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# interfaces.spm.model

## EstimateContrast

Link to code

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()

Inputs:

    [Mandatory]
    beta_images: (an existing file name)
            Parameter estimates of the design matrix
    contrasts: (a list of items which are a tuple of the form: (a string,
             'T', a list of items which are a string, a list of items which are
             a float) or a tuple of the form: (a string, 'T', a list of items
             which are a string, a list of items which are a float, a list of
             items which are a float) or a tuple of the form: (a string, 'F', a
             list of items which are a tuple of the form: (a string, 'T', a list
             of items which are a string, a list of items which are a float) or
             a tuple of the form: (a string, 'T', a list of items which are a
             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_image: (an existing file name)
            Mean-squared image of the residuals
    spm_mat_file: (an existing file name)
            Absolute path to SPM.mat

    [Optional]
    beta_images: (an existing file name)
            Parameter estimates of the design matrix
    contrasts: (a list of items which are a tuple of the form: (a string,
             'T', a list of items which are a string, a list of items which are
             a float) or a tuple of the form: (a string, 'T', a list of items
             which are a string, a list of items which are a float, a list of
             items which are a float) or a tuple of the form: (a string, 'F', a
             list of items which are a tuple of the form: (a string, 'T', a list
             of items which are a string, a list of items which are a float) or
             a tuple of the form: (a string, 'T', a list of items which are a
             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.
    group_contrast: (a boolean)
            higher level contrast
            mutually_exclusive: use_derivs
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    matlab_cmd: (a string)
            matlab command to use
    mfile: (a boolean, nipype default value: True)
            Run m-code using m-file
    paths: (a directory name)
            Paths to add to matlabpath
    residual_image: (an existing file name)
            Mean-squared image of the residuals
    spm_mat_file: (an existing file name)
            Absolute path to SPM.mat
    use_derivs: (a boolean)
            use derivatives for estimation
            mutually_exclusive: group_contrast
    use_mcr: (a boolean)
            Run m-code using SPM MCR
    use_v8struct: (a boolean, nipype default value: True)
            Generate SPM8 and higher compatible jobs

Outputs:

    con_images: (an existing file name)
            contrast images from a t-contrast
    ess_images: (an existing file name)
            contrast images from an F-contrast
    spmF_images: (an existing file name)
            stat images from an F-contrast
    spmT_images: (an existing file name)
            stat images from a t-contrast
    spm_mat_file: (an existing file name)
            Updated SPM mat file

## EstimateModel

Link to code

Use spm_spm to estimate the parameters of a model

http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=71

### Examples

    >>> est = EstimateModel()
    >>> est.inputs.spm_mat_file = 'SPM.mat'
    >>> est.run()

Inputs:

    [Mandatory]
    estimation_method: (a dictionary with keys which are 'Classical' or
             'Bayesian2' or 'Bayesian' and with values which are any value)
            Classical, Bayesian2, Bayesian (dict)
    spm_mat_file: (an existing file name)
            absolute path to SPM.mat

    [Optional]
    estimation_method: (a dictionary with keys which are 'Classical' or
             'Bayesian2' or 'Bayesian' and with values which are any value)
            Classical, Bayesian2, Bayesian (dict)
    flags: (a string)
            optional arguments (opt)
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    matlab_cmd: (a string)
            matlab command to use
    mfile: (a boolean, nipype default value: True)
            Run m-code using m-file
    paths: (a directory name)
            Paths to add to matlabpath
    spm_mat_file: (an existing file name)
            absolute path to SPM.mat
    use_mcr: (a boolean)
            Run m-code using SPM MCR
    use_v8struct: (a boolean, nipype default value: True)
            Generate SPM8 and higher compatible jobs

Outputs:

    RPVimage: (an existing file name)
            Resels per voxel image
    beta_images: (an existing file name)
            design parameter estimates
    mask_image: (an existing file name)
            binary mask to constrain estimation
    residual_image: (an existing file name)
            Mean-squared image of the residuals
    spm_mat_file: (an existing file name)
            Updated SPM mat file

## FactorialDesign

Link to code

Base class for factorial designs

http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=79

Inputs:

    [Mandatory]

