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.inputs.use_mm = True
>>> cl.cmdline 
'cluster --in=zstat1.nii.gz --olmax=stats.txt --thresh=2.3000000000 --mm'

Inputs:

[Mandatory]
in_file: (an existing file name)
        input volume
        flag: --in=%s
threshold: (a float)
        threshold for input volume
        flag: --thresh=%.10f

[Optional]
args: (a unicode string)
        Additional parameters to the command
        flag: %s
connectivity: (an integer (int or long))
        the connectivity of voxels (default 26)
        flag: --connectivity=%d
cope_file: (a file name)
        cope volume
        flag: --cope=%s
dlh: (a float)
        smoothness estimate = sqrt(det(Lambda))
        flag: --dlh=%.10f
environ: (a dictionary with keys which are a newbytes or None or a
         newstr or None and with values which are a newbytes or None or a
         newstr or None, nipype default value: {})
        Environment variables
find_min: (a boolean, nipype default value: False)
        find minima instead of maxima
        flag: --min
fractional: (a boolean, nipype default value: False)
        interprets the threshold as a fraction of the robust range
        flag: --fractional
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, nipype default value: False)
        prints out minimum significant cluster size
        flag: --minclustersize
no_table: (a boolean, nipype default value: False)
        suppresses printing of the table info
        flag: --no_table
num_maxima: (an integer (int or long))
        no of local maxima to report
        flag: --num=%d
out_index_file: (a boolean or a file name)
        output of cluster index (in size order)
        flag: --oindex=%s
out_localmax_txt_file: (a boolean or a file name)
        local maxima text file
        flag: --olmax=%s
out_localmax_vol_file: (a boolean or a file name)
        output of local maxima volume
        flag: --olmaxim=%s
out_max_file: (a boolean or a file name)
        filename for output of max image
        flag: --omax=%s
out_mean_file: (a boolean or a file name)
        filename for output of mean image
        flag: --omean=%s
out_pval_file: (a boolean or a file name)
        filename for image output of log pvals
        flag: --opvals=%s
out_size_file: (a boolean or a file name)
        filename for output of size image
        flag: --osize=%s
out_threshold_file: (a boolean or a file name)
        thresholded image
        flag: --othresh=%s
output_type: (u'NIFTI_PAIR' or u'NIFTI_PAIR_GZ' or u'NIFTI_GZ' or
         u'NIFTI')
        FSL output type
peak_distance: (a float)
        minimum distance between local maxima/minima, in mm (default 0)
        flag: --peakdist=%.10f
pthreshold: (a float)
        p-threshold for clusters
        flag: --pthresh=%.10f
        requires: dlh, volume
std_space_file: (a file name)
        filename for standard-space volume
        flag: --stdvol=%s
terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored
use_mm: (a boolean, nipype default value: False)
        use mm, not voxel, coordinates
        flag: --mm
volume: (an integer (int or long))
        number of voxels in the mask
        flag: --volume=%d
warpfield_file: (a file name)
        file contining warpfield
        flag: --warpvol=%s
xfm_file: (a file name)
        filename for Linear: input->standard-space transform. Non-linear:
        input->highres transform
        flag: --xfm=%s

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: (a list of items which are 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
        flag: %s, position: -1

[Optional]
args: (a unicode string)
        Additional parameters to the command
        flag: %s
contrast_num: (an integer >= 1)
        contrast number to start labeling copes from
        flag: -cope
environ: (a dictionary with keys which are a newbytes or None or a
         newstr or None and with values which are a newbytes or None or a
         newstr or None, nipype default value: {})
        Environment variables
fcon_file: (an existing file name)
        contrast file containing F-contrasts
        flag: -f %s
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: (u'NIFTI_PAIR' or u'NIFTI_PAIR_GZ' or u'NIFTI_GZ' or
         u'NIFTI')
        FSL output type
suffix: (a unicode string)
        suffix to put on the end of the cope filename before the contrast
        number, default is nothing
        flag: -suffix %s
terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored

Outputs:

copes: (a list of items which are an existing file name)
        Contrast estimates for each contrast
fstats: (a list of items which are an existing file name)
        f-stat file for each contrast
neffs: (a list of items which are an existing file name)
        neff file ?? for each contrast
tstats: (a list of items which are an existing file name)
        t-stat file for each contrast
varcopes: (a list of items which are an existing file name)
        Variance estimates for each contrast
zfstats: (a list of items which are an existing file name)
        z-stat file for each F contrast
zstats: (a list of items which are 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
        flag: %s, position: 0

