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sg_execution_times.html
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sg_execution_times.html
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<meta property="og:description" content="72:46.490 total execution time for 93 files from all galleries:,,, Example, Time, Mem (MB),,, Different classifiers in decoding the Haxby dataset(../examples/02_decoding/plot_haxby_different_estima..." />
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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/04_glm_first_level/plot_design_matrix.html">Examples of design matrices</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>
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<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>
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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>
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<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>
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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-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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<li class="toctree-l3"><a class="reference internal" href="modules/description/ABIDE_pcp.html">ABIDE PCP dataset</a></li>
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<section id="computation-times">
<span id="sphx-glr-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Link to this heading">#</a></h1>
<p><strong>72:46.490</strong> total execution time for 93 files <strong>from all galleries</strong>:</p>
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<thead>
<tr class="row-odd"><th class="head"><p>Example</p></th>
<th class="head"><p>Time</p></th>
<th class="head"><p>Mem (MB)</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/02_decoding/plot_haxby_different_estimators.html#sphx-glr-auto-examples-02-decoding-plot-haxby-different-estimators-py"><span class="std std-ref">Different classifiers in decoding the Haxby dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/02_decoding/plot_haxby_different_estimators.py</span></code>)</p></td>
<td><p>12:52.594</p></td>
<td><p>1368.5</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/03_connectivity/plot_compare_decomposition.html#sphx-glr-auto-examples-03-connectivity-plot-compare-decomposition-py"><span class="std std-ref">Deriving spatial maps from group fMRI data using ICA and Dictionary Learning</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/03_connectivity/plot_compare_decomposition.py</span></code>)</p></td>
<td><p>07:30.450</p></td>
<td><p>2966.8</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/02_decoding/plot_haxby_full_analysis.html#sphx-glr-auto-examples-02-decoding-plot-haxby-full-analysis-py"><span class="std std-ref">ROI-based decoding analysis in Haxby et al. dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/02_decoding/plot_haxby_full_analysis.py</span></code>)</p></td>
<td><p>05:23.048</p></td>
<td><p>1367.6</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/03_connectivity/plot_extract_regions_dictlearning_maps.html#sphx-glr-auto-examples-03-connectivity-plot-extract-regions-dictlearning-maps-py"><span class="std std-ref">Regions extraction using dictionary learning and functional connectomes</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/03_connectivity/plot_extract_regions_dictlearning_maps.py</span></code>)</p></td>
<td><p>03:26.005</p></td>
<td><p>1382.1</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/02_decoding/plot_haxby_grid_search.html#sphx-glr-auto-examples-02-decoding-plot-haxby-grid-search-py"><span class="std std-ref">Setting a parameter by cross-validation</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/02_decoding/plot_haxby_grid_search.py</span></code>)</p></td>
<td><p>03:19.233</p></td>
<td><p>915.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/03_connectivity/plot_sphere_based_connectome.html#sphx-glr-auto-examples-03-connectivity-plot-sphere-based-connectome-py"><span class="std std-ref">Extract signals on spheres and plot a connectome</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/03_connectivity/plot_sphere_based_connectome.py</span></code>)</p></td>
<td><p>02:49.715</p></td>
<td><p>957.2</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/04_glm_first_level/plot_first_level_details.html#sphx-glr-auto-examples-04-glm-first-level-plot-first-level-details-py"><span class="std std-ref">Understanding parameters of the first-level model</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/04_glm_first_level/plot_first_level_details.py</span></code>)</p></td>
<td><p>02:48.258</p></td>
<td><p>252.1</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/07_advanced/plot_age_group_prediction_cross_val.html#sphx-glr-auto-examples-07-advanced-plot-age-group-prediction-cross-val-py"><span class="std std-ref">Functional connectivity predicts age group</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/07_advanced/plot_age_group_prediction_cross_val.py</span></code>)</p></td>
<td><p>02:28.122</p></td>
