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interfaces.fsl.model

Cluster

Link to code

Wraps command cluster

Uses FSL cluster to perform clustering on statistical output

Examples

>>> cl = Cluster()
>>> cl.inputs.threshold = 2.3
>>> cl.inputs.in_file = 'zstat1.nii.gz'
>>> cl.inputs.out_localmax_txt_file = 'stats.txt'
>>> cl.cmdline
'cluster --in=zstat1.nii.gz --olmax=stats.txt --thresh=2.3000000000'

Inputs:

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

[Optional]
args: (a string)
        Additional parameters to the command
connectivity: (an integer)
        the connectivity of voxels (default 26)
cope_file: (a file name)
        cope volume
dlh: (a float)
        smoothness estimate = sqrt(det(Lambda))
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
find_min: (a boolean)
        find minima instead of maxima
fractional: (a boolean)
        interprets the threshold as a fraction of the robust range
ignore_exception: (a boolean, nipype default value: False)
        Print an error message instead of throwing an exception in case the interface fails to
        run
minclustersize: (a boolean)
        prints out minimum significant cluster size
no_table: (a boolean)
        suppresses printing of the table info
num_maxima: (an integer)
        no of local maxima to report
out_index_file: (a boolean or a file name)
        output of cluster index (in size order)
out_localmax_txt_file: (a boolean or a file name)
        local maxima text file
out_localmax_vol_file: (a boolean or a file name)
        output of local maxima volume
out_max_file: (a boolean or a file name)
        filename for output of max image
out_mean_file: (a boolean or a file name)
        filename for output of mean image
out_pval_file: (a boolean or a file name)
        filename for image output of log pvals
out_size_file: (a boolean or a file name)
        filename for output of size image
out_threshold_file: (a boolean or a file name)
        thresholded image
output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
        FSL output type
peak_distance: (a float)
        minimum distance between local maxima/minima, in mm (default 0)
pthreshold: (a float)
        p-threshold for clusters
        requires: dlh, volume
std_space_file: (a file name)
        filename for standard-space volume
use_mm: (a boolean)
        use mm, not voxel, coordinates
volume: (an integer)
        number of voxels in the mask
warpfield_file: (a file name)
        file contining warpfield
xfm_file: (a file name)
        filename for Linear: input->standard-space transform. Non-linear: input->highres
        transform

Outputs:

index_file: (a file name)
        output of cluster index (in size order)
localmax_txt_file: (a file name)
        local maxima text file
localmax_vol_file: (a file name)
        output of local maxima volume
max_file: (a file name)
        filename for output of max image
mean_file: (a file name)
        filename for output of mean image
pval_file: (a file name)
        filename for image output of log pvals
size_file: (a file name)
        filename for output of size image
threshold_file: (a file name)
        thresholded image

ContrastMgr

Link to code

Wraps command contrast_mgr

Use FSL contrast_mgr command to evaluate contrasts

In interface mode this file assumes that all the required inputs are in the same location.

Inputs:

[Mandatory]
corrections: (an existing file name)
        statistical corrections used within FILM modelling
dof_file: (an existing file name)
        degrees of freedom
param_estimates: (an existing file name)
        Parameter estimates for each column of the design matrix
sigmasquareds: (an existing file name)
        summary of residuals, See Woolrich, et. al., 2001
tcon_file: (an existing file name)
        contrast file containing T-contrasts
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output

[Optional]
args: (a string)
        Additional parameters to the command
contrast_num: (an integer)
        contrast number to start labeling copes from
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
fcon_file: (an existing file name)
        contrast file containing F-contrasts
ignore_exception: (a boolean, nipype default value: False)
        Print an error message instead of throwing an exception in case the interface fails to
        run
output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
        FSL output type
suffix: (a string)
        suffix to put on the end of the cope filename before the contrast number, default is
        nothing

Outputs:

copes: (an existing file name)
        Contrast estimates for each contrast
fstats: (an existing file name)
        f-stat file for each contrast
neffs: (an existing file name)
        neff file ?? for each contrast
tstats: (an existing file name)
        t-stat file for each contrast
varcopes: (an existing file name)
        Variance estimates for each contrast
zfstats: (an existing file name)
        z-stat file for each F contrast
zstats: (an existing file name)
        z-stat file for each contrast

