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# nipype.interfaces.spm.model module

The spm module provides basic functions for interfacing with matlab and spm to access spm tools.

## EstimateContrast

Link to code

> Bases: SPMCommand

> Use spm_contrasts to estimate contrasts of interest

> Examples

>     >>> import nipype.interfaces.spm as spm
>     >>> est = spm.EstimateContrast()
>     >>> est.inputs.spm_mat_file = 'SPM.mat'
>     >>> cont1 = ('Task>Baseline','T', ['Task-Odd','Task-Even'],[0.5,0.5])
>     >>> cont2 = ('Task-Odd>Task-Even','T', ['Task-Odd','Task-Even'],[1,-1])
>     >>> contrasts = [cont1,cont2]
>     >>> est.inputs.contrasts = contrasts
>     >>> est.run()

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

>   Parameter estimates of the design matrix.

> contrastsa list of items which are a tuple of the form: (a unicode string, ‘T’, a list of items which are a unicode string, a list of items which are a float) or a tuple of the form: (a unicode string, ‘T’, a list of items which are a unicode string, a list of items which are a float, a list of items which are a float) or a tuple of the form: (a unicode string, ‘F’, a list of items which are a tuple of the form: (a unicode string, ‘T’, a list of items which are a unicode string, a list of items which are a float) or a tuple of the form: (a unicode string, ‘T’, a list of items which are a unicode string, a list of items which are a float, a list of items which are a float))
>   List of contrasts with each contrast being a list of the form:

>     [(‘name’, ‘stat’, [condition list], [weight list], [session list])] If session list is None or not provided, all sessions are used. For F contrasts, the condition list should contain previously defined T-contrasts.

> residual_imagea pathlike object or string representing an existing file

>   Mean-squared image of the residuals.

> spm_mat_filea pathlike object or string representing an existing file

>   Absolute path to SPM.mat.

> group_contrasta boolean

>   Higher level contrast. Mutually exclusive with inputs: `use_derivs`.

> 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_derivsa boolean

>   Use derivatives for estimation. Mutually exclusive with inputs: `group_contrast`.

> use_mcra boolean

>   Run m-code using SPM MCR.

> use_v8structa boolean

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

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

>   Contrast images from a t-contrast.

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

>   Contrast images from an F-contrast.

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

>   Stat images from an F-contrast.

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

>   Stat images from a t-contrast.

> spm_mat_filea pathlike object or string representing an existing file

>   Updated SPM mat file.

## EstimateModel

Link to code

> Bases: SPMCommand

> Use spm_spm to estimate the parameters of a model

> http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=69

> Examples

>     >>> est = EstimateModel()
>     >>> est.inputs.spm_mat_file = 'SPM.mat'
>     >>> est.inputs.estimation_method = {'Classical': 1}
>     >>> est.run()

> estimation_methoda dictionary with keys which are ‘Classical’ or ‘Bayesian2’ or ‘Bayesian’ and with values which are any value

>   Dictionary of either Classical: 1, Bayesian: 1, or Bayesian2: 1 (dict).

> spm_mat_filea pathlike object or string representing an existing file

>   Absolute path to SPM.mat.

> flagsa dictionary with keys which are any value and with values which are any value

>   Additional arguments.

> 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`)

> write_residualsa boolean

>   Write individual residual images.

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

>   Images of the AR coefficient.

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

>   Images of the parameter posteriors.

> RPVimagea pathlike object or string representing an existing file

>   Resels per voxel image.

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

>   Images of the standard deviation of parameter posteriors.

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

>   Images of the standard deviation of the error.

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

>   Design parameter estimates.

> labelsa pathlike object or string representing an existing file

>   Label file.

> mask_imagea pathlike object or string representing an existing file

>   Binary mask to constrain estimation.

> residual_imagea pathlike object or string representing an existing file

>   Mean-squared image of the residuals.

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

>   Individual residual images (requires write_residuals.

> spm_mat_filea pathlike object or string representing an existing file

>   Updated SPM mat file.

## FactorialDesign

Link to code

> Bases: SPMCommand

> Base class for factorial designs

> http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=77

> covariatesa list of items which are a dictionary with keys which are ‘vector’ or ‘name’ or ‘interaction’ or ‘centering’ and with values which are any value

>   Covariate dictionary {vector, name, interaction, centering}.

> explicit_mask_filea pathlike object or string representing a file

>   Use an implicit mask file to threshold.

> global_calc_meana boolean

>   Use mean for global calculation. Mutually exclusive with inputs: `global_calc_omit`, `global_calc_values`.

