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# interfaces.freesurfer.model

## Binarize

Link to code

Wraps command mri_binarize

Use FreeSurfer mri_binarize to threshold an input volume

### Examples

    >>> binvol = Binarize(in_file='structural.nii', min=10, binary_file='foo_out.nii')
    >>> binvol.cmdline
    'mri_binarize --o foo_out.nii --i structural.nii --min 10.000000'

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            input volume
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output

    [Optional]
    abs: (a boolean)
            take abs of invol first (ie, make unsigned)
    args: (a string)
            Additional parameters to the command
    bin_col_num: (a boolean)
            set binarized voxel value to its column number
    bin_val: (an integer)
            set vox within thresh to val (default is 1)
    bin_val_not: (an integer)
            set vox outside range to val (default is 0)
    binary_file: (a file name)
            binary output volume
    count_file: (a boolean or a file name)
            save number of hits in ascii file (hits, ntotvox, pct)
    dilate: (an integer)
            niters: dilate binarization in 3D
    environ: (a dictionary with keys which are a value of type 'str' and with values which
             are a value of type 'str', nipype default value: {})
            Environment variables
    erode: (an integer)
            nerode: erode binarization in 3D (after any dilation)
    erode2d: (an integer)
            nerode2d: erode binarization in 2D (after any 3D erosion)
    frame_no: (an integer)
            use 0-based frame of input (default is 0)
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    invert: (a boolean)
            set binval=0, binvalnot=1
    mask_file: (an existing file name)
            must be within mask
    mask_thresh: (a float)
            set thresh for mask
    match: (a list of items which are an integer)
            match instead of threshold
    max: (a float)
            max thresh
    merge_file: (an existing file name)
            merge with mergevol
    min: (a float)
            min thresh
    out_type: ('nii' or 'nii.gz' or 'mgz')
            output file type
    rmax: (a float)
            compute max based on rmax*globalmean
    rmin: (a float)
            compute min based on rmin*globalmean
    subjects_dir: (an existing directory name)
            subjects directory
    ventricles: (a boolean)
            set match vals those for aseg ventricles+choroid (not 4th)
    wm: (a boolean)
            set match vals to 2 and 41 (aseg for cerebral WM)
    wm_ven_csf: (a boolean)
            WM and ventricular CSF, including choroid (not 4th)
    zero_edges: (a boolean)
            zero the edge voxels
    zero_slice_edge: (a boolean)
            zero the edge slice voxels

Outputs:

    binary_file: (an existing file name)
            binarized output volume
    count_file: (a file name)
            ascii file containing number of hits

## Concatenate

Link to code

Wraps command mri_concat

Use Freesurfer mri_concat to combine several input volumes into one output volume. Can concatenate by frames, or compute a variety of statistics on the input volumes.

### Examples

Combine two input volumes into one volume with two frames

    >>> concat = Concatenate()
    >>> concat.inputs.in_files = ['cont1.nii', 'cont2.nii']
    >>> concat.inputs.concatenated_file = 'bar.nii'
    >>> concat.cmdline
    'mri_concat --o bar.nii --i cont1.nii --i cont2.nii'

Inputs:

    [Mandatory]
    in_files: (an existing file name)
            Individual volumes to be concatenated
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output

    [Optional]
    add_val: (a float)
            Add some amount to the input volume
    args: (a string)
            Additional parameters to the command
    combine: (a boolean)
            Combine non-zero values into single frame volume
    concatenated_file: (a file name)
            Output volume
    environ: (a dictionary with keys which are a value of type 'str' and with values which
             are a value of type 'str', nipype default value: {})
            Environment variables
    gmean: (an integer)
            create matrix to average Ng groups, Nper=Ntot/Ng
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    keep_dtype: (a boolean)
            Keep voxelwise precision type (default is float
    mask_file: (an existing file name)
            Mask input with a volume
    max_bonfcor: (a boolean)
            Compute max and bonferroni correct (assumes -log10(ps))
    max_index: (a boolean)
            Compute the index of max voxel in concatenated volumes
    mean_div_n: (a boolean)
            compute mean/nframes (good for var)
    multiply_by: (a float)
            Multiply input volume by some amount
    multiply_matrix_file: (an existing file name)
            Multiply input by an ascii matrix in file
    paired_stats: ('sum' or 'avg' or 'diff' or 'diff-norm' or 'diff-norm1' or 'diff-norm2')
            Compute paired sum, avg, or diff
    sign: ('abs' or 'pos' or 'neg')
            Take only pos or neg voxles from input, or take abs
    sort: (a boolean)
            Sort each voxel by ascending frame value
    stats: ('sum' or 'var' or 'std' or 'max' or 'min' or 'mean')
            Compute the sum, var, std, max, min or mean of the input volumes
    subjects_dir: (an existing directory name)
            subjects directory
    vote: (a boolean)
            Most frequent value at each voxel and fraction of occurances

