

# No RMS displacement

confound2_wm, and confound2_csf

Tissue-based nuisance regressors, including aCompCor.

Tissue-based nuisance regressors are capable of reducing the influence of subject movement (as well as physiological artefacts) on the data. Mean white matter and cerebrospinal fluid signal are most often used to this end (e.g., Windischberger et al., 2002; Satterthwaite et al., 2012), but principal component analysis can also be used to extract signals of no interest from anatomical compartments (Behzadi et al., 2007: aCompCor). The number of aCompCor components removed can either be specified as a fixed number, or by the percent variance explained (usually this is 50% as in Muschelli et al., 2014.) This approach requires a known segmentation of the anatomical image into tissue classes. If you provided an output directory from the ANTsCT routine or the anatomical stream, then a segmentation will automatically be available as the derivative segmentation.:

# Do not use any white matter signals

# Use the mean white matter signal


  • 1 indicates that the mean time series over all voxels inside the tissue boundaries should be used in the confound model.

Use of tissue-based nuisance regressors requires a known segmentation of the anatomical image into tissue classes. If you provided an output directory from the ANTsCT routine or the anatomical stream, then a segmentation will automatically be available as the derivative segmentation. In some segmentations, such as the one output by ANTs Cortical Thickness, each tissue class is assigned a different intensity value in the segmentation volume. For instance, 1 might correspond to CSF, 2 to cortical grey matter, 3 to white matter, etc. If your segmentation is strictly a binary-valued white matter mask, then enter ALL. To enter a range of values, use the colon (:) delimiter; to enter multiple values, use the comma (,) delimiter.:

# Use a custom segmentation for WM

# Use the segmentation from ANTsCT or the anatomical stream for WM

# Use the mean CSF signal. Use the pipeline segmentation for CSF. 1=CSF in the provided CSF segmentation path.

In order to ensure that the signal extracted from the tissue or region of interest is not mixed with signal from adjacent voxels associated with a different tissue class (partial volume effects), it is possible to erode its mask by removing fringe voxels. An optimal degree of erosion will result in a mask comprising ‘deep’ voxels of the tissue, while excessive erosion may result in a mask whose extent is poorly representative of the tissue. For functional connectivity analysis, more aggressive erosion of WM and CSF masks is recommended to reduce collinearity of WM and CSF signal with global and GM signals. Erosion to a target range of 5 to 10 percent is recommended in this case.:

# Erode CSF mask to the deepest 10 percent

# Erode WM mask to the deepest 5 percent

The value of confound_<tissue>_ero specifies the level of erosion that is to be applied to tissue masks. Allowable values range from 0 to 100 and reflect the minimum percentage of tissue remaining after erosion cycles have been applied. For instance, a value of 30 indicates that the tissue mask should be eroded to 30 percent its original size; that is, the mask will comprise only the deepest 30 percent of voxels with the tissue classification. (Depth is computed using ImageMath from ANTs, and the erosion is implemented in the utility erodespare.)

For advanced users: The confound module offers the option of including up to three tissue- or RoI-based regressors. While nominally these are the mean GM, WM, and CSF timeseries, it is possible to include signals from any three RoIs for which a binary mask is available by assigning the appropriate value to the <tissue>_path variable.


Global signal regression.

Removal of the mean signal across the entire brain is one of the simplest and most effective means of attenuating the influence of artefactual sources such as subject motion. While earlier studies suggested that global signal regression might be harmful, for instance by introducing artefactual anticorrelations (Murphy et al., 2009) or group differences (Saad et al., 2012), an emerging consensus (e.g., Power et al., 2014; Chai et al., 2012) suggests instead that it is uniquely effective in removing widespread forms of artefact (due to both motion and physiological processes such as respiration).:

# Enable GSR (recommended for functional connectivity analysis)

# Disable GSR


Expansion: previous time points.

Including forward-shifted realignment and nuisance timeseries in the nuisance model (Friston et al., 1996) provides a means of factoring in the subject’s history of motion and for the lingering effects of motion, which may persist for upwards of 10 seconds following motion itself. confound_past must be a nonnegative integer.:

# Include no previous time points

# Include previous time point

# Include previous 2 time points

Note: Do not include both previous time points (confound2_past) and temporal derivatives (confound2_dx) in the same model. Together with the original time series, they form a collinear triple, which will result in an overspecified model. That is to say, for a time series \(T\), its temporal derivative \(D\), and previous/shifted time series \(P\),

\(D + P = T\)


Expansion: temporal derivatives.

Temporal derivatives of motion parameters encode the relative displacement of the brain from one volume of a timeseries to the next; they are used in major confound models (e.g., Satterthwaite et al., 2012). confound2_dx must be a nonnegative integer.:

# Include no temporal derivatives

# Include first temporal derivative

# Include first and second temporal derivatives

Note: Do not include both previous time points (confound2_past) and temporal derivatives (confound2_dx) in the same model. Together with the original time series, they form a collinear triple, which will result in an overspecified model. That is to say, for a time series \(T\), its temporal derivative \(D\), and previous/shifted time series \(P\),

\(D + P = T\)


Expansion: powers (quadratic, cubic, quartic, etc.).

In addition to the first power of each confound, you may elect to include higher powers to account for potential noise that is proportional to squares or higher powers of motion parameters and nuisance regressors.:

# First power only

# First power and quadratic expansion

# First power, quadratic and cubic expansions


Custom regressors.

In addition to regressors generated from the image data, custom regressors can be added to the nuisance model. For instance, these might include respiratory traces convolved with an appropriate response function or estimates of task-driven activity. Custom regressors should be formatted as a matrix with regressor time series in columns and time points/frames in rows.:

# No custom regressors

# Include a custom regressor file

# Include custom regressors in multiple files


Ordinarily, each module will detect whether a particular analysis has run to completion before beginning it. If re-running is disabled, then the module will immediately skip to the next stage of analysis. Otherwise, any completed analyses will be repeated.If you change the run parameters, you should rerun any modules downstream of the change.:

# Skip processing steps if the pipeline detects the expected output

# Repeat all processing steps


Modules often produce numerous intermediate temporary files and images during the course of an analysis. In many cases, these temporary files are undesirable and unnecessarily consume disk space. If cleanup is enabled, any files stamped as temporary will be deleted when a module successfully runs to completion. If a module fails to detect the output that it expects, then temporary files will be retained to facilitate error diagnosis.:

# Remove temporary files

# Retain temporary files

Example configuration: 36-parameters model

The 36-parameter confound model includes 6 realignment parameters, mean WM and CSF time series, and global signal regression (9 parameters). Additionally, the 36-parameter model includes temporal derivatives of these 9 time series (+9) and squares of the original 9 parameters and of their temporal derivatives (+18) for a total of 36 parameters. As an illustrative example for confound2 module configuration, the variable settings for configuring a 36-parameter model are shown here. The example configuration uses a standard 6-class segmentation, such as that output by the ANTs Cortical Thickness pipeline when provided appropriate priors.: