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# nipype.algorithms.confounds module

Algorithms to compute confounds in fMRI

## ACompCor

Link to code

> Bases: CompCor

> Anatomical compcor: for inputs and outputs, see CompCor. When the mask provided is an anatomical mask, then CompCor is equivalent to ACompCor.

> realigned_filea pathlike object or string representing an existing file

>   Already realigned brain image (4D).

> components_filea string

>   Filename to store physiological components. (Nipype default value: `components_file.txt`)

> failure_mode‘error’ or ‘NaN’

>   When no components are found or convergence fails, raise an error or silently return columns of NaNs. (Nipype default value: `error`)

> header_prefixa string

>   The desired header for the output tsv file (one column). If undefined, will default to “CompCor”.

> high_pass_cutoffa float

>   Cutoff (in seconds) for “cosine” pre-filter. (Nipype default value: `128`)

> ignore_initial_volumesan integer >= 0

>   Number of volumes at start of series to ignore. (Nipype default value: `0`)

> mask_filesa list of items which are a pathlike object or string representing an existing file

>   One or more mask files that determines ROI (3D). When more that one file is provided `merge_method` or `merge_index` must be provided.

> mask_indexan integer >= 0

>   Position of mask in `mask_files` to use - first is the default. Mutually exclusive with inputs: `merge_method`. Requires inputs: `mask_files`.

> mask_namesa list of items which are a string

>   Names for provided masks (for printing into metadata). If provided, it must be as long as the final mask list (after any merge and indexing operations).

> merge_method‘union’ or ‘intersect’ or ‘none’

>   Merge method if multiple masks are present - `union` uses voxels included in at least one input mask, `intersect` uses only voxels present in all input masks, `none` performs CompCor on each mask individually. Mutually exclusive with inputs: `mask_index`. Requires inputs: `mask_files`.

> num_componentsan integer >= 1 or ‘all’

>   Number of components to return from the decomposition. If `num_components` is `all`, then all components will be retained. Mutually exclusive with inputs: `variance_threshold`.

> pre_filter‘polynomial’ or ‘cosine’ or False

>   Detrend time series prior to component extraction. (Nipype default value: `polynomial`)

> regress_poly_degreean integer >= 1

>   The degree polynomial to use. (Nipype default value: `1`)

> repetition_timea float

>   Repetition time (TR) of series - derived from image header if unspecified.

> save_metadataa boolean or a pathlike object or string representing a file

>   Save component metadata as text file. (Nipype default value: `False`)

> save_pre_filtera boolean or a pathlike object or string representing a file

>   Save pre-filter basis as text file. (Nipype default value: `False`)

> use_regress_polya boolean

>   Use polynomial regression pre-component extraction.

> variance_threshold0.0 < a floating point number < 1.0

>   Select the number of components to be returned automatically based on their ability to explain variance in the dataset. `variance_threshold` is a fractional value between 0 and 1; the number of components retained will be equal to the minimum number of components necessary to explain the provided fraction of variance in the masked time series. Mutually exclusive with inputs: `num_components`.

> components_filea pathlike object or string representing an existing file

>   Text file containing the noise components.

> metadata_filea pathlike object or string representing a file

>   Text file containing component metadata.

> pre_filter_filea pathlike object or string representing a file

>   Text file containing high-pass filter basis.

## CompCor

Link to code

> Bases: SimpleInterface

> Interface with core CompCor computation, used in aCompCor and tCompCor.

> CompCor provides three pre-filter options, all of which include per-voxel mean removal:

> > - `'polynomial'`: Legendre polynomial basis

> > - `'cosine'`: Discrete cosine basis

> > - `False`: mean-removal only

> In the case of `polynomial` and `cosine` filters, a pre-filter file may be saved with a row for each volume/timepoint, and a column for each non-constant regressor. If no non-constant (mean-removal) columns are used, this file may be empty.

> If `ignore_initial_volumes` is set, then the specified number of initial volumes are excluded both from pre-filtering and CompCor component extraction. Each column in the components and pre-filter files are prefixe with zeros for each excluded volume so that the number of rows continues to match the number of volumes in the input file. In addition, for each excluded volume, a column is added to the pre-filter file with a 1 in the corresponding row.