Outputs:

    concatenated_file: (an existing file name)
            Path/name of the output volume

## GLMFit

Link to code

Wraps command mri_glmfit

Use FreeSurfer’s mri_glmfit to specify and estimate a general linear model.

### Examples

    >>> glmfit = GLMFit()
    >>> glmfit.inputs.in_file = 'functional.nii'
    >>> glmfit.inputs.one_sample = True
    >>> glmfit.cmdline == 'mri_glmfit --glmdir %s --y functional.nii --osgm'%os.getcwd()
    True

Inputs:

    [Mandatory]
    in_file: (a file name)
            input 4D file
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output

    [Optional]
    allow_ill_cond: (a boolean)
            allow ill-conditioned design matrices
    allow_repeated_subjects: (a boolean)
            allow subject names to repeat in the fsgd file (must appear before --fsgd
    args: (a string)
            Additional parameters to the command
    calc_AR1: (a boolean)
            compute and save temporal AR1 of residual
    check_opts: (a boolean)
            don't run anything, just check options and exit
    compute_log_y: (a boolean)
            compute natural log of y prior to analysis
    contrast: (an existing file name)
            contrast file
    cortex: (a boolean)
            use subjects ?h.cortex.label as label
            mutually_exclusive: label_file
    debug: (a boolean)
            turn on debugging
    design: (an existing file name)
            design matrix file
            mutually_exclusive: fsgd, design, one_sample
    diag: (an integer)
            Gdiag_no : set diagnositc level
    diag_cluster: (a boolean)
            save sig volume and exit from first sim loop
    environ: (a dictionary with keys which are a value of type 'str' and with values which
             are a value of type 'str', nipype default value: {})
            Environment variables
    fixed_fx_dof: (an integer)
            dof for fixed effects analysis
            mutually_exclusive: fixed_fx_dof_file
    fixed_fx_dof_file: (a file name)
            text file with dof for fixed effects analysis
            mutually_exclusive: fixed_fx_dof
    fixed_fx_var: (an existing file name)
            for fixed effects analysis
    force_perm: (a boolean)
            force perumtation test, even when design matrix is not orthog
    fsgd: (a tuple of the form: (an existing file name, 'doss' or 'dods'))
            freesurfer descriptor file
            mutually_exclusive: fsgd, design, one_sample
    fwhm: (a float)
            smooth input by fwhm
    glm_dir: (a string)
            save outputs to dir
    hemi: ('lh' or 'rh')
            surface hemisphere
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    invert_mask: (a boolean)
            invert mask
    label_file: (an existing file name)
            use label as mask, surfaces only
            mutually_exclusive: cortex
    mask_file: (an existing file name)
            binary mask
    no_contrast_sok: (a boolean)
            do not fail if no contrasts specified
    no_est_fwhm: (a boolean)
            turn off FWHM output estimation
    no_mask_smooth: (a boolean)
            do not mask when smoothing
    no_prune: (a boolean)
            do not prune
            mutually_exclusive: prunethresh
    one_sample: (a boolean)
            construct X and C as a one-sample group mean
            mutually_exclusive: one_sample, fsgd, design, contrast
    pca: (a boolean)
            perform pca/svd analysis on residual
    per_voxel_reg: (an existing file name)
            per-voxel regressors
    profile: (an integer)
            niters : test speed
    prune: (a boolean)
            remove voxels that do not have a non-zero value at each frame (def)
    prune_thresh: (a float)
            prune threshold. Default is FLT_MIN
            mutually_exclusive: noprune
    resynth_test: (an integer)
            test GLM by resynthsis
    save_cond: (a boolean)
            flag to save design matrix condition at each voxel
    save_estimate: (a boolean)
            save signal estimate (yhat)
    save_res_corr_mtx: (a boolean)
            save residual error spatial correlation matrix (eres.scm). Big!
    save_residual: (a boolean)
            save residual error (eres)
    seed: (an integer)
            used for synthesizing noise
    self_reg: (a tuple of the form: (an integer, an integer, an integer))
            self-regressor from index col row slice
    sim_done_file: (a file name)
            create file when simulation finished
    sim_sign: ('abs' or 'pos' or 'neg')
            abs, pos, or neg
    simulation: (a tuple of the form: ('perm' or 'mc-full' or 'mc-z', an integer, a float, a
             string))
            nulltype nsim thresh csdbasename
    subject_id: (a string)
            subject id for surface geometry
    subjects_dir: (an existing directory name)
            subjects directory
    surf: (a boolean)
            analysis is on a surface mesh
            requires: subject_id, hemi
    surf_geo: (a string, nipype default value: white)
            surface geometry name (e.g. white, pial)
    synth: (a boolean)
            replace input with gaussian
    uniform: (a tuple of the form: (a float, a float))
            use uniform distribution instead of gaussian
    var_fwhm: (a float)
            smooth variance by fwhm
    vox_dump: (a tuple of the form: (an integer, an integer, an integer))
            dump voxel GLM and exit
    weight_file: (an existing file name)
            weight for each input at each voxel
            mutually_exclusive: weighted_ls
    weight_inv: (a boolean)
            invert weights
            mutually_exclusive: weighted_ls
    weight_sqrt: (a boolean)
            sqrt of weights
            mutually_exclusive: weighted_ls
    weighted_ls: (an existing file name)
            weighted least squares
            mutually_exclusive: weight_file, weight_inv, weight_sqrt