> global_calc_omita boolean

>   Omit global calculation. Mutually exclusive with inputs: `global_calc_mean`, `global_calc_values`.

> global_calc_valuesa list of items which are a float

>   Omit global calculation. Mutually exclusive with inputs: `global_calc_mean`, `global_calc_omit`.

> global_normalization1 or 2 or 3

>   Global normalization None-1, Proportional-2, ANCOVA-3.

> matlab_cmda unicode string

>   Matlab command to use.

> mfilea boolean

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

> no_grand_mean_scalinga boolean

>   Do not perform grand mean scaling.

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

>   Paths to add to matlabpath.

> spm_mat_dira pathlike object or string representing an existing directory

>   Directory to store SPM.mat file (opt).

> threshold_mask_absolutea float

>   Use an absolute threshold. Mutually exclusive with inputs: `threshold_mask_none`, `threshold_mask_relative`.

> threshold_mask_nonea boolean

>   Do not use threshold masking. Mutually exclusive with inputs: `threshold_mask_absolute`, `threshold_mask_relative`.

> threshold_mask_relativea float

>   Threshold using a proportion of the global value. Mutually exclusive with inputs: `threshold_mask_absolute`, `threshold_mask_none`.

> use_implicit_thresholda boolean

>   Use implicit mask NaNs or zeros to threshold.

> use_mcra boolean

>   Run m-code using SPM MCR.

> use_v8structa boolean

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

> spm_mat_filea pathlike object or string representing an existing file

>   SPM mat file.

## Level1Design

Link to code

> Bases: SPMCommand

> Generate an SPM design matrix

> http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=59

> Examples

>     >>> level1design = Level1Design()
>     >>> level1design.inputs.timing_units = 'secs'
>     >>> level1design.inputs.interscan_interval = 2.5
>     >>> level1design.inputs.bases = {'hrf':{'derivs': [0,0]}}
>     >>> level1design.inputs.session_info = 'session_info.npz'
>     >>> level1design.inputs.flags = {'mthresh': 0.4}
>     >>> level1design.run()

> basesa dictionary with keys which are ‘hrf’ or ‘fourier’ or ‘fourier_han’ or ‘gamma’ or ‘fir’ and with values which are any value

>   Dictionary names of the basis function to parameters:

>   > - hrf

>   >   > - derivs – (2-element list) Model HRF Derivatives. No derivatives: [0,0], Time derivatives : [1,0], Time and Dispersion derivatives: [1,1]

>   > - fourier, fourier_han, gamma, or fir:

>   >   > - length – (int) Post-stimulus window length (in seconds)

>   >   > - order – (int) Number of basis functions

> interscan_intervala float

>   Interscan interval in secs.

> session_infoany value

>   Session specific information generated by `modelgen.SpecifyModel`.

> timing_units‘secs’ or ‘scans’

>   Units for specification of onsets.

> factor_infoa list of items which are a dictionary with keys which are ‘name’ or ‘levels’ and with values which are any value

>   Factor specific information file (opt).

> flagsa dictionary with keys which are any value and with values which are any value

>   Additional arguments to the job, e.g., a common SPM operation is to modify the default masking threshold (mthresh).

> global_intensity_normalization‘none’ or ‘scaling’

>   Global intensity normalization - scaling or none.

> mask_imagea pathlike object or string representing an existing file

>   Image for explicitly masking the analysis.

> mask_threshold‘-Inf’ or a float

>   Thresholding for the mask. (Nipype default value: `-Inf`)

> matlab_cmda unicode string

>   Matlab command to use.

> mfilea boolean

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

> microtime_onseta float

>   The onset/time-bin in seconds for alignment (opt).

> microtime_resolutionan integer (int or long)

>   Number of time-bins per scan in secs (opt).

> model_serial_correlations‘AR(1)’ or ‘FAST’ or ‘none’

>   Model serial correlations AR(1), FAST or none. FAST is available in SPM12.

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

>   Paths to add to matlabpath.

> spm_mat_dira pathlike object or string representing an existing directory

>   Directory to store SPM.mat file (opt).

> use_mcra boolean

>   Run m-code using SPM MCR.

> use_v8structa boolean

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

> volterra_expansion_order1 or 2

>   Model interactions - no:1, yes:2.

> spm_mat_filea pathlike object or string representing an existing file

>   SPM mat file.

## MultipleRegressionDesign

Link to code

> Bases: FactorialDesign

> Create SPM design for multiple regression

> Examples

>     >>> mreg = MultipleRegressionDesign()
>     >>> mreg.inputs.in_files = ['cont1.nii','cont2.nii']
>     >>> mreg.run()

> in_filesa list of at least 2 items which are a pathlike object or string representing an existing file

>   List of files.

