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.BaseMasker#

class nilearn.maskers.BaseMasker[source]#

Base class for NiftiMaskers.

__init__(*args, **kwargs)#

abstract transform_single_imgs(imgs, confounds=None, sample_mask=None, copy=True)[source]#

Extract signals from a single 4D niimg.

Parameters:

imgs3D/4D Niimg-like object: See Input and output: neuroimaging data representation. Images to process. If a 3D niimg is provided, a singleton dimension will be added to the output to represent the single scan in the niimg.
confoundsCSV file or array-like, optional: This parameter is passed to signal.clean. Please see the related documentation for details. shape: (number of scans, number of confounds)
sample_maskAny type compatible with numpy-array indexing, optional: shape: (number of scans - number of volumes removed, ) Masks the niimgs along time/fourth dimension to perform scrubbing (remove volumes with high motion) and/or non-steady-state volumes. This parameter is passed to signal.clean.

New in version 0.8.0.
copyBoolean, optional: Indicates whether a copy is returned or not. Default=True.

Returns:

region_signals2D numpy.ndarray: Signal for each element. shape: (number of scans, number of elements)

Warns:

DeprecationWarning: If a 3D niimg input is provided, the current behavior (adding a singleton dimension to produce a 2D array) is deprecated. Starting in version 0.12, a 1D array will be returned for 3D inputs.

transform(imgs, confounds=None, sample_mask=None)[source]#

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 singleton dimension will be added to the output to represent the single scan in the niimg.
confoundsCSV file or array-like, optional: This parameter is passed to signal.clean. Please see the related documentation for details. shape: (number of scans, number of confounds)
sample_maskAny type compatible with numpy-array indexing, optional: shape: (number of scans - number of volumes removed, ) Masks the niimgs along time/fourth dimension to perform scrubbing (remove volumes with high motion) and/or non-steady-state volumes. This parameter is passed to signal.clean.

New in version 0.8.0.

Returns:

region_signals2D numpy.ndarray: Signal for each element. shape: (number of scans, number of elements)

Warns:

DeprecationWarning: If a 3D niimg input is provided, the current behavior (adding a singleton dimension to produce a 2D array) is deprecated. Starting in version 0.12, a 1D array will be returned for 3D inputs.

fit_transform(X, y=None, confounds=None, sample_mask=None, **fit_params)[source]#

Fit to data, then transform it.

Parameters:

XNiimg-like object: See Input and output: neuroimaging data representation.
ynumpy array of shape [n_samples], optional: Target values.
confoundslist of confounds, optional: List of confounds (2D arrays or filenames pointing to CSV files). Must be of same length than imgs_list.
sample_masklist of sample_mask, optional: List of sample_mask (1D arrays) if scrubbing motion outliers. Must be of same length than imgs_list.

New in version 0.8.0.

Returns:

X_newnumpy array of shape [n_samples, n_features_new]: Transformed array.

inverse_transform(X)[source]#

Transform the 2D data matrix back to an image in brain space.

This step only performs spatial unmasking, without inverting any additional processing performed by transform, such as temporal filtering or smoothing.

Parameters:

X1D/2D numpy.ndarray: Signal for each element in the mask. If a 1D array is provided, then the shape should be (number of elements,), and a 3D img will be returned. If a 2D array is provided, then the shape should be (number of scans, number of elements), and a 4D img will be returned. See Input and output: neuroimaging data representation.

Returns:

imgTransformed image in brain space.

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_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
None: Transform configuration is unchanged

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='$UNCHANGED$', 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.

New 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.

Examples using `nilearn.maskers.BaseMasker`#

The haxby dataset: different multi-class strategies

Searchlight analysis of face vs house recognition

ROI-based decoding analysis in Haxby et al. dataset

Voxel-Based Morphometry on Oasis dataset

Encoding models for visual stimuli from Miyawaki et al. 2008

Reconstruction of visual stimuli from Miyawaki et al. 2008

Computing a connectome with sparse inverse covariance

Extracting signals of a probabilistic atlas of functional regions

Producing single subject maps of seed-to-voxel correlation

Group Sparse inverse covariance for multi-subject connectome

Regions extraction using dictionary learning and functional connectomes

Comparing connectomes on different reference atlases

Classification of age groups using functional connectivity

Extracting signals from a brain parcellation

Extract signals on spheres and plot a connectome

Default Mode Network extraction of ADHD dataset

Predicted time series and residuals

Regions Extraction of Default Mode Networks using Smith Atlas

Simple example of NiftiMasker use

Extracting signals from brain regions using the NiftiLabelsMasker

Understanding NiftiMasker and mask computation

Computing a Region of Interest (ROI) mask manually

Massively univariate analysis of a calculation task from the Localizer dataset

Multivariate decompositions: Independent component analysis of fMRI

Functional connectivity predicts age group

Massively univariate analysis of a motor task from the Localizer dataset

NeuroVault cross-study ICA maps

Massively univariate analysis of face vs house recognition

Advanced decoding using scikit learn

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

nilearn.maskers.BaseMasker#

Examples using nilearn.maskers.BaseMasker#

Examples using `nilearn.maskers.BaseMasker`#