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.MultiNiftiMasker¶
- class nilearn.maskers.MultiNiftiMasker(mask_img=None, smoothing_fwhm=None, standardize=False, standardize_confounds=True, detrend=False, high_variance_confounds=False, low_pass=None, high_pass=None, t_r=None, target_affine=None, target_shape=None, mask_strategy='background', mask_args=None, dtype=None, memory=None, memory_level=0, n_jobs=1, verbose=0, **kwargs)[source]¶
Applying a mask to extract time-series from multiple Niimg-like objects.
MultiNiftiMasker is useful when dealing with image sets from multiple subjects.
Use case: integrates well with decomposition by MultiPCA and CanICA (multi-subject models)
- Parameters:
- mask_imgNiimg-like object
See Input and output: neuroimaging data representation. Mask of the data. If not given, a mask is computed in the fit step. Optional parameters can be set using mask_args and mask_strategy to fine tune the mask extraction.
- 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.
- target_affine3x3 or 4x4
numpy.ndarray
, optional This parameter is passed to image.resample_img. Please see the related documentation for details.
- target_shape3-
tuple
ofint
, optional This parameter is passed to image.resample_img. Please see the related documentation for details.
- mask_strategy{“background”, “epi”, “whole-brain-template”,”gm-template”, “wm-template”}, optional
The strategy used to compute the mask:
“background”: Use this option if your images present a clear homogeneous background.
“epi”: Use this option if your images are raw EPI images
“whole-brain-template”: This will extract the whole-brain part of your data by resampling the MNI152 brain mask for your data’s field of view.
Note
This option is equivalent to the previous ‘template’ option which is now deprecated.
“gm-template”: This will extract the gray matter part of your data by resampling the corresponding MNI152 template for your data’s field of view.
Added in version 0.8.1.
“wm-template”: This will extract the white matter part of your data by resampling the corresponding MNI152 template for your data’s field of view.
Added in version 0.8.1.
Note
Depending on this value, the mask will be computed from
nilearn.masking.compute_multi_background_mask
,nilearn.masking.compute_multi_epi_mask
, ornilearn.masking.compute_multi_brain_mask
.Default=’background’.
- mask_args
dict
, optional If mask is None, these are additional parameters passed to masking.compute_background_mask or masking.compute_epi_mask to fine-tune mask computation. 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.
- 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=0 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).
- 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.
- affine_4x4
numpy.ndarray
Affine of the transformed image.
- n_elements_
int
The number of voxels in the mask.
Added in version 0.9.2.
- mask_img_
See also
nilearn.image.resample_img
image resampling
nilearn.masking.compute_epi_mask
mask computation
nilearn.masking.apply_mask
mask application on image
nilearn.signal.clean
confounds removal and general filtering of signals
- __init__(mask_img=None, smoothing_fwhm=None, standardize=False, standardize_confounds=True, detrend=False, high_variance_confounds=False, low_pass=None, high_pass=None, t_r=None, target_affine=None, target_shape=None, mask_strategy='background', mask_args=None, dtype=None, memory=None, memory_level=0, n_jobs=1, verbose=0, **kwargs)[source]¶
- fit(imgs=None, y=None)[source]¶
Compute the mask corresponding to the data.
- Parameters:
- imgs
list
of Niimg-like objects See Input and output: neuroimaging data representation. Data on which the mask must be calculated. If this is a list, the affine is considered the same for all.
- yNone
This parameter is unused. It is solely included for scikit-learn compatibility.
- imgs
- transform_imgs(imgs_list, confounds=None, sample_mask=None, copy=True, n_jobs=1)[source]¶
Prepare multi subject data in parallel.
- Parameters:
- imgs_list
list
of Niimg-like objects See Input and output: neuroimaging data representation. List of imgs file to prepare. One item per subject.
- confounds
list
of confounds, optional List of confounds (2D arrays or filenames pointing to CSV files or pandas DataFrames). Must be of same length than imgs_list.
- sample_mask
list
of sample_mask, optional List of sample_mask (1D arrays) if scrubbing motion outliers. Must be of same length than imgs_list.
Added in version 0.8.0.
- copy
bool
, default=True If True, guarantees that output array has no memory in common with input array.
- n_jobs
int
, default=1 The number of cpus to use to do the computation. -1 means ‘all cpus’.
- imgs_list
- Returns:
- region_signals
list
of 2Dnumpy.ndarray
List of signal for each element per subject. shape: list of (number of scans, number of elements)
- region_signals
- 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.
- 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. Data to be preprocessed
- confoundsCSV file or 2D
numpy.ndarray
orpandas.DataFrame
, optional This parameter is passed to signal.clean. Please see the corresponding documentation for details.
- sample_mask
list
of 1Dnumpy.ndarray
, optional List of sample_mask (1D arrays) if scrubbing motion outliers. Must be of same length than imgs_list.
Added in version 0.8.0.
- imgs
- Returns:
- data
list
ofnumpy.ndarray
preprocessed images
- data
- Warns:
- DeprecationWarning
If 3D niimg inputs are provided, the current behavior (adding a singleton dimension to produce 2D arrays) is deprecated. Starting in version 0.12, 1D arrays will be returned for 3D inputs.
- fit_transform(X, y=None, confounds=None, sample_mask=None, **fit_params)[source]¶
Fit to data, then transform it.
- Parameters:
- XNiimg-like object
- ynumpy array of shape [n_samples], optional
Target values.
- confoundslist of confounds, optional
List of confounds (2D arrays or filenames pointing to CSV files). Must be of same length than imgs_list.
- sample_masklist of sample_mask, optional
List of sample_mask (1D arrays) if scrubbing motion outliers. Must be of same length than imgs_list.
Added in version 0.8.0.
- Returns:
- X_newnumpy array of shape [n_samples, n_features_new]
Transformed array.
- 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(X)[source]¶
Transform the 2D data matrix back to an image in brain space.
This step only performs spatial unmasking, without inverting any additional processing performed by
transform
, such as temporal filtering or smoothing.- Parameters:
- X1D/2D
numpy.ndarray
Signal for each element in the mask. 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. See Input and output: neuroimaging data representation.
- X1D/2D
- Returns:
- imgTransformed image in brain space.
- 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_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, copy=True)[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 or
pandas.DataFrame
, optional This parameter is passed to signal.clean. Please see the related documentation for details:
nilearn.signal.clean
. 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.
- copy
bool
, default=True Indicates whether a copy is returned or not.
- Returns:
- region_signals2D
numpy.ndarray
Signal for each voxel inside the mask. shape: (number of scans, number of voxels)
- region_signals2D
- 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.MultiNiftiMasker
¶
Encoding models for visual stimuli from Miyawaki et al. 2008
Reconstruction of visual stimuli from Miyawaki et al. 2008