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<li class="toctree-l1"><a class="reference internal" href="quickstart.html">Quickstart</a></li>
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<li class="toctree-l3"><a class="reference internal" href="auto_examples/00_tutorials/plot_3d_and_4d_niimg.html">3D and 4D niimgs: handling and visualizing</a></li>
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<li class="toctree-l2 has-children"><a class="reference internal" href="auto_examples/01_plotting/index.html">Visualization of brain images</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Visualization of brain images</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/01_plotting/plot_demo_glass_brain.html">Glass brain plotting in nilearn</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/01_plotting/plot_visualize_megatrawls_netmats.html">Visualizing Megatrawls Network Matrices from Human Connectome Project</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/01_plotting/plot_prob_atlas.html">Visualizing 4D probabilistic atlas maps</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/01_plotting/plot_atlas.html">Basic Atlas plotting</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/01_plotting/plot_overlay.html">Visualizing a probabilistic atlas: the default mode in the MSDL atlas</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/01_plotting/plot_dim_plotting.html">Controlling the contrast of the background when plotting</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/01_plotting/plot_multiscale_parcellations.html">Visualizing multiscale functional brain parcellations</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/01_plotting/plot_colormaps.html">Matplotlib colormaps in Nilearn</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/01_plotting/plot_visualization.html">NeuroImaging volumes visualization</a></li>
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<li class="toctree-l3"><a class="reference internal" href="auto_examples/02_decoding/plot_haxby_stimuli.html">Show stimuli of Haxby et al. dataset</a></li>
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<li class="toctree-l3"><a class="reference internal" href="auto_examples/03_connectivity/plot_inverse_covariance_connectome.html">Computing a connectome with sparse inverse covariance</a></li>
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<li class="toctree-l3"><a class="reference internal" href="auto_examples/03_connectivity/plot_compare_decomposition.html">Deriving spatial maps from group fMRI data using ICA and Dictionary Learning</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/03_connectivity/plot_extract_regions_dictlearning_maps.html">Regions extraction using dictionary learning and functional connectomes</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/03_connectivity/plot_atlas_comparison.html">Comparing connectomes on different reference atlases</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/03_connectivity/plot_group_level_connectivity.html">Classification of age groups using functional connectivity</a></li>
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<li class="toctree-l3"><a class="reference internal" href="auto_examples/03_connectivity/plot_data_driven_parcellations.html">Clustering methods to learn a brain parcellation from fMRI</a></li>
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<li class="toctree-l3"><a class="reference internal" href="auto_examples/04_glm_first_level/plot_design_matrix.html">Examples of design matrices</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/04_glm_first_level/plot_write_events_file.html">Generate an events.tsv file for the NeuroSpin localizer task</a></li>
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<li class="toctree-l3"><a class="reference internal" href="auto_examples/04_glm_first_level/plot_spm_multimodal_faces.html">Single-subject data (two runs) in native space</a></li>
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<li class="toctree-l3"><a class="reference internal" href="auto_examples/04_glm_first_level/plot_first_level_details.html">Understanding parameters of the first-level model</a></li>
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<li class="toctree-l3"><a class="reference internal" href="auto_examples/05_glm_second_level/plot_second_level_design_matrix.html">Example of second level design matrix</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/05_glm_second_level/plot_proportion_activated_voxels.html">Second-level fMRI model: true positive proportion in clusters</a></li>
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<li class="toctree-l3"><a class="reference internal" href="auto_examples/05_glm_second_level/plot_oasis.html">Voxel-Based Morphometry on OASIS dataset</a></li>
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<li class="toctree-l3"><a class="reference internal" href="auto_examples/05_glm_second_level/plot_second_level_association_test.html">Example of generic design in second-level models</a></li>
