Note

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

nilearn.connectome.ConnectivityMeasure

class nilearn.connectome.ConnectivityMeasure(cov_estimator=None, kind='covariance', vectorize=False, discard_diagonal=False, standardize=True)

A class that computes different kinds of functional connectivity matrices.

Added in version 0.2.

Parameters:

cov_estimatorestimator object, default=LedoitWolf(store_precision=False)

The covariance estimator. This implies that correlations are slightly shrunk towards zero compared to a maximum-likelihood estimate

kind{“covariance”, “correlation”, “partial correlation”, “tangent”, “precision”}, default=’covariance’

The matrix kind. For the use of “tangent” see Varoquaux et al.[1].

vectorizebool, default=False

If True, connectivity matrices are reshaped into 1D arrays and only their flattened lower triangular parts are returned.

discard_diagonalbool, default=False

If True, vectorized connectivity coefficients do not include the matrices diagonal elements. Used only when vectorize is set to True.

standardizebool, default=True

If standardize is True, the data are centered and normed: their mean is put to 0 and their variance is put to 1 in the time dimension.

Note

Added to control passing value to standardize of signal.clean to call new behavior since passing “zscore” or True (default) is deprecated. This parameter will be deprecated in version 0.13 and removed in version 0.15.

Attributes:

cov_estimator_estimator object, default=None

A new covariance estimator with the same parameters as cov_estimator. If None is passed, defaults to LedoitWolf(store_precision=False).

mean_numpy.ndarray

The mean connectivity matrix across subjects. For ‘tangent’ kind, it is the geometric mean of covariances (a group covariance matrix that captures information from both correlation and partial correlation matrices). For other values for “kind”, it is the mean of the corresponding matrices

whitening_numpy.ndarray

The inverted square-rooted geometric mean of the covariance matrices.

References

[1]

Gael Varoquaux, Flore Baronnet, Andreas Kleinschmidt, Pierre Fillard, and Bertrand Thirion. Detection of brain functional-connectivity difference in post-stroke patients using group-level covariance modeling. In Tianzi Jiang, Nassir Navab, Josien P. W. Pluim, and Max A. Viergever, editors, Medical image computing and computer-assisted intervention - MICCAI 2010, Lecture notes in computer science, 200–208. Berlin, Heidelberg, 2010. Springer. doi:10/cn2h9c.

__init__(cov_estimator=None, kind='covariance', vectorize=False, discard_diagonal=False, standardize=True)

fit(X, y=None)

Fit the covariance estimator to the given time series for each subject.

Parameters:

Xlist of numpy.ndarray, shape for each (n_samples, n_features)

The input subjects time series. The number of samples may differ from one subject to another.

Returns:

selfConnectivityMatrix instance

The object itself. Useful for chaining operations.

fit_transform(X, y=None, confounds=None)

Fit the covariance estimator to the given time series for each subject. Then apply transform to covariance matrices for the chosen kind.

Parameters:

Xlist of n_subjects numpy.ndarray with shapes (n_samples, n_features)

The input subjects time series. The number of samples may differ from one subject to another.

confoundsnp.ndarray with shape (n_samples) or (n_samples, n_confounds), or pandas DataFrame, optional

Confounds to be cleaned on the vectorized matrices. Only takes into effect when vetorize=True. This parameter is passed to signal.clean. Please see the related documentation for details.

Returns:

outputnumpy.ndarray, shape (n_subjects, n_features, n_features) or (n_subjects, n_features * (n_features + 1) / 2) if vectorize is set to True.

The transformed individual connectivities, as matrices or vectors. Vectors are cleaned when vectorize=True and confounds are provided.

transform(X, confounds=None)

Apply transform to covariances matrices to get the connectivity matrices for the chosen kind.

Parameters:

Xlist of n_subjects numpy.ndarray with shapes (n_samples, n_features)

The input subjects time series. The number of samples may differ from one subject to another.

confoundsnumpy.ndarray with shape (n_samples) or (n_samples, n_confounds), optional

Confounds to be cleaned on the vectorized matrices. Only takes into effect when vetorize=True. This parameter is passed to signal.clean. Please see the related documentation for details.

Returns:

outputnumpy.ndarray, shape (n_subjects, n_features, n_features) or (n_subjects, n_features * (n_features + 1) / 2) if vectorize is set to True.

The transformed individual connectivities, as matrices or vectors. Vectors are cleaned when vectorize=True and confounds are provided.

inverse_transform(connectivities, diagonal=None)

Return connectivity matrices from connectivities, vectorized or not.

If kind is ‘tangent’, the covariance matrices are reconstructed.

Parameters:

connectivitieslist of n_subjects numpy.ndarray with shapes (n_features, n_features) or (n_features * (n_features + 1) / 2,)

or ((n_features - 1) * n_features / 2,) Connectivities of each subject, vectorized or not.

diagonalnumpy.ndarray, shape (n_subjects, n_features), optional

The diagonals of the connectivity matrices.

Returns:

outputnumpy.ndarray, shape (n_subjects, n_features, n_features)

The corresponding connectivity matrices. If kind is ‘correlation’/ ‘partial correlation’, the correlation/partial correlation matrices are returned. If kind is ‘tangent’, the covariance matrices are reconstructed.

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest

A MetadataRequest encapsulating routing information.

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict

Parameter names mapped to their values.

set_inverse_transform_request(*, connectivities='$UNCHANGED$', diagonal='$UNCHANGED$')

Request metadata passed to the inverse_transform method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to inverse_transform if provided. The request is ignored if metadata is not provided.

• False: metadata is not requested and the meta-estimator will not pass it to inverse_transform.

• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

connectivitiesstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for connectivities parameter in inverse_transform.

diagonalstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for diagonal parameter in inverse_transform.

Returns:

selfobject

The updated object.

set_output(*, transform=None)

Set output container.

See Introducing the set_output API for an example on how to use the API.

Parameters:

transform{“default”, “pandas”}, default=None

Configure output of transform and fit_transform.

• “default”: Default output format of a transformer

• “pandas”: DataFrame output

• “polars”: Polars output

• None: Transform configuration is unchanged

Added in version 1.4: “polars” option was added.

Returns:

selfestimator instance

Estimator instance.

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict

Estimator parameters.

Returns:

selfestimator instance

Estimator instance.

set_transform_request(*, confounds='$UNCHANGED$')

Request metadata passed to the transform method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to transform if provided. The request is ignored if metadata is not provided.

• False: metadata is not requested and the meta-estimator will not pass it to transform.

• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

confoundsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for confounds parameter in transform.

Returns:

selfobject

The updated object.

Examples using nilearn.connectome.ConnectivityMeasure

Extracting signals of a probabilistic atlas of functional regions

Extracting signals of a probabilistic atlas of functional regions

Regions extraction using dictionary learning and functional connectomes

Regions extraction using dictionary learning and functional connectomes

Classification of age groups using functional connectivity

Classification of age groups using functional connectivity

Extracting signals from a brain parcellation

Extracting signals from a brain parcellation

Comparing connectomes on different reference atlases

Comparing connectomes on different reference atlases

Extract signals on spheres and plot a connectome

Extract signals on spheres and plot a connectome

Functional connectivity predicts age group

Functional connectivity predicts age group

A short demo of the surface images & maskers

A short demo of the surface images & maskers