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=None, memory_level=1, verbose=0, strategy='mean', reports=True, n_jobs=1, **kwargs)[source]¶
Class for extracting data from multiple Niimg-like objects using labels of non-overlapping brain regions.
MultiNiftiLabelsMasker is useful when data from non-overlapping volumes and from different subjects should be extracted (contrary to
nilearn.maskers.NiftiLabelsMasker
).For more details on the definitions of labels in Nilearn, see the Extraction of signals from regions: NiftiLabelsMasker, NiftiMapsMasker section.
- Parameters:
- labels_imgNiimg-like object
See Input and output: neuroimaging data representation. Region definitions, as one image of labels.
- labels
list
ofstr
, 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_label
int
orfloat
, default=0 Label used in labels_img to represent background. Warning: This value must be consistent with label values and image provided.
- mask_imgNiimg-like object, optional
See Input and output: neuroimaging data representation. Mask to apply to regions before extracting signals.
- smoothing_fwhm
float
, 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_confounds
bool
, 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_confounds
bool
, default=False If True, high variance confounds are computed on provided image with
nilearn.image.high_variance_confounds
and default parameters and regressed out.- detrend
bool
, optional Whether to detrend signals or not.
- low_pass
float
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_pass
float
, default=None High cutoff frequency in Hertz. If specified, signals below this frequency will be filtered out.
- t_r
float
or None, default=None Repetition time, in seconds (sampling period). Set to None if not provided.
- 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}, default=”data”
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
- memoryNone, instance of
joblib.Memory
,str
, orpathlib.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_level
int
, default=1 Rough estimator of the amount of memory used by caching. Higher value means more memory for caching. Zero means no caching.
- n_jobs
int
, default=1 The number of CPUs to use to do the computation. -1 means ‘all CPUs’.
- verbose
int
, default=0 Verbosity level (0 means no message).
- strategy
str
, default=’mean’ The name of a valid function to reduce the region with. Must be one of: sum, mean, median, minimum, maximum, variance, standard_deviation.
- reports
bool
, 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
. Withinclean
, kwargs prefixed with ‘butterworth__’ will be passed to the Butterworth filter (i.e., clean__butterworth__).
- Attributes:
- mask_img_
nibabel.nifti1.Nifti1Image
The mask of the data, or the computed one.
- labels_img_
nibabel.nifti1.Nifti1Image
The labels image.
- n_elements_
int
The number of discrete values in the mask. This is equivalent to the number of unique values in the mask image, ignoring the background value.
Added in version 0.9.2.
- mask_img_
- __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=None, memory_level=1, verbose=0, strategy='mean', reports=True, n_jobs=1, **kwargs)[source]¶
- transform_imgs(imgs_list, confounds=None, n_jobs=1, sample_mask=None)[source]¶
Extract signals from a list of 4D niimgs.
- Parameters:
- imgs
list
of Niimg-like objects See Input and output: neuroimaging data representation. Images to process. Each element of the list is a 4D image.
- confoundsCSV file or array-like, optional
This parameter is passed to
nilearn.signal.clean
. Please see the related documentation for details. shape: list of (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
nilearn.signal.clean
.
- imgs
- 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)
- region_signals: list of 2D
- transform(imgs, confounds=None, sample_mask=None)[source]¶
Apply mask, spatial and temporal preprocessing.
- Parameters:
- imgs
list
of Niimg-like objects See Input and output: neuroimaging data representation. Images to process. Each element of the list is a 4D image.
- confoundsCSV file or array-like, optional
This parameter is passed to
nilearn.signal.clean
. Please see the related documentation for details. shape: list of (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
nilearn.signal.clean
.
- imgs
- Returns:
- region_signalslist of 2D
numpy.ndarray
List of signals for each label per subject. shape: list of (number of scans, number of labels)
- region_signalslist of 2D
- fit(imgs=None, y=None)[source]¶
Prepare signal extraction from regions.
- Parameters:
- imgs
list
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.
- imgs
- 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.
Added in version 0.8.0.
- Returns:
- region_signals2D
numpy.ndarray
Signal for each label. shape: (number of scans, number of labels)
- region_signals2D
- 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.
- 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.
- signals1D/2D
- Returns:
- img
nibabel.nifti1.Nifti1Image
Signal for each voxel shape: (X, Y, Z, number of scans)
- img
- set_fit_request(*, imgs='$UNCHANGED$')¶
Request metadata passed to the
fit
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed tofit
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it tofit
.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.Added 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 infit
.
- Returns:
- selfobject
The updated object.
- set_inverse_transform_request(*, signals='$UNCHANGED$')¶
Request metadata passed to the
inverse_transform
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed toinverse_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 toinverse_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.Added 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:
- signalsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
signals
parameter ininverse_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”, “polars”}, 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
Added 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
(seesklearn.set_config
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed totransform
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it totransform
.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.Added 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 intransform
.- imgsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
imgs
parameter intransform
.- sample_maskstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
sample_mask
parameter intransform
.
- Returns:
- selfobject
The updated object.
- 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.
Added 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