mia_processes.bricks.stat.spm package

Statistical calculations from spm.

Submodules

mia_processes.bricks.stat.spm.model module

The library for the SPM fMRI statistical analysis of the mia_processes package.

The purpose of this module is to customise the main spm statistical analysis bricks provided by nipype and to correct some things that do not work directly in populse_mia.

Contains:
Class:

  • EstimateContrast

  • EstimateModel

  • Level1Design

  • MultipleRegressionDesign

  • OneSampleTTestDesign

  • PairedTTestDesign

  • TwoSampleTTestDesign

class mia_processes.bricks.stat.spm.model.EstimateContrast[source]

Bases: ProcessMIA

Estimate contrasts of interest

Please, see the complete documentation for the EstimateContrast brick in the mia_processes website

Note

  • Type ‘EstimateContrast.help()’ for a full description of this process parameters.

  • Type ‘<EstimateContrast>.get_input_spec()’ for a full description of this process input trait types.

  • Type ‘<EstimateContrast>.get_output_spec()’ for a full description of this process output trait types.

__init__()[source]

Dedicated to the attributes initialisation / instantiation.

The input and output plugs are defined here. The special ‘self.requirement’ attribute (optional) is used to define the third-party products necessary for the running of the brick.

list_outputs(is_plugged=None, iteration=False)[source]

Dedicated to the initialisation step of the brick.

The main objective of this method is to produce the outputs of the bricks (self.outputs) and the associated tags (self.inheritance_dic), if defined here. In order not to include an output in the database, this output must be a value of the optional key ‘notInDb’ of the self.outputs dictionary. To work properly this method must return self.make_initResult() object.

Parameters:

  • is_plugged – the state, linked or not, of the plugs.

  • iteration – the state, iterative or not, of the process.

Returns:

a dictionary with requirement, outputs and inheritance_dict.

run_process_mia()[source]

Dedicated to the process launch step of the brick.

class mia_processes.bricks.stat.spm.model.EstimateModel[source]

Bases: ProcessMIA

Estimation of model parameters using classical method (ReML - Restricted Maximum Likelihood)

Please, see the complete documentation for the EstimateModel brick in the mia_processes website

Note

  • Type ‘EstimateModel.help()’ for a full description of this process parameters.

  • Type ‘<EstimateModel>.get_input_spec()’ for a full description of this process input trait types.

  • Type ‘<EstimateModel>.get_output_spec()’ for a full description of this process output trait types.

__init__()[source]

Dedicated to the attributes initialisation / instantiation.

The input and output plugs are defined here. The special ‘self.requirement’ attribute (optional) is used to define the third-party products necessary for the running of the brick.

list_outputs(is_plugged=None, iteration=False)[source]

Dedicated to the initialisation step of the brick.

The main objective of this method is to produce the outputs of the bricks (self.outputs) and the associated tags (self.inheritance_dict), if defined here. In order not to include an output in the database, this output must be a value of the optional key ‘notInDb’ of the self.outputs dictionary. To work properly this method must return self.make_initResult() object.

Parameters:

  • is_plugged – the state, linked or not, of the plugs.

  • iteration – the state, iterative or not, of the process.

Returns:

a dictionary with requirement, outputs and inheritance_dict.

run_process_mia()[source]

Dedicated to the process launch step of the brick.

class mia_processes.bricks.stat.spm.model.Level1Design[source]

Bases: ProcessMIA

Specification of the GLM design matrix, fMRI data files and filtering

Please, see the complete documentation for the Level1Design brick in the mia_processes website

Note

  • Type ‘Level1Design.help()’ for a full description of this process parameters.

  • Type ‘<Level1Design>.get_input_spec()’ for a full description of this process input trait types.

  • Type ‘<Level1Design>.get_output_spec()’ for a full description of this process output trait types.

__init__()[source]

Dedicated to the attributes initialisation / instantiation.

The input and output plugs are defined here. The special ‘self.requirement’ attribute (optional) is used to define the third-party products necessary for the running of the brick.

list_outputs(is_plugged=None, iteration=False)[source]

Dedicated to the initialisation step of the brick.

The main objective of this method is to produce the outputs of the bricks (self.outputs) and the associated tags. In order not to include an output in the database, the name of the plug related to this output must be an element of the list corresponding to the value of the optional key “notInDb” of the self.outputs dictionary. To work properly this method must return self.make_initResult() object.

Parameters:

  • is_plugged – the state, linked or not, of the plugs.

  • iteration – the state, iterative or not, of the process.

Returns:

a dictionary with requirement, outputs and inheritance_dict.

run_process_mia()[source]

Dedicated to the process launch step of the brick.

class mia_processes.bricks.stat.spm.model.MultipleRegressionDesign[source]

Bases: ProcessMIA

Create SPM design for multiple regression

Please, see the complete documentation for the MultipleRegressionDesign brick in the mia_processes website

Note

  • Type ‘MultipleRegressionDesign.help()’ for a full description of this process parameters.

  • Type ‘<MultipleRegressionDesign>.get_input_spec()’ for a full description of this process input trait types.

  • Type ‘<MultipleRegressionDesign>.get_output_spec()’ for a full description of this process output trait types.

