Wraps command cluster
Uses FSL cluster to perform clustering on statistical output
>>> 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
flag: --in=%s
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
flag: --thresh=%.10f
[Optional]
args: (a string)
Additional parameters to the command
flag: %s
connectivity: (an integer)
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 value of type 'str' and
with values which are a value of type 'str', nipype default value:
{})
Environment variables
find_min: (a boolean)
find minima instead of maxima
fractional: (a boolean)
interprets the threshold as a fraction of the robust range
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
minclustersize: (a boolean)
prints out minimum significant cluster size
flag: --minclustersize
no_table: (a boolean)
suppresses printing of the table info
num_maxima: (an integer)
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: ('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)
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
use_mm: (a boolean)
use mm, not voxel, coordinates
volume: (an integer)
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
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
flag: %s, position: -1
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
flag: %s
contrast_num: (an integer >= 1)
contrast number to start labeling copes from
flag: -cope
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
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: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or
'NIFTI')
FSL output type
suffix: (a string)
suffix to put on the end of the cope filename before the contrast
number, default is nothing
flag: -suffix %s
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
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
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
flag: %s
environ: (a dictionary with keys which are a value of type 'str' and
with values which are a value of type 'str', nipype default value:
{})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or
'NIFTI')
FSL output type
Outputs:
feat_dir: (an existing directory name)
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
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
flag: %s
environ: (a dictionary with keys which are a value of type 'str' and
with values which are a value of type 'str', nipype default value:
{})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or
'NIFTI')
FSL output type
Outputs:
con_file: (an existing file name)
Contrast file containing contrast vectors
design_cov: (an existing file name)
Graphical representation of design covariance
design_file: (an existing file name)
Mat file containing ascii matrix for design
design_image: (an existing file name)
Graphical representation of design matrix
fcon_file: (a file name)
Contrast file containing contrast vectors
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]
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
reg_dof: (an integer, nipype default value: 12)
registration degrees of freedom
Outputs:
fsf_file: (an existing file name)
FSL feat specification file
Wraps command film_gls
Use FSL film_gls command to fit a design matrix to voxel timeseries
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
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
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 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
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)
susan mask size
flag: --ms=%d
mode: ('volumetric' or 'surface')
Type of analysis to be done
flag: --mode=%s
multitaper_product: (an integer)
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: ('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
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
threshold: (a float, nipype default value: 0.0)
threshold
flag: --thr=%f, position: -1
tukey_window: (an integer)
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: (an existing file name)
Contrast estimates for each contrast
dof_file: (an existing file name)
degrees of freedom
fstats: (an existing file name)
f-stat file for each contrast
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
thresholdac: (an existing file name)
The FILM autocorrelation parameters
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
Wraps command flameo
Use FSL flameo command to perform higher level model fits
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: ('fe' or 'ols' or 'flame1' or 'flame12')
inference to perform
flag: --runmode=%s
t_con_file: (an existing file name)
ascii matrix specifying t-contrasts
flag: --tcontrastsfile=%s
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
flag: %s
burnin: (an integer)
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 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
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)
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)
Number of max iterations to use when inferring outliers. Default is
12.
flag: --ioni=%d
output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or
'NIFTI')
FSL output type
sample_every: (an integer)
number of jumps for each sample
flag: --sampleevery=%d
sigma_dofs: (an integer)
sigma (in mm) to use for Gaussian smoothing the DOFs in FLAME 2.
Default is 1mm, -1 indicates no smoothing
flag: --sigma_dofs=%d
var_cope_file: (an existing file name)
varcope weightings data file
flag: --varcopefile=%s
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
Wraps command fsl_glm
FSL GLM:
>>> 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
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
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)
set degrees of freedom explicitly
flag: --dof=%d
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 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: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or
'NIFTI')
FSL output type
var_norm: (a boolean)
perform MELODIC variance-normalisation on data
flag: --vn
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)
Generate subject specific second level model
>>> 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
Generate FEAT specific files
>>> level1design = Level1Design()
>>> level1design.inputs.interscan_interval = 2.5
>>> level1design.inputs.bases = {'dgamma':{'derivs': False}}
>>> level1design.inputs.session_info = 'session_info.npz'
>>> level1design.run()
Inputs:
[Mandatory]
bases: (a dictionary with keys which are 'dgamma' and with values
which are a dictionary with keys which are 'derivs' and with values
which are a boolean or a dictionary with keys which are 'gamma' and
with values which are a dictionary with keys which are 'derivs' and
with values which are a boolean or a dictionary with keys which are
'none' and with values which are None)
name of basis function and options e.g., {'dgamma': {'derivs':
True}}
interscan_interval: (a float)
Interscan interval (in secs)
model_serial_correlations: (a boolean)
Option to model serial correlations using an autoregressive
estimator (order 1). Setting this option is only useful in the
context of the fsf file. If you set this to False, you need to
repeat this option for FILMGLS by setting autocorr_noestimate to
True
session_info
Session specific information generated by ``modelgen.SpecifyModel``
[Optional]
contrasts: (a list of items which are a tuple of the form: (a string,
'T', a list of items which are a string, a list of items which are
a float) or a tuple of the form: (a string, 'T', a list of items
which are a string, a list of items which are a float, a list of
items which are a float) or a tuple of the form: (a string, 'F', a
list of items which are a tuple of the form: (a string, 'T', a list
of items which are a string, a list of items which are a float) or
a tuple of the form: (a string, 'T', a list of items which are a
string, a list of items which are a float, a list of items which
are a float)))
List of contrasts with each contrast being a list of the form -
[('name', 'stat', [condition list], [weight list], [session list])].