<td><p>1143.1</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/05_glm_second_level/plot_oasis.html#sphx-glr-auto-examples-05-glm-second-level-plot-oasis-py"><span class="std std-ref">Voxel-Based Morphometry on OASIS dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/05_glm_second_level/plot_oasis.py</span></code>)</p></td>
<td><p>02:27.139</p></td>
<td><p>1509.7</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/03_connectivity/plot_group_level_connectivity.html#sphx-glr-auto-examples-03-connectivity-plot-group-level-connectivity-py"><span class="std std-ref">Classification of age groups using functional connectivity</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/03_connectivity/plot_group_level_connectivity.py</span></code>)</p></td>
<td><p>02:14.782</p></td>
<td><p>1209.3</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/07_advanced/plot_beta_series.html#sphx-glr-auto-examples-07-advanced-plot-beta-series-py"><span class="std std-ref">Beta-Series Modeling for Task-Based Functional Connectivity and Decoding</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/07_advanced/plot_beta_series.py</span></code>)</p></td>
<td><p>02:12.429</p></td>
<td><p>474.1</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/07_advanced/plot_surface_bids_analysis.html#sphx-glr-auto-examples-07-advanced-plot-surface-bids-analysis-py"><span class="std std-ref">Surface-based dataset first and second level analysis of a dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/07_advanced/plot_surface_bids_analysis.py</span></code>)</p></td>
<td><p>01:51.082</p></td>
<td><p>456.1</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/03_connectivity/plot_atlas_comparison.html#sphx-glr-auto-examples-03-connectivity-plot-atlas-comparison-py"><span class="std std-ref">Comparing connectomes on different reference atlases</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/03_connectivity/plot_atlas_comparison.py</span></code>)</p></td>
<td><p>01:48.750</p></td>
<td><p>1815.3</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/04_glm_first_level/plot_spm_multimodal_faces.html#sphx-glr-auto-examples-04-glm-first-level-plot-spm-multimodal-faces-py"><span class="std std-ref">Single-subject data (two runs) in native space</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/04_glm_first_level/plot_spm_multimodal_faces.py</span></code>)</p></td>
<td><p>01:43.980</p></td>
<td><p>998.9</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/03_connectivity/plot_multi_subject_connectome.html#sphx-glr-auto-examples-03-connectivity-plot-multi-subject-connectome-py"><span class="std std-ref">Group Sparse inverse covariance for multi-subject connectome</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/03_connectivity/plot_multi_subject_connectome.py</span></code>)</p></td>
<td><p>01:42.143</p></td>
<td><p>571.6</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/02_decoding/plot_oasis_vbm.html#sphx-glr-auto-examples-02-decoding-plot-oasis-vbm-py"><span class="std std-ref">Voxel-Based Morphometry on Oasis dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/02_decoding/plot_oasis_vbm.py</span></code>)</p></td>
<td><p>01:33.126</p></td>
<td><p>2485.8</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/07_advanced/plot_localizer_mass_univariate_methods.html#sphx-glr-auto-examples-07-advanced-plot-localizer-mass-univariate-methods-py"><span class="std std-ref">Massively univariate analysis of a motor task from the Localizer dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/07_advanced/plot_localizer_mass_univariate_methods.py</span></code>)</p></td>
<td><p>01:31.502</p></td>
<td><p>165.1</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/03_connectivity/plot_data_driven_parcellations.html#sphx-glr-auto-examples-03-connectivity-plot-data-driven-parcellations-py"><span class="std std-ref">Clustering methods to learn a brain parcellation from fMRI</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/03_connectivity/plot_data_driven_parcellations.py</span></code>)</p></td>
<td><p>01:24.008</p></td>
<td><p>2852.5</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/02_decoding/plot_miyawaki_reconstruction.html#sphx-glr-auto-examples-02-decoding-plot-miyawaki-reconstruction-py"><span class="std std-ref">Reconstruction of visual stimuli from Miyawaki et al. 2008</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/02_decoding/plot_miyawaki_reconstruction.py</span></code>)</p></td>
<td><p>01:18.643</p></td>
<td><p>998.6</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/07_advanced/plot_bids_analysis.html#sphx-glr-auto-examples-07-advanced-plot-bids-analysis-py"><span class="std std-ref">BIDS dataset first and second level analysis</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/07_advanced/plot_bids_analysis.py</span></code>)</p></td>
<td><p>01:02.618</p></td>
<td><p>1078.2</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/04_glm_first_level/plot_two_runs_model.html#sphx-glr-auto-examples-04-glm-first-level-plot-two-runs-model-py"><span class="std std-ref">Simple example of two-runs fMRI model fitting</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/04_glm_first_level/plot_two_runs_model.py</span></code>)</p></td>
