nipype.interfaces.spm.model module¶
The spm module provides basic functions for interfacing with matlab and spm to access spm tools.
EstimateContrast¶
Bases: SPMCommand
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()
- beta_imagesa list of items which are a pathlike object or string representing an existing file
Parameter estimates of the design matrix.
- contrastsa list of items which are a tuple of the form: (a unicode string, ‘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, ‘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, ‘F’, a list of items which are a tuple of the form: (a unicode string, ‘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, ‘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.
- residual_imagea pathlike object or string representing an existing file
Mean-squared image of the residuals.
- spm_mat_filea pathlike object or string representing an existing file
Absolute path to SPM.mat.
- group_contrasta boolean
Higher level contrast. Mutually exclusive with inputs:
use_derivs
.- matlab_cmda unicode string
Matlab command to use.
- mfilea boolean
Run m-code using m-file. (Nipype default value:
True
)- pathsa list of items which are a pathlike object or string representing a directory
Paths to add to matlabpath.
- use_derivsa boolean
Use derivatives for estimation. Mutually exclusive with inputs:
group_contrast
.- use_mcra boolean
Run m-code using SPM MCR.
- use_v8structa boolean
Generate SPM8 and higher compatible jobs. (Nipype default value:
True
)
- con_imagesa list of items which are a pathlike object or string representing an existing file
Contrast images from a t-contrast.
- ess_imagesa list of items which are a pathlike object or string representing an existing file
Contrast images from an F-contrast.
- spmF_imagesa list of items which are a pathlike object or string representing an existing file
Stat images from an F-contrast.
- spmT_imagesa list of items which are a pathlike object or string representing an existing file
Stat images from a t-contrast.
- spm_mat_filea pathlike object or string representing an existing file
Updated SPM mat file.
EstimateModel¶
Bases: SPMCommand
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.inputs.estimation_method = {'Classical': 1} >>> est.run()
- estimation_methoda dictionary with keys which are ‘Classical’ or ‘Bayesian2’ or ‘Bayesian’ and with values which are any value
Dictionary of either Classical: 1, Bayesian: 1, or Bayesian2: 1 (dict).
- spm_mat_filea pathlike object or string representing an existing file
Absolute path to SPM.mat.
- flagsa dictionary with keys which are any value and with values which are any value
Additional arguments.
- matlab_cmda unicode string
Matlab command to use.
- mfilea boolean
Run m-code using m-file. (Nipype default value:
True
)- pathsa list of items which are a pathlike object or string representing a directory
Paths to add to matlabpath.
- use_mcra boolean
Run m-code using SPM MCR.
- use_v8structa boolean
Generate SPM8 and higher compatible jobs. (Nipype default value:
True
)- write_residualsa boolean
Write individual residual images.
- ARcoefa list of items which are a pathlike object or string representing an existing file
Images of the AR coefficient.
- Cbetasa list of items which are a pathlike object or string representing an existing file
Images of the parameter posteriors.
- RPVimagea pathlike object or string representing an existing file
Resels per voxel image.
- SDbetasa list of items which are a pathlike object or string representing an existing file
Images of the standard deviation of parameter posteriors.
- SDerrora list of items which are a pathlike object or string representing an existing file
Images of the standard deviation of the error.
- beta_imagesa list of items which are a pathlike object or string representing an existing file
Design parameter estimates.
- labelsa pathlike object or string representing an existing file
Label file.
- mask_imagea pathlike object or string representing an existing file
Binary mask to constrain estimation.
- residual_imagea pathlike object or string representing an existing file
Mean-squared image of the residuals.
- residual_imagesa list of items which are a pathlike object or string representing an existing file
Individual residual images (requires write_residuals.
- spm_mat_filea pathlike object or string representing an existing file
Updated SPM mat file.
FactorialDesign¶
Bases: SPMCommand
Base class for factorial designs
http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=77
- covariatesa 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_filea pathlike object or string representing a file
Use an implicit mask file to threshold.