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<li class="toctree-l3"><a class="reference internal" href="auto_examples/06_manipulating_images/plot_negate_image.html">Negating an image with math_img</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/06_manipulating_images/plot_compare_mean_image.html">Comparing the means of 2 images</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/06_manipulating_images/plot_smooth_mean_image.html">Smoothing an image</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/06_manipulating_images/plot_extract_regions_labels_image.html">Breaking an atlas of labels in separated regions</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/06_manipulating_images/plot_resample_to_template.html">Resample an image to a template</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/06_manipulating_images/plot_extract_rois_smith_atlas.html">Regions Extraction of Default Mode Networks using Smith Atlas</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/06_manipulating_images/plot_nifti_simple.html">Simple example of NiftiMasker use</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/06_manipulating_images/plot_extract_rois_statistical_maps.html">Region Extraction using a t-statistical map (3D)</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/06_manipulating_images/plot_nifti_labels_simple.html">Extracting signals from brain regions using the NiftiLabelsMasker</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/06_manipulating_images/plot_mask_computation.html">Understanding NiftiMasker and mask computation</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/06_manipulating_images/plot_affine_transformation.html">Visualization of affine resamplings</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/06_manipulating_images/plot_roi_extraction.html">Computing a Region of Interest (ROI) mask manually</a></li>
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<li class="toctree-l2 has-children"><a class="reference internal" href="auto_examples/07_advanced/index.html">Advanced statistical analysis of brain images</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" role="switch" type="checkbox"/><label for="toctree-checkbox-9"><div class="visually-hidden">Toggle navigation of Advanced statistical analysis of brain images</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/07_advanced/plot_ica_resting_state.html">Multivariate decompositions: Independent component analysis of fMRI</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/07_advanced/plot_localizer_simple_analysis.html">Massively univariate analysis of a calculation task from the Localizer dataset</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/07_advanced/plot_bids_analysis.html">BIDS dataset first and second level analysis</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/07_advanced/plot_neurovault_meta_analysis.html">NeuroVault meta-analysis of stop-go paradigm studies</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/07_advanced/plot_age_group_prediction_cross_val.html">Functional connectivity predicts age group</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/07_advanced/plot_surface_bids_analysis.html">Surface-based dataset first and second level analysis of a dataset</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/07_advanced/plot_localizer_mass_univariate_methods.html">Massively univariate analysis of a motor task from the Localizer dataset</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/07_advanced/plot_ica_neurovault.html">NeuroVault cross-study ICA maps</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/07_advanced/plot_haxby_mass_univariate.html">Massively univariate analysis of face vs house recognition</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/07_advanced/plot_advanced_decoding_scikit.html">Advanced decoding using scikit learn</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/07_advanced/plot_beta_series.html">Beta-Series Modeling for Task-Based Functional Connectivity and Decoding</a></li>
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<li class="toctree-l3"><a class="reference internal" href="auto_examples/08_experimental/plot_surface_image_and_maskers.html">A short demo of the surface images & maskers</a></li>
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<li class="toctree-l1 has-children"><a class="reference internal" href="user_guide.html">User guide</a><input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" role="switch" type="checkbox"/><label for="toctree-checkbox-11"><div class="visually-hidden">Toggle navigation of User guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l2"><a class="reference internal" href="introduction.html">1. Introduction</a></li>
<li class="toctree-l2"><a class="reference internal" href="introduction.html#what-is-nilearn">2. What is <code class="docutils literal notranslate"><span class="pre">nilearn</span></code>?</a></li>
