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<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>
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<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>
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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/02_decoding/plot_haxby_multiclass.html">The haxby dataset: different multi-class strategies</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../auto_examples/02_decoding/plot_haxby_full_analysis.html">ROI-based decoding analysis in 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>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/03_connectivity/plot_probabilistic_atlas_extraction.html">Extracting signals of a probabilistic atlas of functional regions</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../auto_examples/03_connectivity/plot_multi_subject_connectome.html">Group Sparse inverse covariance for multi-subject connectome</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/03_connectivity/plot_seed_to_voxel_correlation.html">Producing single subject maps of seed-to-voxel correlation</a></li>
<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>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/03_connectivity/plot_signal_extraction.html">Extracting signals from a brain parcellation</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/03_connectivity/plot_sphere_based_connectome.html">Extract signals on spheres and plot a connectome</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_bids_features.html">First level analysis of a complete BIDS dataset from openneuro</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/04_glm_first_level/plot_localizer_surface_analysis.html">Example of surface-based first-level analysis</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/04_glm_first_level/plot_two_runs_model.html">Simple example of two-runs fMRI model fitting</a></li>
<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_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_second_level_association_test.html">Example of generic design in second-level models</a></li>
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<li class="toctree-l2 has-children"><a class="reference internal" href="../auto_examples/06_manipulating_images/index.html">Manipulating brain image volumes</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" role="switch" type="checkbox"/><label for="toctree-checkbox-8"><div class="visually-hidden">Toggle navigation of Manipulating brain image volumes</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/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>
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<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-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-l1 has-children"><a class="reference internal" href="../modules/index.html">API References</a><input class="toctree-checkbox" id="toctree-checkbox-18" name="toctree-checkbox-18" role="switch" type="checkbox"/><label for="toctree-checkbox-18"><div class="visually-hidden">Toggle navigation of API References</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l2 has-children"><a class="reference internal" href="../modules/connectome.html"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.connectome</span></code>: Functional Connectivity</a><input class="toctree-checkbox" id="toctree-checkbox-19" name="toctree-checkbox-19" role="switch" type="checkbox"/><label for="toctree-checkbox-19"><div class="visually-hidden">Toggle navigation of nilearn.connectome: Functional Connectivity</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.connectome.ConnectivityMeasure.html">nilearn.connectome.ConnectivityMeasure</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.connectome.GroupSparseCovariance.html">nilearn.connectome.GroupSparseCovariance</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.connectome.GroupSparseCovarianceCV.html">nilearn.connectome.GroupSparseCovarianceCV</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.connectome.sym_matrix_to_vec.html">nilearn.connectome.sym_matrix_to_vec</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.connectome.vec_to_sym_matrix.html">nilearn.connectome.vec_to_sym_matrix</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.connectome.group_sparse_covariance.html">nilearn.connectome.group_sparse_covariance</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.connectome.cov_to_corr.html">nilearn.connectome.cov_to_corr</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.connectome.prec_to_partial.html">nilearn.connectome.prec_to_partial</a></li>
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<article role="main">
<section id="id1">
<h1>0.2.5.1<a class="headerlink" href="#id1" title="Link to this heading">#</a></h1>
<p><strong>Released August 2016</strong></p>
<p>This is a bugfix release.
