interfaces.spm.model¶
EstimateContrast¶
use spm_contrasts to estimate contrasts of interest
Examples¶
>>> import nipype.interfaces.spm as spm
>>> est = spm.EstimateContrast()
>>> est.inputs.spm_mat_file = 'SPM.mat'
>>> cont1 = ('Task>Baseline','T', ['Task-Odd','Task-Even'],[0.5,0.5])
>>> cont2 = ('Task-Odd>Task-Even','T', ['Task-Odd','Task-Even'],[1,-1])
>>> contrasts = [cont1,cont2]
>>> est.inputs.contrasts = contrasts
>>> est.run()
Inputs:
[Mandatory]
beta_images: (a list of items which are an existing file name)
Parameter estimates of the design matrix
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.
residual_image: (an existing file name)
Mean-squared image of the residuals
spm_mat_file: (an existing file name)
Absolute path to SPM.mat
[Optional]
group_contrast: (a boolean)
higher level contrast
mutually_exclusive: use_derivs
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
matlab_cmd: (a string)
matlab command to use
mfile: (a boolean, nipype default value: True)
Run m-code using m-file
paths: (a list of items which are a directory name)
Paths to add to matlabpath
use_derivs: (a boolean)
use derivatives for estimation
mutually_exclusive: group_contrast
use_mcr: (a boolean)
Run m-code using SPM MCR
use_v8struct: (a boolean, nipype default value: True)
Generate SPM8 and higher compatible jobs
Outputs:
con_images: (a list of items which are an existing file name)
contrast images from a t-contrast
ess_images: (a list of items which are an existing file name)
contrast images from an F-contrast
spmF_images: (a list of items which are an existing file name)
stat images from an F-contrast
spmT_images: (a list of items which are an existing file name)
stat images from a t-contrast
spm_mat_file: (an existing file name)
Updated SPM mat file
EstimateModel¶
Use spm_spm to estimate the parameters of a model
http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=69
Examples¶
>>> est = EstimateModel()
>>> est.inputs.spm_mat_file = 'SPM.mat'
>>> est.run()
Inputs:
[Mandatory]
estimation_method: (a dictionary with keys which are 'Classical' or
'Bayesian2' or 'Bayesian' and with values which are any value)
Classical, Bayesian2, Bayesian (dict)
spm_mat_file: (an existing file name)
absolute path to SPM.mat
[Optional]
flags: (a string)
optional arguments (opt)
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
matlab_cmd: (a string)
matlab command to use
mfile: (a boolean, nipype default value: True)
Run m-code using m-file
paths: (a list of items which are a directory name)
Paths to add to matlabpath
use_mcr: (a boolean)
Run m-code using SPM MCR
use_v8struct: (a boolean, nipype default value: True)
Generate SPM8 and higher compatible jobs
Outputs:
RPVimage: (an existing file name)
Resels per voxel image
beta_images: (a list of items which are an existing file name)
design parameter estimates
mask_image: (an existing file name)
binary mask to constrain estimation
residual_image: (an existing file name)
Mean-squared image of the residuals
spm_mat_file: (an existing file name)
Updated SPM mat file
FactorialDesign¶
Base class for factorial designs
http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=77
Inputs:
[Mandatory]
[Optional]
covariates: (a list of items which are a dictionary with keys which
are 'vector' or 'name' or 'interaction' or 'centering' and with
values which are any value)
covariate dictionary {vector, name, interaction, centering}
explicit_mask_file: (a file name)
use an implicit mask file to threshold
global_calc_mean: (a boolean)
use mean for global calculation
mutually_exclusive: global_calc_omit, global_calc_values
global_calc_omit: (a boolean)
omit global calculation
mutually_exclusive: global_calc_mean, global_calc_values
global_calc_values: (a list of items which are a float)
omit global calculation
mutually_exclusive: global_calc_mean, global_calc_omit
global_normalization: (1 or 2 or 3)