<td><p>00:55.372</p></td>
<td><p>725.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/05_glm_second_level/plot_second_level_one_sample_test.html#sphx-glr-auto-examples-05-glm-second-level-plot-second-level-one-sample-test-py"><span class="std std-ref">Second-level fMRI model: one sample test</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/05_glm_second_level/plot_second_level_one_sample_test.py</span></code>)</p></td>
<td><p>00:51.227</p></td>
<td><p>8.6</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/04_glm_first_level/plot_bids_features.html#sphx-glr-auto-examples-04-glm-first-level-plot-bids-features-py"><span class="std std-ref">First level analysis of a complete BIDS dataset from openneuro</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/04_glm_first_level/plot_bids_features.py</span></code>)</p></td>
<td><p>00:44.592</p></td>
<td><p>698.6</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/05_glm_second_level/plot_second_level_association_test.html#sphx-glr-auto-examples-05-glm-second-level-plot-second-level-association-test-py"><span class="std std-ref">Example of generic design in second-level models</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/05_glm_second_level/plot_second_level_association_test.py</span></code>)</p></td>
<td><p>00:39.260</p></td>
<td><p>92.9</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/06_manipulating_images/plot_roi_extraction.html#sphx-glr-auto-examples-06-manipulating-images-plot-roi-extraction-py"><span class="std std-ref">Computing a Region of Interest (ROI) mask manually</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/06_manipulating_images/plot_roi_extraction.py</span></code>)</p></td>
<td><p>00:34.936</p></td>
<td><p>2257.9</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/08_experimental/plot_surface_image_and_maskers.html#sphx-glr-auto-examples-08-experimental-plot-surface-image-and-maskers-py"><span class="std std-ref">A short demo of the surface images & maskers</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/08_experimental/plot_surface_image_and_maskers.py</span></code>)</p></td>
<td><p>00:33.525</p></td>
<td><p>1531.5</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/07_advanced/plot_haxby_mass_univariate.html#sphx-glr-auto-examples-07-advanced-plot-haxby-mass-univariate-py"><span class="std std-ref">Massively univariate analysis of face vs house recognition</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/07_advanced/plot_haxby_mass_univariate.py</span></code>)</p></td>
<td><p>00:32.160</p></td>
<td><p>980.3</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/07_advanced/plot_ica_neurovault.html#sphx-glr-auto-examples-07-advanced-plot-ica-neurovault-py"><span class="std std-ref">NeuroVault cross-study ICA maps</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/07_advanced/plot_ica_neurovault.py</span></code>)</p></td>
<td><p>00:28.016</p></td>
<td><p>130.6</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/06_manipulating_images/plot_extract_rois_smith_atlas.html#sphx-glr-auto-examples-06-manipulating-images-plot-extract-rois-smith-atlas-py"><span class="std std-ref">Regions Extraction of Default Mode Networks using Smith Atlas</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/06_manipulating_images/plot_extract_rois_smith_atlas.py</span></code>)</p></td>
<td><p>00:27.548</p></td>
<td><p>465.1</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/04_glm_first_level/plot_predictions_residuals.html#sphx-glr-auto-examples-04-glm-first-level-plot-predictions-residuals-py"><span class="std std-ref">Predicted time series and residuals</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/04_glm_first_level/plot_predictions_residuals.py</span></code>)</p></td>
<td><p>00:25.390</p></td>
<td><p>544.5</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/03_connectivity/plot_inverse_covariance_connectome.html#sphx-glr-auto-examples-03-connectivity-plot-inverse-covariance-connectome-py"><span class="std std-ref">Computing a connectome with sparse inverse covariance</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/03_connectivity/plot_inverse_covariance_connectome.py</span></code>)</p></td>
<td><p>00:24.106</p></td>
<td><p>1337.8</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/07_advanced/plot_advanced_decoding_scikit.html#sphx-glr-auto-examples-07-advanced-plot-advanced-decoding-scikit-py"><span class="std std-ref">Advanced decoding using scikit learn</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/07_advanced/plot_advanced_decoding_scikit.py</span></code>)</p></td>
<td><p>00:19.913</p></td>
<td><p>916.1</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/03_connectivity/plot_seed_to_voxel_correlation.html#sphx-glr-auto-examples-03-connectivity-plot-seed-to-voxel-correlation-py"><span class="std std-ref">Producing single subject maps of seed-to-voxel correlation</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/03_connectivity/plot_seed_to_voxel_correlation.py</span></code>)</p></td>
<td><p>00:19.910</p></td>
