8. 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.

Contents

General examples
Visualization of brain images
Decoding and predicting from brain images
Functional connectivity
Manipulating brain image volumes
Advanced statistical analysis of brain images

8.1. General examples ¶

General-purpose and introductory examples for nilearn.

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Basic numerics and plotting with Python

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Basic nilearn example: manipulating and looking at data

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3D and 4D niimgs: handling and visualizing

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A introduction tutorial to fMRI decoding

8.2. Visualization of brain images ¶

See Plotting brain images for more details.

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Basic Atlas plotting

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Glass brain plotting in nilearn

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Visualizing Megatrawls Network Matrices from Human Connectome Project

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Visualizing multiscale functional brain parcellations

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

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Controling the contrast of the background when plotting

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NeuroImaging volumes visualization

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Visualizing 4D probabilistic atlas maps

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Loading and plotting of a cortical surface atlas

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Plot Haxby masks

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Plotting tools in nilearn

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Seed-based connectivity on the surface

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Glass brain plotting in nilearn (all options)

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More plotting tools from nilearn

8.3. Decoding and predicting from brain images ¶

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

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Show stimuli of Haxby et al. dataset

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SpaceNet on Jimura et al “mixed gambles” dataset.

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Decoding with SpaceNet: face vs house object recognition

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

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The haxby dataset: different multi-class strategies

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Searchlight analysis of face vs house recognition

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

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Setting a parameter by cross-validation

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ROI-based decoding analysis in Haxby et al. dataset

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Voxel-Based Morphometry on Oasis dataset

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Example of pattern recognition on simulated data

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Encoding models for visual stimuli from Miyawaki et al. 2008

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Different classifiers in decoding the Haxby dataset

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Reconstruction of visual stimuli from Miyawaki et al. 2008

8.4. Functional connectivity ¶

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

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Group analysis of resting-state fMRI with ICA: CanICA

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Extracting signals of a probabilistic atlas of rest functional regions

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Extracting brain signal from spheres

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Connectivity structure estimation on simulated data

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Computing a connectome with sparse inverse covariance

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Extracting signals from a brain parcellation

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Dictionary Learning and ICA for doing group analysis of resting-state fMRI

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Extract signals on spheres from an atlas and plot a connectome

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Producing single subject maps of seed-to-voxel correlation

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Ward clustering to learn a brain parcellation from rest fMRI

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Group Sparse inverse covariance for multi-subject connectome

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Functional connectivity matrices for group analysis of connectomes

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Regions extraction using Dictionary Learning and functional connectomes

8.5. Manipulating brain image volumes ¶

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

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Comparing the means of 2 images

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Smoothing an image

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Negating an image with math_img

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Regions Extraction of Default Mode Networks using Smith Atlas

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Breaking an atlas of labels in separated regions

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Simple example of NiftiMasker use

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Resample an image to a template

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

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Understanding NiftiMasker and mask computation

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Visualization of affine resamplings

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Computing a Region of Interest (ROI) mask manually

8.6. Advanced statistical analysis of brain images ¶

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Massively univariate analysis of a calculation task from the Localizer dataset

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Multivariate decompositions: Independent component analysis of fMRI

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NeuroVault meta-analysis of stop-go paradigm studies.

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Massively univariate analysis of a motor task from the Localizer dataset

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NeuroVault cross-study ICA maps.

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Massively univariate analysis of face vs house recognition

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