9. Nilearn usage examples

Warning

If you want to run the examples, make sure you execute them in a directory where you have write permissions, or you copy the examples into such a directory. If you install nilearn manually, make sure you have followed the instructions.

9.1. Tutorial examples

Introductory examples that teach how to use nilearn.

Basic numerics and plotting with Python

Basic nilearn example: manipulating and looking at data

3D and 4D niimgs: handling and visualizing

A introduction tutorial to fMRI decoding

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

9.2. Visualization of brain images

See Plotting brain images for more details.

Basic Atlas plotting

Glass brain plotting in nilearn

Visualizing Megatrawls Network Matrices from Human Connectome Project

Visualizing multiscale functional brain parcellations

Matplotlib colormaps in Nilearn

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

Controling the contrast of the background when plotting

NeuroImaging volumes visualization

Visualizing global patterns with a carpet plot

Plot Haxby masks

Plotting tools in nilearn

Technical point: Illustration of the volume to surface sampling schemes

Visualizing 4D probabilistic atlas maps

Making a surface plot of a 3D statistical map

Seed-based connectivity on the surface

Loading and plotting of a cortical surface atlas

Glass brain plotting in nilearn (all options)

More plotting tools from nilearn

9.3. Decoding and predicting from brain images

See Decoding and MVPA: predicting from brain images for more details.

Show stimuli of Haxby et al. dataset

FREM on Jimura et al “mixed gambles” 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

Decoding of a dataset after GLM fit for signal extraction

Setting a parameter by cross-validation

ROI-based decoding analysis in Haxby et al. dataset

Different classifiers in decoding the Haxby dataset

Example of pattern recognition on simulated data

Voxel-Based Morphometry on Oasis dataset

Encoding models for visual stimuli from Miyawaki et al. 2008

Reconstruction of visual stimuli from Miyawaki et al. 2008

9.4. Functional connectivity

See Clustering to parcellate the brain in regions, Extracting functional brain networks: ICA and related or Extracting times series to build a functional connectome for more details.

Extracting signals of a probabilistic atlas of functional regions

Extracting signals from a brain parcellation

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

Producing single subject maps of seed-to-voxel correlation

Group Sparse inverse covariance for multi-subject connectome

Comparing connectomes on different reference atlases

Classification of age groups using functional connectivity

Regions extraction using Dictionary Learning and functional connectomes

Clustering methods to learn a brain parcellation from fMRI

Extract signals on spheres and plot a connectome

9.5. GLM: First level analysis examples

These are examples focused on showcasing first level models functionality and single subject analysis.

See Analyzing fMRI using GLMs for more details.

Example of hemodynamic reponse functions.

Example of a events.tsv file generation: the neurospin/localizer events.

Example of explicit fixed effects fMRI model fitting

Default Mode Network extraction of AHDH dataset

Examples of design matrices

Analysis of an fMRI dataset with a Finite Impule Response (FIR) model

Single-subject data (two sessions) in native space

Simple example of two-session fMRI model fitting

First level analysis of a complete BIDS dataset from openneuro

Predicted time series and residuals

Example of surface-based first-level analysis

Understanding parameters of the first-level model

9.6. GLM : Second level analysis examples

These are examples focused on showcasing second level models functionality and group level analysis.

See Analyzing fMRI using GLMs for more details.

Example of second level design matrix

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

9.7. Manipulating brain image volumes

See Manipulating images: resampling, smoothing, masking, ROIs… for more details.

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

Extracting signals from brain regions using the NiftiLabelsMasker

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

9.8. Advanced statistical analysis of brain images

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

Functional connectivity predicts age group

NeuroVault meta-analysis of stop-go paradigm studies.

Massively univariate analysis of a motor task from the Localizer dataset

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

NeuroVault cross-study ICA maps.

Massively univariate analysis of face vs house recognition

Advanced decoding using scikit learn