The new minimum required version of <code class="docutils literal notranslate"><span class="pre">scikit-learn</span></code> is <code class="docutils literal notranslate"><span class="pre">0.14.1</span></code>.</p>
<section id="changes">
<h2>Changes<a class="headerlink" href="#changes" title="Link to this heading">#</a></h2>
<ul class="simple">
<li><p><span class="sd-sphinx-override sd-badge sd-bg-dark sd-bg-text-dark">Code</span> Default option for <code class="docutils literal notranslate"><span class="pre">dim</span></code> argument in plotting functions which uses <a class="reference internal" href="../glossary.html#term-MNI"><span class="xref std std-term">MNI</span></a> template as a background image is now changed to ‘auto’ mode. Meaning that an automatic contrast setting on background image is applied by default.</p></li>
<li><p><span class="sd-sphinx-override sd-badge sd-bg-dark sd-bg-text-dark">Code</span> <code class="docutils literal notranslate"><span class="pre">Scikit-learn</span></code> validation tools have been imported and are now used to check consistency of input data, in SpaceNet for example.</p></li>
</ul>
</section>
<section id="new">
<h2>New<a class="headerlink" href="#new" title="Link to this heading">#</a></h2>
<ul class="simple">
<li><p><span class="sd-sphinx-override sd-badge sd-bg-success sd-bg-text-success">API</span> Add an option to select only off-diagonal elements in function <code class="docutils literal notranslate"><span class="pre">sym_to_vec</span></code>. Also, the scaling of matrices is modified: we divide the diagonal by <code class="docutils literal notranslate"><span class="pre">sqrt(2)</span></code> instead of multiplying the off-diagonal elements.</p></li>
<li><p><span class="sd-sphinx-override sd-badge sd-bg-primary sd-bg-text-primary">Doc</span> Connectivity examples rely on <a class="reference internal" href="../modules/generated/nilearn.connectome.ConnectivityMeasure.html#nilearn.connectome.ConnectivityMeasure" title="nilearn.connectome.ConnectivityMeasure"><code class="xref py py-class docutils literal notranslate"><span class="pre">ConnectivityMeasure</span></code></a>.</p></li>
</ul>
</section>
<section id="fixes">
<h2>Fixes<a class="headerlink" href="#fixes" title="Link to this heading">#</a></h2>
<ul class="simple">
<li><p><span class="sd-sphinx-override sd-badge sd-bg-dark sd-bg-text-dark">Code</span> <code class="docutils literal notranslate"><span class="pre">Scipy</span> <span class="pre">0.18</span></code> introduces a bug in a corner-case of resampling. Nilearn <code class="docutils literal notranslate"><span class="pre">0.2.5</span></code> can give wrong results with <code class="docutils literal notranslate"><span class="pre">scipy</span> <span class="pre">0.18</span></code>, but this is fixed in <code class="docutils literal notranslate"><span class="pre">0.2.6</span></code>.</p></li>
<li><p><span class="sd-sphinx-override sd-badge sd-bg-secondary sd-bg-text-secondary">Maint</span> Broken links and references fixed in docs.</p></li>
</ul>
</section>
</section>
<section id="id2">
<h1>0.2.5<a class="headerlink" href="#id2" title="Link to this heading">#</a></h1>
<p><strong>Released June 2016</strong></p>
<p>The <code class="docutils literal notranslate"><span class="pre">0.2.5</span></code> release includes plotting for connectomes and glass brain with
hemisphere-specific projection, as well as more didactic examples and
improved documentation.</p>
<section id="id3">
<h2>New<a class="headerlink" href="#id3" title="Link to this heading">#</a></h2>
<ul class="simple">
<li><p><span class="sd-sphinx-override sd-badge sd-bg-success sd-bg-text-success">API</span> New display_mode options in functions <a class="reference internal" href="../modules/generated/nilearn.plotting.plot_glass_brain.html#nilearn.plotting.plot_glass_brain" title="nilearn.plotting.plot_glass_brain"><code class="xref py py-func docutils literal notranslate"><span class="pre">plot_glass_brain</span></code></a> and <a class="reference internal" href="../modules/generated/nilearn.plotting.plot_connectome.html#nilearn.plotting.plot_connectome" title="nilearn.plotting.plot_connectome"><code class="xref py py-func docutils literal notranslate"><span class="pre">plot_connectome</span></code></a>. It is possible to plot right and left hemisphere projections separately.</p></li>
<li><p><span class="sd-sphinx-override sd-badge sd-bg-success sd-bg-text-success">API</span> New function <a class="reference internal" href="../modules/generated/nilearn.datasets.load_mni152_brain_mask.html#nilearn.datasets.load_mni152_brain_mask" title="nilearn.datasets.load_mni152_brain_mask"><code class="xref py py-func docutils literal notranslate"><span class="pre">load_mni152_brain_mask</span></code></a> to load canonical brain mask image in <a class="reference internal" href="../glossary.html#term-MNI"><span class="xref std std-term">MNI</span></a> template space.</p></li>