Outputs:

    beta_file: (an existing file name)
            map of regression coefficients
    dof_file: (a file name)
            text file with effective degrees-of-freedom for the analysis
    error_file: (a file name)
            map of residual error
    error_stddev_file: (a file name)
            map of residual error standard deviation
    error_var_file: (a file name)
            map of residual error variance
    estimate_file: (a file name)
            map of the estimated Y values
    frame_eigenvectors: (a file name)
            matrix of frame eigenvectors from residual PCA
    ftest_file
            map of test statistic values
    fwhm_file: (a file name)
            text file with estimated smoothness
    gamma_file
            map of contrast of regression coefficients
    gamma_var_file
            map of regression contrast variance
    glm_dir: (an existing directory name)
            output directory
    mask_file: (a file name)
            map of the mask used in the analysis
    sig_file
            map of F-test significance (in -log10p)
    singular_values: (a file name)
            matrix singular values from residual PCA
    spatial_eigenvectors: (a file name)
            map of spatial eigenvectors from residual PCA
    svd_stats_file: (a file name)
            text file summarizing the residual PCA

## Label2Vol

Link to code

Wraps command mri_label2vol

Make a binary volume from a Freesurfer label

### Examples

    >>> binvol = Label2Vol(label_file='cortex.label', template_file='structural.nii', reg_file='register.dat', fill_thresh=0.5, vol_label_file='foo_out.nii')
    >>> binvol.cmdline
    'mri_label2vol --fillthresh 0 --label cortex.label --reg register.dat --temp structural.nii --o foo_out.nii'

Inputs:

    [Mandatory]
    annot_file: (an existing file name)
            surface annotation file
            mutually_exclusive: label_file, annot_file, seg_file, aparc_aseg
            requires: subject_id, hemi
    aparc_aseg: (a boolean)
            use aparc+aseg.mgz in subjectdir as seg
            mutually_exclusive: label_file, annot_file, seg_file, aparc_aseg
    label_file: (an existing file name)
            list of label files
            mutually_exclusive: label_file, annot_file, seg_file, aparc_aseg
    seg_file: (an existing file name)
            segmentation file
            mutually_exclusive: label_file, annot_file, seg_file, aparc_aseg
    template_file: (an existing file name)
            output template volume
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output