- global_calc_meana boolean
Use mean for global calculation. Mutually exclusive with inputs:
global_calc_omit
,global_calc_values
.- global_calc_omita boolean
Omit global calculation. Mutually exclusive with inputs:
global_calc_mean
,global_calc_values
.- global_calc_valuesa list of items which are a float
Omit global calculation. Mutually exclusive with inputs:
global_calc_mean
,global_calc_omit
.- global_normalization1 or 2 or 3
Global normalization None-1, Proportional-2, ANCOVA-3.
- matlab_cmda unicode string
Matlab command to use.
- mfilea boolean
Run m-code using m-file. (Nipype default value:
True
)- no_grand_mean_scalinga boolean
Do not perform grand mean scaling.
- pathsa list of items which are a pathlike object or string representing a directory
Paths to add to matlabpath.
- spm_mat_dira pathlike object or string representing an existing directory
Directory to store SPM.mat file (opt).
- threshold_mask_absolutea float
Use an absolute threshold. Mutually exclusive with inputs:
threshold_mask_none
,threshold_mask_relative
.- threshold_mask_nonea boolean
Do not use threshold masking. Mutually exclusive with inputs:
threshold_mask_absolute
,threshold_mask_relative
.- threshold_mask_relativea float
Threshold using a proportion of the global value. Mutually exclusive with inputs:
threshold_mask_absolute
,threshold_mask_none
.- use_implicit_thresholda boolean
Use implicit mask NaNs or zeros to threshold.
- use_mcra boolean
Run m-code using SPM MCR.
- use_v8structa boolean
Generate SPM8 and higher compatible jobs. (Nipype default value:
True
)
- spm_mat_filea pathlike object or string representing an existing file
SPM mat file.
Level1Design¶
Bases: SPMCommand
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.inputs.flags = {'mthresh': 0.4} >>> level1design.run()
- basesa dictionary with keys which are ‘hrf’ or ‘fourier’ or ‘fourier_han’ or ‘gamma’ or ‘fir’ and with values which are any value
Dictionary names of the basis function to parameters:
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, or fir:
length – (int) Post-stimulus window length (in seconds)
order – (int) Number of basis functions
- interscan_intervala float
Interscan interval in secs.
- session_infoany value
Session specific information generated by
modelgen.SpecifyModel
.- timing_units‘secs’ or ‘scans’
Units for specification of onsets.
- factor_infoa 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).
- flagsa dictionary with keys which are any value and with values which are any value
Additional arguments to the job, e.g., a common SPM operation is to modify the default masking threshold (mthresh).
- global_intensity_normalization‘none’ or ‘scaling’
Global intensity normalization - scaling or none.
- mask_imagea pathlike object or string representing an existing file
Image for explicitly masking the analysis.
- mask_threshold‘-Inf’ or a float
Thresholding for the mask. (Nipype default value:
-Inf
)- matlab_cmda unicode string
Matlab command to use.
- mfilea boolean
Run m-code using m-file. (Nipype default value:
True
)- microtime_onseta float
The onset/time-bin in seconds for alignment (opt).
- microtime_resolutionan 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.
- pathsa list of items which are a pathlike object or string representing a directory
Paths to add to matlabpath.
- spm_mat_dira pathlike object or string representing an existing directory
Directory to store SPM.mat file (opt).
- use_mcra boolean
Run m-code using SPM MCR.
- use_v8structa boolean
Generate SPM8 and higher compatible jobs. (Nipype default value:
True
)- volterra_expansion_order1 or 2
Model interactions - no:1, yes:2.
- spm_mat_filea pathlike object or string representing an existing file
SPM mat file.
MultipleRegressionDesign¶
Bases: FactorialDesign
Create SPM design for multiple regression
Examples
>>> mreg = MultipleRegressionDesign() >>> mreg.inputs.in_files = ['cont1.nii','cont2.nii'] >>> mreg.run()
- in_filesa list of at least 2 items which are a pathlike object or string representing an existing file
List of files.
