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.MultiNiftiLabelsMasker#

class nilearn.maskers.MultiNiftiLabelsMasker(labels_img, labels=None, background_label=0, mask_img=None, smoothing_fwhm=None, standardize=False, standardize_confounds=True, high_variance_confounds=False, detrend=False, low_pass=None, high_pass=None, t_r=None, dtype=None, resampling_target='data', memory=Memory(location=None), memory_level=1, verbose=0, strategy='mean', reports=True, n_jobs=1)[source]#

Class for masking of Niimg-like objects. MultiNiftiLabelsMasker is useful when data from non-overlapping volumes and from different subjects should be extracted (contrary to nilearn.maskers.NiftiLabelsMasker).

Parameters:
labels_imgNiimg-like object

See Input and output: neuroimaging data representation. Region definitions, as one image of labels.

labelslist of str, optional

Full labels corresponding to the labels image. This is used to improve reporting quality if provided.

Warning

The labels must be consistent with the label values provided through labels_img.

background_labelint or float, optional

Label used in labels_img to represent background. Warning: This value must be consistent with label values and image provided. Default=0.

mask_imgNiimg-like object, optional

See Input and output: neuroimaging data representation. Mask to apply to regions before extracting signals.

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{‘zscore’, ‘psc’, True, False}, 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 set to 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

Whether to detrend signals or not.

low_passfloat or None, optional

Low cutoff frequency in Hertz. If specified, signals above this frequency will be filtered out. If None, no low-pass filtering will be performed. Default=None.

high_passfloat, optional

High cutoff frequency in Hertz. If specified, signals below this frequency will be filtered out. Default=None.

t_rfloat or None, optional

Repetition time, in seconds (sampling period). Set to None if not provided. Default=None.

dtype{dtype, “auto”}

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.

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

Gives which image gives the final shape/size:

  • “data” means the atlas is resampled to the shape of the data if needed

  • “labels” means en 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

Default=”data”.

memoryinstance 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, optional.

Rough estimator of the amount of memory used by caching. Higher value means more memory for caching. Zero means no caching. Default=0.

n_jobsint, optional.

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

verboseint, optional

Verbosity level (0 means no message). Default=0.

strategystr, optional

The name of a valid function to reduce the region with. Must be one of: sum, mean, median, minimum, maximum, variance, standard_deviation. Default=’mean’.

reportsbool, optional

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

__init__(labels_img, labels=None, background_label=0, mask_img=None, smoothing_fwhm=None, standardize=False, standardize_confounds=True, high_variance_confounds=False, detrend=False, low_pass=None, high_pass=None, t_r=None, dtype=None, resampling_target='data', memory=Memory(location=None), memory_level=1, verbose=0, strategy='mean', reports=True, n_jobs=1)[source]#
transform_imgs(imgs_list, confounds=None, n_jobs=1, sample_mask=None)[source]#

Extract signals from a list of 4D niimgs.

Parameters:
%(imgs)s

Images to process. Each element of the list is a 4D image.

%(confounds)s
%(sample_mask)s
Returns:
region_signals: list of 2D numpy.ndarray

List of signals for each label per subject. shape: list of (number of scans, number of labels)

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

Apply mask, spatial and temporal preprocessing

Parameters:
%(imgs)s

Images to process. Each element of the list is a 4D image.

%(confounds)s
%(sample_mask)s
Returns:
region_signalslist of 2D numpy.ndarray

List of signals for each label per subject. shape: list of (number of scans, number of labels)

fit(imgs=None, y=None)[source]#

Prepare signal extraction from regions.

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

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

Prepare and perform signal extraction from regions.

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

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(signals)[source]#

Compute voxel signals from region signals

Any mask given at initialization is taken into account.

Changed in version 0.9.2: This method now supports 1D arrays, which will produce 3D images.

Parameters:
signals1D/2D numpy.ndarray

Signal for each region. 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.

Returns:
imgnibabel.nifti1.Nifti1Image

Signal for each voxel shape: (X, Y, Z, number of scans)

set_output(*, transform=None)#

Set output container.

See Introducing the set_output API for an example on how to use the API.

Parameters:
transform{“default”, “pandas”}, default=None

Configure output of transform and fit_transform.

  • “default”: Default output format of a transformer

  • “pandas”: DataFrame output

  • None: Transform configuration is unchanged

Returns:
selfestimator instance

Estimator instance.

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)[source]#

Extract signals from a single 4D niimg.

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

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.MultiNiftiLabelsMasker#

Comparing connectomes on different reference atlases

Comparing connectomes on different reference atlases

Comparing connectomes on different reference atlases