    [Optional]
    args: (a string)
            Additional parameters to the command
    environ: (a dictionary with keys which are a value of type 'str' and with values which
             are a value of type 'str', nipype default value: {})
            Environment variables
    fill_thresh: (0.0 <= a floating point number <= 1.0)
            thresh : between 0 and 1
    hemi: ('lh' or 'rh')
            hemisphere to use lh or rh
    identity: (a boolean)
            set R=I
            mutually_exclusive: reg_file, reg_header, identity
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    invert_mtx: (a boolean)
            Invert the registration matrix
    label_hit_file: (a file name)
            file with each frame is nhits for a label
    label_voxel_volume: (a float)
            volume of each label point (def 1mm3)
    map_label_stat: (a file name)
            map the label stats field into the vol
    native_vox2ras: (a boolean)
            use native vox2ras xform instead of  tkregister-style
    proj: (a tuple of the form: ('abs' or 'frac', a float, a float, a float))
            project along surface normal
    reg_file: (an existing file name)
            tkregister style matrix VolXYZ = R*LabelXYZ
            mutually_exclusive: reg_file, reg_header, identity
    reg_header: (an existing file name)
            label template volume
            mutually_exclusive: reg_file, reg_header, identity
    subject_id: (a string)
            subject id
    subjects_dir: (an existing directory name)
            subjects directory
    surface: (a string)
            use surface instead of white
    vol_label_file: (a file name)
            output volume

Outputs:

    vol_label_file: (an existing file name)
            output volume

## MRISPreproc

Link to code

Wraps command mris_preproc

Use FreeSurfer mris_preproc to prepare a group of contrasts for a second level analysis

### Examples

    >>> preproc = MRISPreproc()
    >>> preproc.inputs.target = 'fsaverage'
    >>> preproc.inputs.hemi = 'lh'
    >>> preproc.inputs.vol_measure_file = [('cont1.nii', 'register.dat'),                                            ('cont1a.nii', 'register.dat')]
    >>> preproc.inputs.out_file = 'concatenated_file.mgz'
    >>> preproc.cmdline
    'mris_preproc --hemi lh --out concatenated_file.mgz --target fsaverage --iv cont1.nii register.dat --iv cont1a.nii register.dat'

Inputs:

    [Mandatory]
    hemi: ('lh' or 'rh')
            hemisphere for source and target
    target: (a string)
            target subject name
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output

    [Optional]
    args: (a string)
            Additional parameters to the command
    environ: (a dictionary with keys which are a value of type 'str' and with values which
             are a value of type 'str', nipype default value: {})
            Environment variables
    fsgd_file: (an existing file name)
            specify subjects using fsgd file
            mutually_exclusive: subjects, fsgd_file, subject_file
    fwhm: (a float)
            smooth by fwhm mm on the target surface
            mutually_exclusive: num_iters
    fwhm_source: (a float)
            smooth by fwhm mm on the source surface
            mutually_exclusive: num_iters_source
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    num_iters: (an integer)
            niters : smooth by niters on the target surface
            mutually_exclusive: fwhm
    num_iters_source: (an integer)
            niters : smooth by niters on the source surface
            mutually_exclusive: fwhm_source
    out_file: (a file name)
            output filename
    proj_frac: (a float)
            projection fraction for vol2surf
    smooth_cortex_only: (a boolean)
            only smooth cortex (ie, exclude medial wall)
    source_format: (a string)
            source format
    subject_file: (an existing file name)
            file specifying subjects separated by white space
            mutually_exclusive: subjects, fsgd_file, subject_file
    subjects: (a list of items which are any value)
            subjects from who measures are calculated
            mutually_exclusive: subjects, fsgd_file, subject_file
    subjects_dir: (an existing directory name)
            subjects directory
    surf_area: (a string)
            Extract vertex area from subject/surf/hemi.surfname to use as input.
            mutually_exclusive: surf_measure, surf_measure_file, surf_area
    surf_dir: (a string)
            alternative directory (instead of surf)
    surf_measure: (a string)
            Use subject/surf/hemi.surf_measure as input
            mutually_exclusive: surf_measure, surf_measure_file, surf_area
    surf_measure_file: (an existing file name)
            file alternative to surfmeas, still requires list of subjects
            mutually_exclusive: surf_measure, surf_measure_file, surf_area
    vol_measure_file: (a tuple of the form: (an existing file name, an existing file name))
            list of volume measure and reg file tuples

