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.MultiSurfaceMasker

class nilearn.maskers.MultiSurfaceMasker(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, n_jobs=1, verbose=0, reports=True, cmap='inferno', clean_args=None)

Extract time-series from multiple SurfaceImage objects.

MultiSurfaceMasker is useful when dealing with image sets from multiple subjects.

Added in version 0.13.0dev.

Parameters:

mask_imgSurfaceImage or None, default=None

smoothing_fwhmfloat or int or None, 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.

standardizeany of: ‘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.

  Deprecated since Nilearn 0.10.1: This option will be removed in Nilearn version 0.14.0. Use zscore_sample instead.

• '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 int 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 or int or None, default=None

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

t_rfloat or int 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.

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).

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 clean called within the masker. Within clean, kwargs prefixed with 'butterworth__' will be passed to the Butterworth filter.

Attributes:

clean_args_dict

Keyword arguments to be passed to clean called within the masker. Within clean, kwargs prefixed with 'butterworth__' will be passed to the Butterworth filter.

mask_img_A 1D binary SurfaceImage

The mask of the data, or the one computed from imgs passed to fit. If a mask_img is passed at masker construction, then mask_img_ is the resulting binarized version of it where each vertex is True if all values across samples (for example across timepoints) is finite value different from 0.

memory_joblib memory cache

n_elements_int or None

number of vertices included in mask

__init__(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, n_jobs=1, verbose=0, reports=True, cmap='inferno', clean_args=None)

fit(imgs=None, y=None)

Prepare signal extraction from regions.

Parameters:

imgsSurfaceImage or list of SurfaceImage or tuple of SurfaceImage or None, default = None

Mesh and data for both hemispheres.

yNone

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

Returns:

SurfaceMasker object

fit_transform(imgs, y=None, confounds=None, sample_mask=None)

Fit to data, then transform it.

Parameters:

imgsImage object, or a list of Image objects

See Input and output: neuroimaging data representation. Data to be preprocessed

yNone

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

confoundslist of confounds, default=None

List of confounds (arrays, dataframes, str or path of files loadable into an array). As confounds are passed to nilearn.signal.clean, please see the related documentation for details about accepted types. Must be of same length than imgs.

sample_masklist of sample_mask, default=None

List of sample_mask (any type compatible with numpy-array indexing) to use for scrubbing outliers. Must be of same length as imgs. shape = (total number of scans - number of scans removed) for explicit index (for example, sample_mask=np.asarray([1, 2, 4])), or shape = (number of scans) for binary mask (for example, sample_mask=np.asarray([False, True, True, False, True])). Masks the images along the last dimension to perform scrubbing: for example to remove volumes with high motion and/or non-steady-state volumes. This parameter is passed to nilearn.signal.clean.

Added in Nilearn 0.8.0.

Returns:

signalslist of numpy.ndarray or numpy.ndarray

Signal for each voxel. Output shape for :

• 3D images: (number of elements,) array

• 4D images: (number of scans, number of elements) array

• list of 3D images: list of (number of elements,) array

• list of 4D images: list of (number of scans, number of elements) array

generate_report()

Generate a report for the SurfaceMasker.

Returns:

list(None) or HTMLReport

get_feature_names_out(input_features=None)

Get output feature names for transformation.

The feature names out will prefixed by the lowercased class name. For example, if the transformer outputs 3 features, then the feature names out are: [“class_name0”, “class_name1”, “class_name2”].

Parameters:

input_featuresarray-like of str or None, default=None

Only used to validate feature names with the names seen in fit.

Returns:

feature_names_outndarray of str objects

Transformed feature names.

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.

inverse_transform(signals)

Transform extracted signal back to surface object.

Parameters:

signals1D/2D numpy.ndarray

Extracted signal. If a 1D array is provided, then the shape should be (number of elements,). If a 2D array is provided, then the shape should be (number of scans, number of elements).

Returns:

imgSurfaceImage

Signal for each vertex projected on the mesh. Output shape for :

• 1D array : 1D SurfaceImage will be returned.

• 2D array : 2D SurfaceImage will be returned.

