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.NiftiLabelsMasker#

class nilearn.maskers.NiftiLabelsMasker(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)[source]#

Class for masking of Niimg-like objects.

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

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{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_confoundsbool, 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_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: 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
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.
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. Default=”data”.
memoryjoblib.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_levelint, optional: Aggressiveness of memory caching. The higher the number, the higher the number of functions that will be cached. Zero means no caching. Default=1.
verboseint, optional: Indicate the level of verbosity. By default, nothing is printed 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.

Examples using `nilearn.maskers.NiftiLabelsMasker`#

Comparing connectomes on different reference atlases

Extracting signals from a brain parcellation

Extracting signals from brain regions using the NiftiLabelsMasker

Computing a Region of Interest (ROI) mask manually

nilearn.maskers.NiftiLabelsMasker#

Examples using nilearn.maskers.NiftiLabelsMasker#

Examples using `nilearn.maskers.NiftiLabelsMasker`#