    [Optional]
    covariates: (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_file: (a file name)
            use an implicit mask file to threshold
    global_calc_mean: (a boolean)
            use mean for global calculation
            mutually_exclusive: global_calc_omit, global_calc_values
    global_calc_omit: (a boolean)
            omit global calculation
            mutually_exclusive: global_calc_mean, global_calc_values
    global_calc_values: (a list of items which are a float)
            omit global calculation
            mutually_exclusive: global_calc_mean, global_calc_omit
    global_normalization: (1 or 2 or 3)
            global normalization None-1, Proportional-2, ANCOVA-3
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    matlab_cmd: (a string)
            matlab command to use
    mfile: (a boolean, nipype default value: True)
            Run m-code using m-file
    no_grand_mean_scaling: (a boolean)
            do not perform grand mean scaling
    paths: (a directory name)
            Paths to add to matlabpath
    spm_mat_dir: (an existing directory name)
            directory to store SPM.mat file (opt)
    threshold_mask_absolute: (a float)
            use an absolute threshold
            mutually_exclusive: threshold_mask_none, threshold_mask_relative
    threshold_mask_none: (a boolean)
            do not use threshold masking
            mutually_exclusive: threshold_mask_absolute, threshold_mask_relative
    threshold_mask_relative: (a float)
            threshold using a proportion of the global value
            mutually_exclusive: threshold_mask_absolute, threshold_mask_none
    use_implicit_threshold: (a boolean)
            use implicit mask NaNs or zeros to threshold
    use_mcr: (a boolean)
            Run m-code using SPM MCR
    use_v8struct: (a boolean, nipype default value: True)
            Generate SPM8 and higher compatible jobs

Outputs:

    spm_mat_file: (an existing file name)
            SPM mat file

## Level1Design

Link to code

Generate an SPM design matrix

http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=61

### 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.run()

Inputs:

    [Mandatory]
    bases: (a dictionary with keys which are 'hrf' or 'fourier' or
             'fourier_han' or 'gamma' or 'fir' and with values which are any
             value)
             dict {'name':{'basesparam1':val,...}}
             name : string
             Name of basis function (hrf, fourier, fourier_han,
             gamma, fir)
             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, fir:
             length : int
             Post-stimulus window length (in seconds)
             order : int
             Number of basis functions
    interscan_interval: (a float)
            Interscan interval in secs
    session_info
            Session specific information generated by ``modelgen.SpecifyModel``
    timing_units: ('secs' or 'scans')
            units for specification of onsets

    [Optional]
    bases: (a dictionary with keys which are 'hrf' or 'fourier' or
             'fourier_han' or 'gamma' or 'fir' and with values which are any
             value)
             dict {'name':{'basesparam1':val,...}}
             name : string
             Name of basis function (hrf, fourier, fourier_han,
             gamma, fir)
             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, fir:
             length : int
             Post-stimulus window length (in seconds)
             order : int
             Number of basis functions
    factor_info: (a 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)
    global_intensity_normalization: ('none' or 'scaling')
            Global intensity normalization - scaling or none (opt)
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    interscan_interval: (a float)
            Interscan interval in secs
    mask_image: (an existing file name)
            Image for explicitly masking the analysis (opt)
    mask_threshold: ('-Inf' or a float, nipype default value: -Inf)
            Thresholding for the mask (opt, '-Inf')
    matlab_cmd: (a string)
            matlab command to use
    mfile: (a boolean, nipype default value: True)
            Run m-code using m-file
    microtime_onset: (a float)
            The onset/time-bin in seconds for alignment (opt)
    microtime_resolution: (an integer)
            Number of time-bins per scan in secs (opt)
    model_serial_correlations: ('AR(1)' or 'none')
            Model serial correlations AR(1) or none (opt)
    paths: (a directory name)
            Paths to add to matlabpath
    session_info
            Session specific information generated by ``modelgen.SpecifyModel``
    spm_mat_dir: (an existing directory name)
            directory to store SPM.mat file (opt)
    timing_units: ('secs' or 'scans')
            units for specification of onsets
    use_mcr: (a boolean)
            Run m-code using SPM MCR
    use_v8struct: (a boolean, nipype default value: True)
            Generate SPM8 and higher compatible jobs
    volterra_expansion_order: (1 or 2)
            Model interactions - yes:1, no:2 (opt)

