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# nipype.interfaces.spm.preprocess module

SPM wrappers for preprocessing data

## ApplyDeformations

Link to code

> Bases: SPMCommand

> deformation_field : a pathlike object or string representing an existing file in_files : a list of items which are a pathlike object or string representing an existing file reference_volume : a pathlike object or string representing an existing file

> interp0 <= a long integer <= 7

>   Degree of b-spline used for interpolation.

> matlab_cmda unicode string

>   Matlab command to use.

> mfilea boolean

>   Run m-code using m-file. (Nipype default value: `True`)

> pathsa list of items which are a pathlike object or string representing a directory

>   Paths to add to matlabpath.

> use_mcra boolean

>   Run m-code using SPM MCR.

> use_v8structa boolean

>   Generate SPM8 and higher compatible jobs. (Nipype default value: `True`)

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

## Coregister

Link to code

> Bases: SPMCommand

> Use spm_coreg for estimating cross-modality rigid body alignment

> http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=39

> Examples

>     >>> import nipype.interfaces.spm as spm
>     >>> coreg = spm.Coregister()
>     >>> coreg.inputs.target = 'functional.nii'
>     >>> coreg.inputs.source = 'structural.nii'
>     >>> coreg.run()

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

>   File to register to target.

> targeta pathlike object or string representing an existing file

>   Reference file to register to.

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

>   Files to apply transformation to.

> cost_function‘mi’ or ‘nmi’ or ‘ecc’ or ‘ncc’
>   Cost function, one of:

>     ‘mi’ - Mutual Information, ‘nmi’ - Normalised Mutual Information, ‘ecc’ - Entropy Correlation Coefficient, ‘ncc’ - Normalised Cross Correlation.

> fwhma list of from 2 to 2 items which are a float

>   Gaussian smoothing kernel width (mm).

> jobtype‘estwrite’ or ‘estimate’ or ‘write’

>   One of: estimate, write, estwrite. (Nipype default value: `estwrite`)

> matlab_cmda unicode string

>   Matlab command to use.

> mfilea boolean

>   Run m-code using m-file. (Nipype default value: `True`)

> out_prefixa string

>   Coregistered output prefix. (Nipype default value: `r`)

> pathsa list of items which are a pathlike object or string representing a directory

>   Paths to add to matlabpath.

> separationa list of items which are a float

>   Sampling separation in mm.

> tolerancea list of items which are a float

>   Acceptable tolerance for each of 12 params.

> use_mcra boolean

>   Run m-code using SPM MCR.

> use_v8structa boolean

>   Generate SPM8 and higher compatible jobs. (Nipype default value: `True`)

> write_interp0 <= a long integer <= 7

>   Degree of b-spline used for interpolation.

> write_maska boolean

>   True/False mask output image.

> write_wrapa list of from 3 to 3 items which are an integer (int or long)

>   Check if interpolation should wrap in [x,y,z].

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

>   Coregistered other files.

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

>   Coregistered source files.

## CreateWarped

Link to code

> Bases: SPMCommand

> Apply a flow field estimated by DARTEL to create warped images

> http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=190

> Examples

>     >>> import nipype.interfaces.spm as spm
>     >>> create_warped = spm.CreateWarped()
>     >>> create_warped.inputs.image_files = ['rc1s1.nii', 'rc1s2.nii']
>     >>> create_warped.inputs.flowfield_files = ['u_rc1s1_Template.nii', 'u_rc1s2_Template.nii']
>     >>> create_warped.run()

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

>   DARTEL flow fields u_rc1*.

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

>   A list of files to be warped.

> interp0 <= a long integer <= 7

>   Degree of b-spline used for interpolation.

> iterations0 <= a long integer <= 9

>   The number of iterations: log2(number of time steps).

> matlab_cmda unicode string

>   Matlab command to use.

> mfilea boolean

>   Run m-code using m-file. (Nipype default value: `True`)

> modulatea boolean

>   Modulate images.

> pathsa list of items which are a pathlike object or string representing a directory

>   Paths to add to matlabpath.

> use_mcra boolean

>   Run m-code using SPM MCR.

> use_v8structa boolean

>   Generate SPM8 and higher compatible jobs. (Nipype default value: `True`)

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

## DARTEL

Link to code

> Bases: SPMCommand

> Use spm DARTEL to create a template and flow fields

> http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=185

> Examples

>     >>> import nipype.interfaces.spm as spm
>     >>> dartel = spm.DARTEL()
>     >>> dartel.inputs.image_files = [['rc1s1.nii','rc1s2.nii'],['rc2s1.nii', 'rc2s2.nii']]
>     >>> dartel.run()

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

>   A list of files to be segmented.

> iteration_parametersa list of from 3 to 12 items which are a tuple of the form: (1 <= a long integer <= 10, a tuple of the form: (a float, a float, a float), 1 or 2 or 4 or 8 or 16 or 32 or 64 or 128 or 256 or 512, 0 or 0.5 or 1 or 2 or 4 or 8 or 16 or 32)

>   List of tuples for each iteration

>   > - Inner iterations

>   > - Regularization parameters

>   > - Time points for deformation model

>   > - smoothing parameter

> matlab_cmda unicode string

>   Matlab command to use.

> mfilea boolean

>   Run m-code using m-file. (Nipype default value: `True`)

> optimization_parametersa tuple of the form: (a float, 1 <= a long integer <= 8, 1 <= a long integer <= 8)

>   Optimization settings a tuple:

>   > - LM regularization

>   > - cycles of multigrid solver

>   > - relaxation iterations

> pathsa list of items which are a pathlike object or string representing a directory

>   Paths to add to matlabpath.

