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.NiftiSpheresMasker#

class nilearn.maskers.NiftiSpheresMasker(seeds, radius=None, mask_img=None, allow_overlap=False, 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, memory=Memory(location=None), memory_level=1, verbose=0)[source]#

Class for masking of Niimg-like objects using seeds.

NiftiSpheresMasker is useful when data from given seeds should be extracted. Use case: Summarize brain signals from seeds that were obtained from prior knowledge.

Parameters

seedslist of triplet of coordinates in native space: Seed definitions. List of coordinates of the seeds in the same space as the images (typically MNI or TAL).
radiusfloat, optional: Indicates, in millimeters, the radius for the sphere around the seed. Default is None (signal is extracted on a single voxel).
mask_imgNiimg-like object, optional: See Input and output: neuroimaging data representation. Mask to apply to regions before extracting signals.
allow_overlapbool, optional: If False, an error is raised if the maps overlaps (ie at least two maps have a non-zero value for the same voxel). Default=False.
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.
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.

Examples using `nilearn.maskers.NiftiSpheresMasker`#

Producing single subject maps of seed-to-voxel correlation

Extract signals on spheres and plot a connectome

Default Mode Network extraction of ADHD dataset

Predicted time series and residuals

Beta-Series Modeling for Task-Based Functional Connectivity and Decoding

nilearn.maskers.NiftiSpheresMasker#

Examples using nilearn.maskers.NiftiSpheresMasker#

Examples using `nilearn.maskers.NiftiSpheresMasker`#