[Optional]
args: (a unicode string)
        Additional parameters to the command
        flag: %s
environ: (a dictionary with keys which are a newbytes or None or a
         newstr or None and with values which are a newbytes or None or a
         newstr or None, 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: (u'NIFTI_PAIR' or u'NIFTI_PAIR_GZ' or u'NIFTI_GZ' or
         u'NIFTI')
        FSL output type
terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `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
        flag: %s, position: 1
fsf_file: (an existing file name)
        File specifying the feat design spec file
        flag: %s, position: 0

[Optional]
args: (a unicode string)
        Additional parameters to the command
        flag: %s
environ: (a dictionary with keys which are a newbytes or None or a
         newstr or None and with values which are a newbytes or None or a
         newstr or None, 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: (u'NIFTI_PAIR' or u'NIFTI_PAIR_GZ' or u'NIFTI_GZ' or
         u'NIFTI')
        FSL output type
terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `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: (a list of items which are an existing directory name)
        Lower level feat dirs
reg_image: (an existing 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 (int or long), 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
        flag: --in=%s, position: -3

[Optional]
args: (a unicode string)
        Additional parameters to the command
        flag: %s
autocorr_estimate_only: (a boolean)
        perform autocorrelation estimation only
        flag: --ac
        mutually_exclusive: autocorr_estimate_only, fit_armodel,
         tukey_window, multitaper_product, use_pava, autocorr_noestimate
autocorr_noestimate: (a boolean)
        do not estimate autocorrs
        flag: --noest
        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
        flag: --epith=%d
design_file: (an existing file name)
        design matrix file
        flag: --pd=%s, position: -2
environ: (a dictionary with keys which are a newbytes or None or a
         newstr or None and with values which are a newbytes or None or a
         newstr or None, nipype default value: {})
        Environment variables
fcon_file: (an existing file name)
        contrast file containing F-contrasts
        flag: --fcon=%s
fit_armodel: (a boolean)
        fits autoregressive model - default is to use tukey with
        M=sqrt(numvols)
        flag: --ar
        mutually_exclusive: autocorr_estimate_only, fit_armodel,
         tukey_window, multitaper_product, use_pava, autocorr_noestimate
full_data: (a boolean)
        output full data
        flag: -v
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 (int or long))
        susan mask size
        flag: --ms=%d
mode: (u'volumetric' or u'surface')
        Type of analysis to be done
        flag: --mode=%s
multitaper_product: (an integer (int or long))
        multitapering with slepian tapers and num is the time-bandwidth
        product
        flag: --mt=%d
        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
        flag: --outputPWdata
output_type: (u'NIFTI_PAIR' or u'NIFTI_PAIR_GZ' or u'NIFTI_GZ' or
         u'NIFTI')
        FSL output type
results_dir: (a directory name, nipype default value: results)
        directory to store results in
        flag: --rn=%s
smooth_autocorr: (a boolean)
        Smooth auto corr estimates
        flag: --sa
surface: (an existing file name)
        input surface for autocorr smoothing in surface-based analyses
        flag: --in2=%s
tcon_file: (an existing file name)
        contrast file containing T-contrasts
        flag: --con=%s
terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored
threshold: (a float, nipype default value: 0.0)
        threshold
        flag: --thr=%f, position: -1
tukey_window: (an integer (int or long))
        tukey window size to estimate autocorr
        flag: --tukey=%d
        mutually_exclusive: autocorr_estimate_only, fit_armodel,
         tukey_window, multitaper_product, use_pava, autocorr_noestimate
use_pava: (a boolean)
        estimates autocorr using PAVA
        flag: --pava

Outputs:

copes: (a list of items which are an existing file name)
        Contrast estimates for each contrast
dof_file: (an existing file name)
        degrees of freedom
fstats: (a list of items which are an existing file name)
        f-stat file for each contrast
logfile: (an existing file name)
        FILM run logfile
param_estimates: (a list of items which are 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
thresholdac: (an existing file name)
        The FILM autocorrelation parameters
tstats: (a list of items which are an existing file name)
        t-stat file for each contrast
varcopes: (a list of items which are an existing file name)
        Variance estimates for each contrast
zfstats: (a list of items which are an existing file name)
        z-stat file for each F contrast
zstats: (a list of items which are an existing file name)
        z-stat file for each contrast