global normalization None-1, Proportional-2, ANCOVA-3
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
matlab_cmd: (a string)
matlab command to use
mfile: (a boolean, nipype default value: True)
Run m-code using m-file
no_grand_mean_scaling: (a boolean)
do not perform grand mean scaling
paths: (a list of items which are a directory name)
Paths to add to matlabpath
spm_mat_dir: (an existing directory name)
directory to store SPM.mat file (opt)
threshold_mask_absolute: (a float)
use an absolute threshold
mutually_exclusive: threshold_mask_none, threshold_mask_relative
threshold_mask_none: (a boolean)
do not use threshold masking
mutually_exclusive: threshold_mask_absolute, threshold_mask_relative
threshold_mask_relative: (a float)
threshold using a proportion of the global value
mutually_exclusive: threshold_mask_absolute, threshold_mask_none
use_implicit_threshold: (a boolean)
use implicit mask NaNs or zeros to threshold
use_mcr: (a boolean)
Run m-code using SPM MCR
use_v8struct: (a boolean, nipype default value: True)
Generate SPM8 and higher compatible jobs
Outputs:
spm_mat_file: (an existing file name)
SPM mat file
Level1Design¶
Generate an SPM design matrix
http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=59
Examples¶
>>> level1design = Level1Design()
>>> level1design.inputs.timing_units = 'secs'
>>> level1design.inputs.interscan_interval = 2.5
>>> level1design.inputs.bases = {'hrf':{'derivs': [0,0]}}
>>> level1design.inputs.session_info = 'session_info.npz'
>>> level1design.run()
Inputs:
[Mandatory]
bases: (a dictionary with keys which are 'hrf' or 'fourier' or
'fourier_han' or 'gamma' or 'fir' and with values which are any
value)
dict {'name':{'basesparam1':val,...}}
name : string
Name of basis function (hrf, fourier, fourier_han,
gamma, fir)
hrf :
derivs : 2-element list
Model HRF Derivatives. No derivatives: [0,0],
Time derivatives : [1,0], Time and Dispersion
derivatives: [1,1]
fourier, fourier_han, gamma, fir:
length : int
Post-stimulus window length (in seconds)
order : int
Number of basis functions
interscan_interval: (a float)
Interscan interval in secs
session_info: (any value)
Session specific information generated by ``modelgen.SpecifyModel``
timing_units: ('secs' or 'scans')
units for specification of onsets
[Optional]
factor_info: (a list of items which are a dictionary with keys which
are 'name' or 'levels' and with values which are any value)
Factor specific information file (opt)
global_intensity_normalization: ('none' or 'scaling')
Global intensity normalization - scaling or none
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_image: (an existing file name)
Image for explicitly masking the analysis
mask_threshold: ('-Inf' or a float, nipype default value: -Inf)
Thresholding for the mask
matlab_cmd: (a string)
matlab command to use
mfile: (a boolean, nipype default value: True)
Run m-code using m-file
microtime_onset: (a float)
The onset/time-bin in seconds for alignment (opt)
microtime_resolution: (an integer (int or long))
Number of time-bins per scan in secs (opt)
model_serial_correlations: ('AR(1)' or 'FAST' or 'none')
Model serial correlations AR(1), FAST or none. FAST is available in
SPM12
paths: (a list of items which are a directory name)
Paths to add to matlabpath
spm_mat_dir: (an existing directory name)
directory to store SPM.mat file (opt)
use_mcr: (a boolean)
Run m-code using SPM MCR
use_v8struct: (a boolean, nipype default value: True)
Generate SPM8 and higher compatible jobs
volterra_expansion_order: (1 or 2)
Model interactions - yes:1, no:2
Outputs:
spm_mat_file: (an existing file name)
SPM mat file
MultipleRegressionDesign¶
Create SPM design for multiple regression
Examples¶
>>> mreg = MultipleRegressionDesign()
>>> mreg.inputs.in_files = ['cont1.nii','cont2.nii']
>>> mreg.run()
Inputs:
[Mandatory]
in_files: (a list of at least 2 items which are an existing file
name)
List of files
[Optional]