<li class="toctree-l2"><a class="reference internal" href="introduction.html#using-nilearn-for-the-first-time">3. Using <code class="docutils literal notranslate"><span class="pre">nilearn</span></code> for the first time</a></li>
<li class="toctree-l2"><a class="reference internal" href="introduction.html#machine-learning-applications-to-neuroimaging">4. Machine learning applications to Neuroimaging</a></li>
<li class="toctree-l2 has-children"><a class="reference internal" href="decoding/index.html">5. Decoding and MVPA: predicting from brain images</a><input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" role="switch" type="checkbox"/><label for="toctree-checkbox-12"><div class="visually-hidden">Toggle navigation of 5. Decoding and MVPA: predicting from brain images</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="decoding/decoding_intro.html">5.1. An introduction to decoding</a></li>
<li class="toctree-l3"><a class="reference internal" href="decoding/estimator_choice.html">5.2. Choosing the right predictive model for neuroimaging</a></li>
<li class="toctree-l3"><a class="reference internal" href="decoding/frem.html">5.3. FREM: fast ensembling of regularized models for robust decoding</a></li>
<li class="toctree-l3"><a class="reference internal" href="decoding/space_net.html">5.4. SpaceNet: decoding with spatial structure for better maps</a></li>
<li class="toctree-l3"><a class="reference internal" href="decoding/searchlight.html">5.5. Searchlight : finding voxels containing information</a></li>
<li class="toctree-l3"><a class="reference internal" href="decoding/going_further.html">5.6. Running scikit-learn functions for more control on the analysis</a></li>
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<li class="toctree-l2 has-children"><a class="reference internal" href="connectivity/index.html">6. Functional connectivity and resting state</a><input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" role="switch" type="checkbox"/><label for="toctree-checkbox-13"><div class="visually-hidden">Toggle navigation of 6. Functional connectivity and resting state</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="connectivity/functional_connectomes.html">6.1. Extracting times series to build a functional connectome</a></li>
<li class="toctree-l3 has-children"><a class="reference internal" href="connectivity/connectome_extraction.html">6.2. Connectome extraction: inverse covariance for direct connections</a><input class="toctree-checkbox" id="toctree-checkbox-14" name="toctree-checkbox-14" role="switch" type="checkbox"/><label for="toctree-checkbox-14"><div class="visually-hidden">Toggle navigation of 6.2. Connectome extraction: inverse covariance for direct connections</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l4"><a class="reference internal" href="developers/group_sparse_covariance.html">6.2.3.1. Group-sparse covariance estimation</a></li>
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<li class="toctree-l3"><a class="reference internal" href="connectivity/resting_state_networks.html">6.3. Extracting functional brain networks: ICA and related</a></li>
<li class="toctree-l3"><a class="reference internal" href="connectivity/region_extraction.html">6.4. Region Extraction for better brain parcellations</a></li>
<li class="toctree-l3"><a class="reference internal" href="connectivity/parcellating.html">6.5. Clustering to parcellate the brain in regions</a></li>
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<li class="toctree-l2"><a class="reference internal" href="plotting/index.html">7. Plotting brain images</a></li>
<li class="toctree-l2 has-children"><a class="reference internal" href="glm/index.html">8. Analyzing fMRI using GLMs</a><input class="toctree-checkbox" id="toctree-checkbox-15" name="toctree-checkbox-15" role="switch" type="checkbox"/><label for="toctree-checkbox-15"><div class="visually-hidden">Toggle navigation of 8. Analyzing fMRI using GLMs</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="glm/glm_intro.html">8.1. An introduction to GLMs in fMRI statistical analysis</a></li>
<li class="toctree-l3"><a class="reference internal" href="glm/first_level_model.html">8.2. First level models</a></li>
<li class="toctree-l3"><a class="reference internal" href="glm/second_level_model.html">8.3. Second level models</a></li>
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<li class="toctree-l2 has-children"><a class="reference internal" href="manipulating_images/index.html">9. Manipulation brain volumes with nilearn</a><input class="toctree-checkbox" id="toctree-checkbox-16" name="toctree-checkbox-16" role="switch" type="checkbox"/><label for="toctree-checkbox-16"><div class="visually-hidden">Toggle navigation of 9. Manipulation brain volumes with nilearn</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="manipulating_images/input_output.html">9.1. Input and output: neuroimaging data representation</a></li>