> regularization_form‘Linear’ or ‘Membrane’ or ‘Bending’

>   Form of regularization energy term.

> template_prefixa unicode string

>   Prefix for template. (Nipype default value: `Template`)

> use_mcra boolean

>   Run m-code using SPM MCR.

> use_v8structa boolean

>   Generate SPM8 and higher compatible jobs. (Nipype default value: `True`)

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

>   DARTEL flow fields.

> final_template_filea pathlike object or string representing an existing file

>   Final DARTEL template.

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

>   Templates from different stages of iteration.

## DARTELNorm2MNI

Link to code

> Bases: SPMCommand

> Use spm DARTEL to normalize data to MNI space

> http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=188

> Examples

>     >>> import nipype.interfaces.spm as spm
>     >>> nm = spm.DARTELNorm2MNI()
>     >>> nm.inputs.template_file = 'Template_6.nii'
>     >>> nm.inputs.flowfield_files = ['u_rc1s1_Template.nii', 'u_rc1s3_Template.nii']
>     >>> nm.inputs.apply_to_files = ['c1s1.nii', 'c1s3.nii']
>     >>> nm.inputs.modulate = True
>     >>> nm.run()

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

>   Files to apply the transform to.

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

>   DARTEL flow fields u_rc1*.

> template_filea pathlike object or string representing an existing file

>   DARTEL template.

> bounding_boxa tuple of the form: (a float, a float, a float, a float, a float, a float)

>   Voxel sizes for output file.

> fwhma list of from 3 to 3 items which are a float or a float

>   3-list of fwhm for each dimension.

> matlab_cmda unicode string

>   Matlab command to use.

> mfilea boolean

>   Run m-code using m-file. (Nipype default value: `True`)

> modulatea boolean

>   Modulate out images - no modulation preserves concentrations.

> pathsa list of items which are a pathlike object or string representing a directory

>   Paths to add to matlabpath.

> use_mcra boolean

>   Run m-code using SPM MCR.

> use_v8structa boolean

>   Generate SPM8 and higher compatible jobs. (Nipype default value: `True`)

> voxel_sizea tuple of the form: (a float, a float, a float)

>   Voxel sizes for output file.

> normalization_parameter_filea pathlike object or string representing an existing file

>   Transform parameters to MNI space.

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

>   Normalized files in MNI space.

## FieldMap

Link to code

> Bases: SPMCommand

> Use the fieldmap toolbox from spm to calculate the voxel displacement map (VDM).

> http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=173

> Important

> This interface does not deal with real/imag magnitude images nor with the two phase files case.

> Examples

>     >>> from nipype.interfaces.spm import FieldMap
>     >>> fm = FieldMap()
>     >>> fm.inputs.phase_file = 'phase.nii'
>     >>> fm.inputs.magnitude_file = 'magnitude.nii'
>     >>> fm.inputs.echo_times = (5.19, 7.65)
>     >>> fm.inputs.blip_direction = 1
>     >>> fm.inputs.total_readout_time = 15.6
>     >>> fm.inputs.epi_file = 'epi.nii'
>     >>> fm.run()

> blip_direction1 or -1

>   Polarity of the phase-encode blips.

> echo_timesa tuple of the form: (a float, a float)

>   Short and long echo times.

> epi_filea pathlike object or string representing an existing file

>   EPI to unwarp.

> magnitude_filea pathlike object or string representing an existing file

>   Presubstracted magnitude file.

> phase_filea pathlike object or string representing an existing file

>   Presubstracted phase file.

> total_readout_timea float

>   Total EPI readout time.

> anat_filea pathlike object or string representing an existing file

>   Anatomical image for comparison.

> epifma boolean

>   Epi-based field map. (Nipype default value: `False`)

> jacobian_modulationa boolean

>   Jacobian modulation. (Nipype default value: `False`)

> jobtype‘calculatevdm’ or ‘applyvdm’

>   One of: calculatevdm, applyvdm. (Nipype default value: `calculatevdm`)

> mask_fwhma long integer >= 0

>   Gaussian smoothing kernel width. (Nipype default value: `5`)

> maskbraina boolean

>   Masking or no masking of the brain. (Nipype default value: `True`)

> matchanata boolean

>   Match anatomical image to EPI. (Nipype default value: `True`)

> matchvdma boolean

>   Match VDM to EPI. (Nipype default value: `True`)

> matlab_cmda unicode string

>   Matlab command to use.

> method‘Mark3D’ or ‘Mark2D’ or ‘Huttonish’

>   One of: Mark3D, Mark2D, Huttonish. (Nipype default value: `Mark3D`)

> mfilea boolean

>   Run m-code using m-file. (Nipype default value: `True`)

> ndilatea long integer >= 0

>   Number of erosions. (Nipype default value: `4`)

> nerodea long integer >= 0

>   Number of erosions. (Nipype default value: `2`)

> pada long integer >= 0

>   Padding kernel width. (Nipype default value: `0`)

> pathsa list of items which are a pathlike object or string representing a directory

>   Paths to add to matlabpath.

> rega float

>   Regularization value used in the segmentation. (Nipype default value: `0.02`)

> sessnamea unicode string

>   VDM filename extension. (Nipype default value: `_run-`)

> templatea pathlike object or string representing an existing file

>   Template image for brain masking.

> thresha float

>   Threshold used to create brain mask from segmented data. (Nipype default value: `0.5`)

> unwarp_fwhma long integer >= 0

>   Gaussian smoothing kernel width. (Nipype default value: `10`)

> use_mcra boolean

>   Run m-code using SPM MCR.