> Example

>     >>> ccinterface = CompCor()
>     >>> ccinterface.inputs.realigned_file = 'functional.nii'
>     >>> ccinterface.inputs.mask_files = 'mask.nii'
>     >>> ccinterface.inputs.num_components = 1
>     >>> ccinterface.inputs.pre_filter = 'polynomial'
>     >>> ccinterface.inputs.regress_poly_degree = 2

> realigned_filea pathlike object or string representing an existing file

>   Already realigned brain image (4D).

> components_filea string

>   Filename to store physiological components. (Nipype default value: `components_file.txt`)

> failure_mode‘error’ or ‘NaN’

>   When no components are found or convergence fails, raise an error or silently return columns of NaNs. (Nipype default value: `error`)

> header_prefixa string

>   The desired header for the output tsv file (one column). If undefined, will default to “CompCor”.

> high_pass_cutoffa float

>   Cutoff (in seconds) for “cosine” pre-filter. (Nipype default value: `128`)

> ignore_initial_volumesan integer >= 0

>   Number of volumes at start of series to ignore. (Nipype default value: `0`)

> mask_filesa list of items which are a pathlike object or string representing an existing file

>   One or more mask files that determines ROI (3D). When more that one file is provided `merge_method` or `merge_index` must be provided.

> mask_indexan integer >= 0

>   Position of mask in `mask_files` to use - first is the default. Mutually exclusive with inputs: `merge_method`. Requires inputs: `mask_files`.

> mask_namesa list of items which are a string

>   Names for provided masks (for printing into metadata). If provided, it must be as long as the final mask list (after any merge and indexing operations).

> merge_method‘union’ or ‘intersect’ or ‘none’

>   Merge method if multiple masks are present - `union` uses voxels included in at least one input mask, `intersect` uses only voxels present in all input masks, `none` performs CompCor on each mask individually. Mutually exclusive with inputs: `mask_index`. Requires inputs: `mask_files`.

> num_componentsan integer >= 1 or ‘all’

>   Number of components to return from the decomposition. If `num_components` is `all`, then all components will be retained. Mutually exclusive with inputs: `variance_threshold`.

> pre_filter‘polynomial’ or ‘cosine’ or False

>   Detrend time series prior to component extraction. (Nipype default value: `polynomial`)

> regress_poly_degreean integer >= 1

>   The degree polynomial to use. (Nipype default value: `1`)

> repetition_timea float

>   Repetition time (TR) of series - derived from image header if unspecified.

> save_metadataa boolean or a pathlike object or string representing a file

>   Save component metadata as text file. (Nipype default value: `False`)

> save_pre_filtera boolean or a pathlike object or string representing a file

>   Save pre-filter basis as text file. (Nipype default value: `False`)

> use_regress_polya boolean

>   Use polynomial regression pre-component extraction.

> variance_threshold0.0 < a floating point number < 1.0

>   Select the number of components to be returned automatically based on their ability to explain variance in the dataset. `variance_threshold` is a fractional value between 0 and 1; the number of components retained will be equal to the minimum number of components necessary to explain the provided fraction of variance in the masked time series. Mutually exclusive with inputs: `num_components`.

> components_filea pathlike object or string representing an existing file

>   Text file containing the noise components.

> metadata_filea pathlike object or string representing a file

>   Text file containing component metadata.

> pre_filter_filea pathlike object or string representing a file

>   Text file containing high-pass filter basis.

## ComputeDVARS

Link to code

> Bases: BaseInterface

> Computes the DVARS.

> in_filea pathlike object or string representing an existing file

>   Functional data, after HMC.

> in_maska pathlike object or string representing an existing file

>   A brain mask.

> figdpian integer

>   Output dpi for the plot. (Nipype default value: `100`)

> figformat‘png’ or ‘pdf’ or ‘svg’

>   Output format for figures. (Nipype default value: `png`)

> figsizea tuple of the form: (a float, a float)

>   Output figure size. (Nipype default value: `(11.7, 2.3)`)

> intensity_normalizationa float

>   Divide value in each voxel at each timepoint by the median calculated across all voxelsand timepoints within the mask (if specified)and then multiply by the value specified bythis parameter. By using the default (1000)output DVARS will be expressed in x10 % BOLD units compatible with Power et al.2012. Set this to 0 to disable intensitynormalization altogether. (Nipype default value: `1000.0`)

> remove_zerovariancea boolean

>   Remove voxels with zero variance. (Nipype default value: `True`)

> save_alla boolean

>   Output all DVARS. (Nipype default value: `False`)

> save_nstda boolean

>   Save non-standardized DVARS. (Nipype default value: `False`)

> save_plota boolean

>   Write DVARS plot. (Nipype default value: `False`)

> save_stda boolean

>   Save standardized DVARS. (Nipype default value: `True`)

> save_vxstda boolean

>   Save voxel-wise standardized DVARS. (Nipype default value: `False`)

> series_tra float

>   Repetition time in sec.