FEAT

Link to code

Wraps command feat

Uses FSL feat to calculate first level stats

Inputs:

[Mandatory]
fsf_file: (a file name)
        File specifying the feat design spec file
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
ignore_exception: (a boolean, nipype default value: False)
        Print an error message instead of throwing an exception in case the interface fails to
        run
output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
        FSL output type

Outputs:

feat_dir: (an existing directory name)

FEATModel

Link to code

Wraps command feat_model

Uses FSL feat_model to generate design.mat files

Inputs:

[Mandatory]
ev_files: (a list of items which are an existing file name)
        Event spec files generated by level1design
fsf_file: (a file name)
        File specifying the feat design spec file
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
ignore_exception: (a boolean, nipype default value: False)
        Print an error message instead of throwing an exception in case the interface fails to
        run
output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
        FSL output type

Outputs:

con_file: (an existing file name)
        Contrast file containing contrast vectors
design_cov: (an existing file name)
        Graphical representation of design covariance
design_file: (an existing file name)
        Mat file containing ascii matrix for design
design_image: (an existing file name)
        Graphical representation of design matrix
fcon_file: (a file name)
        Contrast file containing contrast vectors

FEATRegister

Link to code

Register feat directories to a specific standard

Inputs:

[Mandatory]
feat_dirs: (a directory name)
        Lower level feat dirs
reg_image: (a file name)
        image to register to (will be treated as standard)

[Optional]
ignore_exception: (a boolean, nipype default value: False)
        Print an error message instead of throwing an exception in case the interface fails to
        run
reg_dof: (an integer, nipype default value: 12)
        registration degrees of freedom

Outputs:

fsf_file: (an existing file name)
        FSL feat specification file

FILMGLS

Link to code

Wraps command film_gls

Use FSL film_gls command to fit a design matrix to voxel timeseries

Examples

Initialize with no options, assigning them when calling run:

>>> from nipype.interfaces import fsl
>>> fgls = fsl.FILMGLS()
>>> res = fgls.run('in_file', 'design_file', 'thresh', rn='stats') 

Assign options through the inputs attribute:

>>> fgls = fsl.FILMGLS()
>>> fgls.inputs.in_file = 'functional.nii'
>>> fgls.inputs.design_file = 'design.mat'
>>> fgls.inputs.threshold = 10
>>> fgls.inputs.results_dir = 'stats'
>>> res = fgls.run() 

Specify options when creating an instance:

>>> fgls = fsl.FILMGLS(in_file='functional.nii', design_file='design.mat', threshold=10, results_dir='stats')
>>> res = fgls.run() 

Inputs:

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

[Optional]
args: (a string)
        Additional parameters to the command
autocorr_estimate_only: (a boolean)
        perform autocorrelation estimatation only
        mutually_exclusive: autocorr_estimate_only, fit_armodel, tukey_window,
         multitaper_product, use_pava, autocorr_noestimate
autocorr_noestimate: (a boolean)
        do not estimate autocorrs
        mutually_exclusive: autocorr_estimate_only, fit_armodel, tukey_window,
         multitaper_product, use_pava, autocorr_noestimate
brightness_threshold: (an integer)
        susan brightness threshold, otherwise it is estimated
design_file: (an existing file name)
        design matrix file
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
fit_armodel: (a boolean)
        fits autoregressive model - default is to use tukey with M=sqrt(numvols)
        mutually_exclusive: autocorr_estimate_only, fit_armodel, tukey_window,
         multitaper_product, use_pava, autocorr_noestimate
full_data: (a boolean)
        output full data
ignore_exception: (a boolean, nipype default value: False)
        Print an error message instead of throwing an exception in case the interface fails to
        run
mask_size: (an integer)
        susan mask size
multitaper_product: (an integer)
        multitapering with slepian tapers and num is the time-bandwidth product
        mutually_exclusive: autocorr_estimate_only, fit_armodel, tukey_window,
         multitaper_product, use_pava, autocorr_noestimate
output_pwdata: (a boolean)
        output prewhitened data and average design matrix
output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
        FSL output type
results_dir: (a directory name, nipype default value: results)
        directory to store results in
smooth_autocorr: (a boolean)
        Smooth auto corr estimates
threshold: (a float)
        threshold
tukey_window: (an integer)
        tukey window size to estimate autocorr
        mutually_exclusive: autocorr_estimate_only, fit_armodel, tukey_window,
         multitaper_product, use_pava, autocorr_noestimate
use_pava: (a boolean)
        estimates autocorr using PAVA