> use_v8structa boolean

>   Generate SPM8 and higher compatible jobs. (Nipype default value: `True`)

> writeunwarpeda boolean

>   Write unwarped EPI. (Nipype default value: `False`)

> wsa boolean

>   Weighted smoothing. (Nipype default value: `True`)

> vdma pathlike object or string representing an existing file

>   Voxel difference map.

## MultiChannelNewSegment

Link to code

> Bases: SPMCommand

> Use spm_preproc8 (New Segment) to separate structural images into different tissue classes. Supports multiple modalities and multichannel inputs.

> http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=45

> Examples

>     >>> import nipype.interfaces.spm as spm
>     >>> seg = spm.MultiChannelNewSegment()
>     >>> seg.inputs.channels = [('structural.nii',(0.0001, 60, (True, True)))]
>     >>> seg.run()

> For VBM pre-processing [http://www.fil.ion.ucl.ac.uk/~john/misc/VBMclass10.pdf], TPM.nii should be replaced by /path/to/spm8/toolbox/Seg/TPM.nii

>     >>> seg = MultiChannelNewSegment()
>     >>> channel1= ('T1.nii',(0.0001, 60, (True, True)))
>     >>> channel2= ('T2.nii',(0.0001, 60, (True, True)))
>     >>> seg.inputs.channels = [channel1, channel2]
>     >>> tissue1 = (('TPM.nii', 1), 2, (True,True), (False, False))
>     >>> tissue2 = (('TPM.nii', 2), 2, (True,True), (False, False))
>     >>> tissue3 = (('TPM.nii', 3), 2, (True,False), (False, False))
>     >>> tissue4 = (('TPM.nii', 4), 2, (False,False), (False, False))
>     >>> tissue5 = (('TPM.nii', 5), 2, (False,False), (False, False))
>     >>> seg.inputs.tissues = [tissue1, tissue2, tissue3, tissue4, tissue5]
>     >>> seg.run()

> affine_regularization‘mni’ or ‘eastern’ or ‘subj’ or ‘none’

>   Mni, eastern, subj, none .

> channelsa list of items which are a tuple of the form: (a list of items which are a pathlike object or string representing an existing file, a tuple of the form: (a float, a float, a tuple of the form: (a boolean, a boolean)))
>   A list of tuples (one per each channel) with the following fields:

>     - a list of channel files (only 1rst channel files will be segmented)

>     - a tuple with the following channel-specific info fields: - bias reguralisation (0-10) - FWHM of Gaussian smoothness of bias - which maps to save (Field, Corrected) - a tuple of two boolean values.

> matlab_cmda unicode string

>   Matlab command to use.

> mfilea boolean

>   Run m-code using m-file. (Nipype default value: `True`)

> pathsa list of items which are a pathlike object or string representing a directory

>   Paths to add to matlabpath.

> sampling_distancea float

>   Sampling distance on data for parameter estimation.

> tissuesa list of items which are a tuple of the form: (a tuple of the form: (a pathlike object or string representing an existing file, an integer (int or long)), an integer (int or long), a tuple of the form: (a boolean, a boolean), a tuple of the form: (a boolean, a boolean))
>   A list of tuples (one per tissue) with the following fields:

>     - tissue probability map (4D), 1-based index to frame

>     - number of gaussians

>     - which maps to save [Native, DARTEL] - a tuple of two boolean values

>     - which maps to save [Unmodulated, Modulated] - a tuple of two boolean values.

> use_mcra boolean

>   Run m-code using SPM MCR.

> use_v8structa boolean

>   Generate SPM8 and higher compatible jobs. (Nipype default value: `True`)

> warping_regularizationa list of from 5 to 5 items which are a float or a float

>   Warping regularization parameter(s). Accepts float or list of floats (the latter is required by SPM12).

> write_deformation_fieldsa list of from 2 to 2 items which are a boolean

>   Which deformation fields to write:[Inverse, Forward].

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

>   Bias corrected images.

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

>   Bias field images.

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

>   Dartel imported class images.

> forward_deformation_field : a list of items which are a pathlike object or string representing an existing file inverse_deformation_field : a list of items which are a pathlike object or string representing an existing file modulated_class_images : a list of items which are a list of items which are a pathlike object or string representing an existing file

> > Modulated+normalized class images.

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

>   Native space probability maps.

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

>   Normalized class images.

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

>   Normalization transformation.

## NewSegment

Link to code

> Bases: SPMCommand

> Use spm_preproc8 (New Segment) to separate structural images into different tissue classes. Supports multiple modalities.

> NOTE: This interface currently supports single channel input only

> http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=43

> Examples

>     >>> import nipype.interfaces.spm as spm
>     >>> seg = spm.NewSegment()
>     >>> seg.inputs.channel_files = 'structural.nii'
>     >>> seg.inputs.channel_info = (0.0001, 60, (True, True))
>     >>> seg.run()

> For VBM pre-processing [http://www.fil.ion.ucl.ac.uk/~john/misc/VBMclass10.pdf], TPM.nii should be replaced by /path/to/spm8/toolbox/Seg/TPM.nii

>     >>> seg = NewSegment()
>     >>> seg.inputs.channel_files = 'structural.nii'
>     >>> tissue1 = (('TPM.nii', 1), 2, (True,True), (False, False))
>     >>> tissue2 = (('TPM.nii', 2), 2, (True,True), (False, False))
>     >>> tissue3 = (('TPM.nii', 3), 2, (True,False), (False, False))
>     >>> tissue4 = (('TPM.nii', 4), 2, (False,False), (False, False))
>     >>> tissue5 = (('TPM.nii', 5), 2, (False,False), (False, False))
>     >>> seg.inputs.tissues = [tissue1, tissue2, tissue3, tissue4, tissue5]
>     >>> seg.run()

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

>   A list of files to be segmented.