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
        flag: --copefile=%s
cov_split_file: (an existing file name)
        ascii matrix specifying the groups the covariance is split into
        flag: --covsplitfile=%s
design_file: (an existing file name)
        design matrix file
        flag: --designfile=%s
mask_file: (an existing file name)
        mask file
        flag: --maskfile=%s
run_mode: (u'fe' or u'ols' or u'flame1' or u'flame12')
        inference to perform
        flag: --runmode=%s
t_con_file: (an existing file name)
        ascii matrix specifying t-contrasts
        flag: --tcontrastsfile=%s

[Optional]
args: (a unicode string)
        Additional parameters to the command
        flag: %s
burnin: (an integer (int or long))
        number of jumps at start of mcmc to be discarded
        flag: --burnin=%d
dof_var_cope_file: (an existing file name)
        dof data file for varcope data
        flag: --dofvarcopefile=%s
environ: (a dictionary with keys which are a newbytes or None or a
         newstr or None and with values which are a newbytes or None or a
         newstr or None, nipype default value: {})
        Environment variables
f_con_file: (an existing file name)
        ascii matrix specifying f-contrasts
        flag: --fcontrastsfile=%s
fix_mean: (a boolean)
        fix mean for tfit
        flag: --fixmean
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
        flag: --inferoutliers
log_dir: (a directory name, nipype default value: stats)
        flag: --ld=%s
n_jumps: (an integer (int or long))
        number of jumps made by mcmc
        flag: --njumps=%d
no_pe_outputs: (a boolean)
        do not output pe files
        flag: --nopeoutput
outlier_iter: (an integer (int or long))
        Number of max iterations to use when inferring outliers. Default is
        12.
        flag: --ioni=%d
output_type: (u'NIFTI_PAIR' or u'NIFTI_PAIR_GZ' or u'NIFTI_GZ' or
         u'NIFTI')
        FSL output type
sample_every: (an integer (int or long))
        number of jumps for each sample
        flag: --sampleevery=%d
sigma_dofs: (an integer (int or long))
        sigma (in mm) to use for Gaussian smoothing the DOFs in FLAME 2.
        Default is 1mm, -1 indicates no smoothing
        flag: --sigma_dofs=%d
terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `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
        flag: --varcopefile=%s

Outputs:

copes: (a list of items which are an existing file name)
        Contrast estimates for each contrast
fstats: (a list of items which are an existing file name)
        f-stat file for each contrast
mrefvars: (a list of items which are an existing file name)
        mean random effect variances for each contrast
pes: (a list of items which are an existing file name)
        Parameter estimates for each column of the design matrix for each
        voxel
res4d: (a list of items which are 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: (a list of items which are an existing file name)
        temporal dof file for each contrast
tstats: (a list of items which are an existing file name)
        t-stat file for each contrast
var_copes: (a list of items which are an existing file name)
        Variance estimates for each contrast
weights: (a list of items which are an existing file name)
        weights file for each contrast
zfstats: (a list of items which are an existing file name)
        z stat file for each f contrast
zstats: (a list of items which are 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)
        flag: -d %s, position: 2
in_file: (an existing file name)
        input file name (text matrix or 3D/4D image file)
        flag: -i %s, position: 1

