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.

7.4.1. nilearn.decomposition.CanICA

class nilearn.decomposition.CanICA(mask=None, n_components=20, smoothing_fwhm=6, do_cca=True, threshold='auto', n_init=10, random_state=None, standardize=True, detrend=True, low_pass=None, high_pass=None, t_r=None, target_affine=None, target_shape=None, mask_strategy='epi', mask_args=None, memory=Memory(location=None), memory_level=0, n_jobs=1, verbose=0)

Perform Canonical Independent Component Analysis.

Parameters:

mask: Niimg-like object or MultiNiftiMasker instance, optional

Mask to be used on data. If an instance of masker is passed, then its mask will be used. If no mask is given, it will be computed automatically by a MultiNiftiMasker with default parameters.

n_components: int

Number of components to extract. By default n_components=20.

smoothing_fwhm: float, optional, default 6mm

If smoothing_fwhm is not None, it gives the size in millimeters of the spatial smoothing to apply to the signal.

do_cca: boolean, optional

Indicate if a Canonical Correlation Analysis must be run after the PCA.

standardize: boolean, optional, default True

If standardize is True, the time-series are centered and normed: their variance is put to 1 in the time dimension.

detrend : boolean, optional, default True

If detrend is True, the time-series will be detrended before components extraction.

threshold: None, ‘auto’ or float

If None, no thresholding is applied. If ‘auto’, then we apply a thresholding that will keep the n_voxels, more intense voxels across all the maps, n_voxels being the number of voxels in a brain volume. A float value indicates the ratio of voxels to keep (2. means that the maps will together have 2 x n_voxels non-zero voxels ). The float value must be bounded by [0. and n_components].

n_init: int, optional

The number of times the fastICA algorithm is restarted

random_state: int or RandomState

Pseudo number generator state used for random sampling.

target_affine: 3x3 or 4x4 matrix, optional

This parameter is passed to image.resample_img. Please see the related documentation for details.

target_shape: 3-tuple of integers, optional

This parameter is passed to image.resample_img. Please see the related documentation for details.

low_pass: None or float, optional

This parameter is passed to signal.clean. Please see the related documentation for details

high_pass: None or float, optional

This parameter is passed to signal.clean. Please see the related documentation for details

t_r: float, optional

This parameter is passed to signal.clean. Please see the related documentation for details

mask_strategy: {‘background’, ‘epi’ or ‘template’}, optional

The strategy used to compute the mask: use ‘background’ if your images present a clear homogeneous background, ‘epi’ if they are raw EPI images, or you could use ‘template’ which will extract the gray matter part of your data by resampling the MNI152 brain mask for your data’s field of view. Depending on this value, the mask will be computed from masking.compute_background_mask, masking.compute_epi_mask or masking.compute_gray_matter_mask. Default is ‘epi’.

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.

memory: instance of joblib.Memory or string

Used to cache the masking process. By default, no caching is done. If a string is given, it is the path to the caching directory.

memory_level: integer, optional

Rough estimator of the amount of memory used by caching. Higher value means more memory for caching.

n_jobs: integer, optional

The number of CPUs to use to do the computation. -1 means ‘all CPUs’, -2 ‘all CPUs but one’, and so on.

verbose: integer, optional

Indicate the level of verbosity. By default, nothing is printed

References

  • G. Varoquaux et al. “A group model for stable multi-subject ICA on fMRI datasets”, NeuroImage Vol 51 (2010), p. 288-299
  • G. Varoquaux et al. “ICA-based sparse features recovery from fMRI datasets”, IEEE ISBI 2010, p. 1177

Attributes

components_ (2D numpy array (n_components x n-voxels)) Masked ICA components extracted from the input images. They can be unmasked thanks to the masker_ attribute. Deprecated since version 0.4.1. Use components_img_ instead.
components_img_ (4D Nifti image) 4D image giving the extracted ICA components. Each 3D image is a component. New in version 0.4.1.
masker_ (instance of MultiNiftiMasker) Masker used to filter and mask data as first step. If an instance of MultiNiftiMasker is given in mask parameter, this is a copy of it. Otherwise, a masker is created using the value of mask and other NiftiMasker related parameters as initialization.
mask_img_ (Niimg-like object) See http://nilearn.github.io/manipulating_images/input_output.html The mask of the data. If no mask was given at masker creation, contains the automatically computed mask.
__init__(mask=None, n_components=20, smoothing_fwhm=6, do_cca=True, threshold='auto', n_init=10, random_state=None, standardize=True, detrend=True, low_pass=None, high_pass=None, t_r=None, target_affine=None, target_shape=None, mask_strategy='epi', mask_args=None, memory=Memory(location=None), memory_level=0, n_jobs=1, verbose=0)
fit(imgs, y=None, confounds=None)

Compute the mask and the components across subjects

Parameters:

imgs: list of Niimg-like objects

See http://nilearn.github.io/manipulating_images/input_output.html Data on which the mask is calculated. If this is a list, the affine is considered the same for all.

confounds : list of CSV file paths or 2D matrices

This parameter is passed to nilearn.signal.clean. Please see the related documentation for details. Should match with the list of imgs given.

Returns:

self : object

Returns the instance itself. Contains attributes listed at the object level.

fit_transform(X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:

X : numpy array of shape [n_samples, n_features]

Training set.

y : numpy array of shape [n_samples]

Target values.

Returns:

X_new : numpy array of shape [n_samples, n_features_new]

Transformed array.

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deep : boolean, optional

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

params : mapping of string to any

Parameter names mapped to their values.

inverse_transform(loadings)

Use provided loadings to compute corresponding linear component combination in whole-brain voxel space

Parameters:

loadings: list of numpy array (n_samples x n_components)

Component signals to tranform back into voxel signals

Returns:

reconstructed_imgs: list of nibabel.Nifti1Image

For each loading, reconstructed Nifti1Image

score(imgs, confounds=None)

Score function based on explained variance on imgs.

Should only be used by DecompositionEstimator derived classes

Parameters:

imgs: iterable of Niimg-like objects

confounds: CSV file path or 2D matrix

This parameter is passed to nilearn.signal.clean. Please see the related documentation for details

Returns:

score: float,

Holds the score for each subjects. Score is two dimensional if per_component is True. First dimension is squeezed if the number of subjects is one

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:self
transform(imgs, confounds=None)

Project the data into a reduced representation

Parameters:

imgs: iterable of Niimg-like objects

confounds: CSV file path or 2D matrix

This parameter is passed to nilearn.signal.clean. Please see the related documentation for details

Returns:

loadings: list of 2D ndarray,

For each subject, each sample, loadings for each decomposition components shape: number of subjects * (number of scans, number of regions)