> variance_tola float

>   Maximum variance to consider “close to” zero for the purposes of removal. (Nipype default value: `1e-07`)

> avg_nstd : a float avg_std : a float avg_vxstd : a float fig_nstd : a pathlike object or string representing an existing file

> > Output DVARS plot.

> fig_stda pathlike object or string representing an existing file

>   Output DVARS plot.

> fig_vxstda pathlike object or string representing an existing file

>   Output DVARS plot.

> out_alla pathlike object or string representing an existing file

>   Output text file.

> out_nstda pathlike object or string representing an existing file

>   Output text file.

> out_stda pathlike object or string representing an existing file

>   Output text file.

> out_vxstda pathlike object or string representing an existing file

>   Output text file.

## FramewiseDisplacement

Link to code

> Bases: BaseInterface

> Calculate the FD as in [Power2012]. This implementation reproduces the calculation in fsl_motion_outliers

> [Power2012] (1,2)

> Power et al., Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion, NeuroImage 59(3), 2012. doi:10.1016/j.neuroimage.2011.10.018.

> in_filea pathlike object or string representing an existing file

>   Motion parameters.

> parameter_source‘FSL’ or ‘AFNI’ or ‘SPM’ or ‘FSFAST’ or ‘NIPY’

>   Source of movement parameters.

> figdpian integer

>   Output dpi for the FD plot. (Nipype default value: `100`)

> figsizea tuple of the form: (a float, a float)

>   Output figure size. (Nipype default value: `(11.7, 2.3)`)

> normalizea boolean

>   Calculate FD in mm/s. (Nipype default value: `False`)

> out_figurea pathlike object or string representing a file

>   Output figure name. (Nipype default value: `fd_power_2012.pdf`)

> out_filea pathlike object or string representing a file

>   Output file name. (Nipype default value: `fd_power_2012.txt`)

> radiusa float

>   Radius in mm to calculate angular FDs, 50mm is the default since it is used in Power et al. 2012. (Nipype default value: `50`)

> save_plota boolean

>   Write FD plot. (Nipype default value: `False`)

> series_tra float

>   Repetition time in sec.

> fd_averagea float

>   Average FD.

> out_figurea pathlike object or string representing a file

>   Output image file.

> out_filea pathlike object or string representing a file

>   Calculated FD per timestep.

## NonSteadyStateDetector

Link to code

> Bases: BaseInterface

> Returns the number of non-steady state volumes detected at the beginning of the scan.

> in_filea pathlike object or string representing an existing file

>   4D NIFTI EPI file.

> n_volumes_to_discardan integer

>   Number of non-steady state volumesdetected in the beginning of the scan.

## TCompCor

Link to code

> Bases: CompCor

> Interface for tCompCor. Computes a ROI mask based on variance of voxels.

> Example

>     >>> ccinterface = TCompCor()
>     >>> ccinterface.inputs.realigned_file = 'functional.nii'
>     >>> ccinterface.inputs.mask_files = 'mask.nii'
>     >>> ccinterface.inputs.num_components = 1
>     >>> ccinterface.inputs.pre_filter = 'polynomial'
>     >>> ccinterface.inputs.regress_poly_degree = 2
>     >>> ccinterface.inputs.percentile_threshold = .03

> realigned_filea pathlike object or string representing an existing file

>   Already realigned brain image (4D).

> components_filea string

>   Filename to store physiological components. (Nipype default value: `components_file.txt`)

> failure_mode‘error’ or ‘NaN’

>   When no components are found or convergence fails, raise an error or silently return columns of NaNs. (Nipype default value: `error`)

> header_prefixa string

>   The desired header for the output tsv file (one column). If undefined, will default to “CompCor”.

