interfaces.fsl.model¶
Cluster¶
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¶
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¶
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¶
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¶
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¶
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¶
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¶
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¶
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¶
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¶
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¶
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¶
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¶
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¶
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