covariates: (a list of items which are a dictionary with keys which
are 'vector' or 'name' or 'interaction' or 'centering' and with
values which are any value)
covariate dictionary {vector, name, interaction, centering}
explicit_mask_file: (a file name)
use an implicit mask file to threshold
global_calc_mean: (a boolean)
use mean for global calculation
mutually_exclusive: global_calc_omit, global_calc_values
global_calc_omit: (a boolean)
omit global calculation
mutually_exclusive: global_calc_mean, global_calc_values
global_calc_values: (a list of items which are a float)
omit global calculation
mutually_exclusive: global_calc_mean, global_calc_omit
global_normalization: (1 or 2 or 3)
global normalization None-1, Proportional-2, ANCOVA-3
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
include_intercept: (a boolean, nipype default value: True)
Include intercept in design
matlab_cmd: (a string)
matlab command to use
mfile: (a boolean, nipype default value: True)
Run m-code using m-file
no_grand_mean_scaling: (a boolean)
do not perform grand mean scaling
paths: (a list of items which are a directory name)
Paths to add to matlabpath
spm_mat_dir: (an existing directory name)
directory to store SPM.mat file (opt)
threshold_mask_absolute: (a float)
use an absolute threshold
mutually_exclusive: threshold_mask_none, threshold_mask_relative
threshold_mask_none: (a boolean)
do not use threshold masking
mutually_exclusive: threshold_mask_absolute, threshold_mask_relative
threshold_mask_relative: (a float)
threshold using a proportion of the global value
mutually_exclusive: threshold_mask_absolute, threshold_mask_none
use_implicit_threshold: (a boolean)
use implicit mask NaNs or zeros to threshold
use_mcr: (a boolean)
Run m-code using SPM MCR
use_v8struct: (a boolean, nipype default value: True)
Generate SPM8 and higher compatible jobs
user_covariates: (a list of items which are a dictionary with keys
which are 'vector' or 'name' or 'centering' and with values which
are any value)
covariate dictionary {vector, name, centering}
Outputs:
spm_mat_file: (an existing file name)
SPM mat file
OneSampleTTestDesign¶
Create SPM design for one sample t-test
Examples¶
>>> ttest = OneSampleTTestDesign()
>>> ttest.inputs.in_files = ['cont1.nii', 'cont2.nii']
>>> ttest.run()
Inputs:
[Mandatory]
in_files: (a list of at least 2 items which are an existing file
name)
input files
[Optional]
covariates: (a list of items which are a dictionary with keys which
are 'vector' or 'name' or 'interaction' or 'centering' and with
values which are any value)
covariate dictionary {vector, name, interaction, centering}
explicit_mask_file: (a file name)
use an implicit mask file to threshold
global_calc_mean: (a boolean)
use mean for global calculation
mutually_exclusive: global_calc_omit, global_calc_values
global_calc_omit: (a boolean)
omit global calculation
mutually_exclusive: global_calc_mean, global_calc_values
global_calc_values: (a list of items which are a float)
omit global calculation
mutually_exclusive: global_calc_mean, global_calc_omit
global_normalization: (1 or 2 or 3)
global normalization None-1, Proportional-2, ANCOVA-3
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
matlab_cmd: (a string)
matlab command to use
mfile: (a boolean, nipype default value: True)
Run m-code using m-file
no_grand_mean_scaling: (a boolean)
do not perform grand mean scaling
paths: (a list of items which are a directory name)
Paths to add to matlabpath
spm_mat_dir: (an existing directory name)
directory to store SPM.mat file (opt)
threshold_mask_absolute: (a float)
use an absolute threshold
mutually_exclusive: threshold_mask_none, threshold_mask_relative
threshold_mask_none: (a boolean)
do not use threshold masking
mutually_exclusive: threshold_mask_absolute, threshold_mask_relative
threshold_mask_relative: (a float)
threshold using a proportion of the global value