Outputs:

corrections: (an existing file name)
        statistical corrections used within FILM modelling
dof_file: (an existing file name)
        degrees of freedom
logfile: (an existing file name)
        FILM run logfile
param_estimates: (an existing file name)
        Parameter estimates for each column of the design matrix
residual4d: (an existing file name)
        Model fit residual mean-squared error for each time point
results_dir: (an existing directory name)
        directory storing model estimation output
sigmasquareds: (an existing file name)
        summary of residuals, See Woolrich, et. al., 2001

FLAMEO

Link to code

Wraps command flameo

Use FSL flameo command to perform higher level model fits

Examples

Initialize FLAMEO with no options, assigning them when calling run:

>>> from nipype.interfaces import fsl
>>> import os
>>> flameo = fsl.FLAMEO(cope_file='cope.nii.gz',                             var_cope_file='varcope.nii.gz',                             cov_split_file='cov_split.mat',                             design_file='design.mat',                             t_con_file='design.con',                             mask_file='mask.nii',                             run_mode='fe')
>>> flameo.cmdline
'flameo --copefile=cope.nii.gz --covsplitfile=cov_split.mat --designfile=design.mat --ld=stats --maskfile=mask.nii --runmode=fe --tcontrastsfile=design.con --varcopefile=varcope.nii.gz'

Inputs:

[Mandatory]
cope_file: (an existing file name)
        cope regressor data file
cov_split_file: (an existing file name)
        ascii matrix specifying the groups the covariance is split into
design_file: (an existing file name)
        design matrix file
mask_file: (an existing file name)
        mask file
run_mode: ('fe' or 'ols' or 'flame1' or 'flame12')
        inference to perform
t_con_file: (an existing file name)
        ascii matrix specifying t-contrasts
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output

[Optional]
args: (a string)
        Additional parameters to the command
burnin: (an integer)
        number of jumps at start of mcmc to be discarded
dof_var_cope_file: (an existing file name)
        dof data file for varcope data
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
f_con_file: (an existing file name)
        ascii matrix specifying f-contrasts
fix_mean: (a boolean)
        fix mean for tfit
ignore_exception: (a boolean, nipype default value: False)
        Print an error message instead of throwing an exception in case the interface fails to
        run
infer_outliers: (a boolean)
        infer outliers - not for fe
log_dir: (a directory name, nipype default value: stats)
n_jumps: (an integer)
        number of jumps made by mcmc
no_pe_outputs: (a boolean)
        do not output pe files
outlier_iter: (an integer)
        Number of max iterations to use when inferring outliers. Default is 12.
output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
        FSL output type
sample_every: (an integer)
        number of jumps for each sample
sigma_dofs: (an integer)
        sigma (in mm) to use for Gaussian smoothing the DOFs in FLAME 2. Default is 1mm, -1
        indicates no smoothing
var_cope_file: (an existing file name)
        varcope weightings data file

Outputs:

copes
        Contrast estimates for each contrast
mrefvars
        mean random effect variances for each contrast
pes
        Parameter estimates for each column of the design matrixfor each voxel
res4d
        Model fit residual mean-squared error for each time point
stats_dir: (an existing directory name)
        directory storing model estimation output
tdof
        temporal dof file for each contrast
tstats
        t-stat file for each contrast
var_copes
        Variance estimates for each contrast
weights
        weights file for each contrast
zstats
        z-stat file for each contrast

GLM

Link to code

Wraps command fsl_glm

FSL GLM:

Example

>>> import nipype.interfaces.fsl as fsl
>>> glm = fsl.GLM(in_file='functional.nii', design = 'maps.nii')
>>> glm.cmdline

Inputs:

[Mandatory]
design: (an existing file name)
        design file or image file
in_file: (an existing file name)
        input 3d+t file
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output

[Optional]
args: (a string)
        Additional parameters to the command
contrasts: (an existing file name)
        t-contrasts file
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
mask: (an existing file name)
        mask file
options: (a string)
        fsl_glm options
out_file: (a file name)
        file or image output
output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
        FSL output type

Outputs:

out_file: (an existing file name)
        file or image output

L2Model

Link to code

Generate subject specific second level model

Examples

>>> from nipype.interfaces.fsl import L2Model
>>> model = L2Model(num_copes=3) # 3 sessions

