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py-modindex.html
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py-modindex.html
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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-l3"><a class="reference internal" href="auto_examples/00_tutorials/plot_decoding_tutorial.html">A introduction tutorial to fMRI decoding</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/00_tutorials/plot_single_subject_single_run.html">Intro to GLM Analysis: a single-run, single-subject fMRI dataset</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>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/01_plotting/plot_haxby_masks.html">Plot Haxby masks</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/01_plotting/plot_carpet.html">Visualizing global patterns with a carpet plot</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/01_plotting/plot_surface_projection_strategies.html">Technical point: Illustration of the volume to surface sampling schemes</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/01_plotting/plot_demo_plotting.html">Plotting tools in nilearn</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/01_plotting/plot_surf_atlas.html">Loading and plotting of a cortical surface atlas</a></li>
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<li class="toctree-l3"><a class="reference internal" href="auto_examples/01_plotting/plot_demo_more_plotting.html">More plotting tools from nilearn</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/01_plotting/plot_demo_glass_brain_extensive.html">Glass brain plotting in nilearn (all options)</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/01_plotting/plot_surf_stat_map.html">Seed-based connectivity on the surface</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>
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<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>
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<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>
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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>
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<li class="toctree-l3"><a class="reference internal" href="auto_examples/07_advanced/plot_ica_neurovault.html">NeuroVault cross-study ICA maps</a></li>
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<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-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>
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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-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-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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