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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: `stream` - displays to terminal
        immediately, `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored
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
in_file: (an existing file name)
        input volume
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
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal
        immediately, `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored
threshold: (a float)
        threshold for input 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: `stream` - displays to terminal
        immediately, `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored

[Optional]
args: (a string)
        Additional parameters to the command
contrast_num: (an integer >= 1)
        contrast number to start labeling copes from
corrections: (an existing file name)
        statistical corrections used within FILM modelling
dof_file: (an existing file name)
        degrees of freedom
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
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
suffix: (a string)
        suffix to put on the end of the cope filename before the contrast
        number, default is nothing
tcon_file: (an existing file name)
        contrast file containing T-contrasts
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal
        immediately, `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored

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: (an existing file name)
        File specifying the feat design spec file
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal
        immediately, `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored

[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
fsf_file: (an existing file name)
        File specifying the feat design spec 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
output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or
         'NIFTI')
        FSL output type
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal
        immediately, `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored

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: (an existing file name)
        File specifying the feat design spec file
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal
        immediately, `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored

[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
ev_files: (a list of items which are an existing file name)
        Event spec files generated by level1design
fsf_file: (an existing file name)
        File specifying the feat design spec 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
output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or
         'NIFTI')
        FSL output type
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal
        immediately, `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored

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: (an existing directory name)
        Lower level feat dirs
reg_image: (an existing file name)
        image to register to (will be treated as standard)

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

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: `stream` - displays to terminal
        immediately, `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored

[Optional]
args: (a string)
        Additional parameters to the command
autocorr_estimate_only: (a boolean)
        perform autocorrelation estimation 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 >= 0)
        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
in_file: (an existing file name)
        input data file
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
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal
        immediately, `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored
threshold: (a floating point number >= 0.0, nipype default value:
         0.0)
        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: `stream` - displays to terminal
        immediately, `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored

[Optional]
args: (a string)
        Additional parameters to the command
burnin: (an integer)
        number of jumps at start of mcmc to be discarded
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
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)
mask_file: (an existing file name)
        mask file
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
run_mode: ('fe' or 'ols' or 'flame1' or 'flame12')
        inference to perform
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
t_con_file: (an existing file name)
        ascii matrix specifying t-contrasts
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal
        immediately, `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored
var_cope_file: (an existing file name)
        varcope weightings data file

Outputs:

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

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', output_type='NIFTI')
>>> glm.cmdline
'fsl_glm -i functional.nii -d maps.nii -o functional_glm.nii'

Inputs:

[Mandatory]
design: (an existing file name)
        file name of the GLM design matrix (text time courses for temporal
        regression or an image file for spatial regression)
in_file: (an existing file name)
        input file name (text matrix or 3D/4D image file)
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal
        immediately, `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored

[Optional]
args: (a string)
        Additional parameters to the command
contrasts: (an existing file name)
        matrix of t-statics contrasts
dat_norm: (a boolean)
        switch on normalization of the data time series to unit std
        deviation
demean: (a boolean)
        switch on demeaining of design and data
des_norm: (a boolean)
        switch on normalization of the design matrix columns to unit std
        deviation
design: (an existing file name)
        file name of the GLM design matrix (text time courses for temporal
        regression or an image file for spatial regression)
dof: (an integer)
        set degrees of freedom explicitly
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
in_file: (an existing file name)
        input file name (text matrix or 3D/4D image file)
mask: (an existing file name)
        mask image file name if input is image
out_cope: (a file name)
        output file name for COPE (either as txt or image
out_data_name: (a file name)
        output file name for pre-processed data
out_f_name: (a file name)
        output file name for F-value of full model fit
out_file: (a file name)
        filename for GLM parameter estimates (GLM betas)
out_p_name: (a file name)
        output file name for p-values of Z-stats (either as text file or
        image)
out_pf_name: (a file name)
        output file name for p-value for full model fit
out_res_name: (a file name)
        output file name for residuals
out_sigsq_name: (a file name)
        output file name for residual noise variance sigma-square
out_t_name: (a file name)
        output file name for t-stats (either as txt or image
out_varcb_name: (a file name)
        output file name for variance of COPEs
out_vnscales_name: (a file name)
        output file name for scaling factors for variance normalisation
out_z_name: (a file name)
        output file name for Z-stats (either as txt or image
output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or
         'NIFTI')
        FSL output type
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal
        immediately, `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored
var_norm: (a boolean)
        perform MELODIC variance-normalisation on data

Outputs:

out_cope: (an existing file name)
        output file name for COPEs (either as text file or image)
out_data: (an existing file name)
        output file for preprocessed data
out_f: (an existing file name)
        output file name for F-value of full model fit
out_file: (an existing file name)
        file name of GLM parameters (if generated)
out_p: (an existing file name)
        output file name for p-values of Z-stats (either as text file or
        image)
out_pf: (an existing file name)
        output file name for p-value for full model fit
out_res: (an existing file name)
        output file name for residuals
out_sigsq: (an existing file name)
        output file name for residual noise variance sigma-square
out_t: (an existing file name)
        output file name for t-stats (either as text file or image)
out_varcb: (an existing file name)
        output file name for variance of COPEs
out_vnscales: (an existing file name)
        output file name for scaling factors for variance normalisation
out_z: (an existing file name)
        output file name for COPEs (either as text file or image)

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 >= 1)
        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
num_copes: (an integer >= 1)
        number of copes to be combined

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]
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}}
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
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``

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: `stream` - displays to terminal
        immediately, `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored

[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
in_files: (an existing file name)
        input file names (either single file name or a list)
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
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal
        immediately, `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored
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]
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.
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
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

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: `stream` - displays to terminal
        immediately, `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored

[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
in_file: (an existing file name)
        4D input file
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
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal
        immediately, `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored
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
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

Link to code

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: (an existing 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: `stream` - displays to terminal
        immediately, `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored

[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
mask: (an existing file name)
        mask file
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
spatial_data_file: (an existing file name)
        statistics spatial map
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal
        immediately, `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored

Outputs:

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

SmoothEstimate

Link to code

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: `stream` - displays to terminal
        immediately, `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored

[Optional]
args: (a string)
        Additional parameters to the command
dof: (an integer)
        number of degrees of freedom
        mutually_exclusive: zstat_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_file: (an existing file name)
        brain mask volume
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
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal
        immediately, `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored
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