if session list is None or not provided, all sessions are used. For
F contrasts, the condition list should contain previously defined
T-contrasts.
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
Outputs:
ev_files: (a list of items which are an existing file name)
condition information files
fsf_files: (an existing file name)
FSL feat specification files
Wraps command melodic
Multivariate Exploratory Linear Optimised Decomposition into Independent Components
>>> 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: (an existing file name)
input file names (either single file name or a list)
flag: -i %s, position: 0
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
flag: --ICs=%s
approach: (a string)
approach for decomposition, 2D: defl, symm (default), 3D: tica
(default), concat
flag: -a %s
args: (a 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)
dimensionality reduction into #num dimensions(default: automatic
estimation)
flag: -d %d
dim_est: (a string)
use specific dim. estimation technique: lap, bic, mdl, aic, mean
(default: lap)
flag: --dimest=%s
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
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)
maximum number of restarts
flag: --maxrestart=%d
maxit: (an integer)
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 string)
nonlinearity: gauss, tanh, pow3, pow4
flag: --nl=%s
num_ICs: (an integer)
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: ('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
flag: --pbsc
rem_cmp: (a list of items which are an integer)
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 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
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)
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.
>>> from nipype.interfaces.fsl import MultipleRegressDesign
>>> model = MultipleRegressDesign()
>>> model.inputs.contrasts = [['group mean', 'T',['reg1'],[1]]]
>>> model.inputs.regressors = dict(reg1=[1, 1, 1], reg2=[2.,-4, 3])
>>> model.run()
Inputs:
[Mandatory]
contrasts: (a list of items which are a tuple of the form: (a string,
'T', a list of items which are a string, a list of items which are
a float) or a tuple of the form: (a string, 'F', a list of items
which are a tuple of the form: (a string, 'T', a list of items
which are a string, a list of items which are a float)))
List of contrasts with each contrast being a list of the form -
[('name', 'stat', [condition list], [weight list])]. if session list
is None or not provided, all sessions are used. For F contrasts, the
condition list should contain previously defined T-contrasts without
any weight list.
regressors: (a dictionary with keys which are a string and with
values which are a list of items which are a float)
dictionary containing named lists of regressors
[Optional]
groups: (a list of items which are an integer)
list of group identifiers (defaults to single group)
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
Outputs:
design_con: (an existing file name)
design t-contrast file
design_fts: (an existing file name)
design f-contrast file
design_grp: (an existing file name)
design group file
design_mat: (an existing file name)
design matrix file
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
>>> 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
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
flag: %s
base_name: (a 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 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
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)
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: ('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
flag: -P
raw_stats_imgs: (a boolean)
output raw ( unpermuted ) statistic images
flag: -R
seed: (an integer)
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
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)
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
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
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
flag: %s
environ: (a dictionary with keys which are a value of type 'str' and
with values which are a value of type 'str', nipype default value:
{})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
no_deactivation_class: (a boolean)
enforces no deactivation class
flag: --zfstatmode, position: 2
output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or
'NIFTI')
FSL output type
Outputs:
activation_p_map: (an existing file name)
deactivation_p_map: (an existing file name)
null_p_map: (an existing file name)
Wraps command smoothest
Estimates the smoothness of an image
>>> 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
flag: --dof=%d
mutually_exclusive: zstat_file
mask_file: (an existing file name)
brain mask volume
flag: --mask=%s
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
flag: %s
environ: (a dictionary with keys which are a value of type 'str' and
with values which are a value of type 'str', nipype default value:
{})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or
'NIFTI')
FSL output type
residual_fit_file: (an existing file name)
residual-fit image file
flag: --res=%s
requires: dof
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)
number of voxels in mask