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.

7.6.2. nilearn.input_data.MultiNiftiMasker

class nilearn.input_data.MultiNiftiMasker(mask_img=None, smoothing_fwhm=None, standardize=False, detrend=False, low_pass=None, high_pass=None, t_r=None, target_affine=None, target_shape=None, mask_strategy='background', mask_args=None, memory=Memory(cachedir=None), memory_level=0, n_jobs=1, verbose=0)

Class for masking of 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_img: Niimg-like object

See http://nilearn.github.io/manipulating_images/input_output.html 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.

smoothing_fwhm: float, optional

If smoothing_fwhm is not None, it gives the size in millimeters of the spatial smoothing to apply to the signal.

standardize: boolean, optional

If standardize is True, the time-series are centered and normed: their mean is put to 0 and their variance to 1 in the time dimension.

detrend: boolean, optional

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

low_pass: None or float, optional

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

high_pass: None or float, optional

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

t_r: float, optional

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

target_affine: 3x3 or 4x4 matrix, optional

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

target_shape: 3-tuple of integers, optional

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

mask_strategy: {‘background’ or ‘epi’}, optional

The strategy used to compute the mask: use ‘background’ if your images present a clear homogeneous background, and ‘epi’ if they are raw EPI images. Depending on this value, the mask will be computed from masking.compute_background_mask or masking.compute_epi_mask. Default is ‘background’.

mask_args : dict, 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.

memory: instance of joblib.Memory or string

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_level: integer, optional

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

n_jobs: integer, optional

The number of CPUs to use to do the computation. -1 means ‘all CPUs’, -2 ‘all CPUs but one’, and so on.

verbose: integer, optional

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

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

Attributes

mask_img_ (nibabel.Nifti1Image object) The mask of the data.
affine_ (4x4 numpy.ndarray) Affine of the transformed image.
__init__(mask_img=None, smoothing_fwhm=None, standardize=False, detrend=False, low_pass=None, high_pass=None, t_r=None, target_affine=None, target_shape=None, mask_strategy='background', mask_args=None, memory=Memory(cachedir=None), memory_level=0, n_jobs=1, verbose=0)
fit(imgs=None, y=None)

Compute the mask corresponding to the data

Parameters:

imgs: list of Niimg-like objects

See http://nilearn.github.io/manipulating_images/input_output.html Data on which the mask must be calculated. If this is a list, the affine is considered the same for all.

fit_transform(X, y=None, confounds=None, **fit_params)

Fit to data, then transform it

Parameters:

X : Niimg-like object

y : numpy array of shape [n_samples]

Target values.

confounds: list of confounds, optional

List of confounds (2D arrays or filenames pointing to CSV files). Must be of same length than imgs_list.

Returns:

X_new : numpy array of shape [n_samples, n_features_new]

Transformed array.

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deep : boolean, optional

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

params : mapping of string to any

Parameter names mapped to their values.

inverse_transform(X)

Transform the 2D data matrix back to an 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 pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:self
transform(imgs, confounds=None)

Apply mask, spatial and temporal preprocessing

Parameters:

imgs: list of Niimg-like objects

confounds: CSV file path or 2D matrix

This parameter is passed to signal.clean. Please see the corresponding documentation for details.

Returns:

data: {list of numpy arrays}

preprocessed images

transform_imgs(imgs_list, confounds=None, copy=True, n_jobs=1)

Prepare multi subject data in parallel

Parameters:

imgs_list: list of Niimg-like objects

See http://nilearn.github.io/manipulating_images/input_output.html List of imgs file to prepare. One item per subject.

confounds: list of confounds, optional

List of confounds (2D arrays or filenames pointing to CSV files). Must be of same length than imgs_list.

copy: boolean, optional

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

n_jobs: integer, optional

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

Returns:

region_signals: list of 2D numpy.ndarray

List of signal for each element per subject. shape: list of (number of scans, number of elements)

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

Apply mask, spatial and temporal preprocessing

Parameters:

imgs: 3D/4D Niimg-like object

See http://nilearn.github.io/manipulating_images/input_output.html Images to process. It must boil down to a 4D image with scans number as last dimension.

confounds: CSV 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)

Returns:

region_signals: 2D numpy.ndarray

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