- covariatesa 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_filea pathlike object or string representing a file
Use an implicit mask file to threshold.
- global_calc_meana boolean
Use mean for global calculation. Mutually exclusive with inputs:
global_calc_omit
,global_calc_values
.- global_calc_omita boolean
Omit global calculation. Mutually exclusive with inputs:
global_calc_mean
,global_calc_values
.- global_calc_valuesa list of items which are a float
Omit global calculation. Mutually exclusive with inputs:
global_calc_mean
,global_calc_omit
.- global_normalization1 or 2 or 3
Global normalization None-1, Proportional-2, ANCOVA-3.
- include_intercepta boolean
Include intercept in design. (Nipype default value:
True
)- matlab_cmda unicode string
Matlab command to use.
- mfilea boolean
Run m-code using m-file. (Nipype default value:
True
)- no_grand_mean_scalinga boolean
Do not perform grand mean scaling.
- pathsa list of items which are a pathlike object or string representing a directory
Paths to add to matlabpath.
- spm_mat_dira pathlike object or string representing an existing directory
Directory to store SPM.mat file (opt).
- threshold_mask_absolutea float
Use an absolute threshold. Mutually exclusive with inputs:
threshold_mask_none
,threshold_mask_relative
.- threshold_mask_nonea boolean
Do not use threshold masking. Mutually exclusive with inputs:
threshold_mask_absolute
,threshold_mask_relative
.- threshold_mask_relativea float
Threshold using a proportion of the global value. Mutually exclusive with inputs:
threshold_mask_absolute
,threshold_mask_none
.- use_implicit_thresholda boolean
Use implicit mask NaNs or zeros to threshold.
- use_mcra boolean
Run m-code using SPM MCR.
- use_v8structa boolean
Generate SPM8 and higher compatible jobs. (Nipype default value:
True
)- user_covariatesa 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}.
- spm_mat_filea pathlike object or string representing an existing file
SPM mat file.
OneSampleTTestDesign¶
Bases: FactorialDesign
Create SPM design for one sample t-test
Examples
>>> ttest = OneSampleTTestDesign() >>> ttest.inputs.in_files = ['cont1.nii', 'cont2.nii'] >>> ttest.run()
- in_filesa list of at least 2 items which are a pathlike object or string representing an existing file
Input files.
- covariatesa 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_filea pathlike object or string representing a file
Use an implicit mask file to threshold.
- global_calc_meana boolean
Use mean for global calculation. Mutually exclusive with inputs:
global_calc_omit
,global_calc_values
.- global_calc_omita boolean
Omit global calculation. Mutually exclusive with inputs:
global_calc_mean
,global_calc_values
.- global_calc_valuesa list of items which are a float
Omit global calculation. Mutually exclusive with inputs:
global_calc_mean
,global_calc_omit
.- global_normalization1 or 2 or 3
Global normalization None-1, Proportional-2, ANCOVA-3.
- matlab_cmda unicode string
Matlab command to use.
- mfilea boolean
Run m-code using m-file. (Nipype default value:
True
)- no_grand_mean_scalinga boolean
Do not perform grand mean scaling.
- pathsa list of items which are a pathlike object or string representing a directory
Paths to add to matlabpath.
- spm_mat_dira pathlike object or string representing an existing directory
Directory to store SPM.mat file (opt).
- threshold_mask_absolutea float
Use an absolute threshold. Mutually exclusive with inputs:
threshold_mask_none
,threshold_mask_relative
.- threshold_mask_nonea boolean
Do not use threshold masking. Mutually exclusive with inputs:
threshold_mask_absolute
,threshold_mask_relative
.- threshold_mask_relativea float
Threshold using a proportion of the global value. Mutually exclusive with inputs:
threshold_mask_absolute
,threshold_mask_none
.- use_implicit_thresholda boolean
Use implicit mask NaNs or zeros to threshold.