<li><p><span class="sd-sphinx-override sd-badge sd-bg-success sd-bg-text-success">API</span> New function <a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_icbm152_brain_gm_mask.html#nilearn.datasets.fetch_icbm152_brain_gm_mask" title="nilearn.datasets.fetch_icbm152_brain_gm_mask"><code class="xref py py-func docutils literal notranslate"><span class="pre">fetch_icbm152_brain_gm_mask</span></code></a> to load brain grey matter mask image.</p></li>
<li><p><span class="sd-sphinx-override sd-badge sd-bg-success sd-bg-text-success">API</span> New function <a class="reference internal" href="../modules/generated/nilearn.image.load_img.html#nilearn.image.load_img" title="nilearn.image.load_img"><code class="xref py py-func docutils literal notranslate"><span class="pre">load_img</span></code></a> loads data from a filename or a list of filenames.</p></li>
<li><p><span class="sd-sphinx-override sd-badge sd-bg-success sd-bg-text-success">API</span> New function <a class="reference internal" href="../modules/generated/nilearn.image.clean_img.html#nilearn.image.clean_img" title="nilearn.image.clean_img"><code class="xref py py-func docutils literal notranslate"><span class="pre">clean_img</span></code></a> applies the cleaning function <a class="reference internal" href="../modules/generated/nilearn.signal.clean.html#nilearn.signal.clean" title="nilearn.signal.clean"><code class="xref py py-func docutils literal notranslate"><span class="pre">clean</span></code></a> on all <a class="reference internal" href="../glossary.html#term-voxel"><span class="xref std std-term">voxels</span></a>.</p></li>
<li><p><span class="sd-sphinx-override sd-badge sd-bg-success sd-bg-text-success">API</span> New simple data downloader <a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_localizer_button_task.html#nilearn.datasets.fetch_localizer_button_task" title="nilearn.datasets.fetch_localizer_button_task"><code class="xref py py-func docutils literal notranslate"><span class="pre">fetch_localizer_button_task</span></code></a> to simplify some examples.</p></li>
<li><p><span class="sd-sphinx-override sd-badge sd-bg-success sd-bg-text-success">API</span> New function <a class="reference internal" href="../modules/generated/nilearn.datasets.get_data_dirs.html#nilearn.datasets.get_data_dirs" title="nilearn.datasets.get_data_dirs"><code class="xref py py-func docutils literal notranslate"><span class="pre">get_data_dirs</span></code></a> to check where nilearn downloads data.</p></li>
<li><p><span class="sd-sphinx-override sd-badge sd-bg-dark sd-bg-text-dark">Code</span> Function <a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_localizer_contrasts.html#nilearn.datasets.fetch_localizer_contrasts" title="nilearn.datasets.fetch_localizer_contrasts"><code class="xref py py-func docutils literal notranslate"><span class="pre">fetch_localizer_contrasts</span></code></a> can now download a specific list of subjects rather than a range of subjects.</p></li>
</ul>
</section>
<section id="contributors">
<h2>Contributors<a class="headerlink" href="#contributors" title="Link to this heading">#</a></h2>
<p>Contributors (from <code class="docutils literal notranslate"><span class="pre">git</span> <span class="pre">shortlog</span> <span class="pre">-ns</span> <span class="pre">0.2.4..0.2.5</span></code>):</p>
<blockquote>
<div><ul class="simple">
<li><p><a class="reference external" href="https://gael-varoquaux.info/">Gael Varoquaux</a> (55)</p></li>
<li><p><a class="reference external" href="https://twinee.fr/">Alexandre Abraham</a> (39)</p></li>
<li><p><a class="reference external" href="https://github.com/MartinPerez">Martin Perez-Guevara</a> (26)</p></li>
<li><p><a class="reference external" href="https://github.com/KamalakerDadi">Kamalakar Reddy Daddy</a> (20)</p></li>
<li><p><a class="reference external" href="https://github.com/amadeuskanaan">Amadeus Kanaan</a> (8)</p></li>
<li><p><a class="reference external" href="https://github.com/aabadie">Alexandre Abadie</a> (3)</p></li>
<li><p><a class="reference external" href="https://www.amensch.fr/">Arthur Mensch</a> (3)</p></li>
<li><p><a class="reference external" href="https://dohmatob.github.io/">Elvis Dohmatob</a> (3)</p></li>
<li><p><a class="reference external" href="https://github.com/lesteve">Loic Estève</a> (3)</p></li>
<li><p><a class="reference external" href="https://jeromedockes.github.io/">Jerome Dockes</a> (2)</p></li>
<li><p>Alexandre M. S (1)</p></li>
<li><p><a class="reference external" href="https://pages.saclay.inria.fr/bertrand.thirion/">Bertrand Thirion</a> (1)</p></li>
<li><p>Ivan Gonzalez (1)</p></li>
<li><p><a class="reference external" href="https://github.com/robbisg">Roberto Guidotti</a> (1)</p></li>
</ul>
</div></blockquote>
</section>
</section>
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<li><a class="reference internal" href="#">0.2.5.1</a><ul>
<li><a class="reference internal" href="#changes">Changes</a></li>
<li><a class="reference internal" href="#new">New</a></li>
<li><a class="reference internal" href="#fixes">Fixes</a></li>
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<li><a class="reference internal" href="#id2">0.2.5</a><ul>
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