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# nipype.algorithms.rapidart module

The rapidart module provides routines for artifact detection and region of interest analysis.

These functions include:

> - ArtifactDetect: performs artifact detection on functional images

> - StimulusCorrelation: determines correlation between stimuli schedule and movement/intensity parameters

## ArtifactDetect

Link to code

> Bases: BaseInterface

> Detects outliers in a functional imaging series

> Uses intensity and motion parameters to infer outliers. If use_norm is True, it computes the movement of the center of each face a cuboid centered around the head and returns the maximal movement across the centers. If you wish to use individual thresholds instead, import Undefined from nipype.interfaces.base and set ….inputs.use_norm = Undefined

> Examples

>     >>> ad = ArtifactDetect()
>     >>> ad.inputs.realigned_files = 'functional.nii'
>     >>> ad.inputs.realignment_parameters = 'functional.par'
>     >>> ad.inputs.parameter_source = 'FSL'
>     >>> ad.inputs.norm_threshold = 1
>     >>> ad.inputs.use_differences = [True, False]
>     >>> ad.inputs.zintensity_threshold = 3
>     >>> ad.run()

> mask_type‘spm_global’ or ‘file’ or ‘thresh’

>   Type of mask that should be used to mask the functional data. spm_global uses an spm_global like calculation to determine the brain mask. file specifies a brain mask file (should be an image file consisting of 0s and 1s). thresh specifies a threshold to use. By default all voxels are used,unless one of these mask types are defined.

> norm_thresholda float

>   Threshold to use to detect motion-related outliers when composite motion is being used. Mutually exclusive with inputs: `rotation_threshold`, `translation_threshold`.

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

>   Source of movement parameters.

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

>   Names of realigned functional data files.

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

>   Names of realignment parameters corresponding to the functional data files.

> rotation_thresholda float

>   Threshold (in radians) to use to detect rotation-related outliers. Mutually exclusive with inputs: `norm_threshold`.

> translation_thresholda float

>   Threshold (in mm) to use to detect translation-related outliers. Mutually exclusive with inputs: `norm_threshold`.

> zintensity_thresholda float

>   Intensity Z-threshold use to detection images that deviate from the mean.

> bound_by_brainmaska boolean

>   Use the brain mask to determine bounding boxfor composite norm (worksfor SPM and Nipy - currentlyinaccurate for FSL, AFNI. (Nipype default value: `False`)

> global_thresholda float

>   Use this threshold when mask type equal’s spm_global. (Nipype default value: `8.0`)

> intersect_maska boolean

>   Intersect the masks when computed from spm_global. (Nipype default value: `True`)

> mask_filea pathlike object or string representing an existing file

>   Mask file to be used if mask_type is ‘file’.

> mask_thresholda float

>   Mask threshold to be used if mask_type is ‘thresh’.

> plot_type‘png’ or ‘svg’ or ‘eps’ or ‘pdf’

>   File type of the outlier plot. (Nipype default value: `png`)

> save_plota boolean

>   Save plots containing outliers. (Nipype default value: `True`)

> use_differencesa list of items which are a bool or None

>   Use differences between successive motion (first element) and intensity parameter (second element) estimates in order to determine outliers. (default is [True, False]). (Nipype default value: `[True, False]`)

> use_norma boolean

>   Uses a composite of the motion parameters in order to determine outliers. Requires inputs: `norm_threshold`. (Nipype default value: `True`)

> displacement_filesa list of items which are a pathlike object or string representing a file

>   One image file for each functional run containing the voxel displacement timeseries.

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

>   One file for each functional run containing the global intensity values determined from the brainmask.

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

>   One image file for each functional run containing the mask used for global signal calculation.

> norm_filesa list of items which are a pathlike object or string representing a file

>   One file for each functional run containing the composite norm.

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

>   One file for each functional run containing a list of 0-based indices corresponding to outlier volumes.

> plot_filesa list of items which are a pathlike object or string representing a file

>   One image file for each functional run containing the detected outliers.

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

>   One file for each functional run containing information about the different types of artifacts and if design info is provided then details of stimulus correlated motion and a listing or artifacts by event type.

## StimulusCorrelation

Link to code

> Bases: BaseInterface

> Determines if stimuli are correlated with motion or intensity parameters.

> Currently this class supports an SPM generated design matrix and requires intensity parameters. This implies that one must run ArtifactDetect and Level1Design prior to running this or provide an SPM.mat file and intensity parameters through some other means.

> Examples

>     >>> sc = StimulusCorrelation()
>     >>> sc.inputs.realignment_parameters = 'functional.par'
>     >>> sc.inputs.intensity_values = 'functional.rms'
>     >>> sc.inputs.spm_mat_file = 'SPM.mat'
>     >>> sc.inputs.concatenated_design = False
>     >>> sc.run()

> concatenated_designa boolean

>   State if the design matrix contains concatenated sessions.

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

>   Name of file containing intensity values.

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

>   Names of realignment parameters corresponding to the functional data files.

> spm_mat_filea pathlike object or string representing an existing file

>   SPM mat file (use pre-estimate SPM.mat file).

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

>   List of files containing correlation values.