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(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.
- 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).
If specified, the image is resampled corresponding to this new affine. target_affine can be a 3x3 or a 4x4 matrix.
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.
Controls the amount of verbosity: higher numbers give more messages (0 means no messages). Default=0.
The number of CPUs to use to do the computation (-1 means ‘all CPUs’). Default=1.
For more general operations on images.
A introduction tutorial to fMRI decoding
Intro to GLM Analysis: a single-session, single-subject fMRI dataset
NeuroImaging volumes visualization
More plotting tools from nilearn
Decoding with FREM: face vs house vs chair object recognition
Searchlight analysis of face vs house recognition
Decoding of a dataset after GLM fit for signal extraction
Different classifiers in decoding the Haxby dataset
Clustering methods to learn a brain parcellation from fMRI
Example of explicit fixed effects fMRI model fitting
Single-subject data (two sessions) in native space
Simple example of two-session fMRI model fitting
Predicted time series and residuals
Comparing the means of 2 images
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