<li class="toctree-l3"><a class="reference internal" href="manipulating_images/manipulating_images.html">9.2. Manipulating images: resampling, smoothing, masking, ROIs…</a></li>
<li class="toctree-l3"><a class="reference internal" href="manipulating_images/masker_objects.html">9.3. From neuroimaging volumes to data matrices: the masker objects</a></li>
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<li class="toctree-l2 has-children"><a class="reference internal" href="building_blocks/index.html">10. Advanced usage: manual pipelines and scaling up</a><input class="toctree-checkbox" id="toctree-checkbox-17" name="toctree-checkbox-17" role="switch" type="checkbox"/><label for="toctree-checkbox-17"><div class="visually-hidden">Toggle navigation of 10. Advanced usage: manual pipelines and scaling up</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="building_blocks/manual_pipeline.html">10.1. Building your own neuroimaging machine-learning pipeline</a></li>
<li class="toctree-l3"><a class="reference internal" href="building_blocks/neurovault.html">10.2. Downloading statistical maps from the Neurovault repository</a></li>
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<section id="nilearn">
<h1>Nilearn<a class="headerlink" href="#nilearn" title="Link to this heading">¶</a></h1>
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<p>Nilearn enables <strong>approachable and versatile analyses of brain
volumes</strong>. It provides statistical and machine-learning tools, with
<strong>instructive documentation & open community</strong>.</p>
<p>It supports general linear model (GLM) based analysis and leverages
the <a class="reference external" href="https://scikit-learn.org/stable/">scikit-learn</a> Python toolbox
for multivariate statistics with applications such as predictive modelling,
classification, decoding, or connectivity analysis.</p>
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<span class="fas fa-rocket"></span> Quickstart</div>
<p class="sd-card-text">Get started with Nilearn</p>
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<span class="fas fa-th"></span> Examples</div>
<p class="sd-card-text">Discover functionalities by reading examples</p>
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<span class="fas fa-book"></span> User guide</div>
<p class="sd-card-text">Learn about neuroimaging analysis</p>
</div>
<a class="sd-stretched-link reference internal" href="user_guide.html#user-guide"><span class="std std-ref"></span></a></div>
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</div>
</div>
<section id="featured-examples">
<h2>Featured examples<a class="headerlink" href="#featured-examples" title="Link to this heading">¶</a></h2>
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<img alt="_images/sphx_glr_plot_demo_glass_brain_002.png" src="_images/sphx_glr_plot_demo_glass_brain_002.png" />
</div>
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<p class="sd-card-text">Glass brain plotting</p>
</div>
<p class="sd-card-text">Explore how to retrieve data and plot whole brain cuts
in glass mode.</p>
</div>
</div>
</div>
</div>
<a class="sd-stretched-link reference external" href="auto_examples/01_plotting/plot_demo_glass_brain.html"></a></div>
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<img alt="_images/sphx_glr_plot_inverse_covariance_connectome_004.png" src="_images/sphx_glr_plot_inverse_covariance_connectome_004.png" />
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<p class="sd-card-text">Computing a connectome with sparse inverse covariance</p>
</div>
<p class="sd-card-text">Construct a functional connectome using the sparse inverse covariance,
and display the corresponding graph and matrix.</p>
</div>
</div>
</div>
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<a class="sd-stretched-link reference external" href="auto_examples/03_connectivity/plot_inverse_covariance_connectome.html"></a></div>
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<img alt="_images/sphx_glr_plot_3d_map_to_surface_projection_001.png" src="_images/sphx_glr_plot_3d_map_to_surface_projection_001.png" />
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<p class="sd-card-text">Making a surface plot of a 3D statistical map</p>
</div>
<p class="sd-card-text">Project a 3D statistical map onto a cortical mesh
and display the surface map as png or in interactive mode.</p>
</div>
</div>
</div>
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<a class="sd-stretched-link reference external" href="auto_examples/01_plotting/plot_3d_map_to_surface_projection.html"></a></div>
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<img alt="_images/sphx_glr_plot_decoding_tutorial_001.png" src="_images/sphx_glr_plot_decoding_tutorial_001.png" />
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<p class="sd-card-text">Introduction tutorial to fMRI decoding</p>
</div>
<p class="sd-card-text">Learn to perform decoding with nilearn. Reproduce the Haxby 2001 study on a face vs cat discrimination task in a mask of the ventral stream.</p>