[Optional]
args: (a unicode string)
        Additional parameters to the command
        flag: %s
contrasts: (an existing file name)
        matrix of t-statics contrasts
        flag: -c %s
dat_norm: (a boolean)
        switch on normalization of the data time series to unit std
        deviation
        flag: --dat_norm
demean: (a boolean)
        switch on demeaining of design and data
        flag: --demean
des_norm: (a boolean)
        switch on normalization of the design matrix columns to unit std
        deviation
        flag: --des_norm
dof: (an integer (int or long))
        set degrees of freedom explicitly
        flag: --dof=%d
environ: (a dictionary with keys which are a newbytes or None or a
         newstr or None and with values which are a newbytes or None or a
         newstr or None, 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 image file name if input is image
        flag: -m %s
out_cope: (a file name)
        output file name for COPE (either as txt or image
        flag: --out_cope=%s
out_data_name: (a file name)
        output file name for pre-processed data
        flag: --out_data=%s
out_f_name: (a file name)
        output file name for F-value of full model fit
        flag: --out_f=%s
out_file: (a file name)
        filename for GLM parameter estimates (GLM betas)
        flag: -o %s, position: 3
out_p_name: (a file name)
        output file name for p-values of Z-stats (either as text file or
        image)
        flag: --out_p=%s
out_pf_name: (a file name)
        output file name for p-value for full model fit
        flag: --out_pf=%s
out_res_name: (a file name)
        output file name for residuals
        flag: --out_res=%s
out_sigsq_name: (a file name)
        output file name for residual noise variance sigma-square
        flag: --out_sigsq=%s
out_t_name: (a file name)
        output file name for t-stats (either as txt or image
        flag: --out_t=%s
out_varcb_name: (a file name)
        output file name for variance of COPEs
        flag: --out_varcb=%s
out_vnscales_name: (a file name)
        output file name for scaling factors for variance normalisation
        flag: --out_vnscales=%s
out_z_name: (a file name)
        output file name for Z-stats (either as txt or image
        flag: --out_z=%s
output_type: (u'NIFTI_PAIR' or u'NIFTI_PAIR_GZ' or u'NIFTI_GZ' or
         u'NIFTI')
        FSL output type
terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `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
        flag: --vn

Outputs:

out_cope: (a list of items which are an existing file name)
        output file name for COPEs (either as text file or image)
out_data: (a list of items which are an existing file name)
        output file for preprocessed data
out_f: (a list of items which are 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: (a list of items which are an existing file name)
        output file name for p-values of Z-stats (either as text file or
        image)
out_pf: (a list of items which are an existing file name)
        output file name for p-value for full model fit
out_res: (a list of items which are an existing file name)
        output file name for residuals
out_sigsq: (a list of items which are an existing file name)
        output file name for residual noise variance sigma-square
out_t: (a list of items which are an existing file name)
        output file name for t-stats (either as text file or image)
out_varcb: (a list of items which are an existing file name)
        output file name for variance of COPEs
out_vnscales: (a list of items which are an existing file name)
        output file name for scaling factors for variance normalisation
out_z: (a list of items which are 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

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 u'dgamma' and with values
         which are a dictionary with keys which are u'derivs' and with
         values which are a boolean or a dictionary with keys which are
         u'gamma' and with values which are a dictionary with keys which are
         u'derivs' and with values which are a boolean or a dictionary with
         keys which are u'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: (any value)
        Session specific information generated by ``modelgen.SpecifyModel``

[Optional]
contrasts: (a list of items which are a tuple of the form: (a unicode
         string, u'T', a list of items which are a unicode string, a list of
         items which are a float) or a tuple of the form: (a unicode string,
         u'T', a list of items which are a unicode string, a list of items
         which are a float, a list of items which are a float) or a tuple of
         the form: (a unicode string, u'F', a list of items which are a
         tuple of the form: (a unicode string, u'T', a list of items which
         are a unicode string, a list of items which are a float) or a tuple
         of the form: (a unicode string, u'T', a list of items which are a
         unicode string, a list of items which are a float, a list of items
         which are a float)))
        List of contrasts with each contrast being a list of the form -
        [('name', 'stat', [condition list], [weight list], [session list])].
        if session list is None or not provided, all sessions are used. For
        F contrasts, the condition list should contain previously defined
        T-contrasts.
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 a list of items which are an
         existing file name)
        condition information files
fsf_files: (a list of items which are 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.cmdline 
'melodic -i functional.nii,functional2.nii,functional3.nii -a tica --bgthreshold=10.000000 --mmthresh=0.500000 --nobet -o groupICA.out --Ostats --Scon=subjectDesign.con --Sdes=subjectDesign.mat --Tcon=timeDesign.con --Tdes=timeDesign.mat --tr=1.500000'
>>> melodic_setup.run() 

Inputs:

[Mandatory]
in_files: (a list of items which are an existing file name)
        input file names (either single file name or a list)
        flag: -i %s, position: 0

