Note

This page is a reference documentation. It only explains the function signature, and not how to use it. Please refer to the user guide for the big picture.

nilearn.image.index_img#

nilearn.image.index_img(imgs, index)[source]#

Indexes into a 4D Niimg-like object in the fourth dimension.

Common use cases include extracting a 3D image out of img or creating a 4D image whose data is a subset of img data.

Parameters:

imgs4D Niimg-like object: See Input and output: neuroimaging data representation.
indexAny type compatible with numpy array indexing: Used for indexing the 4D data array in the fourth dimension.

Returns:

Nifti1Image: Indexed image.

See also

nilearn.image.concat_imgs
nilearn.image.iter_img

Examples

First we concatenate two MNI152 images to create a 4D-image:

>>> from nilearn import datasets
>>> from nilearn.image import concat_imgs, index_img
>>> joint_mni_image = concat_imgs([datasets.load_mni152_template(),
...                                datasets.load_mni152_template()])
>>> print(joint_mni_image.shape)
(197, 233, 189, 2)

We can now select one slice from the last dimension of this 4D-image:

>>> single_mni_image = index_img(joint_mni_image, 1)
>>> print(single_mni_image.shape)
(197, 233, 189)

We can also select multiple frames using the slice constructor:

>>> five_mni_images = concat_imgs([datasets.load_mni152_template()] * 5)
>>> print(five_mni_images.shape)
(197, 233, 189, 5)

>>> first_three_images = index_img(five_mni_images,
...                                slice(0, 3))
>>> print(first_three_images.shape)
(197, 233, 189, 3)

Examples using `nilearn.image.index_img`#

3D and 4D niimgs: handling and visualizing

A introduction tutorial to fMRI decoding

Visualizing a probabilistic atlas: the default mode in the MSDL atlas

Cortical surface-based searchlight decoding

Decoding with FREM: face vs house vs chair object recognition

Decoding with ANOVA + SVM: face vs house in the Haxby dataset

Searchlight analysis of face vs house recognition

Decoding of a dataset after GLM fit for signal extraction

ROI-based decoding analysis in Haxby et al. dataset

Setting a parameter by cross-validation

Different classifiers in decoding the Haxby dataset

Regions extraction using dictionary learning and functional connectomes

Clustering methods to learn a brain parcellation from fMRI

Regions Extraction of Default Mode Networks using Smith Atlas

Simple example of NiftiMasker use

Understanding NiftiMasker and mask computation

Multivariate decompositions: Independent component analysis of fMRI

Massively univariate analysis of face vs house recognition

Advanced decoding using scikit learn

nilearn.image.index_img#

Examples using nilearn.image.index_img#

Examples using `nilearn.image.index_img`#