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.NiftiMasker

class nilearn.maskers.NiftiMasker(mask_img=None, runs=None, smoothing_fwhm=None, standardize=False, standardize_confounds=True, detrend=False, high_variance_confounds=False, low_pass=None, high_pass=None, t_r=None, target_affine=None, target_shape=None, mask_strategy='background', mask_args=None, dtype=None, memory_level=1, memory=Memory(location=None), verbose=0, reports=True)

Applying a mask to extract time-series from Niimg-like objects.

NiftiMasker is useful when preprocessing (detrending, standardization, resampling, etc.) of in-mask voxels is necessary. Use case: working with time series of resting-state or task maps.

Parameters

mask_imgNiimg-like object, optional

See Input and output: neuroimaging data representation. Mask for the data. If not given, a mask is computed in the fit step. Optional parameters (mask_args and mask_strategy) can be set to fine tune the mask extraction. If the mask and the images have different resolutions, the images are resampled to the mask resolution. If target_shape and/or target_affine are provided, the mask is resampled first. After this, the images are resampled to the resampled mask.

runsnumpy.ndarray, optional

Add a run level to the preprocessing. Each run will be detrended independently. Must be a 1D array of n_samples elements.

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.

standardize{False, True, ‘zscore’, ‘psc’}, optional

Strategy to standardize the signal. ‘zscore’: the signal is z-scored. Timeseries are shifted to zero mean and scaled to unit variance. ‘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. Timeseries are shifted to zero mean and scaled to unit variance. False : Do not standardize the data. Default=False.

standardize_confoundsbool, optional

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

high_variance_confoundsbool, optional

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

detrendbool, optional

This parameter is passed to signal.clean. Please see the related documentation for details: nilearn.signal.clean. Default=False.

low_passNone or float, optional

This parameter is passed to signal.clean. Please see the related documentation for details: nilearn.signal.clean.

high_passNone or float, optional

This parameter is passed to signal.clean. Please see the related documentation for details: nilearn.signal.clean.

t_rfloat, optional

This parameter is passed to signal.clean. Please see the related documentation for details: nilearn.signal.clean.

target_affine3x3 or 4x4 numpy.ndarray, optional

This parameter is passed to image.resample_img. Please see the related documentation for details.

target_shape3-tuple of int, optional

This parameter is passed to image.resample_img. Please see the related documentation for details.

mask_strategy{‘background’, ‘epi’, ‘whole-brain-template’,’gm-template’, ‘wm-template’}, optional

The strategy used to compute the mask:

• ‘background’: Use this option if your images present a clear homogeneous background.

• ‘epi’: Use this option if your images are raw EPI images

• ‘whole-brain-template’: This will extract the whole-brain part of your data by resampling the MNI152 brain mask for your data’s field of view.

  Note

  This option is equivalent to the previous ‘template’ option which is now deprecated.

• ‘gm-template’: This will extract the gray matter part of your data by resampling the corresponding MNI152 template for your data’s field of view.

  New in version 0.8.1.

• ‘wm-template’: This will extract the white matter part of your data by resampling the corresponding MNI152 template for your data’s field of view.

  New in version 0.8.1.

Note

Depending on this value, the mask will be computed from nilearn.masking.compute_background_mask, nilearn.masking.compute_epi_mask, or nilearn.masking.compute_brain_mask.

Default is ‘background’.

mask_argsdict, optional

If mask is None, these are additional parameters passed to masking.compute_background_mask or masking.compute_epi_mask to fine-tune mask computation. Please see the related documentation for details.

dtype{dtype, “auto”}, optional

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.

memoryinstance of joblib.Memory or str, optional

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

memory_levelint, optional

Rough estimator of the amount of memory used by caching. Higher value means more memory for caching. Default=1.

verboseint, optional

Indicate the level of verbosity. By default, nothing is printed. Default=0.

reportsbool, optional

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

See also

nilearn.masking.compute_background_mask

nilearn.masking.compute_epi_mask

nilearn.image.resample_img

nilearn.image.high_variance_confounds

nilearn.masking.apply_mask

nilearn.signal.clean

Attributes

mask_img_nibabel.nifti1.Nifti1Image

The mask of the data, or the computed one.

affine_4x4 numpy.ndarray

Affine of the transformed image.

n_elements_int

The number of voxels in the mask.

New in version 0.9.2.

__init__(mask_img=None, runs=None, smoothing_fwhm=None, standardize=False, standardize_confounds=True, detrend=False, high_variance_confounds=False, low_pass=None, high_pass=None, t_r=None, target_affine=None, target_shape=None, mask_strategy='background', mask_args=None, dtype=None, memory_level=1, memory=Memory(location=None), verbose=0, reports=True)

generate_report()

fit(imgs=None, y=None)

Compute the mask corresponding to the data.

Parameters

imgslist of Niimg-like objects

See Input and output: neuroimaging data representation. Data on which the mask must be calculated. If this is a list, the affine is considered the same for all.

yNone

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

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

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 or pandas.DataFrame, optional

This parameter is passed to signal.clean. Please see the related documentation for details: nilearn.signal.clean. 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.

copybool, optional

Indicates whether a copy is returned or not. Default=True.

Returns

region_signals2D numpy.ndarray

Signal for each voxel inside the mask. shape: (number of scans, number of voxels)

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)

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.

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(X)

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.

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.

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 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.

Examples using nilearn.maskers.NiftiMasker

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

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

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

Producing single subject maps of seed-to-voxel correlation

Producing single subject maps of seed-to-voxel correlation

First level analysis of a complete BIDS dataset from openneuro

First level analysis of a complete BIDS dataset from openneuro

Simple example of NiftiMasker use

Simple example of NiftiMasker use

Understanding NiftiMasker and mask computation

Understanding NiftiMasker and mask computation

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 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.

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

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

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