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# algorithms.rapidart

## ArtifactDetect

Link to code

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.

### 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()

Inputs:

    [Mandatory]
    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 voxelsare used, unless one of these mask types are defined.
    norm_threshold: (a float)
            Threshold to use to detect motion-related outliers when composite motion is being used
            mutually_exclusive: rotation_threshold, translation_threshold
    parameter_source: ('SPM' or 'FSL' or 'AFNI' or 'NiPy')
            Source of movement parameters
    realigned_files: (an existing file name)
            Names of realigned functional data files
    realignment_parameters: (an existing file name)
            Names of realignment parameterscorresponding to the functional data files
    rotation_threshold: (a float)
            Threshold (in radians) to use to detect rotation-related outliers
            mutually_exclusive: norm_threshold
    translation_threshold: (a float)
            Threshold (in mm) to use to detect translation-related outliers
            mutually_exclusive: norm_threshold
    zintensity_threshold: (a float)
            Intensity Z-threshold use to detection images that deviate from the mean

    [Optional]
    bound_by_brainmask: (a boolean, nipype default value: False)
            use the brain mask to determine bounding boxfor composite norm (worksfor SPM and Nipy -
            currentlyinaccurate for FSL, AFNI
    global_threshold: (a float, nipype default value: 8.0)
            use this threshold when mask type equal's spm_global
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    intersect_mask: (a boolean)
            Intersect the masks when computed from spm_global.
    mask_file: (an existing file name)
            Mask file to be used if mask_type is 'file'.
    mask_threshold: (a float)
            Mask threshold to be used if mask_type is 'thresh'.
    plot_type: ('png' or 'svg' or 'eps' or 'pdf', nipype default value: png)
            file type of the outlier plot
    save_plot: (a boolean, nipype default value: True)
            save plots containing outliers
    use_differences: (a list of items which are an implementor of, or can be adapted to
             implement, bool or None, nipype default value: [True, False])
            Use differences between successive motion (first element)and intensity paramter (second
            element) estimates in orderto determine outliers.  (default is [True, False])
    use_norm: (a boolean, nipype default value: True)
            Uses a composite of the motion parameters in order to determine outliers.
            requires: norm_threshold

Outputs:

    displacement_files: (a file name)
            One image file for each functional run containing the voxeldisplacement timeseries
    intensity_files: (an existing file name)
            One file for each functional run containing the global intensity values determined from
            the brainmask
    mask_files: (a file name)
            One image file for each functional run containing the maskused for global signal
            calculation
    norm_files: (a file name)
            One file for each functional run containing the composite norm
    outlier_files: (an existing file name)
            One file for each functional run containing a list of 0-based indices corresponding to
            outlier volumes
    plot_files: (a file name)
            One image file for each functional run containing the detected outliers
    statistic_files: (an existing file name)
            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

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()

Inputs:

    [Mandatory]
    concatenated_design: (a boolean)
            state if the design matrix contains concatenated sessions
    intensity_values: (an existing file name)
            Name of file containing intensity values
    realignment_parameters: (an existing file name)
            Names of realignment parameters corresponding to the functional data files
    spm_mat_file: (an existing file name)
            SPM mat file (use pre-estimate SPM.mat file)

    [Optional]
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run

Outputs:

    stimcorr_files: (an existing file name)
            List of files containing correlation values