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.

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_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_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.

Examples using nilearn.maskers.BaseMasker#

The haxby dataset: different multi-class strategies

The haxby dataset: different multi-class strategies

The haxby dataset: different multi-class strategies
Searchlight analysis of face vs house recognition

Searchlight analysis of face vs house recognition

Searchlight analysis of face vs house recognition
ROI-based decoding analysis in Haxby et al. dataset

ROI-based decoding analysis in Haxby et al. dataset

ROI-based decoding analysis in Haxby et al. dataset
Voxel-Based Morphometry on Oasis dataset

Voxel-Based Morphometry on Oasis dataset

Voxel-Based Morphometry on Oasis dataset
Encoding models for visual stimuli from Miyawaki et al. 2008

Encoding models for visual stimuli from Miyawaki et al. 2008

Encoding models for visual stimuli from Miyawaki et al. 2008
Reconstruction of visual stimuli from Miyawaki et al. 2008

Reconstruction of visual stimuli from Miyawaki et al. 2008

Reconstruction of visual stimuli from Miyawaki et al. 2008
Extracting signals of a probabilistic atlas of functional regions

Extracting signals of a probabilistic atlas of functional regions

Extracting signals of a probabilistic atlas of functional regions
Computing a connectome with sparse inverse covariance

Computing a connectome with sparse inverse covariance

Computing a connectome with sparse inverse covariance
Producing single subject maps of seed-to-voxel correlation

Producing single subject maps of seed-to-voxel correlation

Producing single subject maps of seed-to-voxel correlation
Group Sparse inverse covariance for multi-subject connectome

Group Sparse inverse covariance for multi-subject connectome

Group Sparse inverse covariance for multi-subject connectome
Regions extraction using dictionary learning and functional connectomes

Regions extraction using dictionary learning and functional connectomes

Regions extraction using dictionary learning and functional connectomes
Comparing connectomes on different reference atlases

Comparing connectomes on different reference atlases

Comparing connectomes on different reference atlases
Classification of age groups using functional connectivity

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

Extracting signals from a brain parcellation
Extract signals on spheres and plot a connectome

Extract signals on spheres and plot a connectome

Extract signals on spheres and plot a connectome
Default Mode Network extraction of ADHD dataset

Default Mode Network extraction of ADHD dataset

Default Mode Network extraction of ADHD dataset
Predicted time series and residuals

Predicted time series and residuals

Predicted time series and residuals
Regions Extraction of Default Mode Networks using Smith Atlas

Regions Extraction of Default Mode Networks using Smith Atlas

Regions Extraction of Default Mode Networks using Smith Atlas
Simple example of NiftiMasker use

Simple example of NiftiMasker use

Simple example of NiftiMasker use
Extracting signals from brain regions using the NiftiLabelsMasker

Extracting signals from brain regions using the NiftiLabelsMasker

Extracting signals from brain regions using the NiftiLabelsMasker
Understanding NiftiMasker and mask computation

Understanding NiftiMasker and mask computation

Understanding NiftiMasker and mask computation
Computing a Region of Interest (ROI) mask manually

Computing a Region of Interest (ROI) mask manually

Computing a Region of Interest (ROI) mask manually
Multivariate decompositions: Independent component analysis of fMRI

Multivariate decompositions: Independent component analysis of fMRI

Multivariate decompositions: Independent component analysis of fMRI
Massively univariate analysis of a calculation task from the Localizer dataset

Massively univariate analysis of a calculation task from the Localizer dataset

Massively univariate analysis of a calculation task from the Localizer dataset
Functional connectivity predicts age group

Functional connectivity predicts age group

Functional connectivity predicts age group
Massively univariate analysis of a motor task from the Localizer dataset

Massively univariate analysis of a motor task from the Localizer dataset

Massively univariate analysis of a motor task from the Localizer dataset
NeuroVault cross-study ICA maps.

NeuroVault cross-study ICA maps.

NeuroVault cross-study ICA maps.
Massively univariate analysis of face vs house recognition

Massively univariate analysis of face vs house recognition

Massively univariate analysis of face vs house recognition
Advanced decoding using scikit learn

Advanced decoding using scikit learn

Advanced decoding using scikit learn
Beta-Series Modeling for Task-Based Functional Connectivity and Decoding

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

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