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.NiftiSpheresMasker

class nilearn.maskers.NiftiSpheresMasker(seeds=None, radius=None, mask_img=None, allow_overlap=False, smoothing_fwhm=None, standardize=False, standardize_confounds=True, high_variance_confounds=False, detrend=False, low_pass=None, high_pass=None, t_r=None, dtype=None, memory=None, memory_level=1, verbose=0, reports=True, clean_args=None, **kwargs)

Class for masking of Niimg-like objects using seeds.

NiftiSpheresMasker is useful when data from given seeds should be extracted.

Use case: summarize brain signals from seeds that were obtained from prior knowledge.

Parameters:

seedslist of triplet of coordinates in native space or None, default=None

Seed definitions. List of coordinates of the seeds in the same space as the images (typically MNI or TAL).

radiusfloat, default=None

Indicates, in millimeters, the radius for the sphere around the seed. By default signal is extracted on a single voxel.

mask_imgNiimg-like object, default=None

See Input and output: neuroimaging data representation. Mask to apply to regions before extracting signals.

allow_overlapbool, default=False

If False, an error is raised if the maps overlaps (ie at least two maps have a non-zero value for the same voxel).

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.

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.

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.

detrendbool, optional

Whether to detrend signals or not.

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.

dtypedtype like, “auto” or None, default=None

Data type toward which the data should be converted. If “auto”, the data will be converted to int32 if dtype is discrete and float32 if it is continuous.

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).

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. .. versionadded:: 0.11.2dev

kwargsdict

Keyword arguments to be passed to functions called within the masker. Kwargs prefixed with ‘clean__’ will be passed to clean. Within clean, kwargs prefixed with ‘butterworth__’ will be passed to the Butterworth filter (i.e., clean__butterworth__).

Deprecated since version 0.11.2dev.

Use clean_args instead!

It is recommended to pass parameters to use for data cleaning via dict to the clean_args parameter.

Passing parameters via “kwargs” is mutually exclusive with passing cleaning parameters via clean_args.

Attributes:

mask_img_A 3D binary nibabel.nifti1.Nifti1Image or None.

The mask of the data. If no mask_img was passed at masker construction, then mask_img_ is None, otherwise is the resulting binarized version of mask_img where each voxel is True if all values across samples (for example across timepoints) is finite value different from 0.

n_elements_int

The number of seeds in the masker.

Added in version 0.9.2.

seeds_list of list

The coordinates of the seeds in the masker.

reportsboolean, default=True

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

See also

nilearn.maskers.NiftiMasker

__init__(seeds=None, radius=None, mask_img=None, allow_overlap=False, smoothing_fwhm=None, standardize=False, standardize_confounds=True, high_variance_confounds=False, detrend=False, low_pass=None, high_pass=None, t_r=None, dtype=None, memory=None, memory_level=1, verbose=0, reports=True, clean_args=None, **kwargs)

fit(imgs=None, y=None)

Prepare signal extraction from regions.

All parameters are unused; they are for scikit-learn compatibility.

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

Prepare and perform signal extraction.

Parameters:

imgs3D/4D Niimg-like object

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

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

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.

Added in version 0.8.0.

Returns:

signalsnumpy.ndarray

Signal for each voxel. Output shape for :

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

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

generate_report(displayed_spheres='all')

Generate an HTML report for current NiftiSpheresMasker object.

Note

This functionality requires to have Matplotlib installed.

Parameters:

displayed_spheresint, or list, or ndarray, or “all”, default=”all”

Indicates which spheres will be displayed in the HTML report.

• If “all”: All spheres will be displayed in the report.

masker.generate_report("all")

Warning

If there are too many spheres, this might be time and memory consuming, and will result in very heavy reports.

• If a list or ndarray: This indicates the indices of the spheres to be displayed in the report. For example, the following code will generate a report with spheres 6, 3, and 12, displayed in this specific order:

masker.generate_report([6, 3, 12])

• If an int: This will only display the first n spheres, n being the value of the parameter. By default, the report will only contain the first 10 spheres. Example to display the first 16 spheres:

masker.generate_report(16)

Returns:

reportnilearn.reporting.html_report.HTMLReport

HTML report for the masker.

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(region_signals)

Compute voxel signals from spheres signals.

Any mask given at initialization is taken into account. Throws an error if mask_img==None

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:

imgnibabel.nifti1.Nifti1Image

Transformed image in brain space. Output shape for :

• 1D array : 3D nibabel.nifti1.Nifti1Image will be returned.

• 2D array : 4D nibabel.nifti1.Nifti1Image 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(*, region_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:

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

Metadata routing for region_signals parameter in inverse_transform.

Returns:

selfobject

The updated object.

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:

imgs3D/4D Niimg-like object

See Input and output: neuroimaging data representation. Images to process. If a 3D niimg is provided, a 1D array is returned.

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.

Added in version 0.8.0.

Returns:

signalsnumpy.ndarray

Signal for each voxel. Output shape for :

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

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

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

Extract signals from a single 4D niimg.

Parameters:

imgs3D/4D Niimg-like object

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

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.

Added in version 0.8.0.

Returns:

signalsnumpy.ndarray

Signal for each voxel. Output shape for :

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

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

Examples using nilearn.maskers.NiftiSpheresMasker

Extract signals on spheres and plot a connectome

Extract signals on spheres and plot a connectome

Producing single subject maps of seed-to-voxel correlation

Producing single subject maps of seed-to-voxel correlation

Default Mode Network extraction of ADHD dataset

Default Mode Network extraction of ADHD dataset

Predicted time series and residuals

Predicted time series and residuals

Beta-Series Modeling for Task-Based Functional Connectivity and Decoding

Beta-Series Modeling for Task-Based Functional Connectivity and Decoding