> high_pass_cutoffa float

>   Cutoff (in seconds) for “cosine” pre-filter. (Nipype default value: `128`)

> ignore_initial_volumesan integer >= 0

>   Number of volumes at start of series to ignore. (Nipype default value: `0`)

> mask_filesa list of items which are a pathlike object or string representing an existing file

>   One or more mask files that determines ROI (3D). When more that one file is provided `merge_method` or `merge_index` must be provided.

> mask_indexan integer >= 0

>   Position of mask in `mask_files` to use - first is the default. Mutually exclusive with inputs: `merge_method`. Requires inputs: `mask_files`.

> mask_namesa list of items which are a string

>   Names for provided masks (for printing into metadata). If provided, it must be as long as the final mask list (after any merge and indexing operations).

> merge_method‘union’ or ‘intersect’ or ‘none’

>   Merge method if multiple masks are present - `union` uses voxels included in at least one input mask, `intersect` uses only voxels present in all input masks, `none` performs CompCor on each mask individually. Mutually exclusive with inputs: `mask_index`. Requires inputs: `mask_files`.

> num_componentsan integer >= 1 or ‘all’

>   Number of components to return from the decomposition. If `num_components` is `all`, then all components will be retained. Mutually exclusive with inputs: `variance_threshold`.

> percentile_threshold0.0 < a floating point number < 1.0

>   The percentile used to select highest-variance voxels, represented by a number between 0 and 1, exclusive. By default, this value is set to .02. That is, the 2% of voxels with the highest variance are used. (Nipype default value: `0.02`)

> pre_filter‘polynomial’ or ‘cosine’ or False

>   Detrend time series prior to component extraction. (Nipype default value: `polynomial`)

> regress_poly_degreean integer >= 1

>   The degree polynomial to use. (Nipype default value: `1`)

> repetition_timea float

>   Repetition time (TR) of series - derived from image header if unspecified.

> save_metadataa boolean or a pathlike object or string representing a file

>   Save component metadata as text file. (Nipype default value: `False`)

> save_pre_filtera boolean or a pathlike object or string representing a file

>   Save pre-filter basis as text file. (Nipype default value: `False`)

> use_regress_polya boolean

>   Use polynomial regression pre-component extraction.

> variance_threshold0.0 < a floating point number < 1.0

>   Select the number of components to be returned automatically based on their ability to explain variance in the dataset. `variance_threshold` is a fractional value between 0 and 1; the number of components retained will be equal to the minimum number of components necessary to explain the provided fraction of variance in the masked time series. Mutually exclusive with inputs: `num_components`.

> components_filea pathlike object or string representing an existing file

>   Text file containing the noise components.

> high_variance_masksa list of items which are a pathlike object or string representing an existing file

>   Voxels exceeding the variance threshold.

> metadata_filea pathlike object or string representing a file

>   Text file containing component metadata.

> pre_filter_filea pathlike object or string representing a file

>   Text file containing high-pass filter basis.

## TSNR

Link to code

> Bases: BaseInterface

> Computes the time-course SNR for a time series

> Typically you want to run this on a realigned time-series.

> Example

>     >>> tsnr = TSNR()
>     >>> tsnr.inputs.in_file = 'functional.nii'
>     >>> res = tsnr.run()

> in_filea list of items which are a pathlike object or string representing an existing file

>   Realigned 4D file or a list of 3D files.

> detrended_filea pathlike object or string representing a file

>   Input file after detrending. (Nipype default value: `detrend.nii.gz`)

> mean_filea pathlike object or string representing a file

>   Output mean file. (Nipype default value: `mean.nii.gz`)

> regress_polyan integer >= 1

>   Remove polynomials.

> stddev_filea pathlike object or string representing a file

>   Output tSNR file. (Nipype default value: `stdev.nii.gz`)

> tsnr_filea pathlike object or string representing a file

>   Output tSNR file. (Nipype default value: `tsnr.nii.gz`)

> detrended_filea pathlike object or string representing a file

>   Detrended input file.

> mean_filea pathlike object or string representing an existing file

>   Mean image file.

> stddev_filea pathlike object or string representing an existing file

>   Std dev image file.

> tsnr_filea pathlike object or string representing an existing file

>   Tsnr image file.

nipype.algorithms.confounds.combine_mask_files(mask_files, mask_method=None, mask_index=None)

  Combines input mask files into a single nibabel image

  A helper function for CompCor

  Parameters:

    - mask_files (a list) – one or more binary mask files

    - mask_method (enum ('union', 'intersect', 'none')) – determines how to combine masks

    - mask_index (an integer) – determines which file to return (mutually exclusive with mask_method)

  Returns:

    masks

  Return type:

    a list of nibabel images

nipype.algorithms.confounds.compute_dvars(in_file, in_mask, remove_zerovariance=False, intensity_normalization=1000, variance_tol=0.0)

  Compute the DVARS [Power2012].

