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.MultiNiftiMasker

class nilearn.maskers.MultiNiftiMasker(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=None, memory_level=0, n_jobs=1, verbose=0, reports=True, cmap='gray', clean_args=None)

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

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

Use case: integrates well with decomposition by MultiPCA and CanICA (multi-subject models)

Parameters:

mask_imgNiimg-like object

See Input and output: neuroimaging data representation. Mask of the data. If not given, a mask is computed in the fit step. Optional parameters can be set using mask_args and mask_strategy to fine tune the mask extraction.

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 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.

standardizeany of: ‘zscore_sample’, ‘zscore’, ‘psc’, True, False or None; 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.

  Deprecated since Nilearn 0.13.0dev: In nilearn version 0.15.0, True will be replaced by 'zscore_sample'.

• False: Do not standardize the data.

  Deprecated since Nilearn 0.13.0dev: In nilearn version 0.15.0, False will be replaced by None.

Deprecated since Nilearn 0.15.0dev: The default will be changed to None in version 0.15.0.

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.

target_affine3x3 or a 4x4 array-like, or None, default=None

If specified, the image is resampled corresponding to this new affine.

Note

This parameter is passed to nilearn.image.resample_img.

target_shapetuple or list or None, default=None

If specified, the image will be resized to match this new shape. len(target_shape) must be equal to 3.

Note

If target_shape is specified, a target_affine of shape (4, 4) must also be given.

Note

This parameter is passed to nilearn.image.resample_img.

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. Uses nilearn.masking.compute_background_mask under the hood.

• "epi": Use this option if your images are raw EPI images. Uses nilearn.masking.compute_epi_mask.

• "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. Uses nilearn.masking.compute_brain_mask with mask_type="whole-brain".

  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. Uses nilearn.masking.compute_brain_mask with mask_type="gm".

  Added in Nilearn 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. Uses nilearn.masking.compute_brain_mask with mask_type="wm".

  Added in Nilearn 0.8.1.

Note

Depending on this value, the mask will be computed from nilearn.masking.compute_multi_background_mask, nilearn.masking.compute_multi_epi_mask, or nilearn.masking.compute_multi_brain_mask.

Default=’background’.

mask_argsdict, optional

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

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=0

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=”gray” 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:

affine_4x4 numpy.ndarray

Affine of the transformed image.

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 3D binary nibabel.nifti1.Nifti1Image

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 voxel is True if all values across samples (for example across timepoints) is finite value different from 0.

memory_joblib memory cache

n_elements_int

The number of voxels in the mask.

Added in Nilearn 0.9.2.

See also

nilearn.image.resample_img

image resampling

nilearn.masking.compute_epi_mask

mask computation

nilearn.masking.apply_mask

mask application on image

nilearn.signal.clean

confounds removal and general filtering of signals

__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=None, memory_level=0, n_jobs=1, verbose=0, reports=True, cmap='gray', clean_args=None)

fit(imgs=None, y=None)

Compute the mask corresponding to the data.

Parameters:

imgsNiimg-like objects, list of Niimg-like objects or None, default=None

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.

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 of the masker.

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(X)

Transform the 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:

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='$UNCHANGED$')

Configure whether metadata should be requested to be passed to the fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config). Please check the 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.

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_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='$UNCHANGED$')

Configure whether metadata should be requested to be passed to the transform method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config). Please check the 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.

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, sample_mask=None, copy=True, n_jobs=1)

Prepare multi subject data in parallel.

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.

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.

copybool, default=True

If True, guarantees that output array has no memory in common with input array.

n_jobsint, default=1

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

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, copy=True)

Apply mask, spatial and temporal preprocessing.

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.

copybool, default=True

Indicates whether a copy is returned or not.

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:

  • 3D images: (number of elements,)

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

• For DataFrame outputs:

  • 3D or 4D images: (number of scans, number of elements) array

Examples using nilearn.maskers.MultiNiftiMasker

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