algorithms.rapidart

Module: algorithms.rapidart

Inheritance diagram for nipype.algorithms.rapidart:

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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

Classes

ArtifactDetect

class nipype.algorithms.rapidart.ArtifactDetect(*args, **inputs)

Bases: nipype.interfaces.base.Interface

Detects outliers in a functional imaging series depending on the intensity and motion parameters. It also generates stimulus correlated motion information and other statistics.

__init__(*args, **inputs)

aggregate_outputs()

get_input_info()

inputs_help()

Parameters:

realigned_files : filename(s)

Names of realigned functional data files

realignment_parameters : filename(s)

Names of realignment parameters corresponding to the functional data files

parameter_source : string

Are the movement parameters from SPM or FSL or from Siemens PACE data. Options: SPM, FSL or Siemens

use_differences : 2-element boolean list

Use differences between successive motion (first element) and intensity paramter (second element) estimates in order to determine outliers. (default is [True, True])

use_norm : boolean, optional

Use the norm of the motion parameters in order to determine outliers. Requires norm_threshold to be set. (default is True)

norm_threshold: float :

Threshold to use to detect motion-related outliers when normalized motion is being used (see use_norm)

rotation_threshold : float

Threshold to use to detect rotation-related outliers

translation_threshold : float

Threshold to use to detect translation-related outliers

zintensity_threshold : float

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

mask_type : {‘spm_global’, ‘file’, ‘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.

mask_file : filename

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

mask_threshold : float

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

intersect_mask : boolean

Intersect the masks when computed from spm_global. (default is True)

outputs()

Generate a bunch containing the output fields.

Parameters:

outlier_files : filename(s)

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

intensity_files : filename(s)

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

statistic_files : filename(s)

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.

outputs_help()

print out the help from the outputs routine

run(**inputs)

Execute this module.

StimulusCorrelation

class nipype.algorithms.rapidart.StimulusCorrelation(*args, **inputs)

Bases: nipype.interfaces.base.Interface

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 nipype.interfaces.spm.Level1Design prior to running this or provide an SPM.mat file and intensity parameters through some other means.

__init__(*args, **inputs)

aggregate_outputs()

get_input_info()

inputs_help()

Parameters:

realignment_parameters : filename(s)

Names of realignment parameters corresponding to the functional data files

intensity_values : filename(s)

Name of file containing intensity values

spm_mat_file : filename

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

concatenated_design : boolean

state if the design matrix contains concatenated sessions

outputs()

Generate a bunch containing the output fields.

Parameters:

stimcorr_files: file/string :

List of files containing correlation values

outputs_help()

print out the help from the outputs routine

run(**inputs)

Execute this module.