xcp_d.interfaces.restingstate.ReHoNamePatch

class xcp_d.interfaces.restingstate.ReHoNamePatch(from_file=None, resource_monitor=None, **inputs)

Wrapped executable: 3dReHo.

Compute ReHo for a given neighbourhood, based on a local neighborhood of that voxel.

For complete details, see the 3dReHo Documentation.

Examples

>>> from nipype.interfaces import afni
>>> reho = afni.ReHo()
>>> reho.inputs.in_file = 'functional.nii'
>>> reho.inputs.out_file = 'reho.nii.gz'
>>> reho.inputs.neighborhood = 'vertices'
>>> reho.cmdline
'3dReHo -prefix reho.nii.gz -inset functional.nii -nneigh 27'
>>> res = reho.run()  

Mandatory Inputs:

in_file (a pathlike object or string representing an existing file) – Input dataset. Maps to a command-line argument: -inset %s (position: 1).

Optional Inputs:

• args (a string) – Additional parameters to the command. Maps to a command-line argument: %s.

• chi_sq (a boolean) – Output the Friedman chi-squared value in addition to the Kendall’s W. This option is currently compatible only with the AFNI (BRIK/HEAD) output type; the chi-squared value will be the second sub-brick of the output dataset. Maps to a command-line argument: -chi_sq.

• ellipsoid (a tuple of the form: (a float, a float, a float)) – Tuple indicating the x, y, and z radius of an ellipsoid defining the neighbourhood of each voxel. The ‘hood is then made according to the following relation: \((i/A)^2 + (j/B)^2 + (k/C)^2 \le 1.\) which will have approx. \(V=4 \pi \, A B C/3\). The impetus for this freedom was for use with data having anisotropic voxel edge lengths. Maps to a command-line argument: -neigh_X %s -neigh_Y %s -neigh_Z %s. Mutually exclusive with inputs: sphere, neighborhood.

• environ (a dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’) – Environment variables. (Nipype default value: {})

• label_set (a pathlike object or string representing an existing file) – A set of ROIs, each labelled with distinct integers. ReHo will then be calculated per ROI. Maps to a command-line argument: -in_rois %s.

• mask_file (a pathlike object or string representing a file) – Mask within which ReHo should be calculated voxelwise. Maps to a command-line argument: -mask %s.

• neighborhood (‘faces’ or ‘edges’ or ‘vertices’) – voxels in neighborhood. can be: faces (for voxel and 6 facewise neighbors, only), edges (for voxel and 18 face- and edge-wise neighbors), vertices (for voxel and 26 face-, edge-, and node-wise neighbors). Maps to a command-line argument: -nneigh %s. Mutually exclusive with inputs: sphere, ellipsoid.

• out_file (a pathlike object or string representing a file) – Output dataset. Maps to a command-line argument: -prefix %s (position: 0).

• overwrite (a boolean) – Overwrite output file if it already exists. Maps to a command-line argument: -overwrite.

• sphere (a float) – For additional voxelwise neighborhood control, the radius R of a desired neighborhood can be put in; R is a floating point number, and must be >1. Examples of the numbers of voxels in a given radius are as follows (you can roughly approximate with the ol’ \(4\pi\,R^3/3\) thing):

  • R=2.0 -> V=33

  • R=2.3 -> V=57,

  • R=2.9 -> V=93,

  • R=3.1 -> V=123,

  • R=3.9 -> V=251,

  • R=4.5 -> V=389,

  • R=6.1 -> V=949,

  but you can choose most any value. Maps to a command-line argument: -neigh_RAD %s. Mutually exclusive with inputs: neighborhood, ellipsoid.

Outputs:

• out_file (a pathlike object or string representing an existing file) – Voxelwise regional homogeneity map.

• out_vals (a pathlike object or string representing a file) – Table of labelwise regional homogeneity values.

__init__(from_file=None, resource_monitor=None, **inputs)

Subclasses must implement __init__

Methods

__init__([from_file, resource_monitor])	Subclasses must implement __init__
aggregate_outputs([runtime, needed_outputs])	Collate expected outputs and apply output traits validation.
help([returnhelp])	Prints class help
load_inputs_from_json(json_file[, overwrite])	A convenient way to load pre-set inputs from a JSON file.
run([cwd, ignore_exception])	Execute this interface.
save_inputs_to_json(json_file)	A convenient way to save current inputs to a JSON file.

Attributes

always_run	Should the interface be always run even if the inputs were not changed? Only applies to interfaces being run within a workflow context.
can_resume	Defines if the interface can reuse partial results after interruption.
resource_monitor
version	interfaces should implement a version property