mutually_exclusive: threshold_mask_absolute, threshold_mask_none
use_implicit_threshold: (a boolean)
use implicit mask NaNs or zeros to threshold
use_mcr: (a boolean)
Run m-code using SPM MCR
use_v8struct: (a boolean, nipype default value: True)
Generate SPM8 and higher compatible jobs
Outputs:
spm_mat_file: (an existing file name)
SPM mat file
PairedTTestDesign¶
Create SPM design for paired t-test
Examples¶
>>> pttest = PairedTTestDesign()
>>> pttest.inputs.paired_files = [['cont1.nii','cont1a.nii'],['cont2.nii','cont2a.nii']]
>>> pttest.run()
Inputs:
[Mandatory]
paired_files: (a list of at least 2 items which are a list of from 2
to 2 items which are an existing file name)
List of paired files
[Optional]
ancova: (a boolean)
Specify ancova-by-factor regressors
covariates: (a list of items which are a dictionary with keys which
are 'vector' or 'name' or 'interaction' or 'centering' and with
values which are any value)
covariate dictionary {vector, name, interaction, centering}
explicit_mask_file: (a file name)
use an implicit mask file to threshold
global_calc_mean: (a boolean)
use mean for global calculation
mutually_exclusive: global_calc_omit, global_calc_values
global_calc_omit: (a boolean)
omit global calculation
mutually_exclusive: global_calc_mean, global_calc_values
global_calc_values: (a list of items which are a float)
omit global calculation
mutually_exclusive: global_calc_mean, global_calc_omit
global_normalization: (1 or 2 or 3)
global normalization None-1, Proportional-2, ANCOVA-3
grand_mean_scaling: (a boolean)
Perform grand mean scaling
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
matlab_cmd: (a string)
matlab command to use
mfile: (a boolean, nipype default value: True)
Run m-code using m-file
no_grand_mean_scaling: (a boolean)
do not perform grand mean scaling
paths: (a list of items which are a directory name)
Paths to add to matlabpath
spm_mat_dir: (an existing directory name)
directory to store SPM.mat file (opt)
threshold_mask_absolute: (a float)
use an absolute threshold
mutually_exclusive: threshold_mask_none, threshold_mask_relative
threshold_mask_none: (a boolean)
do not use threshold masking
mutually_exclusive: threshold_mask_absolute, threshold_mask_relative
threshold_mask_relative: (a float)
threshold using a proportion of the global value
mutually_exclusive: threshold_mask_absolute, threshold_mask_none
use_implicit_threshold: (a boolean)
use implicit mask NaNs or zeros to threshold
use_mcr: (a boolean)
Run m-code using SPM MCR
use_v8struct: (a boolean, nipype default value: True)
Generate SPM8 and higher compatible jobs
Outputs:
spm_mat_file: (an existing file name)
SPM mat file
Threshold¶
Topological FDR thresholding based on cluster extent/size. Smoothness is estimated from GLM residuals but is assumed to be the same for all of the voxels.
Examples¶
>>> thresh = Threshold()
>>> thresh.inputs.spm_mat_file = 'SPM.mat'
>>> thresh.inputs.stat_image = 'spmT_0001.img'
>>> thresh.inputs.contrast_index = 1
>>> thresh.inputs.extent_fdr_p_threshold = 0.05
>>> thresh.run()
Inputs:
[Mandatory]
contrast_index: (an integer (int or long))
which contrast in the SPM.mat to use
spm_mat_file: (an existing file name)
absolute path to SPM.mat
stat_image: (an existing file name)
stat image
[Optional]
extent_fdr_p_threshold: (a float, nipype default value: 0.05)
p threshold on FDR corrected cluster size probabilities
extent_threshold: (an integer (int or long), nipype default value: 0)
Minimum cluster size in voxels
force_activation: (a boolean, nipype default value: False)
In case no clusters survive the topological inference step this will
pick a culster with the highes sum of t-values. Use with care.
height_threshold: (a float, nipype default value: 0.05)
value for initial thresholding (defining clusters)
height_threshold_type: ('p-value' or 'stat', nipype default value:
p-value)
Is the cluster forming threshold a stat value or p-value?