- use_mcra boolean
Run m-code using SPM MCR.
- use_v8structa boolean
Generate SPM8 and higher compatible jobs. (Nipype default value:
True
)
- spm_mat_filea pathlike object or string representing an existing file
SPM mat file.
PairedTTestDesign¶
Bases: FactorialDesign
Create SPM design for paired t-test
Examples
>>> pttest = PairedTTestDesign() >>> pttest.inputs.paired_files = [['cont1.nii','cont1a.nii'],['cont2.nii','cont2a.nii']] >>> pttest.run()
- paired_filesa list of at least 2 items which are a list of from 2 to 2 items which are a pathlike object or string representing an existing file
List of paired files.
- ancovaa boolean
Specify ancova-by-factor regressors.
- covariatesa 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_filea pathlike object or string representing a file
Use an implicit mask file to threshold.
- global_calc_meana boolean
Use mean for global calculation. Mutually exclusive with inputs:
global_calc_omit
,global_calc_values
.- global_calc_omita boolean
Omit global calculation. Mutually exclusive with inputs:
global_calc_mean
,global_calc_values
.- global_calc_valuesa list of items which are a float
Omit global calculation. Mutually exclusive with inputs:
global_calc_mean
,global_calc_omit
.- global_normalization1 or 2 or 3
Global normalization None-1, Proportional-2, ANCOVA-3.
- grand_mean_scalinga boolean
Perform grand mean scaling.
- matlab_cmda unicode string
Matlab command to use.
- mfilea boolean
Run m-code using m-file. (Nipype default value:
True
)- no_grand_mean_scalinga boolean
Do not perform grand mean scaling.
- pathsa list of items which are a pathlike object or string representing a directory
Paths to add to matlabpath.
- spm_mat_dira pathlike object or string representing an existing directory
Directory to store SPM.mat file (opt).
- threshold_mask_absolutea float
Use an absolute threshold. Mutually exclusive with inputs:
threshold_mask_none
,threshold_mask_relative
.- threshold_mask_nonea boolean
Do not use threshold masking. Mutually exclusive with inputs:
threshold_mask_absolute
,threshold_mask_relative
.- threshold_mask_relativea float
Threshold using a proportion of the global value. Mutually exclusive with inputs:
threshold_mask_absolute
,threshold_mask_none
.- use_implicit_thresholda boolean
Use implicit mask NaNs or zeros to threshold.
- use_mcra boolean
Run m-code using SPM MCR.
- use_v8structa boolean
Generate SPM8 and higher compatible jobs. (Nipype default value:
True
)
- spm_mat_filea pathlike object or string representing an existing file
SPM mat file.
Threshold¶
Bases: SPMCommand
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()
- contrast_indexan integer (int or long)
Which contrast in the SPM.mat to use.
- spm_mat_filea pathlike object or string representing an existing file
Absolute path to SPM.mat.
- stat_imagea pathlike object or string representing an existing file
Stat image.
- extent_fdr_p_thresholda float
P threshold on FDR corrected cluster size probabilities. (Nipype default value:
0.05
)- extent_thresholdan integer (int or long)
Minimum cluster size in voxels. (Nipype default value:
0
)- force_activationa boolean
In case no clusters survive the topological inference step this will pick a culster with the highes sum of t-values. Use with care. (Nipype default value:
False
)- height_thresholda float
Value for initial thresholding (defining clusters). (Nipype default value:
0.05
)- height_threshold_type‘p-value’ or ‘stat’
Is the cluster forming threshold a stat value or p-value?. (Nipype default value:
p-value
)- matlab_cmda unicode string
Matlab command to use.
- mfilea boolean
Run m-code using m-file. (Nipype default value:
True
)- pathsa list of items which are a pathlike object or string representing a directory
Paths to add to matlabpath.
- use_fwe_correctiona boolean
Whether to use FWE (Bonferroni) correction for initial threshold (height_threshold_type has to be set to p-value). (Nipype default value:
True
)- use_mcra boolean
Run m-code using SPM MCR.