[Optional]
ICs: (an existing file name)
        filename of the IC components file for mixture modelling
        flag: --ICs=%s
approach: (a unicode string)
        approach for decomposition, 2D: defl, symm (default), 3D: tica
        (default), concat
        flag: -a %s
args: (a unicode string)
        Additional parameters to the command
        flag: %s
bg_image: (an existing file name)
        specify background image for report (default: mean image)
        flag: --bgimage=%s
bg_threshold: (a float)
        brain/non-brain threshold used to mask non-brain voxels, as a
        percentage (only if --nobet selected)
        flag: --bgthreshold=%f
cov_weight: (a float)
        voxel-wise weights for the covariance matrix (e.g. segmentation
        information)
        flag: --covarweight=%f
dim: (an integer (int or long))
        dimensionality reduction into #num dimensions (default: automatic
        estimation)
        flag: -d %d
dim_est: (a unicode string)
        use specific dim. estimation technique: lap, bic, mdl, aic, mean
        (default: lap)
        flag: --dimest=%s
environ: (a dictionary with keys which are a newbytes or None or a
         newstr or None and with values which are a newbytes or None or a
         newstr or None, nipype default value: {})
        Environment variables
epsilon: (a float)
        minimum error change
        flag: --eps=%f
epsilonS: (a float)
        minimum error change for rank-1 approximation in TICA
        flag: --epsS=%f
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
        flag: --logPower
mask: (an existing file name)
        file name of mask for thresholding
        flag: -m %s
max_restart: (an integer (int or long))
        maximum number of restarts
        flag: --maxrestart=%d
maxit: (an integer (int or long))
        maximum number of iterations before restart
        flag: --maxit=%d
mix: (an existing file name)
        mixing matrix for mixture modelling / filtering
        flag: --mix=%s
mm_thresh: (a float)
        threshold for Mixture Model based inference
        flag: --mmthresh=%f
no_bet: (a boolean)
        switch off BET
        flag: --nobet
no_mask: (a boolean)
        switch off masking
        flag: --nomask
no_mm: (a boolean)
        switch off mixture modelling on IC maps
        flag: --no_mm
non_linearity: (a unicode string)
        nonlinearity: gauss, tanh, pow3, pow4
        flag: --nl=%s
num_ICs: (an integer (int or long))
        number of IC's to extract (for deflation approach)
        flag: -n %d
out_all: (a boolean)
        output everything
        flag: --Oall
out_dir: (a directory name)
        output directory name
        flag: -o %s
out_mean: (a boolean)
        output mean volume
        flag: --Omean
out_orig: (a boolean)
        output the original ICs
        flag: --Oorig
out_pca: (a boolean)
        output PCA results
        flag: --Opca
out_stats: (a boolean)
        output thresholded maps and probability maps
        flag: --Ostats
out_unmix: (a boolean)
        output unmixing matrix
        flag: --Ounmix
out_white: (a boolean)
        output whitening/dewhitening matrices
        flag: --Owhite
output_type: (u'NIFTI_PAIR' or u'NIFTI_PAIR_GZ' or u'NIFTI_GZ' or
         u'NIFTI')
        FSL output type
pbsc: (a boolean)
        switch off conversion to percent BOLD signal change
        flag: --pbsc
rem_cmp: (a list of items which are an integer (int or long))
        component numbers to remove
        flag: -f %d
remove_deriv: (a boolean)
        removes every second entry in paradigm file (EV derivatives)
        flag: --remove_deriv
report: (a boolean)
        generate Melodic web report
        flag: --report
report_maps: (a unicode string)
        control string for spatial map images (see slicer)
        flag: --report_maps=%s
s_con: (an existing file name)
        t-contrast matrix across subject-domain
        flag: --Scon=%s
s_des: (an existing file name)
        design matrix across subject-domain
        flag: --Sdes=%s
sep_vn: (a boolean)
        switch off joined variance normalization
        flag: --sep_vn
sep_whiten: (a boolean)
        switch on separate whitening
        flag: --sep_whiten
smode: (an existing file name)
        matrix of session modes for report generation
        flag: --smode=%s
t_con: (an existing file name)
        t-contrast matrix across time-domain
        flag: --Tcon=%s
t_des: (an existing file name)
        design matrix across time-domain
        flag: --Tdes=%s
terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `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
        flag: --tr=%f
update_mask: (a boolean)
        switch off mask updating
        flag: --update_mask
var_norm: (a boolean)
        switch off variance normalization
        flag: --vn

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 unicode
         string, u'T', a list of items which are a unicode string, a list of
         items which are a float) or a tuple of the form: (a unicode string,
         u'F', a list of items which are a tuple of the form: (a unicode
         string, u'T', a list of items which are a unicode 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 unicode 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 (int or long))
        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