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
matlab_cmd: (a string)
matlab command to use
mfile: (a boolean, nipype default value: True)
Run m-code using m-file
paths: (a list of items which are a directory name)
Paths to add to matlabpath
use_fwe_correction: (a boolean, nipype default value: True)
whether to use FWE (Bonferroni) correction for initial threshold
(height_threshold_type has to be set to p-value)
use_mcr: (a boolean)
Run m-code using SPM MCR
use_topo_fdr: (a boolean, nipype default value: True)
whether to use FDR over cluster extent probabilities
use_v8struct: (a boolean, nipype default value: True)
Generate SPM8 and higher compatible jobs
Outputs:
activation_forced: (a boolean)
cluster_forming_thr: (a float)
n_clusters: (an integer (int or long))
pre_topo_fdr_map: (an existing file name)
pre_topo_n_clusters: (an integer (int or long))
thresholded_map: (an existing file name)
ThresholdStatistics¶
Given height and cluster size threshold calculate theoretical probabilities concerning false positives
Examples¶
>>> thresh = ThresholdStatistics()
>>> thresh.inputs.spm_mat_file = 'SPM.mat'
>>> thresh.inputs.stat_image = 'spmT_0001.img'
>>> thresh.inputs.contrast_index = 1
>>> thresh.inputs.height_threshold = 4.56
>>> thresh.run()
Inputs:
[Mandatory]
contrast_index: (an integer (int or long))
which contrast in the SPM.mat to use
height_threshold: (a float)
stat value for initial thresholding (defining clusters)
spm_mat_file: (an existing file name)
absolute path to SPM.mat
stat_image: (an existing file name)
stat image
[Optional]
extent_threshold: (an integer (int or long), nipype default value: 0)
Minimum cluster size in voxels
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
matlab_cmd: (a string)
matlab command to use
mfile: (a boolean, nipype default value: True)
Run m-code using m-file
paths: (a list of items which are a directory name)
Paths to add to matlabpath
use_mcr: (a boolean)
Run m-code using SPM MCR
use_v8struct: (a boolean, nipype default value: True)
Generate SPM8 and higher compatible jobs
Outputs:
clusterwise_P_FDR: (a float)
clusterwise_P_RF: (a float)
voxelwise_P_Bonf: (a float)
voxelwise_P_FDR: (a float)
voxelwise_P_RF: (a float)
voxelwise_P_uncor: (a float)
TwoSampleTTestDesign¶
Create SPM design for two sample t-test
Examples¶
>>> ttest = TwoSampleTTestDesign()
>>> ttest.inputs.group1_files = ['cont1.nii', 'cont2.nii']
>>> ttest.inputs.group2_files = ['cont1a.nii', 'cont2a.nii']
>>> ttest.run()
Inputs:
[Mandatory]
group1_files: (a list of at least 2 items which are an existing file
name)
Group 1 input files
group2_files: (a list of at least 2 items which are an existing file
name)
Group 2 input files
[Optional]
covariates: (a list of items which are a dictionary with keys which
are 'vector' or 'name' or 'interaction' or 'centering' and with
values which are any value)
covariate dictionary {vector, name, interaction, centering}
dependent: (a boolean)
Are the measurements dependent between levels
explicit_mask_file: (a file name)
use an implicit mask file to threshold
global_calc_mean: (a boolean)
use mean for global calculation
mutually_exclusive: global_calc_omit, global_calc_values
global_calc_omit: (a boolean)
omit global calculation
mutually_exclusive: global_calc_mean, global_calc_values
global_calc_values: (a list of items which are a float)
omit global calculation
mutually_exclusive: global_calc_mean, global_calc_omit
global_normalization: (1 or 2 or 3)
global normalization None-1, Proportional-2, ANCOVA-3
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
matlab_cmd: (a string)
matlab command to use
mfile: (a boolean, nipype default value: True)
Run m-code using m-file
no_grand_mean_scaling: (a boolean)
do not perform grand mean scaling
paths: (a list of items which are a directory name)
Paths to add to matlabpath
spm_mat_dir: (an existing directory name)
directory to store SPM.mat file (opt)
threshold_mask_absolute: (a float)
use an absolute threshold
mutually_exclusive: threshold_mask_none, threshold_mask_relative
threshold_mask_none: (a boolean)
do not use threshold masking
mutually_exclusive: threshold_mask_absolute, threshold_mask_relative
threshold_mask_relative: (a float)
threshold using a proportion of the global value
mutually_exclusive: threshold_mask_absolute, threshold_mask_none
unequal_variance: (a boolean)
Are the variances equal or unequal between groups
use_implicit_threshold: (a boolean)
use implicit mask NaNs or zeros to threshold
use_mcr: (a boolean)
Run m-code using SPM MCR
use_v8struct: (a boolean, nipype default value: True)
Generate SPM8 and higher compatible jobs
Outputs:
spm_mat_file: (an existing file name)
SPM mat file