Outputs:

    spm_mat_file: (an existing file name)
            SPM mat file

## MultipleRegressionDesign

Link to code

Create SPM design for multiple regression

### Examples

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

Inputs:

    [Mandatory]
    in_files: (a list of at least 2 items which are an existing file
             name)
            List of files

    [Optional]
    covariates: (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_file: (a file name)
            use an implicit mask file to threshold
    global_calc_mean: (a boolean)
            use mean for global calculation
            mutually_exclusive: global_calc_omit, global_calc_values
    global_calc_omit: (a boolean)
            omit global calculation
            mutually_exclusive: global_calc_mean, global_calc_values
    global_calc_values: (a list of items which are a float)
            omit global calculation
            mutually_exclusive: global_calc_mean, global_calc_omit
    global_normalization: (1 or 2 or 3)
            global normalization None-1, Proportional-2, ANCOVA-3
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    in_files: (a list of at least 2 items which are an existing file
             name)
            List of files
    include_intercept: (a boolean, nipype default value: True)
            Include intercept in design
    matlab_cmd: (a string)
            matlab command to use
    mfile: (a boolean, nipype default value: True)
            Run m-code using m-file
    no_grand_mean_scaling: (a boolean)
            do not perform grand mean scaling
    paths: (a directory name)
            Paths to add to matlabpath
    spm_mat_dir: (an existing directory name)
            directory to store SPM.mat file (opt)
    threshold_mask_absolute: (a float)
            use an absolute threshold
            mutually_exclusive: threshold_mask_none, threshold_mask_relative
    threshold_mask_none: (a boolean)
            do not use threshold masking
            mutually_exclusive: threshold_mask_absolute, threshold_mask_relative
    threshold_mask_relative: (a float)
            threshold using a proportion of the global value
            mutually_exclusive: threshold_mask_absolute, threshold_mask_none
    use_implicit_threshold: (a boolean)
            use implicit mask NaNs or zeros to threshold
    use_mcr: (a boolean)
            Run m-code using SPM MCR
    use_v8struct: (a boolean, nipype default value: True)
            Generate SPM8 and higher compatible jobs
    user_covariates: (a dictionary with keys which are 'vector' or 'name'
             or 'centering' and with values which are any value)
            covariate dictionary {vector, name, centering}

Outputs:

    spm_mat_file: (an existing file name)
            SPM mat file

## OneSampleTTestDesign

Link to code

Create SPM design for one sample t-test

### Examples

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

Inputs:

    [Mandatory]
    in_files: (a list of at least 2 items which are an existing file
             name)
            input files

    [Optional]
    covariates: (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_file: (a file name)
            use an implicit mask file to threshold
    global_calc_mean: (a boolean)
            use mean for global calculation
            mutually_exclusive: global_calc_omit, global_calc_values
    global_calc_omit: (a boolean)
            omit global calculation
            mutually_exclusive: global_calc_mean, global_calc_values
    global_calc_values: (a list of items which are a float)
            omit global calculation
            mutually_exclusive: global_calc_mean, global_calc_omit
    global_normalization: (1 or 2 or 3)
            global normalization None-1, Proportional-2, ANCOVA-3
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    in_files: (a list of at least 2 items which are an existing file
             name)
            input files
    matlab_cmd: (a string)
            matlab command to use
    mfile: (a boolean, nipype default value: True)
            Run m-code using m-file
    no_grand_mean_scaling: (a boolean)
            do not perform grand mean scaling
    paths: (a directory name)
            Paths to add to matlabpath
    spm_mat_dir: (an existing directory name)
            directory to store SPM.mat file (opt)
    threshold_mask_absolute: (a float)
            use an absolute threshold
            mutually_exclusive: threshold_mask_none, threshold_mask_relative
    threshold_mask_none: (a boolean)
            do not use threshold masking
            mutually_exclusive: threshold_mask_absolute, threshold_mask_relative
    threshold_mask_relative: (a float)
            threshold using a proportion of the global value
            mutually_exclusive: threshold_mask_absolute, threshold_mask_none
    use_implicit_threshold: (a boolean)
            use implicit mask NaNs or zeros to threshold
    use_mcr: (a boolean)
            Run m-code using SPM MCR
    use_v8struct: (a boolean, nipype default value: True)
            Generate SPM8 and higher compatible jobs