> affine_regularization‘mni’ or ‘eastern’ or ‘subj’ or ‘none’

>   Mni, eastern, subj, none .

> channel_infoa tuple of the form: (a float, a float, a tuple of the form: (a boolean, a boolean))
>   A tuple with the following fields:

>     - bias reguralisation (0-10)

>     - FWHM of Gaussian smoothness of bias

>     - which maps to save (Field, Corrected) - a tuple of two boolean values.

> matlab_cmda unicode string

>   Matlab command to use.

> mfilea boolean

>   Run m-code using m-file. (Nipype default value: `True`)

> pathsa list of items which are a pathlike object or string representing a directory

>   Paths to add to matlabpath.

> sampling_distancea float

>   Sampling distance on data for parameter estimation.

> tissuesa list of items which are a tuple of the form: (a tuple of the form: (a pathlike object or string representing an existing file, an integer (int or long)), an integer (int or long), a tuple of the form: (a boolean, a boolean), a tuple of the form: (a boolean, a boolean))
>   A list of tuples (one per tissue) with the following fields:

>     - tissue probability map (4D), 1-based index to frame

>     - number of gaussians

>     - which maps to save [Native, DARTEL] - a tuple of two boolean values

>     - which maps to save [Unmodulated, Modulated] - a tuple of two boolean values.

> use_mcra boolean

>   Run m-code using SPM MCR.

> use_v8structa boolean

>   Generate SPM8 and higher compatible jobs. (Nipype default value: `True`)

> warping_regularizationa list of from 5 to 5 items which are a float or a float

>   Warping regularization parameter(s). Accepts float or list of floats (the latter is required by SPM12).

> write_deformation_fieldsa list of from 2 to 2 items which are a boolean

>   Which deformation fields to write:[Inverse, Forward].

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

>   Bias corrected images.

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

>   Bias field images.

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

>   Dartel imported class images.

> forward_deformation_field : a list of items which are a pathlike object or string representing an existing file inverse_deformation_field : a list of items which are a pathlike object or string representing an existing file modulated_class_images : a list of items which are a list of items which are a pathlike object or string representing an existing file

> > Modulated+normalized class images.

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

>   Native space probability maps.

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

>   Normalized class images.

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

>   Normalization transformation.

## Normalize

Link to code

> Bases: SPMCommand

> use spm_normalise for warping an image to a template

> http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=203

> Examples

>     >>> import nipype.interfaces.spm as spm
>     >>> norm = spm.Normalize()
>     >>> norm.inputs.source = 'functional.nii'
>     >>> norm.run()

> parameter_filea pathlike object or string representing a file

>   Normalization parameter file*_sn.mat. Mutually exclusive with inputs: `source`, `template`.

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

>   File to normalize to template. Mutually exclusive with inputs: `parameter_file`.

> templatea pathlike object or string representing an existing file

>   Template file to normalize to. Mutually exclusive with inputs: `parameter_file`.

> DCT_period_cutoffa float

>   Cutoff of for DCT bases.

> affine_regularization_type‘mni’ or ‘size’ or ‘none’

>   Mni, size, none.

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

>   Files to apply transformation to.

> jobtype‘estwrite’ or ‘est’ or ‘write’

>   Estimate, Write or do both. (Nipype default value: `estwrite`)

> matlab_cmda unicode string

>   Matlab command to use.

> mfilea boolean

>   Run m-code using m-file. (Nipype default value: `True`)

> nonlinear_iterationsan integer (int or long)

>   Number of iterations of nonlinear warping.

> nonlinear_regularizationa float

>   The amount of the regularization for the nonlinear part of the normalization.

> out_prefixa string

>   Normalized output prefix. (Nipype default value: `w`)

> pathsa list of items which are a pathlike object or string representing a directory

>   Paths to add to matlabpath.

> source_image_smoothinga float

>   Source smoothing.

> source_weighta pathlike object or string representing a file

>   Name of weighting image for source.

> template_image_smoothinga float

>   Template smoothing.

> template_weighta pathlike object or string representing a file

>   Name of weighting image for template.

> use_mcra boolean

>   Run m-code using SPM MCR.

> use_v8structa boolean

>   Generate SPM8 and higher compatible jobs. (Nipype default value: `True`)

> write_bounding_boxa list of from 2 to 2 items which are a list of from 3 to 3 items which are a float

>   3x2-element list of lists.

> write_interp0 <= a long integer <= 7

>   Degree of b-spline used for interpolation.

> write_preservea boolean

>   True/False warped images are modulated.

> write_voxel_sizesa list of from 3 to 3 items which are a float

>   3-element list.

> write_wrapa list of items which are an integer (int or long)

>   Check if interpolation should wrap in [x,y,z] - list of bools.

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

>   MAT files containing the normalization parameters.

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

>   Normalized other files.

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

>   Normalized source files.

## Normalize12

Link to code

> Bases: SPMCommand

> uses SPM12’s new Normalise routine for warping an image to a template. Spatial normalisation is now done via the segmentation routine (which was known as `New Segment` in SPM8). Note that the normalisation in SPM12 is done towards a file containing multiple tissue probability maps, which was not the case in SPM8.

> http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=49

> Examples

>     >>> import nipype.interfaces.spm as spm
>     >>> norm12 = spm.Normalize12()
>     >>> norm12.inputs.image_to_align = 'structural.nii'
>     >>> norm12.inputs.apply_to_files = 'functional.nii'
>     >>> norm12.run()

> deformation_filea pathlike object or string representing a file

>   File y_*.nii containing 3 deformation fields for the deformation in x, y and z dimension. Mutually exclusive with inputs: `image_to_align`, `tpm`.