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
        flag: -i %s, position: 0

[Optional]
args: (a unicode string)
        Additional parameters to the command
        flag: %s
base_name: (a unicode string, nipype default value: tbss_)
        the rootname that all generated files will have
        flag: -o "%s", position: 1
c_thresh: (a float)
        carry out cluster-based thresholding
        flag: -c %.2f
cm_thresh: (a float)
        carry out cluster-mass-based thresholding
        flag: -C %.2f
demean: (a boolean)
        demean data temporally before model fitting
        flag: -D
design_mat: (an existing file name)
        design matrix file
        flag: -d %s, position: 2
environ: (a dictionary with keys which are a newbytes or None or a
         newstr or None and with values which are a newbytes or None or a
         newstr or None, nipype default value: {})
        Environment variables
f_c_thresh: (a float)
        carry out f cluster thresholding
        flag: -F %.2f
f_cm_thresh: (a float)
        carry out f cluster-mass thresholding
        flag: -S %.2f
f_only: (a boolean)
        calculate f-statistics only
        flag: --f_only
fcon: (an existing file name)
        f contrasts file
        flag: -f %s
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
        flag: -m %s
num_perm: (an integer (int or long))
        number of permutations (default 5000, set to 0 for exhaustive)
        flag: -n %d
one_sample_group_mean: (a boolean)
        perform 1-sample group-mean test instead of generic permutation test
        flag: -1
output_type: (u'NIFTI_PAIR' or u'NIFTI_PAIR_GZ' or u'NIFTI_GZ' or
         u'NIFTI')
        FSL output type
p_vec_n_dist_files: (a boolean)
        output permutation vector and null distribution text files
        flag: -P
raw_stats_imgs: (a boolean)
        output raw ( unpermuted ) statistic images
        flag: -R
seed: (an integer (int or long))
        specific integer seed for random number generator
        flag: --seed=%d
show_info_parallel_mode: (a boolean)
        print out information required for parallel mode and exit
        flag: -Q
show_total_perms: (a boolean)
        print out how many unique permutations would be generated and exit
        flag: -q
tcon: (an existing file name)
        t contrasts file
        flag: -t %s, position: 3
terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `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
        flag: -T
tfce2D: (a boolean)
        carry out Threshold-Free Cluster Enhancement with 2D optimisation
        flag: --T2
tfce_C: (a float)
        TFCE connectivity (6 or 26; default=6)
        flag: --tfce_C=%.2f
tfce_E: (a float)
        TFCE extent parameter (default=0.5)
        flag: --tfce_E=%.2f
tfce_H: (a float)
        TFCE height parameter (default=2)
        flag: --tfce_H=%.2f
var_smooth: (an integer (int or long))
        use variance smoothing (std is in mm)
        flag: -v %d
vox_p_values: (a boolean)
        output voxelwise (corrected and uncorrected) p-value images
        flag: -x
x_block_labels: (an existing file name)
        exchangeability block labels file
        flag: -e %s

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
        flag: --mask="%s", position: 1
spatial_data_file: (an existing file name)
        statistics spatial map
        flag: --sdf="%s", position: 0

[Optional]
args: (a unicode string)
        Additional parameters to the command
        flag: %s
environ: (a dictionary with keys which are a newbytes or None or a
         newstr or None and with values which are a newbytes or None or a
         newstr or None, 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
        flag: --zfstatmode, position: 2
output_type: (u'NIFTI_PAIR' or u'NIFTI_PAIR_GZ' or u'NIFTI_GZ' or
         u'NIFTI')
        FSL output type
terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `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 (int or long))
        number of degrees of freedom
        flag: --dof=%d
        mutually_exclusive: zstat_file
mask_file: (an existing file name)
        brain mask volume
        flag: --mask=%s

[Optional]
args: (a unicode string)
        Additional parameters to the command
        flag: %s
environ: (a dictionary with keys which are a newbytes or None or a
         newstr or None and with values which are a newbytes or None or a
         newstr or None, 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: (u'NIFTI_PAIR' or u'NIFTI_PAIR_GZ' or u'NIFTI_GZ' or
         u'NIFTI')
        FSL output type
residual_fit_file: (an existing file name)
        residual-fit image file
        flag: --res=%s
        requires: dof
terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `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
        flag: --zstat=%s
        mutually_exclusive: dof

Outputs:

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