Outputs:

    out_file: (an existing file name)
            preprocessed output file

## MS_LDA

Link to code

Wraps command mri_ms_LDA

Perform LDA reduction on the intensity space of an arbitrary # of FLASH images

### Examples

    >>> grey_label = 2
    >>> white_label = 3
    >>> zero_value = 1
    >>> optimalWeights = MS_LDA(lda_labels=[grey_label, white_label],                                 label_file='label.mgz', weight_file='weights.txt',                                 shift=zero_value, vol_synth_file='synth_out.mgz',                                 conform=True, use_weights=True,                                 images=['FLASH1.mgz', 'FLASH2.mgz', 'FLASH3.mgz'])
    >>> optimalWeights.cmdline
    'mri_ms_LDA -conform -label label.mgz -lda 2 3 -shift 1 -W -synth synth_out.mgz -weight weights.txt FLASH1.mgz FLASH2.mgz FLASH3.mgz'

Inputs:

    [Mandatory]
    images: (an existing file name)
            list of input FLASH images
    lda_labels: (a list of from 2 to 2 items which are an integer)
            pair of class labels to optimize
    output_synth: (a file name)
            filename for the synthesized output volume
            mutually_exclusive: vol_synth_file, output_synth
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output
    vol_synth_file: (a file name)
            filename for the synthesized output volume
            mutually_exclusive: vol_synth_file, output_synth
    weight_file: (a file name)
            filename for the LDA weights (input or output)

    [Optional]
    args: (a string)
            Additional parameters to the command
    conform: (a boolean)
            Conform the input volumes (brain mask typically already conformed)
    environ: (a dictionary with keys which are a value of type 'str' and with values which
             are a value of type 'str', nipype default value: {})
            Environment variables
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    label_file: (a file name)
            filename of the label volume
    mask_file: (a file name)
            filename of the brain mask volume
    shift: (an integer)
            shift all values equal to the given value to zero
    subjects_dir: (an existing directory name)
            subjects directory
    use_weights: (a boolean)
            Use the weights from a previously generated weight file

Outputs:

    vol_synth_file: (an existing file name)
    weight_file: (an existing file name)

## OneSampleTTest

Link to code

Wraps command mri_glmfit

Inputs:

    [Mandatory]
    in_file: (a file name)
            input 4D file
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output