</div>
</div>
</div>
</div>
<a class="sd-stretched-link reference external" href="auto_examples/00_tutorials/plot_decoding_tutorial.html"></a></div>
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<img alt="_images/sphx_glr_plot_oasis_vbm_002.png" src="_images/sphx_glr_plot_oasis_vbm_002.png" />
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<p class="sd-card-text">Voxel-Based Morphometry on Oasis dataset</p>
</div>
<p class="sd-card-text">Study the relationship between aging and gray matter density
using data from the OASIS project.</p>
</div>
</div>
</div>
</div>
<a class="sd-stretched-link reference external" href="auto_examples/02_decoding/plot_oasis_vbm.html"></a></div>
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<img alt="_images/sphx_glr_plot_data_driven_parcellations_001.png" src="_images/sphx_glr_plot_data_driven_parcellations_001.png" />
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<p class="sd-card-text">Clustering methods to learn a brain parcellation from fMRI</p>
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<p class="sd-card-text">Use spatially-constrained Ward-clustering, KMeans, Hierarchical KMeans
and Recursive Neighbor Agglomeration (ReNA) to create a set of parcels,
and display them.</p>
</div>
</div>
</div>
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<a class="sd-stretched-link reference external" href="auto_examples/03_connectivity/plot_data_driven_parcellations.html"></a></div>
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<div class="sd-col sd-d-flex-column sd-col-12 sd-col-xs-12 sd-col-sm-4 sd-col-md-4 sd-col-lg-4 docutils">
<img alt="_images/sphx_glr_plot_compare_decomposition_001.png" src="_images/sphx_glr_plot_compare_decomposition_001.png" />
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<div class="sd-col sd-d-flex-column sd-col-12 sd-col-xs-12 sd-col-sm-8 sd-col-md-8 sd-col-lg-8 docutils">
<div class="sd-font-weight-bold docutils">
<p class="sd-card-text">Deriving spatial maps from group fMRI data using ICA and Dictionary Learning</p>
</div>
<p class="sd-card-text">Derive spatial maps or networks from group fMRI data
using two popular decomposition methods, ICA and Dictionary learning
on data of children and young adults watching movies.</p>
</div>
</div>
</div>
</div>
<a class="sd-stretched-link reference external" href="auto_examples/03_connectivity/plot_compare_decomposition.html"></a></div>
</div>
<div class="sd-col sd-d-flex-row sd-col-12 sd-col-xs-12 sd-col-sm-12 sd-col-md-12 sd-col-lg-12 sd-mt-2 sd-mb-2 sd-ml-auto sd-mr-auto docutils">
<div class="sd-card sd-sphinx-override sd-w-100 sd-shadow-sm sd-card-hover docutils">
<div class="sd-card-body docutils">
<div class="sd-container-fluid sd-sphinx-override sd-m-0 sd-p-0 docutils">
<div class="sd-row sd-g-3 sd-g-xs-3 sd-g-sm-3 sd-g-md-3 sd-g-lg-3 docutils">
<div class="sd-col sd-d-flex-column sd-col-12 sd-col-xs-12 sd-col-sm-4 sd-col-md-4 sd-col-lg-4 docutils">
<img alt="_images/sphx_glr_plot_haxby_frem_001.png" src="_images/sphx_glr_plot_haxby_frem_001.png" />
</div>
<div class="sd-col sd-d-flex-column sd-col-12 sd-col-xs-12 sd-col-sm-8 sd-col-md-8 sd-col-lg-8 docutils">
<div class="sd-font-weight-bold docutils">
<p class="sd-card-text">Decoding with FREM: face vs house object recognition</p>
</div>
<p class="sd-card-text">Use fast ensembling of regularized models (FREM)
to decode a face vs house discrimination task from Haxby 2001 study.</p>
</div>
</div>
</div>
</div>
<a class="sd-stretched-link reference external" href="auto_examples/02_decoding/plot_haxby_frem.html"></a></div>
</div>
<div class="sd-col sd-d-flex-row sd-col-12 sd-col-xs-12 sd-col-sm-12 sd-col-md-12 sd-col-lg-12 sd-mt-2 sd-mb-2 sd-ml-auto sd-mr-auto docutils">
<div class="sd-card sd-sphinx-override sd-w-100 sd-shadow-sm sd-card-hover docutils">
<div class="sd-card-body docutils">
<div class="sd-container-fluid sd-sphinx-override sd-m-0 sd-p-0 docutils">
<div class="sd-row sd-g-3 sd-g-xs-3 sd-g-sm-3 sd-g-md-3 sd-g-lg-3 docutils">
<div class="sd-col sd-d-flex-column sd-col-12 sd-col-xs-12 sd-col-sm-4 sd-col-md-4 sd-col-lg-4 docutils">
<img alt="_images/sphx_glr_plot_haxby_searchlight_001.png" src="_images/sphx_glr_plot_haxby_searchlight_001.png" />
</div>
<div class="sd-col sd-d-flex-column sd-col-12 sd-col-xs-12 sd-col-sm-8 sd-col-md-8 sd-col-lg-8 docutils">
<div class="sd-font-weight-bold docutils">
<p class="sd-card-text">Searchlight analysis of face vs house recognition</p>
</div>
<p class="sd-card-text">Fit a classifier a large amount of times
in order to distinguish between face- and house-related
cortical areas.</p>
</div>
</div>
</div>
</div>
<a class="sd-stretched-link reference external" href="auto_examples/02_decoding/plot_haxby_searchlight.html"></a></div>
</div>
</div>
</div>
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<p>Nilearn is part of the <a class="reference external" href="https://nipy.org/">NiPy ecosystem</a>.</p>
</section>
</section>
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