> covariatesa list of items which are a dictionary with keys which are ‘vector’ or ‘name’ or ‘interaction’ or ‘centering’ and with values which are any value

>   Covariate dictionary {vector, name, interaction, centering}.

> explicit_mask_filea pathlike object or string representing a file

>   Use an implicit mask file to threshold.

> global_calc_meana boolean

>   Use mean for global calculation. Mutually exclusive with inputs: `global_calc_omit`, `global_calc_values`.

> global_calc_omita boolean

>   Omit global calculation. Mutually exclusive with inputs: `global_calc_mean`, `global_calc_values`.

> global_calc_valuesa list of items which are a float

>   Omit global calculation. Mutually exclusive with inputs: `global_calc_mean`, `global_calc_omit`.

> global_normalization1 or 2 or 3

>   Global normalization None-1, Proportional-2, ANCOVA-3.

> include_intercepta boolean

>   Include intercept in design. (Nipype default value: `True`)

> matlab_cmda unicode string

>   Matlab command to use.

> mfilea boolean

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

> no_grand_mean_scalinga boolean

>   Do not perform grand mean scaling.

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

>   Paths to add to matlabpath.

> spm_mat_dira pathlike object or string representing an existing directory

>   Directory to store SPM.mat file (opt).

> threshold_mask_absolutea float

>   Use an absolute threshold. Mutually exclusive with inputs: `threshold_mask_none`, `threshold_mask_relative`.

> threshold_mask_nonea boolean

>   Do not use threshold masking. Mutually exclusive with inputs: `threshold_mask_absolute`, `threshold_mask_relative`.

> threshold_mask_relativea float

>   Threshold using a proportion of the global value. Mutually exclusive with inputs: `threshold_mask_absolute`, `threshold_mask_none`.

> use_implicit_thresholda boolean

>   Use implicit mask NaNs or zeros to threshold.

> use_mcra boolean

>   Run m-code using SPM MCR.

> use_v8structa boolean

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

> user_covariatesa list of items which are a dictionary with keys which are ‘vector’ or ‘name’ or ‘centering’ and with values which are any value

>   Covariate dictionary {vector, name, centering}.

> spm_mat_filea pathlike object or string representing an existing file

>   SPM mat file.

## OneSampleTTestDesign

Link to code

> Bases: FactorialDesign

> Create SPM design for one sample t-test

> Examples

>     >>> ttest = OneSampleTTestDesign()
>     >>> ttest.inputs.in_files = ['cont1.nii', 'cont2.nii']
>     >>> ttest.run()

> in_filesa list of at least 2 items which are a pathlike object or string representing an existing file

>   Input files.

> covariatesa list of items which are a dictionary with keys which are ‘vector’ or ‘name’ or ‘interaction’ or ‘centering’ and with values which are any value

>   Covariate dictionary {vector, name, interaction, centering}.

> explicit_mask_filea pathlike object or string representing a file

>   Use an implicit mask file to threshold.

> global_calc_meana boolean

>   Use mean for global calculation. Mutually exclusive with inputs: `global_calc_omit`, `global_calc_values`.

> global_calc_omita boolean

>   Omit global calculation. Mutually exclusive with inputs: `global_calc_mean`, `global_calc_values`.

> global_calc_valuesa list of items which are a float

>   Omit global calculation. Mutually exclusive with inputs: `global_calc_mean`, `global_calc_omit`.

> global_normalization1 or 2 or 3

>   Global normalization None-1, Proportional-2, ANCOVA-3.

> matlab_cmda unicode string

>   Matlab command to use.

> mfilea boolean

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

> no_grand_mean_scalinga boolean

>   Do not perform grand mean scaling.

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

>   Paths to add to matlabpath.

> spm_mat_dira pathlike object or string representing an existing directory

>   Directory to store SPM.mat file (opt).

> threshold_mask_absolutea float

>   Use an absolute threshold. Mutually exclusive with inputs: `threshold_mask_none`, `threshold_mask_relative`.

> threshold_mask_nonea boolean

>   Do not use threshold masking. Mutually exclusive with inputs: `threshold_mask_absolute`, `threshold_mask_relative`.

> threshold_mask_relativea float

>   Threshold using a proportion of the global value. Mutually exclusive with inputs: `threshold_mask_absolute`, `threshold_mask_none`.

> use_implicit_thresholda boolean

>   Use implicit mask NaNs or zeros to threshold.