Outputs:

    spm_mat_file: (an existing file name)
            SPM mat file

## PairedTTestDesign

Link to code

Create SPM design for paired t-test

### Examples

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

Inputs:

    [Mandatory]
    paired_files: (a list of at least 2 items which are a list of from 2
             to 2 items which are an existing file name)
            List of paired files

    [Optional]
    ancova: (a boolean)
            Specify ancova-by-factor regressors
    covariates: (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_file: (a file name)
            use an implicit mask file to threshold
    global_calc_mean: (a boolean)
            use mean for global calculation
            mutually_exclusive: global_calc_omit, global_calc_values
    global_calc_omit: (a boolean)
            omit global calculation
            mutually_exclusive: global_calc_mean, global_calc_values
    global_calc_values: (a list of items which are a float)
            omit global calculation
            mutually_exclusive: global_calc_mean, global_calc_omit
    global_normalization: (1 or 2 or 3)
            global normalization None-1, Proportional-2, ANCOVA-3
    grand_mean_scaling: (a boolean)
            Perform grand mean scaling
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    matlab_cmd: (a string)
            matlab command to use
    mfile: (a boolean, nipype default value: True)
            Run m-code using m-file
    no_grand_mean_scaling: (a boolean)
            do not perform grand mean scaling
    paired_files: (a list of at least 2 items which are a list of from 2
             to 2 items which are an existing file name)
            List of paired files
    paths: (a directory name)
            Paths to add to matlabpath
    spm_mat_dir: (an existing directory name)
            directory to store SPM.mat file (opt)
    threshold_mask_absolute: (a float)
            use an absolute threshold
            mutually_exclusive: threshold_mask_none, threshold_mask_relative
    threshold_mask_none: (a boolean)
            do not use threshold masking
            mutually_exclusive: threshold_mask_absolute, threshold_mask_relative
    threshold_mask_relative: (a float)
            threshold using a proportion of the global value
            mutually_exclusive: threshold_mask_absolute, threshold_mask_none
    use_implicit_threshold: (a boolean)
            use implicit mask NaNs or zeros to threshold
    use_mcr: (a boolean)
            Run m-code using SPM MCR
    use_v8struct: (a boolean, nipype default value: True)
            Generate SPM8 and higher compatible jobs

Outputs:

    spm_mat_file: (an existing file name)
            SPM mat file

## Threshold

Link to code

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()

Inputs:

    [Mandatory]
    contrast_index: (an integer)
            which contrast in the SPM.mat to use
    spm_mat_file: (an existing file name)
            absolute path to SPM.mat
    stat_image: (an existing file name)
            stat image

    [Optional]
    contrast_index: (an integer)
            which contrast in the SPM.mat to use
    extent_fdr_p_threshold: (a float, nipype default value: 0.05)
            p threshold on FDR corrected cluster size probabilities
    extent_threshold: (an integer, nipype default value: 0)
            Minimum cluster size in voxels
    force_activation: (a boolean, nipype default value: False)
            In case no clusters survive the topological inference step this will
            pick a culster with the highes sum of t-values. Use with care.
    height_threshold: (a float, nipype default value: 0.05)
            value for initial thresholding (defining clusters)
    height_threshold_type: ('p-value' or 'stat', nipype default value:
             p-value)
            Is the cluster forming threshold a stat value or p-value?
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    matlab_cmd: (a string)
            matlab command to use
    mfile: (a boolean, nipype default value: True)
            Run m-code using m-file
    paths: (a directory name)
            Paths to add to matlabpath
    spm_mat_file: (an existing file name)
            absolute path to SPM.mat
    stat_image: (an existing file name)
            stat image
    use_fwe_correction: (a boolean, nipype default value: True)
            whether to use FWE (Bonferroni) correction for initial threshold
            (height_threshold_type has to be set to p-value)
    use_mcr: (a boolean)
            Run m-code using SPM MCR
    use_topo_fdr: (a boolean, nipype default value: True)
            whether to use FDR over cluster extent probabilities
    use_v8struct: (a boolean, nipype default value: True)
            Generate SPM8 and higher compatible jobs

