nipype.interfaces.nipy.model module

EstimateContrast

Link to code

Bases: NipyBaseInterface

Estimate contrast of a fitted model.

Mandatory Inputs:

  • axis (any value)

  • beta (a pathlike object or string representing an existing file) – Beta coefficients of the fitted model.

  • constants (any value)

  • 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.

  • dof (any value) – Degrees of freedom.

  • nvbeta (any value)

  • reg_names (a list of items which are any value)

  • s2 (a pathlike object or string representing an existing file) – Squared variance of the residuals.

Optional Inputs:

mask (a pathlike object or string representing an existing file)

Outputs:

  • p_maps (a list of items which are a pathlike object or string representing an existing file)

  • stat_maps (a list of items which are a pathlike object or string representing an existing file)

  • z_maps (a list of items which are a pathlike object or string representing an existing file)

FitGLM

Link to code

Bases: NipyBaseInterface

Fit GLM model based on the specified design. Supports only single or concatenated runs.

Mandatory Inputs:

  • TR (a float)

  • session_info (a list of from 1 to 1 items which are any value) – Session specific information generated by modelgen.SpecifyModel, FitGLM does not support multiple runs unless they are concatenated (see SpecifyModel options).

Optional Inputs:

  • drift_model (‘Cosine’ or ‘Polynomial’ or ‘Blank’) – String that specifies the desired drift model, to be chosen among ‘Polynomial’, ‘Cosine’, ‘Blank’. (Nipype default value: Cosine)

  • hrf_model (‘Canonical’ or ‘Canonical With Derivative’ or ‘FIR’) – That specifies the hemodynamic response function it can be ‘Canonical’, ‘Canonical With Derivative’ or ‘FIR’. (Nipype default value: Canonical)

  • mask (a pathlike object or string representing an existing file) – Restrict the fitting only to the region defined by this mask.

  • method (‘kalman’ or ‘ols’) – Method to fit the model, ols or kalma; kalman is more time consuming but it supports autoregressive model. (Nipype default value: kalman)

  • model (‘ar1’ or ‘spherical’) – Autoregressive mode is available only for the kalman method. (Nipype default value: ar1)

  • normalize_design_matrix (a boolean) – Normalize (zscore) the regressors before fitting. (Nipype default value: False)

  • plot_design_matrix (a boolean) – (Nipype default value: False)

  • save_residuals (a boolean) – (Nipype default value: False)

Outputs:

  • a (a pathlike object or string representing an existing file)

  • axis (any value)

  • beta (a pathlike object or string representing an existing file)

  • constants (any value)

  • dof (any value)

  • nvbeta (any value)

  • reg_names (a list of items which are any value)

  • residuals (a pathlike object or string representing a file)

  • s2 (a pathlike object or string representing an existing file)