  Particularly, the standardized DVARS [Nichols2013] are computed.

  [Nichols2013]

  Nichols T, Notes on creating a standardized version of DVARS, 2013.

  Note

  Implementation details

  Uses the implementation of the Yule-Walker equations from nitime for the AR filtering of the fMRI signal.

  Parameters:

    - func (numpy.ndarray) – functional data, after head-motion-correction.

    - mask (numpy.ndarray) – a 3D mask of the brain

    - output_all (bool) – write out all dvars

    - out_file (str) – a path to which the standardized dvars should be saved.

  Returns:

    the standardized DVARS

nipype.algorithms.confounds.compute_noise_components(imgseries, mask_images, components_criterion=0.5, filter_type=False, degree=0, period_cut=128, repetition_time=None, failure_mode='error', mask_names=None)

  Compute the noise components from the image series for each mask.

  Parameters:

    - imgseries (nibabel image) – Time series data to be decomposed.

    - mask_images (list) – List of nibabel images. Time series data from `img_series` is subset according to the spatial extent of each mask, and the subset data is then decomposed using principal component analysis. Masks should be coextensive with either anatomical or spatial noise ROIs.

    - components_criterion (float) – Number of noise components to return. If this is a decimal value between 0 and 1, then `create_noise_components` will instead return the smallest number of components necessary to explain the indicated fraction of variance. If `components_criterion` is `all`, then all components will be returned.

    - filter_type (str) –

      Type of filter to apply to time series before computing noise components.

      > - ’polynomial’ - Legendre polynomial basis

      > - ’cosine’ - Discrete cosine (DCT) basis

      > - False - None (mean-removal only)

    - failure_mode (str) – Action to be taken in the event that any decomposition fails to identify any components. `error` indicates that the routine should raise an exception and exit, while any other value indicates that the routine should return a matrix of NaN values equal in size to the requested decomposition matrix.

    - mask_names (list or None) – List of names for each image in `mask_images`. This should be equal in length to `mask_images`, with the ith element of `mask_names` naming the ith element of `mask_images`.

    - degree (int) – Order of polynomial used to remove trends from the timeseries

    - period_cut (float) – Minimum period (in sec) for DCT high-pass filter

    - repetition_time (float) – Time (in sec) between volume acquisitions. This must be defined if the `filter_type` is `cosine`.

  Returns:

    - components (numpy array) – Numpy array containing the requested set of noise components

    - basis (numpy array) – Numpy array containing the (non-constant) filter regressors

    - metadata (OrderedDict{str: numpy array}) – Dictionary of eigenvalues, fractional explained variances, and cumulative explained variances.

nipype.algorithms.confounds.cosine_filter(data, timestep, period_cut, remove_mean=True, axis=-1, failure_mode='error')

nipype.algorithms.confounds.fallback_svd(a, full_matrices=True, compute_uv=True)

nipype.algorithms.confounds.is_outlier(points, thresh=3.5)

  Returns a boolean array with True if points are outliers and False otherwise.

  Parameters:

    - points (nparray) – an numobservations by numdimensions numpy array of observations

    - thresh (float) – the modified z-score to use as a threshold. Observations with a modified z-score (based on the median absolute deviation) greater than this value will be classified as outliers.

  Returns:

    A boolean mask, of size numobservations-length array.

  Note

  References

  Boris Iglewicz and David Hoaglin (1993), “Volume 16: How to Detect and Handle Outliers”, The ASQC Basic References in Quality Control: Statistical Techniques, Edward F. Mykytka, Ph.D., Editor.

nipype.algorithms.confounds.plot_confound(tseries, figsize, name, units=None, series_tr=None, normalize=False)

  A helper function to plot fMRI confounds.

nipype.algorithms.confounds.regress_poly(degree, data, remove_mean=True, axis=-1, failure_mode='error')

  Returns data with degree polynomial regressed out.

  Parameters:

    - remove_mean (bool) – whether or not demean data (i.e. degree 0),

    - axis (int) – numpy array axes along which regression is performed