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.SurfaceMapsMasker¶

class nilearn.maskers.SurfaceMapsMasker(maps_img=None, mask_img=None, allow_overlap=True, smoothing_fwhm=None, standardize=False, standardize_confounds=True, detrend=False, high_variance_confounds=False, low_pass=None, high_pass=None, t_r=None, memory=None, memory_level=1, verbose=0, reports=True, cmap='inferno', clean_args=None)[source]¶

Extract data from a SurfaceImage, using maps of potentially overlapping brain regions.

Added in version 0.11.1.

Parameters:

maps_imgSurfaceImage

Set of maps that define the regions. representative time course per map is extracted using least square regression. The data for each hemisphere is of shape (n_vertices_per_hemisphere, n_regions).

mask_imgSurfaceImage, optional, default=None

Mask to apply to regions before extracting signals. Defines the overall area of the brain to consider. The data for each hemisphere is of shape (n_vertices_per_hemisphere, n_regions).

allow_overlapbool, default=True

If False, an error is raised if the maps overlaps (ie at least two maps have a non-zero value for the same voxel).

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. This parameter is not implemented yet.

standardize{‘zscore_sample’, ‘zscore’, ‘psc’, True, False}, default=False

Strategy to standardize the signal:

'zscore_sample': The signal is z-scored. Timeseries are shifted to zero mean and scaled to unit variance. Uses sample std.
'zscore': The signal is z-scored. Timeseries are shifted to zero mean and scaled to unit variance. Uses population std by calling default numpy.std with N - ddof=0.
'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 (same as option zscore). Timeseries are shifted to zero mean and scaled to unit variance.
False: Do not standardize the data.

standardize_confoundsbool, default=True

If set to True, the confounds are z-scored: their mean is put to 0 and their variance to 1 in the time dimension.

detrendbool, optional

Whether to detrend signals or not.

high_variance_confoundsbool, default=False

If True, high variance confounds are computed on provided image with nilearn.image.high_variance_confounds and default parameters and regressed out.

low_passfloat or None, default=None

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

high_passfloat, default=None

High cutoff frequency in Hertz. If specified, signals below this frequency will be filtered out.

t_rfloat or None, default=None

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

memoryNone, instance 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, default=1

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

verboseint, default=0

Verbosity level (0 means no message).

reportsbool, default=True

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

cmapmatplotlib.colors.Colormap, or str, optional

The colormap to use. Either a string which is a name of a matplotlib colormap, or a matplotlib colormap object. default=”inferno” Only relevant for the report figures.

clean_argsdict or None, default=None

Keyword arguments to be passed to nilearn.signal.clean called within the masker.

Attributes:

maps_img_SurfaceImage: The same as the input maps_img, kept solely for consistency across maskers.
n_elements_int: The number of regions in the maps image.