See Input and output: neuroimaging data representation.

set_fit_request(*, imgs='$UNCHANGED$')

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:

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

Metadata routing for imgs 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 the output container when "transform" is called.

Warning

This has not been implemented yet.

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='$UNCHANGED$', imgs='$UNCHANGED$', 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.

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

Metadata routing for imgs 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.

transform(imgs, confounds=None, sample_mask=None)

Apply mask, spatial and temporal preprocessing.

Parameters:

imgs :Image object, or a :obj:`list` of Image objects

See Input and output: neuroimaging data representation. Data to be preprocessed

confoundslist of confounds, default=None

List of confounds (arrays, dataframes, str or path of files loadable into an array). As confounds are passed to nilearn.signal.clean, please see the related documentation for details about accepted types. Must be of same length than imgs.

sample_masklist of sample_mask, default=None

List of sample_mask (any type compatible with numpy-array indexing) to use for scrubbing outliers. Must be of same length as imgs. shape = (total number of scans - number of scans removed) for explicit index (for example, sample_mask=np.asarray([1, 2, 4])), or shape = (number of scans) for binary mask (for example, sample_mask=np.asarray([False, True, True, False, True])). Masks the images along the last dimension to perform scrubbing: for example to remove volumes with high motion and/or non-steady-state volumes. This parameter is passed to nilearn.signal.clean.

Returns:

signalslist of numpy.ndarray or numpy.ndarray

Signal for each voxel. Output shape for :

• 3D images: (number of elements,) array

• 4D images: (number of scans, number of elements) array

• list of 3D images: list of (number of elements,) array

• list of 4D images: list of (number of scans, number of elements) array

transform_imgs(imgs_list, confounds=None, n_jobs=1, sample_mask=None)

Extract signals from a list of 4D niimgs.

Parameters:

imgslist of Niimg-like objects

See Input and output: neuroimaging data representation. Images to process.

confoundslist of confounds, default=None

List of confounds (arrays, dataframes, str or path of files loadable into an array). As confounds are passed to nilearn.signal.clean, please see the related documentation for details about accepted types. Must be of same length than imgs.

n_jobsint, default=1

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

sample_masklist of sample_mask, default=None

List of sample_mask (any type compatible with numpy-array indexing) to use for scrubbing outliers. Must be of same length as imgs. shape = (total number of scans - number of scans removed) for explicit index (for example, sample_mask=np.asarray([1, 2, 4])), or shape = (number of scans) for binary mask (for example, sample_mask=np.asarray([False, True, True, False, True])). Masks the images along the last dimension to perform scrubbing: for example to remove volumes with high motion and/or non-steady-state volumes. This parameter is passed to nilearn.signal.clean.

Returns:

signalslist of numpy.ndarray

Signal for each voxel. Output shape for :

• list of 3D images: list of (number of elements,) array

• list of 4D images: list of (number of scans, number of elements) array

transform_single_imgs(imgs, confounds=None, sample_mask=None)

Extract signals from fitted surface object.

Parameters:

imgsimgsSurfaceImage object or iterable of SurfaceImage

Images to process. Mesh and data for both hemispheres/parts.

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

This parameter is passed to nilearn.signal.clean. Please see the related documentation for details. shape: (number of scans, number of confounds)

sample_maskAny type compatible with numpy-array indexing, default=None

shape = (total number of scans - number of scans removed) for explicit index (for example, sample_mask=np.asarray([1, 2, 4])), or shape = (number of scans) for binary mask (for example, sample_mask=np.asarray([False, True, True, False, True])). Masks the images along the last dimension to perform scrubbing: for example to remove volumes with high motion and/or non-steady-state volumes. This parameter is passed to nilearn.signal.clean.

Returns:

signalsnumpy.ndarray, pandas.DataFrame or polars.DataFrame

Signal for each element.

Changed in Nilearn 0.13.0dev: Added set_output support.

The type of the output is determined by set_output(): see the scikit-learn documentation.

Output shape for :

• For Numpy outputs:

  • 1D images: (number of elements,)

  • 2D images: (number of scans, number of elements) array

• For DataFrame outputs:

  • 1D or 2D images: (number of scans, number of elements) array