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.maskers.SurfaceLabelsMasker

class nilearn.maskers.SurfaceLabelsMasker(labels_img=None, labels=None, background_label=0, mask_img=None, smoothing_fwhm=None, standardize=False, standardize_confounds=True, detrend=False, high_variance_confounds=False, low_pass=None, high_pass=None, t_r=None, memory=None, memory_level=1, verbose=0, reports=True, cmap='inferno', clean_args=None)[source]

Extract data from a SurfaceImage, averaging over atlas regions.

Added in version 0.11.0.

Parameters:
labels_imgSurfaceImage object

Region definitions, as one image of labels. The data for each hemisphere is of shape (n_vertices_per_hemisphere, n_regions).

labelslist of str, default=None

Full labels corresponding to the labels image. This is used to improve reporting quality if provided.

Warning

The labels must be consistent with the label values provided through labels_img.

background_labelint or float, default=0

Label used in labels_img to represent background.

Warning

This value must be consistent with label values and image provided.

mask_imgSurfaceImage object, optional

Mask to apply to labels_img before extracting signals. Defines the overall area of the brain to consider. The data for each hemisphere is of shape (n_vertices_per_hemisphere, n_regions).

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. This parameter is not implemented yet.

standardize{‘zscore_sample’, ‘zscore’, ‘psc’, True, False}, default=False

Strategy to standardize the signal:

  • 'zscore_sample': The signal is z-scored. Timeseries are shifted to zero mean and scaled to unit variance. Uses sample std.

  • 'zscore': The signal is z-scored. Timeseries are shifted to zero mean and scaled to unit variance. Uses population std by calling default numpy.std with N - ddof=0.

  • 'psc': Timeseries are shifted to zero mean value and scaled to percent signal change (as compared to original mean signal).

  • True: The signal is z-scored (same as option zscore). Timeseries are shifted to zero mean and scaled to unit variance.

  • False: Do not standardize the data.

standardize_confoundsbool, default=True

If set to True, the confounds are z-scored: their mean is put to 0 and their variance to 1 in the time dimension.

detrendbool, optional

Whether to detrend signals or not.

high_variance_confoundsbool, default=False

If True, high variance confounds are computed on provided image with nilearn.image.high_variance_confounds and default parameters and regressed out.

low_passfloat or None, default=None

Low cutoff frequency in Hertz. If specified, signals above this frequency will be filtered out. If None, no low-pass filtering will be performed.

high_passfloat, default=None

High cutoff frequency in Hertz. If specified, signals below this frequency will be filtered out.

t_rfloat or None, default=None

Repetition time, in seconds (sampling period). Set to None if not provided.

memoryNone, instance of joblib.Memory, str, or pathlib.Path

Used to cache the masking process. By default, no caching is done. If a str is given, it is the path to the caching directory.

memory_levelint, default=1

Rough estimator of the amount of memory used by caching. Higher value means more memory for caching. Zero means no caching.

verboseint, default=0

Verbosity level (0 means no message).

reportsbool, default=True

If set to True, data is saved in order to produce a report.

cmapmatplotlib.colors.Colormap, or str, optional

The colormap to use. Either a string which is a name of a matplotlib colormap, or a matplotlib colormap object. default=”inferno” Only relevant for the report figures.

clean_argsdict or None, default=None

Keyword arguments to be passed to nilearn.signal.clean called within the masker.

Attributes:
n_elements_int

The number of discrete values in the mask. This is equivalent to the number of unique values in the mask image, ignoring the background value.

__init__(labels_img=None, labels=None, background_label=0, mask_img=None, smoothing_fwhm=None, standardize=False, standardize_confounds=True, detrend=False, high_variance_confounds=False, low_pass=None, high_pass=None, t_r=None, memory=None, memory_level=1, verbose=0, reports=True, cmap='inferno', clean_args=None)[source]
fit(img=None, y=None)[source]

Prepare signal extraction from regions.

Parameters:
imgSurfaceImage object or None, default=None

This parameter is currently unused.

yNone

This parameter is unused. It is solely included for scikit-learn compatibility.

Returns:
SurfaceLabelsMasker object
transform(img, confounds=None, sample_mask=None)[source]

Extract signals from surface object.

Parameters:
imgSurfaceImage object or list of SurfaceImage or tuple of SurfaceImage

Mesh and data for both hemispheres.

confoundsnumpy.ndarray, str, pathlib.Path, pandas.DataFrame or list of confounds timeseries, default=None

Confounds to pass to nilearn.signal.clean.

sample_maskNone, Any type compatible with numpy-array indexing, or list of shape: (total number of scans - number of scans removed) for explicit index, or (number of scans) for binary mask, default=None

sample_mask to pass to nilearn.signal.clean.

Returns:
outputnumpy.ndarray

Signal for each element. shape: (img data shape, total number of vertices)

fit_transform(img, y=None, confounds=None, sample_mask=None)[source]

Prepare and perform signal extraction from regions.

Parameters:
imgSurfaceImage object or list of SurfaceImage or tuple of SurfaceImage

Mesh and data for both hemispheres.

yNone

This parameter is unused. It is solely included for scikit-learn compatibility.

confoundsnumpy.ndarray, str, pathlib.Path, pandas.DataFrame or list of confounds timeseries, default=None

Confounds to pass to nilearn.signal.clean.

sample_maskNone, Any type compatible with numpy-array indexing, or list of shape: (total number of scans - number of scans removed) for explicit index, or (number of scans) for binary mask, default=None

sample_mask to pass to nilearn.signal.clean.

Returns:
numpy.ndarray

Signal for each element. shape: (img data shape, total number of vertices)

inverse_transform(signals)[source]

Transform extracted signal back to surface image.

Parameters:
signalsnumpy.ndarray

Extracted signal for each region. If a 1D array is provided, then the shape of each hemisphere’s data should be (number of elements,) in the returned surface image. If a 2D array is provided, then it would be (number of scans, number of elements).

Returns:
SurfaceImage object

Mesh and data for both hemispheres.

generate_report()[source]

Generate a report.

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_fit_request(*, img='$UNCHANGED$')

Request metadata passed to the fit 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 fit 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 fit.

  • 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:
imgstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for img parameter in fit.

Returns:
selfobject

The updated object.

set_inverse_transform_request(*, signals='$UNCHANGED$')

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

Metadata routing for signals 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='$UNCHANGED$', img='$UNCHANGED$', sample_mask='$UNCHANGED$')

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.

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

Metadata routing for img parameter in transform.

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

Metadata routing for sample_mask parameter in transform.

Returns:
selfobject

The updated object.

Examples using nilearn.maskers.SurfaceLabelsMasker

Seed-based connectivity on the surface

Seed-based connectivity on the surface

A short demo of the surface images & maskers

A short demo of the surface images & maskers