Inputs:

[Mandatory]
num_copes: (an integer)
        number of copes to be combined

[Optional]
ignore_exception: (a boolean, nipype default value: False)
        Print an error message instead of throwing an exception in case the interface fails to
        run

Outputs:

design_con: (an existing file name)
        design contrast file
design_grp: (an existing file name)
        design group file
design_mat: (an existing file name)
        design matrix file

Level1Design

Link to code

Generate FEAT specific files

Examples

>>> level1design = Level1Design()
>>> level1design.inputs.interscan_interval = 2.5
>>> level1design.inputs.bases = {'dgamma':{'derivs': False}}
>>> level1design.inputs.session_info = 'session_info.npz'
>>> level1design.run() 

Inputs:

[Mandatory]
bases: (a dictionary with keys which are 'dgamma' and with values which are a dictionary
         with keys which are 'derivs' and with values which are a boolean or a dictionary with
         keys which are 'gamma' and with values which are a dictionary with keys which are
         'derivs' and with values which are a boolean or a dictionary with keys which are 'none'
         and with values which are None)
        name of basis function and options e.g., {'dgamma': {'derivs': True}}
interscan_interval: (a float)
        Interscan  interval (in secs)
model_serial_correlations: (a boolean)
        Option to model serial correlations using an autoregressive estimator (order 1). Setting
        this option is only useful in the context of the fsf file. If you set this to False, you
        need to repeat this option for FILMGLS by setting autocorr_noestimate to True
session_info
        Session specific information generated by ``modelgen.SpecifyModel``

[Optional]
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.
ignore_exception: (a boolean, nipype default value: False)
        Print an error message instead of throwing an exception in case the interface fails to
        run

Outputs:

ev_files: (a list of items which are an existing file name)
        condition information files
fsf_files: (an existing file name)
        FSL feat specification files

MELODIC

Link to code

Wraps command melodic

Multivariate Exploratory Linear Optimised Decomposition into Independent Components

Examples

>>> melodic_setup = MELODIC()
>>> melodic_setup.inputs.approach = 'tica'
>>> melodic_setup.inputs.in_files = ['functional.nii', 'functional2.nii', 'functional3.nii']
>>> melodic_setup.inputs.no_bet = True
>>> melodic_setup.inputs.bg_threshold = 10
>>> melodic_setup.inputs.tr_sec = 1.5
>>> melodic_setup.inputs.mm_thresh = 0.5
>>> melodic_setup.inputs.out_stats = True
>>> melodic_setup.inputs.t_des = 'timeDesign.mat'
>>> melodic_setup.inputs.t_con = 'timeDesign.con'
>>> melodic_setup.inputs.s_des = 'subjectDesign.mat'
>>> melodic_setup.inputs.s_con = 'subjectDesign.con'
>>> melodic_setup.inputs.out_dir = 'groupICA.out'
>>> melodic_setup.run() 

Inputs:

[Mandatory]
in_files: (an existing file name)
        input file names (either single file name or a list)
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output