__init__()[source]

Dedicated to the attributes initialisation / instantiation.

The input and output plugs are defined here. The special ‘self.requirement’ attribute (optional) is used to define the third-party products necessary for the running of the brick.

list_outputs(is_plugged=None, iteration=False)[source]

Dedicated to the initialisation step of the brick.

The main objective of this method is to produce the outputs of the bricks (self.outputs) and the associated tags. In order not to include an output in the database, the name of the plug related to this output must be an element of the list corresponding to the value of the optional key “notInDb” of the self.outputs dictionary. To work properly this method must return self.make_initResult() object.

Parameters:

  • is_plugged – the state, linked or not, of the plugs.

  • iteration – the state, iterative or not, of the process.

Returns:

a dictionary with requirement, outputs and inheritance_dict.

run_process_mia()[source]

Dedicated to the process launch step of the brick.

class mia_processes.bricks.stat.spm.model.OneSampleTTestDesign[source]

Bases: ProcessMIA

Create SPM design for one sample t-test

Please, see the complete documentation for the OneSampleTTestDesign brick in the mia_processes website

Note

  • Type ‘OneSampleTTestDesign.help()’ for a full description of this process parameters.

  • Type ‘<OneSampleTTestDesign>.get_input_spec()’ for a full description of this process input trait types.

  • Type ‘<OneSampleTTestDesign>.get_output_spec()’ for a full description of this process output trait types.

__init__()[source]

Dedicated to the attributes initialisation / instantiation.

The input and output plugs are defined here. The special ‘self.requirement’ attribute (optional) is used to define the third-party products necessary for the running of the brick.

list_outputs(is_plugged=None, iteration=False)[source]

Dedicated to the initialisation step of the brick.

The main objective of this method is to produce the outputs of the bricks (self.outputs) and the associated tags. In order not to include an output in the database, the name of the plug related to this output must be an element of the list corresponding to the value of the optional key “notInDb” of the self.outputs dictionary. To work properly this method must return self.make_initResult() object.

Parameters:

  • is_plugged – the state, linked or not, of the plugs.

  • iteration – the state, iterative or not, of the process.

Returns:

a dictionary with requirement, outputs and inheritance_dict.

run_process_mia()[source]

Dedicated to the process launch step of the brick.

class mia_processes.bricks.stat.spm.model.PairedTTestDesign[source]

Bases: ProcessMIA

Create SPM design for one sample t-test

Please, see the complete documentation for the OneSampleTTestDesign brick in the mia_processes website

Note

  • Type ‘PairedTTestDesign.help()’ for a full description of this process parameters.

  • Type ‘<PairedTTestDesign>.get_input_spec()’ for a full description of this process input trait types.

  • Type ‘<PairedTTestDesign>.get_output_spec()’ for a full description of this process output trait types.

__init__()[source]

Dedicated to the attributes initialisation / instantiation.

The input and output plugs are defined here. The special ‘self.requirement’ attribute (optional) is used to define the third-party products necessary for the running of the brick.

list_outputs(is_plugged=None, iteration=False)[source]

Dedicated to the initialisation step of the brick.

The main objective of this method is to produce the outputs of the bricks (self.outputs) and the associated tags. In order not to include an output in the database, the name of the plug related to this output must be an element of the list corresponding to the value of the optional key “notInDb” of the self.outputs dictionary. To work properly this method must return self.make_initResult() object.

Parameters:

  • is_plugged – the state, linked or not, of the plugs.

  • iteration – the state, iterative or not, of the process.

Returns:

a dictionary with requirement, outputs and inheritance_dict.

run_process_mia()[source]

Dedicated to the process launch step of the brick.

class mia_processes.bricks.stat.spm.model.TwoSampleTTestDesign[source]

Bases: ProcessMIA

Create SPM design for two sample t-test

Please, see the complete documentation for the TwoSampleTTestDesign brick in the mia_processes website

Note

  • Type ‘TwoSampleTTestDesign.help()’ for a full description of this process parameters.

  • Type ‘<TwoSampleTTestDesign>.get_input_spec()’ for a full description of this process input trait types.

  • Type ‘<TwoSampleTTestDesign>.get_output_spec()’ for a full description of this process output trait types.

__init__()[source]

Dedicated to the attributes initialisation / instantiation.

The input and output plugs are defined here. The special ‘self.requirement’ attribute (optional) is used to define the third-party products necessary for the running of the brick.

list_outputs(is_plugged=None, iteration=False)[source]

Dedicated to the initialisation step of the brick.

The main objective of this method is to produce the outputs of the bricks (self.outputs) and the associated tags. In order not to include an output in the database, the name of the plug related to this output must be an element of the list corresponding to the value of the optional key “notInDb” of the self.outputs dictionary. To work properly this method must return self.make_initResult() object.

Parameters:

  • is_plugged – the state, linked or not, of the plugs.

  • iteration – the state, iterative or not, of the process.

Returns:

a dictionary with requirement, outputs and inheritance_dict.

run_process_mia()[source]

Dedicated to the process launch step of the brick.

mia_processes.bricks.stat.spm.model.get_covariates(names, vectors, centerings, interactions=None)[source]

Generate the covariates list containing dictionaries with the following key : name, vector, interaction, centering