> image_to_aligna pathlike object or string representing an existing file

>   File to estimate normalization parameters with. Mutually exclusive with inputs: `deformation_file`.

> affine_regularization_type‘mni’ or ‘size’ or ‘none’

>   Mni, size, none.

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

>   Files to apply transformation to.

> bias_fwhm30 or 40 or 50 or 60 or 70 or 80 or 90 or 100 or 110 or 120 or 130 or 140 or 150 or ‘Inf’

>   FWHM of Gaussian smoothness of bias.

> bias_regularization0 or 1e-05 or 0.0001 or 0.001 or 0.01 or 0.1 or 1 or 10

>   No(0) - extremely heavy (10).

> jobtype‘estwrite’ or ‘est’ or ‘write’

>   Estimate, Write or do Both. (Nipype default value: `estwrite`)

> matlab_cmda unicode string

>   Matlab command to use.

> mfilea boolean

>   Run m-code using m-file. (Nipype default value: `True`)

> out_prefixa string

>   Normalized output prefix. (Nipype default value: `w`)

> pathsa list of items which are a pathlike object or string representing a directory

>   Paths to add to matlabpath.

> sampling_distancea float

>   Sampling distance on data for parameter estimation.

> smoothnessa float

>   Value (in mm) to smooth the data before normalization.

> tpma pathlike object or string representing an existing file

>   Template in form of tissue probablitiy maps to normalize to. Mutually exclusive with inputs: `deformation_file`.

> use_mcra boolean

>   Run m-code using SPM MCR.

> use_v8structa boolean

>   Generate SPM8 and higher compatible jobs. (Nipype default value: `True`)

> warping_regularizationa list of from 5 to 5 items which are a float

>   Controls balance between parameters and data.

> write_bounding_boxa list of from 2 to 2 items which are a list of from 3 to 3 items which are a float

>   3x2-element list of lists representing the bounding box (in mm) to be written.

> write_interp0 <= a long integer <= 7

>   Degree of b-spline used for interpolation.

> write_voxel_sizesa list of from 3 to 3 items which are a float

>   3-element list representing the voxel sizes (in mm) of the written normalised images.

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

>   NIfTI file containing 3 deformation fields for the deformation in x, y and z dimension.

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

>   Normalized other files.

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

>   Normalized file that needed to be aligned.

## Realign

Link to code

> Bases: SPMCommand

> Use spm_realign for estimating within modality rigid body alignment

> http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=25

> Examples

>     >>> import nipype.interfaces.spm as spm
>     >>> realign = spm.Realign()
>     >>> realign.inputs.in_files = 'functional.nii'
>     >>> realign.inputs.register_to_mean = True
>     >>> realign.run()

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

>   List of filenames to realign.

> fwhma floating point number >= 0.0

>   Gaussian smoothing kernel width.

> interp0 <= a long integer <= 7

>   Degree of b-spline used for interpolation.

> jobtype‘estwrite’ or ‘estimate’ or ‘write’

>   One of: estimate, write, estwrite. (Nipype default value: `estwrite`)

> matlab_cmda unicode string

>   Matlab command to use.

> mfilea boolean

>   Run m-code using m-file. (Nipype default value: `True`)

> out_prefixa string

>   Realigned output prefix. (Nipype default value: `r`)

> pathsa list of items which are a pathlike object or string representing a directory

>   Paths to add to matlabpath.

> quality0.0 <= a floating point number <= 1.0

>   0.1 = fast, 1.0 = precise.

> register_to_meana boolean

>   Indicate whether realignment is done to the mean image.

> separationa floating point number >= 0.0

>   Sampling separation in mm.

> use_mcra boolean

>   Run m-code using SPM MCR.

> use_v8structa boolean

>   Generate SPM8 and higher compatible jobs. (Nipype default value: `True`)

> weight_imga pathlike object or string representing an existing file

>   Filename of weighting image.

> wrapa list of from 3 to 3 items which are an integer (int or long)

>   Check if interpolation should wrap in [x,y,z].

> write_interp0 <= a long integer <= 7

>   Degree of b-spline used for interpolation.

> write_maska boolean

>   True/False mask output image.

> write_whicha list of items which are a value of class ‘int’

>   Determines which images to reslice. (Nipype default value: `[2, 1]`)

> write_wrapa list of from 3 to 3 items which are an integer (int or long)

>   Check if interpolation should wrap in [x,y,z].

> mean_imagea pathlike object or string representing an existing file

>   Mean image file from the realignment.

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

>   Copies of all files passed to in_files. Headers will have been modified to align all images with the first, or optionally to first do that, extract a mean image, and re-align to that mean image.

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

>   If jobtype is write or estwrite, these will be the resliced files. Otherwise, they will be copies of in_files that have had their headers rewritten.

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

>   Estimated translation and rotation parameters.

## RealignUnwarp

Link to code

> Bases: SPMCommand

> Use spm_uw_estimate for estimating within subject registration and unwarping of time series. Function accepts only one single field map. If in_files is a list of files they will be treated as separate sessions but associated to the same fieldmap.

> http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=31

> Examples

>     >>> import nipype.interfaces.spm as spm
>     >>> realignUnwarp = spm.RealignUnwarp()
>     >>> realignUnwarp.inputs.in_files = ['functional.nii', 'functional2.nii']
>     >>> realignUnwarp.inputs.phase_map = 'voxeldisplacemap.vdm'
>     >>> realignUnwarp.inputs.register_to_mean = True
>     >>> realignUnwarp.run()

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

>   List of filenames to realign and unwarp.

