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

Tutorial 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. Tutorial examples ¶

Introductory examples that teach how to use nilearn.

../_images/sphx_glr_plot_python_101_thumb.png

Basic numerics and plotting with Python¶

../_images/sphx_glr_plot_nilearn_101_thumb.png

Basic nilearn example: manipulating and looking at data¶

../_images/sphx_glr_plot_3d_and_4d_niimg_thumb.png

3D and 4D niimgs: handling and visualizing¶

../_images/sphx_glr_plot_decoding_tutorial_thumb.png

A introduction tutorial to fMRI decoding¶

8.2. Visualization of brain images ¶

See Plotting brain images for more details.

../_images/sphx_glr_plot_atlas_thumb.png

Basic Atlas plotting¶

../_images/sphx_glr_plot_demo_glass_brain_thumb.png

Glass brain plotting in nilearn¶

../_images/sphx_glr_plot_visualize_megatrawls_netmats_thumb.png

Visualizing Megatrawls Network Matrices from Human Connectome Project¶

../_images/sphx_glr_plot_multiscale_parcellations_thumb.png

Visualizing multiscale functional brain parcellations¶

../_images/sphx_glr_plot_surface_projection_strategies_thumb.png

Technical point: Illustration of the volume to surface sampling schemes¶

../_images/sphx_glr_plot_colormaps_thumb.png

Matplotlib colormaps in Nilearn¶

../_images/sphx_glr_plot_overlay_thumb.png

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

../_images/sphx_glr_plot_dim_plotting_thumb.png

Controling the contrast of the background when plotting¶

../_images/sphx_glr_plot_visualization_thumb.png

NeuroImaging volumes visualization¶

../_images/sphx_glr_plot_prob_atlas_thumb.png

Visualizing 4D probabilistic atlas maps¶

../_images/sphx_glr_plot_3d_map_to_surface_projection_thumb.png

Making a surface plot of a 3D statistical map¶

../_images/sphx_glr_plot_haxby_masks_thumb.png

Plot Haxby masks¶

../_images/sphx_glr_plot_demo_plotting_thumb.png

Plotting tools in nilearn¶

../_images/sphx_glr_plot_surf_stat_map_thumb.png

Seed-based connectivity on the surface¶

../_images/sphx_glr_plot_surf_atlas_thumb.png

Loading and plotting of a cortical surface atlas¶

../_images/sphx_glr_plot_demo_glass_brain_extensive_thumb.png

Glass brain plotting in nilearn (all options)¶

../_images/sphx_glr_plot_demo_more_plotting_thumb.png

More plotting tools from nilearn¶

8.3. Decoding and predicting from brain images ¶

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

../_images/sphx_glr_plot_haxby_stimuli_thumb.png

Show stimuli of Haxby et al. dataset¶

../_images/sphx_glr_plot_mixed_gambles_space_net_thumb.png

SpaceNet on Jimura et al “mixed gambles” dataset.¶

../_images/sphx_glr_plot_haxby_space_net_thumb.png

Decoding with SpaceNet: face vs house object recognition¶

../_images/sphx_glr_plot_oasis_vbm_space_net_thumb.png

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

../_images/sphx_glr_plot_haxby_multiclass_thumb.png

The haxby dataset: different multi-class strategies¶

../_images/sphx_glr_plot_haxby_searchlight_thumb.png

Searchlight analysis of face vs house recognition¶

../_images/sphx_glr_plot_haxby_grid_search_thumb.png

Setting a parameter by cross-validation¶

../_images/sphx_glr_plot_haxby_anova_svm_thumb.png

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

../_images/sphx_glr_plot_haxby_full_analysis_thumb.png

ROI-based decoding analysis in Haxby et al. dataset¶

../_images/sphx_glr_plot_oasis_vbm_thumb.png

Voxel-Based Morphometry on Oasis dataset¶

../_images/sphx_glr_plot_simulated_data_thumb.png

Example of pattern recognition on simulated data¶

../_images/sphx_glr_plot_miyawaki_encoding_thumb.png

Encoding models for visual stimuli from Miyawaki et al. 2008¶

../_images/sphx_glr_plot_haxby_different_estimators_thumb.png

Different classifiers in decoding the Haxby dataset¶

../_images/sphx_glr_plot_miyawaki_reconstruction_thumb.png

Reconstruction of visual stimuli from Miyawaki et al. 2008¶

8.4. 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.

../_images/sphx_glr_plot_probabilistic_atlas_extraction_thumb.png

Extracting signals of a probabilistic atlas of functional regions¶

../_images/sphx_glr_plot_signal_extraction_thumb.png

Extracting signals from a brain parcellation¶

../_images/sphx_glr_plot_inverse_covariance_connectome_thumb.png

Computing a connectome with sparse inverse covariance¶

../_images/sphx_glr_plot_simulated_connectome_thumb.png

Connectivity structure estimation on simulated data¶

../_images/sphx_glr_plot_compare_decomposition_thumb.png

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

../_images/sphx_glr_plot_atlas_comparison_thumb.png

Comparing connectomes on different reference atlases¶

../_images/sphx_glr_plot_seed_to_voxel_correlation_thumb.png

Producing single subject maps of seed-to-voxel correlation¶

../_images/sphx_glr_plot_multi_subject_connectome_thumb.png

Group Sparse inverse covariance for multi-subject connectome¶

../_images/sphx_glr_plot_group_level_connectivity_thumb.png

Classification of age groups using functional connectivity¶

../_images/sphx_glr_plot_extract_regions_dictlearning_maps_thumb.png

Regions extraction using Dictionary Learning and functional connectomes¶

../_images/sphx_glr_plot_sphere_based_connectome_thumb.png

Extract signals on spheres and plot a connectome¶

../_images/sphx_glr_plot_data_driven_parcellations_thumb.png

Clustering methods to learn a brain parcellation from fMRI¶

8.6. Advanced statistical analysis of brain images ¶

../_images/sphx_glr_plot_ica_resting_state_thumb.png

Multivariate decompositions: Independent component analysis of fMRI¶

../_images/sphx_glr_plot_localizer_simple_analysis_thumb.png

Massively univariate analysis of a calculation task from the Localizer dataset¶

../_images/sphx_glr_plot_age_group_prediction_cross_val_thumb.png

Functional connectivity predicts age group¶

../_images/sphx_glr_plot_neurovault_meta_analysis_thumb.png

NeuroVault meta-analysis of stop-go paradigm studies.¶

../_images/sphx_glr_plot_localizer_mass_univariate_methods_thumb.png

Massively univariate analysis of a motor task from the Localizer dataset¶

../_images/sphx_glr_plot_ica_neurovault_thumb.png

NeuroVault cross-study ICA maps.¶

../_images/sphx_glr_plot_haxby_mass_univariate_thumb.png

Massively univariate analysis of face vs house recognition¶

Gallery generated by Sphinx-Gallery