    [Optional]
    allow_ill_cond: (a boolean)
            allow ill-conditioned design matrices
    allow_repeated_subjects: (a boolean)
            allow subject names to repeat in the fsgd file (must appear before --fsgd
    args: (a string)
            Additional parameters to the command
    calc_AR1: (a boolean)
            compute and save temporal AR1 of residual
    check_opts: (a boolean)
            don't run anything, just check options and exit
    compute_log_y: (a boolean)
            compute natural log of y prior to analysis
    contrast: (an existing file name)
            contrast file
    cortex: (a boolean)
            use subjects ?h.cortex.label as label
            mutually_exclusive: label_file
    debug: (a boolean)
            turn on debugging
    design: (an existing file name)
            design matrix file
            mutually_exclusive: fsgd, design, one_sample
    diag: (an integer)
            Gdiag_no : set diagnositc level
    diag_cluster: (a boolean)
            save sig volume and exit from first sim loop
    environ: (a dictionary with keys which are a value of type 'str' and with values which
             are a value of type 'str', nipype default value: {})
            Environment variables
    fixed_fx_dof: (an integer)
            dof for fixed effects analysis
            mutually_exclusive: fixed_fx_dof_file
    fixed_fx_dof_file: (a file name)
            text file with dof for fixed effects analysis
            mutually_exclusive: fixed_fx_dof
    fixed_fx_var: (an existing file name)
            for fixed effects analysis
    force_perm: (a boolean)
            force perumtation test, even when design matrix is not orthog
    fsgd: (a tuple of the form: (an existing file name, 'doss' or 'dods'))
            freesurfer descriptor file
            mutually_exclusive: fsgd, design, one_sample
    fwhm: (a float)
            smooth input by fwhm
    glm_dir: (a string)
            save outputs to dir
    hemi: ('lh' or 'rh')
            surface hemisphere
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    invert_mask: (a boolean)
            invert mask
    label_file: (an existing file name)
            use label as mask, surfaces only
            mutually_exclusive: cortex
    mask_file: (an existing file name)
            binary mask
    no_contrast_sok: (a boolean)
            do not fail if no contrasts specified
    no_est_fwhm: (a boolean)
            turn off FWHM output estimation
    no_mask_smooth: (a boolean)
            do not mask when smoothing
    no_prune: (a boolean)
            do not prune
            mutually_exclusive: prunethresh
    one_sample: (a boolean)
            construct X and C as a one-sample group mean
            mutually_exclusive: one_sample, fsgd, design, contrast
    pca: (a boolean)
            perform pca/svd analysis on residual
    per_voxel_reg: (an existing file name)
            per-voxel regressors
    profile: (an integer)
            niters : test speed
    prune: (a boolean)
            remove voxels that do not have a non-zero value at each frame (def)
    prune_thresh: (a float)
            prune threshold. Default is FLT_MIN
            mutually_exclusive: noprune
    resynth_test: (an integer)
            test GLM by resynthsis
    save_cond: (a boolean)
            flag to save design matrix condition at each voxel
    save_estimate: (a boolean)
            save signal estimate (yhat)
    save_res_corr_mtx: (a boolean)
            save residual error spatial correlation matrix (eres.scm). Big!
    save_residual: (a boolean)
            save residual error (eres)
    seed: (an integer)
            used for synthesizing noise
    self_reg: (a tuple of the form: (an integer, an integer, an integer))
            self-regressor from index col row slice
    sim_done_file: (a file name)
            create file when simulation finished
    sim_sign: ('abs' or 'pos' or 'neg')
            abs, pos, or neg
    simulation: (a tuple of the form: ('perm' or 'mc-full' or 'mc-z', an integer, a float, a
             string))
            nulltype nsim thresh csdbasename
    subject_id: (a string)
            subject id for surface geometry
    subjects_dir: (an existing directory name)
            subjects directory
    surf: (a boolean)
            analysis is on a surface mesh
            requires: subject_id, hemi
    surf_geo: (a string, nipype default value: white)
            surface geometry name (e.g. white, pial)
    synth: (a boolean)
            replace input with gaussian
    uniform: (a tuple of the form: (a float, a float))
            use uniform distribution instead of gaussian
    var_fwhm: (a float)
            smooth variance by fwhm
    vox_dump: (a tuple of the form: (an integer, an integer, an integer))
            dump voxel GLM and exit
    weight_file: (an existing file name)
            weight for each input at each voxel
            mutually_exclusive: weighted_ls
    weight_inv: (a boolean)
            invert weights
            mutually_exclusive: weighted_ls
    weight_sqrt: (a boolean)
            sqrt of weights
            mutually_exclusive: weighted_ls
    weighted_ls: (an existing file name)
            weighted least squares
            mutually_exclusive: weight_file, weight_inv, weight_sqrt

Outputs:

    beta_file: (an existing file name)
            map of regression coefficients
    dof_file: (a file name)
            text file with effective degrees-of-freedom for the analysis
    error_file: (a file name)
            map of residual error
    error_stddev_file: (a file name)
            map of residual error standard deviation
    error_var_file: (a file name)
            map of residual error variance
    estimate_file: (a file name)
            map of the estimated Y values
    frame_eigenvectors: (a file name)
            matrix of frame eigenvectors from residual PCA
    ftest_file
            map of test statistic values
    fwhm_file: (a file name)
            text file with estimated smoothness
    gamma_file
            map of contrast of regression coefficients
    gamma_var_file
            map of regression contrast variance
    glm_dir: (an existing directory name)
            output directory
    mask_file: (a file name)
            map of the mask used in the analysis
    sig_file
            map of F-test significance (in -log10p)
    singular_values: (a file name)
            matrix singular values from residual PCA
    spatial_eigenvectors: (a file name)
            map of spatial eigenvectors from residual PCA
    svd_stats_file: (a file name)
            text file summarizing the residual PCA