> est_basis_funca list of from 2 to 2 items which are an integer (int or long)

>   Number of basis functions to use for each dimension.

> est_first_order_effectsa list of from 1 to 6 items which are an integer (int or long)

>   First order effects should only depend on pitch and roll, i.e. [4 5].

> est_jacobian_deformationsa boolean

>   Jacobian deformations. In theory a good idea to include them, in practice a bad idea. Default: No.

> est_num_of_iterationsa list of items which are a value of class ‘int’

>   Number of iterations. (Nipype default value: `[5]`)

> est_re_est_mov_para boolean

>   Re-estimate movement parameters at each unwarping iteration.

> est_reg_factora list of items which are a value of class ‘int’

>   Regularisation factor. Default: 100000 (medium). (Nipype default value: `[100000]`)

> est_reg_order0 <= a long integer <= 3

>   This parameter determines how to balance the compromise between likelihood maximization and smoothness maximization of the estimated field.

> est_second_order_effectsa list of from 1 to 6 items which are an integer (int or long)

>   List of second order terms to model second derivatives of.

> est_taylor_expansion_pointa string

>   Point in position space to perform Taylor-expansion around. (Nipype default value: `Average`)

> est_unwarp_fwhma floating point number >= 0.0

>   Gaussian smoothing kernel width for unwarp.

> fwhma floating point number >= 0.0

>   Gaussian smoothing kernel width.

> interp0 <= a long integer <= 7

>   Degree of b-spline used for interpolation.

> matlab_cmda unicode string

>   Matlab command to use.

> mfilea boolean

>   Run m-code using m-file. (Nipype default value: `True`)

> out_prefixa string

>   Realigned and unwarped output prefix. (Nipype default value: `u`)

> pathsa list of items which are a pathlike object or string representing a directory

>   Paths to add to matlabpath.

> phase_mapa pathlike object or string representing a file

>   Voxel displacement map to use in unwarping. Unlike SPM standard behaviour, the same map will be used for all sessions.

> quality0.0 <= a floating point number <= 1.0

>   0.1 = fast, 1.0 = precise.

> register_to_meana boolean

>   Indicate whether realignment is done to the mean image.

> reslice_interp0 <= a long integer <= 7

>   Degree of b-spline used for interpolation.

> reslice_maska boolean

>   True/False mask output image.

> reslice_whicha list of items which are a value of class ‘int’

>   Determines which images to reslice. (Nipype default value: `[2, 1]`)

> reslice_wrapa list of from 3 to 3 items which are an integer (int or long)

>   Check if interpolation should wrap in [x,y,z].

> separationa floating point number >= 0.0

>   Sampling separation in mm.

> use_mcra boolean

>   Run m-code using SPM MCR.

> use_v8structa boolean

>   Generate SPM8 and higher compatible jobs. (Nipype default value: `True`)

> weight_imga pathlike object or string representing an existing file

>   Filename of weighting image.

> wrapa list of from 3 to 3 items which are an integer (int or long)

>   Check if interpolation should wrap in [x,y,z].

> mean_imagea pathlike object or string representing an existing file

>   Mean image file from the realignment & unwarping.

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

>   Copies of all files passed to in_files. Headers will have been modified to align all images with the first, or optionally to first do that, extract a mean image, and re-align to that mean image.

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

>   Realigned and unwarped files written to disc.

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

>   Estimated translation and rotation parameters.

## Segment

Link to code

> Bases: SPMCommand

> use spm_segment to separate structural images into different tissue classes.

> http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=209

> Examples

>     >>> import nipype.interfaces.spm as spm
>     >>> seg = spm.Segment()
>     >>> seg.inputs.data = 'structural.nii'
>     >>> seg.run()

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

>   One scan per subject.

> affine_regularization‘mni’ or ‘eastern’ or ‘subj’ or ‘none’ or ‘’

>   Possible options: “mni”, “eastern”, “subj”, “none” (no reguralisation), “” (no affine registration).

> bias_fwhm30 or 40 or 50 or 60 or 70 or 80 or 90 or 100 or 110 or 120 or 130 or ‘Inf’

>   FWHM of Gaussian smoothness of bias.

> bias_regularization0 or 1e-05 or 0.0001 or 0.001 or 0.01 or 0.1 or 1 or 10

>   No(0) - extremely heavy (10).

> clean_masks‘no’ or ‘light’ or ‘thorough’

>   Clean using estimated brain mask (‘no’,’light’,’thorough’).

> csf_output_type : a list of from 3 to 3 items which are a boolean

> > Options to produce CSF images: c3*.img, wc3*.img and mwc3*.img. None: [False,False,False], Native Space: [False,False,True], Unmodulated Normalised: [False,True,False], Modulated Normalised: [True,False,False], Native + Unmodulated Normalised: [False,True,True], Native + Modulated Normalised: [True,False,True], Native + Modulated + Unmodulated: [True,True,True], Modulated + Unmodulated Normalised: [True,True,False].

> gaussians_per_classa list of items which are an integer (int or long)

>   Num Gaussians capture intensity distribution.

> gm_output_typea list of from 3 to 3 items which are a boolean
>   Options to produce grey matter images: c1*.img, wc1*.img and mwc1*.img.

>     None: [False,False,False], Native Space: [False,False,True], Unmodulated Normalised: [False,True,False], Modulated Normalised: [True,False,False], Native + Unmodulated Normalised: [False,True,True], Native + Modulated Normalised: [True,False,True], Native + Modulated + Unmodulated: [True,True,True], Modulated + Unmodulated Normalised: [True,True,False].

