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.NiftiMapsMasker#

class nilearn.maskers.NiftiMapsMasker(maps_img, mask_img=None, allow_overlap=True, 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', keep_masked_maps=True, memory=Memory(location=None), memory_level=0, verbose=0, reports=True, **kwargs)[source]#

Class for extracting data from Niimg-like objects using maps of potentially overlapping brain regions.

NiftiMapsMasker is useful when data from overlapping volumes should be extracted (contrarily to nilearn.maskers.NiftiLabelsMasker).

Use case: summarize brain signals from large-scale networks obtained by prior PCA or ICA.

Note

Inf or NaN present in the given input images are automatically put to zero rather than considered as missing data.

For more details on the definitions of maps in Nilearn, see the Extraction of signals from regions: NiftiLabelsMasker, NiftiMapsMasker section.

Parameters:

maps_img4D niimg-like object

See Input and output: neuroimaging data representation. Set of continuous maps. One representative time course per map is extracted using least square regression.

mask_img3D niimg-like object, optional

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

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.

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.

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.

detrendbool, optional

Whether to detrend signals or not.

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.

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”, “mask”, “maps”, None}, default=”data”

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

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, default=0

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).

keep_masked_mapsbool, optional

If True, masked atlas with invalid maps (maps that contain only zeros after applying the mask) will be retained in the output, resulting in corresponding time series containing zeros only. If False, the invalid maps will be removed from the trimmed atlas, resulting in no empty time series in the output.

Deprecated since version 0.10.2: The ‘True’ option for keep_masked_maps is deprecated. The default value will change to ‘False’ in 0.13, and the keep_masked_maps parameter will be removed in 0.15.

reportsbool, default=True

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

kwargsdict

Keyword arguments to be passed to functions called within the masker. Kwargs prefixed with ‘clean__’ will be passed to clean. Within clean, kwargs prefixed with ‘butterworth__’ will be passed to the Butterworth filter (i.e., clean__butterworth__).

See also

nilearn.maskers.NiftiMasker
nilearn.maskers.NiftiLabelsMasker

Notes

If resampling_target is set to “maps”, every 3D image processed by transform() will be resampled to the shape of maps_img. It may lead to a very large memory consumption if the voxel number in maps_img is large.

Attributes:

maps_img_nibabel.nifti1.Nifti1Image: The maps mask of the data.
n_elements_int: The number of overlapping maps in the mask. This is equivalent to the number of volumes in the mask image.

New in version 0.9.2.

__init__(maps_img, mask_img=None, allow_overlap=True, 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', keep_masked_maps=True, memory=Memory(location=None), memory_level=0, verbose=0, reports=True, **kwargs)[source]#

generate_report(displayed_maps=10)[source]#

Generate an HTML report for the current NiftiMapsMasker object.

Note

This functionality requires to have Matplotlib installed.

Parameters:

displayed_mapsint, or list, or ndarray, or “all”, default=10

Indicates which maps will be displayed in the HTML report.

If “all”: All maps will be displayed in the report.
masker.generate_report("all")
If a list or ndarray: This indicates the indices of the maps to be displayed in the report. For example, the following code will generate a report with maps 6, 3, and 12, displayed in this specific order:
masker.generate_report([6, 3, 12])
If an int: This will only display the first n maps, n being the value of the parameter. By default, the report will only contain the first 10 maps. Example to display the first 16 maps:
masker.generate_report(16)

Returns:

reportnilearn.reporting.html_report.HTMLReport: HTML report for the masker.

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

Prepare signal extraction from regions.

Parameters:

imgslist of Niimg-like objects: See Input and output: neuroimaging data representation. Image data passed to the reporter.
yNone: This parameter is unused. It is solely included for scikit-learn compatibility.

fit_transform(imgs, confounds=None, sample_mask=None)[source]#: Prepare and perform signal extraction.

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, 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 map. shape: (number of scans, number of maps)

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.

inverse_transform(region_signals)[source]#

Compute voxel signals from region signals.

Any mask given at initialization is taken into account.

Parameters:

region_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:

voxel_signalsnibabel.Nifti1Image: Signal for each voxel. shape: that of maps.

get_metadata_routing()#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

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.

set_fit_request(*, imgs='$UNCHANGED$')#

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to fit.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

imgsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for imgs parameter in fit.

Returns:

selfobject: The updated object.

set_inverse_transform_request(*, region_signals='$UNCHANGED$')#

Request metadata passed to the inverse_transform method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to inverse_transform if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to inverse_transform.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

region_signalsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for region_signals parameter in inverse_transform.

Returns:

selfobject: The updated object.

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
“polars”: Polars output
None: Transform configuration is unchanged

New in version 1.4: “polars” option was added.

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.

set_transform_request(*, confounds='$UNCHANGED$', imgs='$UNCHANGED$', sample_mask='$UNCHANGED$')#

Request metadata passed to the transform method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to transform if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to transform.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

confoundsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for confounds parameter in transform.
imgsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for imgs parameter in transform.
sample_maskstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for sample_mask parameter in transform.

Returns:

selfobject: The updated object.

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

Apply mask, spatial and temporal preprocessing.

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

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.NiftiMapsMasker`#

Computing a connectome with sparse inverse covariance

Extracting signals of a probabilistic atlas of functional regions

Group Sparse inverse covariance for multi-subject connectome

Regions extraction using dictionary learning and functional connectomes

Comparing connectomes on different reference atlases

Classification of age groups using functional connectivity

Regions Extraction of Default Mode Networks using Smith Atlas

Functional connectivity predicts age group

nilearn.maskers.NiftiMapsMasker#

Examples using nilearn.maskers.NiftiMapsMasker#

Examples using `nilearn.maskers.NiftiMapsMasker`#