Outputs:

    activation_forced: (a boolean)
    cluster_forming_thr: (a float)
    n_clusters: (an integer)
    pre_topo_fdr_map: (an existing file name)
    pre_topo_n_clusters: (an integer)
    thresholded_map: (an existing file name)

## ThresholdStatistics

Link to code

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()

Inputs:

    [Mandatory]
    contrast_index: (an integer)
            which contrast in the SPM.mat to use
    height_threshold: (a float)
            stat value for initial thresholding (defining clusters)
    spm_mat_file: (an existing file name)
            absolute path to SPM.mat
    stat_image: (an existing file name)
            stat image

    [Optional]
    contrast_index: (an integer)
            which contrast in the SPM.mat to use
    extent_threshold: (an integer, nipype default value: 0)
            Minimum cluster size in voxels
    height_threshold: (a float)
            stat value for initial thresholding (defining clusters)
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    matlab_cmd: (a string)
            matlab command to use
    mfile: (a boolean, nipype default value: True)
            Run m-code using m-file
    paths: (a directory name)
            Paths to add to matlabpath
    spm_mat_file: (an existing file name)
            absolute path to SPM.mat
    stat_image: (an existing file name)
            stat image
    use_mcr: (a boolean)
            Run m-code using SPM MCR
    use_v8struct: (a boolean, nipype default value: True)
            Generate SPM8 and higher compatible jobs

Outputs:

    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)

## TwoSampleTTestDesign

Link to code

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()

Inputs:

    [Mandatory]
    group1_files: (a list of at least 2 items which are an existing file
             name)
            Group 1 input files
    group2_files: (a list of at least 2 items which are an existing file
             name)
            Group 2 input files

    [Optional]
    covariates: (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}
    dependent: (a boolean)
            Are the measurements dependent between levels
    explicit_mask_file: (a file name)
            use an implicit mask file to threshold
    global_calc_mean: (a boolean)
            use mean for global calculation
            mutually_exclusive: global_calc_omit, global_calc_values
    global_calc_omit: (a boolean)
            omit global calculation
            mutually_exclusive: global_calc_mean, global_calc_values
    global_calc_values: (a list of items which are a float)
            omit global calculation
            mutually_exclusive: global_calc_mean, global_calc_omit
    global_normalization: (1 or 2 or 3)
            global normalization None-1, Proportional-2, ANCOVA-3
    group1_files: (a list of at least 2 items which are an existing file
             name)
            Group 1 input files
    group2_files: (a list of at least 2 items which are an existing file
             name)
            Group 2 input files
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    matlab_cmd: (a string)
            matlab command to use
    mfile: (a boolean, nipype default value: True)
            Run m-code using m-file
    no_grand_mean_scaling: (a boolean)
            do not perform grand mean scaling
    paths: (a directory name)
            Paths to add to matlabpath
    spm_mat_dir: (an existing directory name)
            directory to store SPM.mat file (opt)
    threshold_mask_absolute: (a float)
            use an absolute threshold
            mutually_exclusive: threshold_mask_none, threshold_mask_relative
    threshold_mask_none: (a boolean)
            do not use threshold masking
            mutually_exclusive: threshold_mask_absolute, threshold_mask_relative
    threshold_mask_relative: (a float)
            threshold using a proportion of the global value
            mutually_exclusive: threshold_mask_absolute, threshold_mask_none
    unequal_variance: (a boolean)
            Are the variances equal or unequal between groups
    use_implicit_threshold: (a boolean)
            use implicit mask NaNs or zeros to threshold
    use_mcr: (a boolean)
            Run m-code using SPM MCR
    use_v8struct: (a boolean, nipype default value: True)
            Generate SPM8 and higher compatible jobs

Outputs:

    spm_mat_file: (an existing file name)
            SPM mat file