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, copy_header=False)[source]¶

Compute the mean over images.

This can be a mean over time or the 4th dimension for a volume, or the 2nd dimension for a surface image.

Note that if list of 4D volume images (or 2D surface images) are given, the mean of each image is computed separately, and the resulting mean is computed after.

Parameters:

imgsNiimg-like or or SurfaceImage object, or iterable of Niimg-like or SurfaceImage.

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 or None, default=None

If specified, the image is resampled corresponding to this new affine. target_affine can be a 3x3 or a 4x4 matrix. Ignored for SurfaceImage.

target_shapetuple or list or None, default=None

If specified, the image will be resized to match this new shape. len(target_shape) must be equal to 3.

Note

If target_shape is specified, a target_affine of shape (4, 4) must also be given.

Ignored for SurfaceImage.

verboseint, default=0

Verbosity level (0 means no message).

n_jobsint, default=1

The number of CPUs to use to do the computation (-1 means ‘all CPUs’). Ignored for SurfaceImage.

copy_headerbool, default=False

Whether to copy the header of the input image to the output. Ignored for SurfaceImage.

Added in version 0.11.0.

This parameter will be set to True by default in 0.13.0.

Returns:

Nifti1Image or SurfaceImage: Mean image.

Examples using `nilearn.image.mean_img`¶

A introduction tutorial to fMRI decoding

Intro to GLM Analysis: a single-run, single-subject fMRI dataset

More plotting tools from nilearn

NeuroImaging volumes visualization

Plot Haxby masks

Plotting images with transparent thresholding

Plotting tools in nilearn

Decoding of a dataset after GLM fit for signal extraction

Decoding with FREM: face vs house vs chair object recognition

Different classifiers in decoding the Haxby dataset

Searchlight analysis of face vs house recognition

Clustering methods to learn a brain parcellation from fMRI

Predicted time series and residuals

Simple example of two-runs fMRI model fitting

Single-subject data (two runs) in native space

Comparing the means of 2 images

Computing a Region of Interest (ROI) mask manually

Simple example of NiftiMasker use

Smoothing an image

Understanding NiftiMasker and mask computation

Massively univariate analysis of face vs house recognition

Multivariate decompositions: Independent component analysis of fMRI

nilearn.image.mean_img¶

Examples using nilearn.image.mean_img¶

Examples using `nilearn.image.mean_img`¶