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.

7.6.3. nilearn.input_data.NiftiLabelsMasker

class nilearn.input_data.NiftiLabelsMasker(labels_img, background_label=0, mask_img=None, smoothing_fwhm=None, standardize=False, detrend=False, low_pass=None, high_pass=None, t_r=None, resampling_target='data', memory=Memory(cachedir=None), memory_level=1, verbose=0)

Class for masking of Niimg-like objects.

NiftiLabelsMasker is useful when data from non-overlapping volumes should be extracted (contrarily to NiftiMapsMasker). Use case: Summarize brain signals from clusters that were obtained by prior K-means or Ward clustering.

Parameters:

labels_img: Niimg-like object

See http://nilearn.github.io/manipulating_images/input_output.html Region definitions, as one image of labels.

background_label: number, optional

Label used in labels_img to represent background.

mask_img: Niimg-like object, optional

See http://nilearn.github.io/manipulating_images/input_output.html Mask to apply to regions before extracting signals.

smoothing_fwhm: float, optional

If smoothing_fwhm is not None, it gives the full-width half maximum 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

resampling_target: {“data”, “labels”, None}, optional.

Gives which image gives the final shape/size. For example, if resampling_target is “data”, the atlas is resampled to the shape of the data if needed. If it is “labels” then mask_img and images provided to fit() are resampled to the shape and affine of maps_img. “None” means no resampling: if shapes and affines do not match, a ValueError is raised. Defaults to “data”.

memory: joblib.Memory or str, optional

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

memory_level: int, optional

Aggressiveness of memory caching. The higher the number, the higher the number of functions that will be cached. Zero means no caching.

verbose: integer, optional

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

__init__(labels_img, background_label=0, mask_img=None, smoothing_fwhm=None, standardize=False, detrend=False, low_pass=None, high_pass=None, t_r=None, resampling_target='data', memory=Memory(cachedir=None), memory_level=1, verbose=0)
fit(X=None, y=None)

Prepare signal extraction from regions.

All parameters are unused, they are for scikit-learn compatibility.

fit_transform(imgs, confounds=None)

Prepare and perform signal extraction from regions.

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(signals)

Compute voxel signals from region signals

Any mask given at initialization is taken into account.

Parameters:

signals (2D numpy.ndarray)

Signal for each region. shape: (number of scans, number of regions)

Returns:

voxel_signals (Nifti1Image)

Signal for each voxel shape: (number of scans, number of voxels)

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: 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 element. shape: (number of scans, number of elements)

transform_single_imgs(imgs, confounds=None)

Extract signals from a single 4D niimg.

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 label. shape: (number of scans, number of labels)

7.6.3.1. Examples using nilearn.input_data.NiftiLabelsMasker