- use_topo_fdra boolean
Whether to use FDR over cluster extent probabilities. (Nipype default value:
True
)- use_v8structa boolean
Generate SPM8 and higher compatible jobs. (Nipype default value:
True
)activation_forced : a boolean cluster_forming_thr : a float n_clusters : an integer (int or long) pre_topo_fdr_map : a pathlike object or string representing an existing file pre_topo_n_clusters : an integer (int or long) thresholded_map : a pathlike object or string representing an existing file
Threshold.
aggregate_outputs
(runtime=None)¶Collate expected outputs and apply output traits validation.
ThresholdStatistics¶
Bases: SPMCommand
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()
- contrast_indexan integer (int or long)
Which contrast in the SPM.mat to use.
- height_thresholda float
Stat value for initial thresholding (defining clusters).
- spm_mat_filea pathlike object or string representing an existing file
Absolute path to SPM.mat.
- stat_imagea pathlike object or string representing an existing file
Stat image.
- extent_thresholdan integer (int or long)
Minimum cluster size in voxels. (Nipype default value:
0
)- matlab_cmda unicode string
Matlab command to use.
- mfilea boolean
Run m-code using m-file. (Nipype default value:
True
)- pathsa list of items which are a pathlike object or string representing a directory
Paths to add to matlabpath.
- use_mcra boolean
Run m-code using SPM MCR.
- use_v8structa boolean
Generate SPM8 and higher compatible jobs. (Nipype default value:
True
)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
ThresholdStatistics.
aggregate_outputs
(runtime=None, needed_outputs=None)¶Collate expected outputs and apply output traits validation.
TwoSampleTTestDesign¶
Bases: FactorialDesign
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()
- group1_filesa list of at least 2 items which are a pathlike object or string representing an existing file
Group 1 input files.
- group2_filesa list of at least 2 items which are a pathlike object or string representing an existing file
Group 2 input files.
- covariatesa 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}.
- dependenta boolean
Are the measurements dependent between levels.
- explicit_mask_filea pathlike object or string representing a file
Use an implicit mask file to threshold.
- global_calc_meana boolean
Use mean for global calculation. Mutually exclusive with inputs:
global_calc_omit
,global_calc_values
.- global_calc_omita boolean
Omit global calculation. Mutually exclusive with inputs:
global_calc_mean
,global_calc_values
.- global_calc_valuesa list of items which are a float
Omit global calculation. Mutually exclusive with inputs:
global_calc_mean
,global_calc_omit
.- global_normalization1 or 2 or 3
Global normalization None-1, Proportional-2, ANCOVA-3.
- matlab_cmda unicode string
Matlab command to use.
- mfilea boolean
Run m-code using m-file. (Nipype default value:
True
)- no_grand_mean_scalinga boolean
Do not perform grand mean scaling.
- pathsa list of items which are a pathlike object or string representing a directory
Paths to add to matlabpath.
- spm_mat_dira pathlike object or string representing an existing directory
Directory to store SPM.mat file (opt).
- threshold_mask_absolutea float
Use an absolute threshold. Mutually exclusive with inputs:
threshold_mask_none
,threshold_mask_relative
.- threshold_mask_nonea boolean
Do not use threshold masking. Mutually exclusive with inputs:
threshold_mask_absolute
,threshold_mask_relative
.- threshold_mask_relativea float
Threshold using a proportion of the global value. Mutually exclusive with inputs:
threshold_mask_absolute
,threshold_mask_none
.- unequal_variancea boolean
Are the variances equal or unequal between groups.
- use_implicit_thresholda boolean
Use implicit mask NaNs or zeros to threshold.
- use_mcra boolean
Run m-code using SPM MCR.
- use_v8structa boolean
Generate SPM8 and higher compatible jobs. (Nipype default value:
True
)
- spm_mat_filea pathlike object or string representing an existing file
SPM mat file.