> mask_imagea pathlike object or string representing an existing file

>   Binary image to restrict parameter estimation .

> matlab_cmda unicode string

>   Matlab command to use.

> mfilea boolean

>   Run m-code using m-file. (Nipype default value: `True`)

> pathsa list of items which are a pathlike object or string representing a directory

>   Paths to add to matlabpath.

> sampling_distancea float

>   Sampling distance on data for parameter estimation.

> save_bias_correcteda boolean

>   True/False produce a bias corrected image.

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

>   List of gray, white & csf prob. (opt,).

> use_mcra boolean

>   Run m-code using SPM MCR.

> use_v8structa boolean

>   Generate SPM8 and higher compatible jobs. (Nipype default value: `True`)

> warp_frequency_cutoffa float

>   Cutoff of DCT bases.

> warping_regularizationa float

>   Controls balance between parameters and data.

> wm_output_type : a list of from 3 to 3 items which are a boolean

> > Options to produce white matter images: c2*.img, wc2*.img and mwc2*.img. None: [False,False,False], Native Space: [False,False,True], Unmodulated Normalised: [False,True,False], Modulated Normalised: [True,False,False], Native + Unmodulated Normalised: [False,True,True], Native + Modulated Normalised: [True,False,True], Native + Modulated + Unmodulated: [True,True,True], Modulated + Unmodulated Normalised: [True,True,False].

> bias_corrected_imagea pathlike object or string representing a file

>   Bias-corrected version of input image.

> inverse_transformation_mata pathlike object or string representing an existing file

>   Inverse normalization info.

> modulated_csf_imagea pathlike object or string representing a file

>   Modulated, normalized csf probability map.

> modulated_gm_imagea pathlike object or string representing a file

>   Modulated, normalized grey probability map.

> modulated_input_imagea pathlike object or string representing a file

>   Bias-corrected version of input image.

> modulated_wm_imagea pathlike object or string representing a file

>   Modulated, normalized white probability map.

> native_csf_imagea pathlike object or string representing a file

>   Native space csf probability map.

> native_gm_imagea pathlike object or string representing a file

>   Native space grey probability map.

> native_wm_imagea pathlike object or string representing a file

>   Native space white probability map.

> normalized_csf_imagea pathlike object or string representing a file

>   Normalized csf probability map.

> normalized_gm_imagea pathlike object or string representing a file

>   Normalized grey probability map.

> normalized_wm_imagea pathlike object or string representing a file

>   Normalized white probability map.

> transformation_mata pathlike object or string representing an existing file

>   Normalization transformation.

## SliceTiming

Link to code

> Bases: SPMCommand

> Use spm to perform slice timing correction.

> http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=19

> Examples

>     >>> from nipype.interfaces.spm import SliceTiming
>     >>> st = SliceTiming()
>     >>> st.inputs.in_files = 'functional.nii'
>     >>> st.inputs.num_slices = 32
>     >>> st.inputs.time_repetition = 6.0
>     >>> st.inputs.time_acquisition = 6. - 6./32.
>     >>> st.inputs.slice_order = list(range(32,0,-1))
>     >>> st.inputs.ref_slice = 1
>     >>> st.run()

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

>   List of filenames to apply slice timing.

> num_slicesan integer (int or long)

>   Number of slices in a volume.

> ref_slicean integer (int or long) or a float

>   1-based Number of the reference slice or reference time point if slice_order is in onsets (ms).

> slice_ordera list of items which are an integer (int or long) or a float

>   1-based order or onset (in ms) in which slices are acquired.

> time_acquisitiona float

>   Time of volume acquisition. usually calculated as TR-(TR/num_slices).

> time_repetitiona float

>   Time between volume acquisitions (start to start time).

> matlab_cmda unicode string

>   Matlab command to use.

> mfilea boolean

>   Run m-code using m-file. (Nipype default value: `True`)

> out_prefixa string

>   Slicetimed output prefix. (Nipype default value: `a`)

> pathsa list of items which are a pathlike object or string representing a directory

>   Paths to add to matlabpath.

> use_mcra boolean

>   Run m-code using SPM MCR.

> use_v8structa boolean

>   Generate SPM8 and higher compatible jobs. (Nipype default value: `True`)

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

>   Slice time corrected files.

## Smooth

Link to code

> Bases: SPMCommand

> Use spm_smooth for 3D Gaussian smoothing of image volumes.

> http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=55

> Examples

>     >>> import nipype.interfaces.spm as spm
>     >>> smooth = spm.Smooth()
>     >>> smooth.inputs.in_files = 'functional.nii'
>     >>> smooth.inputs.fwhm = [4, 4, 4]
>     >>> smooth.run()

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

>   List of files to smooth.

> data_typean integer (int or long)

>   Data type of the output images.

> fwhma list of from 3 to 3 items which are a float or a float

>   3-list of fwhm for each dimension.

> implicit_maskinga boolean

>   A mask implied by a particular voxel value.

> matlab_cmda unicode string

>   Matlab command to use.

> mfilea boolean

>   Run m-code using m-file. (Nipype default value: `True`)

> out_prefixa string

>   Smoothed output prefix. (Nipype default value: `s`)

> pathsa list of items which are a pathlike object or string representing a directory

>   Paths to add to matlabpath.

> use_mcra boolean

>   Run m-code using SPM MCR.

> use_v8structa boolean

>   Generate SPM8 and higher compatible jobs. (Nipype default value: `True`)

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

>   Smoothed files.

## VBMSegment

Link to code

> Bases: SPMCommand

> Use VBM8 toolbox to separate structural images into different tissue classes.