## SegStats

Link to code

Wraps command mri_segstats

Use FreeSurfer mri_segstats for ROI analysis

### Examples

    >>> import nipype.interfaces.freesurfer as fs
    >>> ss = fs.SegStats()
    >>> ss.inputs.annot = ('PWS04', 'lh', 'aparc')
    >>> ss.inputs.in_file = 'functional.nii'
    >>> ss.inputs.subjects_dir = '.'
    >>> ss.inputs.avgwf_txt_file = './avgwf.txt'
    >>> ss.inputs.summary_file = './summary.stats'
    >>> ss.cmdline
    'mri_segstats --annot PWS04 lh aparc --avgwf ./avgwf.txt --i functional.nii --sum ./summary.stats'

Inputs:

    [Mandatory]
    annot: (a tuple of the form: (a string, 'lh' or 'rh', a string))
            subject hemi parc : use surface parcellation
            mutually_exclusive: segmentation_file, annot, surf_label
    segmentation_file: (an existing file name)
            segmentation volume path
            mutually_exclusive: segmentation_file, annot, surf_label
    surf_label: (a tuple of the form: (a string, 'lh' or 'rh', a string))
            subject hemi label : use surface label
            mutually_exclusive: segmentation_file, annot, surf_label
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output

    [Optional]
    args: (a string)
            Additional parameters to the command
    avgwf_file: (a boolean or a file name)
            Save as binary volume (bool or filename)
    avgwf_txt_file: (a boolean or a file name)
            Save average waveform into file (bool or filename)
    brain_vol: ('brain-vol-from-seg' or 'brainmask' or '--%s')
            Compute brain volume either with ``brainmask`` or ``brain-vol-from-seg``
    calc_power: ('sqr' or 'sqrt')
            Compute either the sqr or the sqrt of the input
    calc_snr: (a boolean)
            save mean/std as extra column in output table
    color_table_file: (an existing file name)
            color table file with seg id names
            mutually_exclusive: color_table_file, default_color_table, gca_color_table
    cortex_vol_from_surf: (a boolean)
            Compute cortex volume from surf
    default_color_table: (a boolean)
            use $FREESURFER_HOME/FreeSurferColorLUT.txt
            mutually_exclusive: color_table_file, default_color_table, gca_color_table
    environ: (a dictionary with keys which are a value of type 'str' and with values which
             are a value of type 'str', nipype default value: {})
            Environment variables
    etiv: (a boolean)
            Compute ICV from talairach transform
    etiv_only: ('etiv' or 'old-etiv' or '--%s-only')
            Compute etiv and exit.  Use ``etiv`` or ``old-etiv``
    exclude_ctx_gm_wm: (a boolean)
            exclude cortical gray and white matter
    exclude_id: (an integer)
            Exclude seg id from report
    frame: (an integer)
            Report stats on nth frame of input volume
    gca_color_table: (an existing file name)
            get color table from GCA (CMA)
            mutually_exclusive: color_table_file, default_color_table, gca_color_table
    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_file: (an existing file name)
            Use the segmentation to report stats on this volume
    mask_erode: (an integer)
            Erode mask by some amount
    mask_file: (an existing file name)
            Mask volume (same size as seg
    mask_frame: (an integer)
            Mask with this (0 based) frame of the mask volume
            requires: mask_file
    mask_invert: (a boolean)
            Invert binarized mask volume
    mask_sign: ('abs' or 'pos' or 'neg' or '--masksign %s')
            Sign for mask threshold: pos, neg, or abs
    mask_thresh: (a float)
            binarize mask with this threshold <0.5>
    multiply: (a float)
            multiply input by val
    non_empty_only: (a boolean)
            Only report nonempty segmentations
    partial_volume_file: (an existing file name)
            Compensate for partial voluming
    segment_id: (a list of items which are any value)
            Manually specify segmentation ids
    sf_avg_file: (a boolean or a file name)
            Save mean across space and time
    subjects_dir: (an existing directory name)
            subjects directory
    summary_file: (a file name)
            Segmentation stats summary table file
    vox: (a list of items which are an integer)
            Replace seg with all 0s except at C R S (three int inputs)
    wm_vol_from_surf: (a boolean)
            Compute wm volume from surf

Outputs:

    avgwf_file: (a file name)
            Volume with functional statistics averaged over segs
    avgwf_txt_file: (a file name)
            Text file with functional statistics averaged over segs
    sf_avg_file: (a file name)
            Text file with func statistics averaged over segs and framss
    summary_file: (an existing file name)
            Segmentation summary statistics table