> use_mcra boolean

>   Run m-code using SPM MCR.

> use_v8structa boolean

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

> spm_mat_filea pathlike object or string representing an existing file

>   SPM mat file.

## PairedTTestDesign

Link to code

> Bases: FactorialDesign

> Create SPM design for paired t-test

> Examples

>     >>> pttest = PairedTTestDesign()
>     >>> pttest.inputs.paired_files = [['cont1.nii','cont1a.nii'],['cont2.nii','cont2a.nii']]
>     >>> pttest.run()

> paired_filesa list of at least 2 items which are a list of from 2 to 2 items which are a pathlike object or string representing an existing file

>   List of paired files.

> ancovaa boolean

>   Specify ancova-by-factor regressors.

> covariatesa list of items which are a dictionary with keys which are ‘vector’ or ‘name’ or ‘interaction’ or ‘centering’ and with values which are any value

>   Covariate dictionary {vector, name, interaction, centering}.

> explicit_mask_filea pathlike object or string representing a file

>   Use an implicit mask file to threshold.

> global_calc_meana boolean

>   Use mean for global calculation. Mutually exclusive with inputs: `global_calc_omit`, `global_calc_values`.

> global_calc_omita boolean

>   Omit global calculation. Mutually exclusive with inputs: `global_calc_mean`, `global_calc_values`.

> global_calc_valuesa list of items which are a float

>   Omit global calculation. Mutually exclusive with inputs: `global_calc_mean`, `global_calc_omit`.

> global_normalization1 or 2 or 3

>   Global normalization None-1, Proportional-2, ANCOVA-3.

> grand_mean_scalinga boolean

>   Perform grand mean scaling.

> matlab_cmda unicode string

>   Matlab command to use.

> mfilea boolean

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

> no_grand_mean_scalinga boolean

>   Do not perform grand mean scaling.

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

>   Paths to add to matlabpath.

> spm_mat_dira pathlike object or string representing an existing directory

>   Directory to store SPM.mat file (opt).

> threshold_mask_absolutea float

>   Use an absolute threshold. Mutually exclusive with inputs: `threshold_mask_none`, `threshold_mask_relative`.

> threshold_mask_nonea boolean

>   Do not use threshold masking. Mutually exclusive with inputs: `threshold_mask_absolute`, `threshold_mask_relative`.

> threshold_mask_relativea float

>   Threshold using a proportion of the global value. Mutually exclusive with inputs: `threshold_mask_absolute`, `threshold_mask_none`.

> use_implicit_thresholda boolean

>   Use implicit mask NaNs or zeros to threshold.

> use_mcra boolean

>   Run m-code using SPM MCR.

> use_v8structa boolean

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

> spm_mat_filea pathlike object or string representing an existing file

>   SPM mat file.

## Threshold

Link to code

> Bases: SPMCommand

> Topological FDR thresholding based on cluster extent/size. Smoothness is estimated from GLM residuals but is assumed to be the same for all of the voxels.

> Examples

>     >>> thresh = Threshold()
>     >>> thresh.inputs.spm_mat_file = 'SPM.mat'
>     >>> thresh.inputs.stat_image = 'spmT_0001.img'
>     >>> thresh.inputs.contrast_index = 1
>     >>> thresh.inputs.extent_fdr_p_threshold = 0.05
>     >>> thresh.run()

> contrast_indexan integer (int or long)

>   Which contrast in the SPM.mat to use.

> spm_mat_filea pathlike object or string representing an existing file

>   Absolute path to SPM.mat.

> stat_imagea pathlike object or string representing an existing file

>   Stat image.

> extent_fdr_p_thresholda float

>   P threshold on FDR corrected cluster size probabilities. (Nipype default value: `0.05`)

> extent_thresholdan integer (int or long)

>   Minimum cluster size in voxels. (Nipype default value: `0`)

> force_activationa boolean

>   In case no clusters survive the topological inference step this will pick a culster with the highes sum of t-values. Use with care. (Nipype default value: `False`)

> height_thresholda float

>   Value for initial thresholding (defining clusters). (Nipype default value: `0.05`)

> height_threshold_type‘p-value’ or ‘stat’

>   Is the cluster forming threshold a stat value or p-value?. (Nipype default value: `p-value`)

> 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_fwe_correctiona boolean

>   Whether to use FWE (Bonferroni) correction for initial threshold (height_threshold_type has to be set to p-value). (Nipype default value: `True`)

> use_mcra boolean

>   Run m-code using SPM MCR.