> Example

>     >>> import nipype.interfaces.spm as spm
>     >>> seg = spm.VBMSegment()
>     >>> seg.inputs.tissues = 'TPM.nii'
>     >>> seg.inputs.dartel_template = 'Template_1_IXI550_MNI152.nii'
>     >>> seg.inputs.bias_corrected_native = True
>     >>> seg.inputs.gm_native = True
>     >>> seg.inputs.wm_native = True
>     >>> seg.inputs.csf_native = True
>     >>> seg.inputs.pve_label_native = True
>     >>> seg.inputs.deformation_field = (True, False)
>     >>> seg.run()

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

>   A list of files to be segmented.

> bias_corrected_affinea boolean

>   (Nipype default value: `False`)

> bias_corrected_nativea boolean

>   (Nipype default value: `False`)

> bias_corrected_normalizeda boolean

>   (Nipype default value: `True`)

> bias_fwhm30 or 40 or 50 or 60 or 70 or 80 or 90 or 100 or 110 or 120 or 130 or ‘Inf’

>   FWHM of Gaussian smoothness of bias. (Nipype default value: `60`)

> bias_regularization0 or 1e-05 or 0.0001 or 0.001 or 0.01 or 0.1 or 1 or 10

>   No(0) - extremely heavy (10). (Nipype default value: `0.0001`)

> cleanup_partitionsan integer (int or long)

>   0=None,1=light,2=thorough. (Nipype default value: `1`)

> csf_dartel0 <= a long integer <= 2

>   0=None,1=rigid(SPM8 default),2=affine. (Nipype default value: `0`)

> csf_modulated_normalized0 <= a long integer <= 2

>   0=none,1=affine+non-linear(SPM8 default),2=non-linear only. (Nipype default value: `2`)

> csf_nativea boolean

>   (Nipype default value: `False`)

> csf_normalizeda boolean

>   (Nipype default value: `False`)

> dartel_template : a pathlike object or string representing an existing file deformation_field : a tuple of the form: (a boolean, a boolean)

> > Forward and inverse field. (Nipype default value: `(0, 0)`)

> display_resultsa boolean

>   (Nipype default value: `True`)

> gaussians_per_classa tuple of the form: (an integer (int or long), an integer (int or long), an integer (int or long), an integer (int or long), an integer (int or long), an integer (int or long))

>   Number of gaussians for each tissue class. (Nipype default value: `(2, 2, 2, 3, 4, 2)`)

> gm_dartel0 <= a long integer <= 2

>   0=None,1=rigid(SPM8 default),2=affine. (Nipype default value: `0`)

> gm_modulated_normalized0 <= a long integer <= 2

>   0=none,1=affine+non-linear(SPM8 default),2=non-linear only. (Nipype default value: `2`)

> gm_nativea boolean

>   (Nipype default value: `False`)

> gm_normalizeda boolean

>   (Nipype default value: `False`)

> jacobian_determinanta boolean

>   (Nipype default value: `False`)

> matlab_cmda unicode string

>   Matlab command to use.

> mfilea boolean

>   Run m-code using m-file. (Nipype default value: `True`)

> mrf_weightinga float

>   (Nipype default value: `0.15`)

> pathsa list of items which are a pathlike object or string representing a directory

>   Paths to add to matlabpath.

> pve_label_dartel0 <= a long integer <= 2

>   0=None,1=rigid(SPM8 default),2=affine. (Nipype default value: `0`)

> pve_label_nativea boolean

>   (Nipype default value: `False`)

> pve_label_normalizeda boolean

>   (Nipype default value: `False`)

> sampling_distancea float

>   Sampling distance on data for parameter estimation. (Nipype default value: `3`)

> spatial_normalization‘high’ or ‘low’

>   (Nipype default value: `high`)

> tissuesa pathlike object or string representing an existing file

>   Tissue probability map.

> use_mcra boolean

>   Run m-code using SPM MCR.

> use_sanlm_denoising_filter0 <= a long integer <= 2

>   0=No denoising, 1=denoising,2=denoising multi-threaded. (Nipype default value: `2`)

> use_v8structa boolean

>   Generate SPM8 and higher compatible jobs. (Nipype default value: `True`)

> warping_regularizationa float

>   Controls balance between parameters and data. (Nipype default value: `4`)

> wm_dartel0 <= a long integer <= 2

>   0=None,1=rigid(SPM8 default),2=affine. (Nipype default value: `0`)

> wm_modulated_normalized0 <= a long integer <= 2

>   0=none,1=affine+non-linear(SPM8 default),2=non-linear only. (Nipype default value: `2`)

> wm_nativea boolean

>   (Nipype default value: `False`)

> wm_normalizeda boolean

>   (Nipype default value: `False`)

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

>   Bias corrected images.

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

>   Dartel imported class images.

> forward_deformation_field : a list of items which are a pathlike object or string representing an existing file inverse_deformation_field : a list of items which are a pathlike object or string representing an existing file jacobian_determinant_images : a list of items which are a pathlike object or string representing an existing file modulated_class_images : a list of items which are a list of items which are a pathlike object or string representing an existing file

> > Modulated+normalized class images.

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

>   Native space probability maps.

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

>   Bias corrected images.

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

>   Normalized class images.

> pve_label_native_images : a list of items which are a pathlike object or string representing an existing file pve_label_normalized_images : a list of items which are a pathlike object or string representing an existing file pve_label_registered_images : a list of items which are a pathlike object or string representing an existing file transformation_mat : a list of items which are a pathlike object or string representing an existing file

> > Normalization transformation.