[Optional]
ICs: (an existing file name)
        filename of the IC components file for mixture modelling
approach: (a string)
        approach for decomposition, 2D: defl, symm (default),  3D: tica (default), concat
args: (a string)
        Additional parameters to the command
bg_image: (an existing file name)
        specify background image for report (default: mean image)
bg_threshold: (a float)
        brain/non-brain threshold used to mask non-brain voxels, as a percentage (only if
        --nobet selected)
cov_weight: (a float)
        voxel-wise weights for the covariance matrix (e.g. segmentation information)
dim: (an integer)
        dimensionality reduction into #num dimensions(default: automatic estimation)
dim_est: (a string)
        use specific dim. estimation technique: lap, bic, mdl, aic, mean (default: lap)
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
epsilon: (a float)
        minimum error change
epsilonS: (a float)
        minimum error change for rank-1 approximation in TICA
ignore_exception: (a boolean, nipype default value: False)
        Print an error message instead of throwing an exception in case the interface fails to
        run
log_power: (a boolean)
        calculate log of power for frequency spectrum
mask: (an existing file name)
        file name of mask for thresholding
max_restart: (an integer)
        maximum number of restarts
maxit: (an integer)
        maximum number of iterations before restart
mix: (an existing file name)
        mixing matrix for mixture modelling / filtering
mm_thresh: (a float)
        threshold for Mixture Model based inference
no_bet: (a boolean)
        switch off BET
no_mask: (a boolean)
        switch off masking
no_mm: (a boolean)
        switch off mixture modelling on IC maps
non_linearity: (a string)
        nonlinearity: gauss, tanh, pow3, pow4
num_ICs: (an integer)
        number of IC's to extract (for deflation approach)
out_all: (a boolean)
        output everything
out_dir: (a directory name)
        output directory name
out_mean: (a boolean)
        output mean volume
out_orig: (a boolean)
        output the original ICs
out_pca: (a boolean)
        output PCA results
out_stats: (a boolean)
        output thresholded maps and probability maps
out_unmix: (a boolean)
        output unmixing matrix
out_white: (a boolean)
        output whitening/dewhitening matrices
output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
        FSL output type
pbsc: (a boolean)
        switch off conversion to percent BOLD signal change
rem_cmp: (a list of items which are an integer)
        component numbers to remove
remove_deriv: (a boolean)
        removes every second entry in paradigm file (EV derivatives)
report: (a boolean)
        generate Melodic web report
report_maps: (a string)
        control string for spatial map images (see slicer)
s_con: (an existing file name)
        t-contrast matrix across subject-domain
s_des: (an existing file name)
        design matrix across subject-domain
sep_vn: (a boolean)
        switch off joined variance normalization
sep_whiten: (a boolean)
        switch on separate whitening
smode: (an existing file name)
        matrix of session modes for report generation
t_con: (an existing file name)
        t-contrast matrix across time-domain
t_des: (an existing file name)
        design matrix across time-domain
tr_sec: (a float)
        TR in seconds
update_mask: (a boolean)
        switch off mask updating
var_norm: (a boolean)
        switch off variance normalization

Outputs:

out_dir: (an existing directory name)
report_dir: (an existing directory name)

MultipleRegressDesign

Link to code

Generate multiple regression design

Note

FSL does not demean columns for higher level analysis.

Please see FSL documentation for more details on model specification for higher level analysis.

Examples

>>> from nipype.interfaces.fsl import MultipleRegressDesign
>>> model = MultipleRegressDesign()
>>> model.inputs.contrasts = [['group mean', 'T',['reg1'],[1]]]
>>> model.inputs.regressors = dict(reg1=[1, 1, 1], reg2=[2.,-4, 3])
>>> model.run() 

Inputs:

[Mandatory]
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, '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)))
        List of contrasts with each contrast being a list of the form - [('name', 'stat',
        [condition list], [weight 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 without any weight list.
regressors: (a dictionary with keys which are a string and with values which are a list
         of items which are a float)
        dictionary containing named lists of regressors

[Optional]
groups: (a list of items which are an integer)
        list of group identifiers (defaults to single group)
ignore_exception: (a boolean, nipype default value: False)
        Print an error message instead of throwing an exception in case the interface fails to
        run

Outputs:

design_con: (an existing file name)
        design t-contrast file
design_fts: (an existing file name)
        design f-contrast file
design_grp: (an existing file name)
        design group file
design_mat: (an existing file name)
        design matrix file

Randomise

Link to code

Wraps command randomise

XXX UNSTABLE DO NOT USE

FSL Randomise: feeds the 4D projected FA data into GLM modelling and thresholding in order to find voxels which correlate with your model

Example

>>> import nipype.interfaces.fsl as fsl
>>> rand = fsl.Randomise(in_file='allFA.nii',     mask = 'mask.nii',     tcon='design.con',     design_mat='design.mat')
>>> rand.cmdline
'randomise -i allFA.nii -o tbss_ -d design.mat -t design.con -m mask.nii'

Inputs:

