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.mean_img#

nilearn.image.mean_img(imgs, target_affine=None, target_shape=None, verbose=0, n_jobs=1)[source]#

Compute the mean of the images over time or the 4th dimension.

Note that if list of 4D images are given, the mean of each 4D image is computed separately, and the resulting mean is computed after.

Parameters:

imgsNiimg-like object or iterable of Niimg-like objects: Images to be averaged over time (see Input and output: neuroimaging data representation for a detailed description of the valid input types).
target_affinenumpy.ndarray, optional: If specified, the image is resampled corresponding to this new affine. target_affine can be a 3x3 or a 4x4 matrix.
target_shapetuple or list, optional: If specified, the image will be resized to match this new shape. len(target_shape) must be equal to 3. A target_affine has to be specified jointly with target_shape.
verboseint, default=0: Controls the amount of verbosity: higher numbers give more messages (0 means no messages).
n_jobsint, default=1: The number of CPUs to use to do the computation (-1 means ‘all CPUs’).

Returns:

Nifti1Image: Mean image.

Examples using `nilearn.image.mean_img`#

Different classifiers in decoding the Haxby dataset

Clustering methods to learn a brain parcellation from fMRI

Single-subject data (two runs) in native space

Predicted time series and residuals

Simple example of two-runs fMRI model fitting

Simple example of NiftiMasker use

Understanding NiftiMasker and mask computation

Computing a Region of Interest (ROI) mask manually

Multivariate decompositions: Independent component analysis of fMRI

Massively univariate analysis of face vs house recognition

nilearn.image.mean_img#

Examples using nilearn.image.mean_img#

Examples using `nilearn.image.mean_img`#