> use_topo_fdra boolean

>   Whether to use FDR over cluster extent probabilities. (Nipype default value: `True`)

> use_v8structa boolean

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

> activation_forced : a boolean cluster_forming_thr : a float n_clusters : an integer (int or long) pre_topo_fdr_map : a pathlike object or string representing an existing file pre_topo_n_clusters : an integer (int or long) thresholded_map : a pathlike object or string representing an existing file

> Threshold.aggregate_outputs(runtime=None)

>   Collate expected outputs and apply output traits validation.

## ThresholdStatistics

Link to code

> Bases: SPMCommand

> Given height and cluster size threshold calculate theoretical probabilities concerning false positives

> Examples

>     >>> thresh = ThresholdStatistics()
>     >>> thresh.inputs.spm_mat_file = 'SPM.mat'
>     >>> thresh.inputs.stat_image = 'spmT_0001.img'
>     >>> thresh.inputs.contrast_index = 1
>     >>> thresh.inputs.height_threshold = 4.56
>     >>> thresh.run()

> contrast_indexan integer (int or long)

>   Which contrast in the SPM.mat to use.

> height_thresholda float

>   Stat value for initial thresholding (defining clusters).

> spm_mat_filea pathlike object or string representing an existing file

>   Absolute path to SPM.mat.

> stat_imagea pathlike object or string representing an existing file

>   Stat image.

> extent_thresholdan integer (int or long)

>   Minimum cluster size in voxels. (Nipype default value: `0`)

> 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`)

> clusterwise_P_FDR : a float clusterwise_P_RF : a float voxelwise_P_Bonf : a float voxelwise_P_FDR : a float voxelwise_P_RF : a float voxelwise_P_uncor : a float

> ThresholdStatistics.aggregate_outputs(runtime=None, needed_outputs=None)

>   Collate expected outputs and apply output traits validation.

## TwoSampleTTestDesign

Link to code

> Bases: FactorialDesign

> Create SPM design for two sample t-test

> Examples

>     >>> ttest = TwoSampleTTestDesign()
>     >>> ttest.inputs.group1_files = ['cont1.nii', 'cont2.nii']
>     >>> ttest.inputs.group2_files = ['cont1a.nii', 'cont2a.nii']
>     >>> ttest.run()

> group1_filesa list of at least 2 items which are a pathlike object or string representing an existing file

>   Group 1 input files.

> group2_filesa list of at least 2 items which are a pathlike object or string representing an existing file

>   Group 2 input files.

> covariatesa list of items which are a dictionary with keys which are ‘vector’ or ‘name’ or ‘interaction’ or ‘centering’ and with values which are any value

>   Covariate dictionary {vector, name, interaction, centering}.

> dependenta boolean

>   Are the measurements dependent between levels.

> explicit_mask_filea pathlike object or string representing a file

>   Use an implicit mask file to threshold.

> global_calc_meana boolean

>   Use mean for global calculation. Mutually exclusive with inputs: `global_calc_omit`, `global_calc_values`.

> global_calc_omita boolean

>   Omit global calculation. Mutually exclusive with inputs: `global_calc_mean`, `global_calc_values`.

> global_calc_valuesa list of items which are a float

>   Omit global calculation. Mutually exclusive with inputs: `global_calc_mean`, `global_calc_omit`.

> global_normalization1 or 2 or 3

>   Global normalization None-1, Proportional-2, ANCOVA-3.

> matlab_cmda unicode string

>   Matlab command to use.

> mfilea boolean

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

> no_grand_mean_scalinga boolean

>   Do not perform grand mean scaling.

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

>   Paths to add to matlabpath.

> spm_mat_dira pathlike object or string representing an existing directory

>   Directory to store SPM.mat file (opt).

> threshold_mask_absolutea float

>   Use an absolute threshold. Mutually exclusive with inputs: `threshold_mask_none`, `threshold_mask_relative`.

> threshold_mask_nonea boolean

>   Do not use threshold masking. Mutually exclusive with inputs: `threshold_mask_absolute`, `threshold_mask_relative`.

> threshold_mask_relativea float

>   Threshold using a proportion of the global value. Mutually exclusive with inputs: `threshold_mask_absolute`, `threshold_mask_none`.

> unequal_variancea boolean

>   Are the variances equal or unequal between groups.

> use_implicit_thresholda boolean

>   Use implicit mask NaNs or zeros to threshold.

> use_mcra boolean

>   Run m-code using SPM MCR.

> use_v8structa boolean

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

> spm_mat_filea pathlike object or string representing an existing file

>   SPM mat file.