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.
Basic tutorials#
Introductory examples that teach how to use nilearn.
Basic nilearn example: manipulating and looking at data
Intro to GLM Analysis: a single-session, single-subject fMRI dataset
Visualization of brain images#
See Plotting brain images for more details.
Visualizing Megatrawls Network Matrices from Human Connectome Project
Visualizing a probabilistic atlas: the default mode in the MSDL atlas
Controlling the contrast of the background when plotting
Visualizing multiscale functional brain parcellations
Technical point: Illustration of the volume to surface sampling schemes
Decoding and predicting from brain images#
See Decoding and MVPA: predicting from brain images for more details.
Voxel-Based Morphometry on Oasis dataset with Space-Net prior
Decoding with ANOVA + SVM: face vs house in the Haxby dataset
Decoding with FREM: face vs house vs chair object recognition
The haxby dataset: different multi-class strategies
Decoding of a dataset after GLM fit for signal extraction
ROI-based decoding analysis in Haxby et al. dataset
Different classifiers in decoding the Haxby dataset
Encoding models for visual stimuli from Miyawaki et al. 2008
Reconstruction of visual stimuli from Miyawaki et al. 2008
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.
Computing a connectome with sparse inverse covariance
Extracting signals of a probabilistic atlas of functional regions
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
Regions extraction using dictionary learning and functional connectomes
Comparing connectomes on different reference atlases
Classification of age groups using functional connectivity
Clustering methods to learn a brain parcellation from fMRI
GLM: First level analysis#
These are examples focused on showcasing first level models functionality and single subject analysis.
See Analyzing fMRI using GLMs for more details.
Example of explicit fixed effects fMRI model fitting
Generate an events.tsv file for the NeuroSpin localizer task
Analysis of an fMRI dataset with a Finite Impule Response (FIR) model
Single-subject data (two sessions) in native space
First level analysis of a complete BIDS dataset from openneuro
GLM: Second level analysis#
These are examples focused on showcasing second level models functionality and group level analysis.
See Analyzing fMRI using GLMs for more details.
Second-level fMRI model: true positive proportion in clusters
Second-level fMRI model: two-sample test, unpaired and paired
Manipulating brain image volumes#
See Manipulating images: resampling, smoothing, masking, ROIs… for more details.
Regions Extraction of Default Mode Networks using Smith Atlas
Extracting signals from brain regions using the NiftiLabelsMasker
Computing a Region of Interest (ROI) mask manually
Advanced statistical analysis of brain images#
Massively univariate analysis of a calculation task from the Localizer dataset
Multivariate decompositions: Independent component analysis of fMRI
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
Massively univariate analysis of face vs house recognition
Beta-Series Modeling for Task-Based Functional Connectivity and Decoding
Examples for experimental modules#
These are examples focused on showcasing experimental features and are subject to change without any notice.