<td><p>1140.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/03_connectivity/plot_signal_extraction.html#sphx-glr-auto-examples-03-connectivity-plot-signal-extraction-py"><span class="std std-ref">Extracting signals from a brain parcellation</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/03_connectivity/plot_signal_extraction.py</span></code>)</p></td>
<td><p>00:19.534</p></td>
<td><p>755.1</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/04_glm_first_level/plot_adhd_dmn.html#sphx-glr-auto-examples-04-glm-first-level-plot-adhd-dmn-py"><span class="std std-ref">Default Mode Network extraction of ADHD dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/04_glm_first_level/plot_adhd_dmn.py</span></code>)</p></td>
<td><p>00:19.287</p></td>
<td><p>759.4</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/02_decoding/plot_miyawaki_encoding.html#sphx-glr-auto-examples-02-decoding-plot-miyawaki-encoding-py"><span class="std std-ref">Encoding models for visual stimuli from Miyawaki et al. 2008</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/02_decoding/plot_miyawaki_encoding.py</span></code>)</p></td>
<td><p>00:19.237</p></td>
<td><p>1028.6</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/06_manipulating_images/plot_nifti_simple.html#sphx-glr-auto-examples-06-manipulating-images-plot-nifti-simple-py"><span class="std std-ref">Simple example of NiftiMasker use</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/06_manipulating_images/plot_nifti_simple.py</span></code>)</p></td>
<td><p>00:17.455</p></td>
<td><p>897.8</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/05_glm_second_level/plot_second_level_two_sample_test.html#sphx-glr-auto-examples-05-glm-second-level-plot-second-level-two-sample-test-py"><span class="std std-ref">Second-level fMRI model: two-sample test, unpaired and paired</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/05_glm_second_level/plot_second_level_two_sample_test.py</span></code>)</p></td>
<td><p>00:16.958</p></td>
<td><p>8.6</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/04_glm_first_level/plot_localizer_surface_analysis.html#sphx-glr-auto-examples-04-glm-first-level-plot-localizer-surface-analysis-py"><span class="std std-ref">Example of surface-based first-level analysis</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/04_glm_first_level/plot_localizer_surface_analysis.py</span></code>)</p></td>
<td><p>00:15.780</p></td>
<td><p>210.2</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/07_advanced/plot_neurovault_meta_analysis.html#sphx-glr-auto-examples-07-advanced-plot-neurovault-meta-analysis-py"><span class="std std-ref">NeuroVault meta-analysis of stop-go paradigm studies</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/07_advanced/plot_neurovault_meta_analysis.py</span></code>)</p></td>
<td><p>00:14.937</p></td>
<td><p>8.6</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/06_manipulating_images/plot_mask_computation.html#sphx-glr-auto-examples-06-manipulating-images-plot-mask-computation-py"><span class="std std-ref">Understanding NiftiMasker and mask computation</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/06_manipulating_images/plot_mask_computation.py</span></code>)</p></td>
<td><p>00:14.699</p></td>
<td><p>507.6</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/05_glm_second_level/plot_thresholding.html#sphx-glr-auto-examples-05-glm-second-level-plot-thresholding-py"><span class="std std-ref">Statistical testing of a second-level analysis</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/05_glm_second_level/plot_thresholding.py</span></code>)</p></td>
<td><p>00:13.176</p></td>
<td><p>8.8</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/07_advanced/plot_ica_resting_state.html#sphx-glr-auto-examples-07-advanced-plot-ica-resting-state-py"><span class="std std-ref">Multivariate decompositions: Independent component analysis of fMRI</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/07_advanced/plot_ica_resting_state.py</span></code>)</p></td>
<td><p>00:12.777</p></td>
<td><p>532.8</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/04_glm_first_level/plot_fir_model.html#sphx-glr-auto-examples-04-glm-first-level-plot-fir-model-py"><span class="std std-ref">Analysis of an fMRI dataset with a Finite Impule Response (FIR) model</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/04_glm_first_level/plot_fir_model.py</span></code>)</p></td>
<td><p>00:12.085</p></td>
<td><p>255.4</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/00_tutorials/plot_nilearn_101.html#sphx-glr-auto-examples-00-tutorials-plot-nilearn-101-py"><span class="std std-ref">Basic nilearn example: manipulating and looking at data</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/00_tutorials/plot_nilearn_101.py</span></code>)</p></td>
<td><p>00:09.033</p></td>
<td><p>286.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/02_decoding/plot_simulated_data.html#sphx-glr-auto-examples-02-decoding-plot-simulated-data-py"><span class="std std-ref">Example of pattern recognition on simulated data</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/02_decoding/plot_simulated_data.py</span></code>)</p></td>
