Note

This page is a reference documentation. It only explains the function signature, and not how to use it. Please refer to the user guide for the big picture.

nilearn.glm.second_level.non_parametric_inference#

nilearn.glm.second_level.non_parametric_inference(second_level_input, confounds=None, design_matrix=None, second_level_contrast=None, first_level_contrast=None, mask=None, smoothing_fwhm=None, model_intercept=True, n_perm=10000, two_sided_test=False, random_state=None, n_jobs=1, verbose=0, threshold=None, tfce=False)[source]#

Generate p-values corresponding to the contrasts provided based on permutation testing.

This function is a light wrapper around permuted_ols, with additional steps to ensure compatibility with the second_level module.

Parameters:

second_level_inputlist of FirstLevelModel objects or pandas.DataFrame or list of Niimg-like objects or pandas.Series of Niimg-like objects.

Giving FirstLevelModel objects will allow to easily compute the second level contrast of arbitrary first level contrasts thanks to the first_level_contrast argument of compute_contrast. Effect size images will be computed for each model to contrast at the second level.
If a DataFrame, then it has to contain subject_label, map_name and effects_map_path. It can contain multiple maps that would be selected during contrast estimation with the argument first_level_contrast of compute_contrast. The DataFrame will be sorted based on the subject_label column to avoid order inconsistencies when extracting the maps. So the rows of the automatically computed design matrix, if not provided, will correspond to the sorted subject_label column.
If a list of Niimg-like objects then this is taken literally as Y for the model fit and design_matrix must be provided.

confoundspandas.DataFrame or None, optional

Must contain a subject_label column. All other columns are considered as confounds and included in the model. If design_matrix is provided then this argument is ignored. The resulting second level design matrix uses the same column names as in the given DataFrame for confounds. At least two columns are expected, subject_label and at least one confound.

design_matrixpandas.DataFrame or None, optional

Design matrix to fit the GLM. The number of rows in the design matrix must agree with the number of maps derived from second_level_input. Ensure that the order of maps given by a second_level_input list of Niimgs matches the order of the rows in the design matrix.

second_level_contraststr or numpy.ndarray of shape(n_col), optional

Where n_col is the number of columns of the design matrix. The string can be a formula compatible with pandas.DataFrame.eval. Basically one can use the name of the conditions as they appear in the design matrix of the fitted model combined with operators +- and combined with numbers with operators +-*/. The default None is accepted if the design matrix has a single column, in which case the only possible contrast array((1)) is applied; when the design matrix has multiple columns, an error is raised.

first_level_contraststr, optional

In case a pandas DataFrame was provided as second_level_input this is the map name to extract from the pandas dataframe map_name column. It has to be a ‘t’ contrast.

New in version 0.9.0.

maskNiimg-like, NiftiMasker or MultiNiftiMasker object, optional

Mask to be used on data. If an instance of masker is passed, then its mask will be used. If no mask is given, it will be computed automatically by a MultiNiftiMasker with default parameters. Automatic mask computation assumes first level imgs have already been masked.

smoothing_fwhmfloat, optional.

If smoothing_fwhm is not None, it gives the full-width at half maximum in millimeters of the spatial smoothing to apply to the signal.

model_interceptbool, default=True

If True, a constant column is added to the confounding variates unless the tested variate is already the intercept.

n_permint, default=10000

Number of permutations to perform. Permutations are costly but the more are performed, the more precision one gets in the p-values estimation.

two_sided_testbool, default=False

If True, performs an unsigned t-test. Both positive and negative effects are considered; the null hypothesis is that the effect is zero.
If False, only positive effects are considered as relevant. The null hypothesis is that the effect is zero or negative.

random_stateint or RandomState, optional

Pseudo-random number generator state used for random sampling. Use this parameter to have the same permutations in each computing units.

n_jobsint, default=1

The number of CPUs to use to do the computation. -1 means ‘all CPUs’.

verboseint, default=0

Verbosity level (0 means no message).

thresholdNone or float, default=None

Cluster-forming threshold in p-scale. This is only used for cluster-level inference. If None, no cluster-level inference will be performed.

Warning

Performing cluster-level inference will increase the computation time of the permutation procedure.

New in version 0.9.2.

tfcebool, default=False

Whether to calculate TFCE as part of the permutation procedure or not. The TFCE calculation is implemented as described in Smith and Nichols[1].

Warning

Performing TFCE-based inference will increase the computation time of the permutation procedure considerably. The permutations may take multiple hours, depending on how many permutations are requested and how many jobs are performed in parallel.

New in version 0.9.2.

Returns:

neg_log10_vfwe_pvals_imgNifti1Image

The image which contains negative logarithm of the voxel-level FWER-corrected p-values.

Note

This is returned if threshold is None (the default).

outputsdict

Output images, organized in a dictionary. Each image is 3D/4D, with the potential fourth dimension corresponding to the regressors.

Note

This is returned if tfce is False or threshold is not None.

New in version 0.9.2.

Here are the keys:

key	description
t	T-statistics associated with the significance test of the n_regressors explanatory variates against the n_descriptors target variates.
logp_max_t	Negative log10 family-wise error rate-corrected p-values corrected based on the distribution of maximum t-statistics from permutations.
size	Cluster size values associated with the significance test of the n_regressors explanatory variates against the n_descriptors target variates. Returned only if `threshold` is not `None`.
logp_max_size	Negative log10 family-wise error rate-corrected p-values corrected based on the distribution of maximum cluster sizes from permutations. This map is generated through cluster-level methods, so the values in the map describe the significance of clusters, rather than individual voxels. Returned only if `threshold` is not `None`.
mass	Cluster mass values associated with the significance test of the n_regressors explanatory variates against the n_descriptors target variates. Returned only if `threshold` is not `None`.
logp_max_mass	Negative log10 family-wise error rate-corrected p-values corrected based on the distribution of maximum cluster masses from permutations. This map is generated through cluster-level methods, so the values in the map describe the significance of clusters, rather than individual voxels. Returned only if `threshold` is not `None`.
tfce	TFCE values associated with the significance test of the n_regressors explanatory variates against the n_descriptors target variates. Returned only if `tfce` is `True`.
logp_max_tfce	Negative log10 family-wise error rate-corrected p-values corrected based on the distribution of maximum TFCE values from permutations. Returned only if `tfce` is `True`.

Examples using `nilearn.glm.second_level.non_parametric_inference`#

Second-level fMRI model: one sample test

Example of generic design in second-level models

nilearn.glm.second_level.non_parametric_inference#

Examples using nilearn.glm.second_level.non_parametric_inference#

Examples using `nilearn.glm.second_level.non_parametric_inference`#