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.input_data.MultiNiftiMasker#

class nilearn.input_data.MultiNiftiMasker(mask_img=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=Memory(location=None), memory_level=0, n_jobs=1, verbose=0)[source]#

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_imgNiimg-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_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_confoundsboolean, 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_confoundsboolean, 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.

detrendboolean, optional

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

low_passNone or float, optional

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

high_passNone or float, optional

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

t_rfloat, optional

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

target_affine3x3 or 4x4 matrix, optional

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

target_shape3-tuple of integers, 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_multi_background_mask, nilearn.masking.compute_multi_epi_mask, or nilearn.masking.compute_multi_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 string, 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_levelinteger, optional

Rough estimator of the amount of memory used by caching. Higher value means more memory for caching. Default=0.

n_jobsinteger, optional

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

verboseinteger, optional

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

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, 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=Memory(location=None), memory_level=0, n_jobs=1, verbose=0)[source]#
fit(imgs=None, y=None)[source]#

Compute the mask corresponding to the data

Parameters
imgslist 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.

transform_imgs(imgs_list, confounds=None, sample_mask=None, copy=True, n_jobs=1)[source]#

Prepare multi subject data in parallel

Parameters
imgs_listlist of Niimg-like objects

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

confoundslist of confounds, optional

List of confounds (2D arrays or filenames pointing to CSV files or pandas DataFrames). 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.

copyboolean, optional

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

n_jobsinteger, optional

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

Returns
region_signalslist of 2D numpy.ndarray

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

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

Apply mask, spatial and temporal preprocessing

Parameters
imgslist of Niimg-like objects

See http://nilearn.github.io/manipulating_images/input_output.html Data to be preprocessed

confoundsCSV file path or 2D array or pandas DataFrame, optional

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

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
data{list of numpy arrays}

preprocessed images

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

Fit to data, then transform it

Parameters
XNiimg-like object

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

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.

generate_report()[source]#
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)[source]#

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

Parameters
XNiimg-like object

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

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

Apply mask, spatial and temporal preprocessing

Parameters
imgs3D/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.

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.

copyBoolean, 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)