<td><p>00:07.817</p></td>
<td><p>42.8</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/03_connectivity/plot_probabilistic_atlas_extraction.html#sphx-glr-auto-examples-03-connectivity-plot-probabilistic-atlas-extraction-py"><span class="std std-ref">Extracting signals of a probabilistic atlas of functional regions</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/03_connectivity/plot_probabilistic_atlas_extraction.py</span></code>)</p></td>
<td><p>00:07.555</p></td>
<td><p>560.9</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/06_manipulating_images/plot_nifti_labels_simple.html#sphx-glr-auto-examples-06-manipulating-images-plot-nifti-labels-simple-py"><span class="std std-ref">Extracting signals from brain regions using the NiftiLabelsMasker</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/06_manipulating_images/plot_nifti_labels_simple.py</span></code>)</p></td>
<td><p>00:07.345</p></td>
<td><p>639.8</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/06_manipulating_images/plot_extract_rois_statistical_maps.html#sphx-glr-auto-examples-06-manipulating-images-plot-extract-rois-statistical-maps-py"><span class="std std-ref">Region Extraction using a t-statistical map (3D)</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/06_manipulating_images/plot_extract_rois_statistical_maps.py</span></code>)</p></td>
<td><p>00:06.950</p></td>
<td><p>8.6</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/05_glm_second_level/plot_proportion_activated_voxels.html#sphx-glr-auto-examples-05-glm-second-level-plot-proportion-activated-voxels-py"><span class="std std-ref">Second-level fMRI model: true positive proportion in clusters</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/05_glm_second_level/plot_proportion_activated_voxels.py</span></code>)</p></td>
<td><p>00:06.859</p></td>
<td><p>8.8</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/03_connectivity/plot_simulated_connectome.html#sphx-glr-auto-examples-03-connectivity-plot-simulated-connectome-py"><span class="std std-ref">Connectivity structure estimation on simulated data</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/03_connectivity/plot_simulated_connectome.py</span></code>)</p></td>
<td><p>00:04.029</p></td>
<td><p>8.9</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/07_advanced/plot_localizer_simple_analysis.html#sphx-glr-auto-examples-07-advanced-plot-localizer-simple-analysis-py"><span class="std std-ref">Massively univariate analysis of a calculation task from the Localizer dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/07_advanced/plot_localizer_simple_analysis.py</span></code>)</p></td>
<td><p>00:03.785</p></td>
<td><p>8.8</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/06_manipulating_images/plot_resample_to_template.html#sphx-glr-auto-examples-06-manipulating-images-plot-resample-to-template-py"><span class="std std-ref">Resample an image to a template</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/06_manipulating_images/plot_resample_to_template.py</span></code>)</p></td>
<td><p>00:03.396</p></td>
<td><p>8.6</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/04_glm_first_level/plot_write_events_file.html#sphx-glr-auto-examples-04-glm-first-level-plot-write-events-file-py"><span class="std std-ref">Generate an events.tsv file for the NeuroSpin localizer task</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/04_glm_first_level/plot_write_events_file.py</span></code>)</p></td>
<td><p>00:02.324</p></td>
<td><p>8.9</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/04_glm_first_level/plot_design_matrix.html#sphx-glr-auto-examples-04-glm-first-level-plot-design-matrix-py"><span class="std std-ref">Examples of design matrices</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/04_glm_first_level/plot_design_matrix.py</span></code>)</p></td>
<td><p>00:02.194</p></td>
<td><p>9.4</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/06_manipulating_images/plot_affine_transformation.html#sphx-glr-auto-examples-06-manipulating-images-plot-affine-transformation-py"><span class="std std-ref">Visualization of affine resamplings</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/06_manipulating_images/plot_affine_transformation.py</span></code>)</p></td>
<td><p>00:02.058</p></td>
<td><p>8.6</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="auto_examples/05_glm_second_level/plot_second_level_design_matrix.html#sphx-glr-auto-examples-05-glm-second-level-plot-second-level-design-matrix-py"><span class="std std-ref">Example of second level design matrix</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/05_glm_second_level/plot_second_level_design_matrix.py</span></code>)</p></td>
<td><p>00:01.862</p></td>
<td><p>8.8</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="auto_examples/04_glm_first_level/plot_hrf.html#sphx-glr-auto-examples-04-glm-first-level-plot-hrf-py"><span class="std std-ref">Example of MRI response functions</span></a> (<code class="docutils literal notranslate"><span class="pre">../examples/04_glm_first_level/plot_hrf.py</span></code>)</p></td>
<td><p>00:01.778</p></td>