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.

8.11.30. nilearn.plotting.show

nilearn.plotting.show()

Show all the figures generated by nilearn and/or matplotlib.

This function is equivalent to matplotlib.pyplot.show, but is skipped on the ‘Agg’ backend where it has no effect other than to emit a warning.

8.11.30.1. Examples using nilearn.plotting.show

Basic nilearn example: manipulating and looking at data

3D and 4D niimgs: handling and visualizing

Glass brain plotting in nilearn

Visualizing Megatrawls Network Matrices from Human Connectome Project

Basic Atlas plotting

Visualizing multiscale functional brain parcellations

Matplotlib colormaps in Nilearn

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

Controlling the contrast of the background when plotting

NeuroImaging volumes visualization

Plot Haxby masks

Technical point: Illustration of the volume to surface sampling schemes

Plotting tools in nilearn

Visualizing 4D probabilistic atlas maps

Seed-based connectivity on the surface

Loading and plotting of a cortical surface atlas

Making a surface plot of a 3D statistical map

Glass brain plotting in nilearn (all options)

More plotting tools from nilearn

Show stimuli of Haxby et al. dataset

Decoding with FREM: face vs house object recognition

Voxel-Based Morphometry on Oasis dataset with Space-Net prior

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

Cortical surface-based searchlight decoding

The haxby dataset: different multi-class strategies

Searchlight analysis of face vs house recognition

Setting a parameter by cross-validation

ROI-based decoding analysis in Haxby et al. dataset

Different classifiers in decoding the Haxby dataset

Voxel-Based Morphometry on Oasis dataset

Example of pattern recognition on simulated data

Reconstruction of visual stimuli from Miyawaki et al. 2008

Extracting signals of a probabilistic atlas of functional regions

Computing a connectome with sparse inverse covariance

Connectivity structure estimation on simulated data

Deriving spatial maps from group fMRI data using ICA and Dictionary Learning

Group Sparse inverse covariance for multi-subject connectome

Regions extraction using Dictionary learning and functional connectomes

Comparing connectomes on different reference atlases

Classification of age groups using functional connectivity

Extracting signals from a brain parcellation

Extract signals on spheres and plot a connectome

Example of explicit fixed effects fMRI model fitting

Single-subject data (two sessions) in native space

Simple example of two-session fMRI model fitting

Example of surface-based first-level analysis

Second-level fMRI model: true positive proportion in clusters

Statistical testing of a second-level analysis

Voxel-Based Morphometry on Oasis dataset

Second-level fMRI model: two-sample test, unpaired and paired

Second-level fMRI model: one sample test

Example of generic design in second-level models

Negating an image with math_img

Comparing the means of 2 images

Smoothing an image

Regions Extraction of Default Mode Networks using Smith Atlas

Breaking an atlas of labels in separated regions

Resample an image to a template

Simple example of NiftiMasker use

Region Extraction using a t-statistical map (3D)

Understanding NiftiMasker and mask computation

Visualization of affine resamplings

Computing a Region of Interest (ROI) mask manually

Multivariate decompositions: Independent component analysis of fMRI

Massively univariate analysis of a calculation task from the Localizer dataset

BIDS dataset first and second level analysis

NeuroVault meta-analysis of stop-go paradigm studies.

Surface-based dataset first and second level analysis of a dataset

Massively univariate analysis of a motor task from the Localizer dataset

NeuroVault cross-study ICA maps.

Massively univariate analysis of face vs house recognition