algorithms.rapidart¶
ArtifactDetect¶
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()
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 voxels are 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' or 'FSFAST')
Source of movement parameters
realigned_files: (a list of items which are an existing file name)
Names of realigned functional data files
realignment_parameters: (a list of items which are an existing file
name)
Names of realignment parameters corresponding 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 a bool or None, nipype
default value: [True, False])
Use differences between successive motion (first element) and
intensity parameter (second element) estimates in order to 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 list of items which are a file name)
One image file for each functional run containing the voxel
displacement timeseries
intensity_files: (a list of items which are an existing file name)
One file for each functional run containing the global intensity
values determined from the brainmask
mask_files: (a list of items which are a file name)
One image file for each functional run containing the mask used for
global signal calculation
norm_files: (a list of items which are a file name)
One file for each functional run containing the composite norm
outlier_files: (a list of items which are an existing file name)
One file for each functional run containing a list of 0-based
indices corresponding to outlier volumes
plot_files: (a list of items which are a file name)
One image file for each functional run containing the detected
outliers
statistic_files: (a list of items which are 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¶
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: (a list of items which are an existing file name)
Name of file containing intensity values
realignment_parameters: (a list of items which are 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: (a list of items which are an existing file name)
List of files containing correlation values