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

[Optional]
args: (a string)
        Additional parameters to the command
base_name: (a string, nipype default value: tbss_)
        the rootname that all generated files will have
c_thresh: (a float)
        carry out cluster-based thresholding
cm_thresh: (a float)
        carry out cluster-mass-based thresholding
demean: (a boolean)
        demean data temporally before model fitting
design_mat: (an existing file name)
        design matrix file
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
f_c_thresh: (a float)
        carry out f cluster thresholding
f_cm_thresh: (a float)
        carry out f cluster-mass thresholding
f_only: (a boolean)
        calculate f-statistics only
fcon: (an existing file name)
        f contrasts file
ignore_exception: (a boolean, nipype default value: False)
        Print an error message instead of throwing an exception in case the interface fails to
        run
mask: (an existing file name)
        mask image
num_perm: (an integer)
        number of permutations (default 5000, set to 0 for exhaustive)
one_sample_group_mean: (a boolean)
        perform 1-sample group-mean test instead of generic permutation test
output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
        FSL output type
p_vec_n_dist_files: (a boolean)
        output permutation vector and null distribution text files
raw_stats_imgs: (a boolean)
        output raw ( unpermuted ) statistic images
seed: (an integer)
        specific integer seed for random number generator
show_info_parallel_mode: (a boolean)
        print out information required for parallel mode and exit
show_total_perms: (a boolean)
        print out how many unique permutations would be generated and exit
tcon: (an existing file name)
        t contrasts file
tfce: (a boolean)
        carry out Threshold-Free Cluster Enhancement
tfce2D: (a boolean)
        carry out Threshold-Free Cluster Enhancement with 2D optimisation
tfce_C: (a float)
        TFCE connectivity (6 or 26; default=6)
tfce_E: (a float)
        TFCE extent parameter (default=0.5)
tfce_H: (a float)
        TFCE height parameter (default=2)
var_smooth: (an integer)
        use variance smoothing (std is in mm)
vox_p_values: (a boolean)
        output voxelwise (corrected and uncorrected) p-value images
vxf: (a list of items which are an integer)
        list of 4D images containing voxelwise EVs(list order corresponds to numbers in vxl
        option)
vxl: (a list of items which are an integer)
        list of numbers indicating voxelwise EVsposition in the design matrix (list order
        corresponds to files in vxf option)
x_block_labels: (an existing file name)
        exchangeability block labels file

Outputs:

f_corrected_p_files: (a list of items which are an existing file name)
        f contrast FWE (Family-wise error) corrected p values files
f_p_files: (a list of items which are an existing file name)
        f contrast uncorrected p values files
fstat_files: (a list of items which are an existing file name)
        f contrast raw statistic
t_corrected_p_files: (a list of items which are an existing file name)
        t contrast FWE (Family-wise error) corrected p values files
t_p_files: (a list of items which are an existing file name)
        f contrast uncorrected p values files
tstat_files: (a list of items which are an existing file name)
        t contrast raw statistic

SMM

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Wraps command mm –ld=logdir

Spatial Mixture Modelling. For more detail on the spatial mixture modelling see Mixture Models with Adaptive Spatial Regularisation for Segmentation with an Application to FMRI Data; Woolrich, M., Behrens, T., Beckmann, C., and Smith, S.; IEEE Trans. Medical Imaging, 24(1):1-11, 2005.

Inputs:

[Mandatory]
mask: (a file name)
        mask file
spatial_data_file: (an existing file name)
        statistics spatial map
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
ignore_exception: (a boolean, nipype default value: False)
        Print an error message instead of throwing an exception in case the interface fails to
        run
no_deactivation_class: (a boolean)
        enforces no deactivation class
output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
        FSL output type

Outputs:

activation_p_map: (an existing file name)
deactivation_p_map: (an existing file name)
null_p_map: (an existing file name)

SmoothEstimate

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Wraps command smoothest

Estimates the smoothness of an image

Examples

>>> est = SmoothEstimate()
>>> est.inputs.zstat_file = 'zstat1.nii.gz'
>>> est.inputs.mask_file = 'mask.nii'
>>> est.cmdline
'smoothest --mask=mask.nii --zstat=zstat1.nii.gz'

Inputs:

[Mandatory]
dof: (an integer)
        number of degrees of freedom
        mutually_exclusive: zstat_file
mask_file: (an existing file name)
        brain mask 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
ignore_exception: (a boolean, nipype default value: False)
        Print an error message instead of throwing an exception in case the interface fails to
        run
output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
        FSL output type
residual_fit_file: (an existing file name)
        residual-fit image file
        requires: dof
zstat_file: (an existing file name)
        zstat image file
        mutually_exclusive: dof

Outputs:

dlh: (a float)
        smoothness estimate sqrt(det(Lambda))
resels: (a float)
        number of resels
volume: (an integer)
        number of voxels in mask