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introduction.html
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<!doctype html>
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<meta property="og:title" content="Introduction" />
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<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_atlas.html">Basic Atlas 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_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_overlay.html#visualizing-a-probabilistic-atlas-with-plot-prob-atlas">Visualizing a probabilistic atlas with plot_prob_atlas</a></li>
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<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_carpet.html">Visualizing global patterns with a carpet plot</a></li>
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<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>
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<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_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/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>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/02_decoding/plot_mixed_gambles_frem.html">FREM on Jimura et al “mixed gambles” dataset.</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/02_decoding/plot_haxby_frem.html">Decoding with FREM: face vs house object recognition</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/02_decoding/plot_oasis_vbm_space_net.html">Voxel-Based Morphometry on Oasis dataset with Space-Net prior</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/02_decoding/plot_haxby_anova_svm.html">Decoding with ANOVA + SVM: face vs house in the Haxby dataset</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/02_decoding/plot_haxby_searchlight_surface.html">Cortical surface-based searchlight decoding</a></li>
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<li class="toctree-l3"><a class="reference internal" href="auto_examples/02_decoding/plot_haxby_glm_decoding.html">Decoding of a dataset after GLM fit for signal extraction</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/02_decoding/plot_haxby_grid_search.html">Setting a parameter by cross-validation</a></li>
<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>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/02_decoding/plot_haxby_different_estimators.html">Different classifiers in decoding the Haxby dataset</a></li>
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<li class="toctree-l3"><a class="reference internal" href="auto_examples/02_decoding/plot_miyawaki_encoding.html">Encoding models for visual stimuli from Miyawaki et al. 2008</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/02_decoding/plot_miyawaki_reconstruction.html">Reconstruction of visual stimuli from Miyawaki et al. 2008</a></li>
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<li class="toctree-l2 has-children"><a class="reference internal" href="auto_examples/03_connectivity/index.html">Functional connectivity</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle child pages in navigation</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/03_connectivity/plot_probabilistic_atlas_extraction.html">Extracting signals of a probabilistic atlas of functional regions</a></li>
<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_multi_subject_connectome.html">Group Sparse inverse covariance for multi-subject connectome</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>
<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>
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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_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 sessions) 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_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_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_extract_regions_labels_image.html">Breaking an atlas of labels in separated regions</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>
<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_extract_rois_statistical_maps.html">Region Extraction using a t-statistical map (3D)</a></li>
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<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_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_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_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_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_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-l1 current has-children"><a class="reference internal" href="user_guide.html">User guide</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" role="switch" type="checkbox"/><label for="toctree-checkbox-10"><div class="visually-hidden">Toggle child pages in navigation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="current">
<li class="toctree-l2 current current-page"><a class="current reference internal" href="#">Introduction</a></li>
<li class="toctree-l2 has-children"><a class="reference internal" href="decoding/index.html">Decoding and MVPA: predicting from brain images</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 child pages in navigation</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">An introduction to decoding</a></li>
<li class="toctree-l3"><a class="reference internal" href="decoding/estimator_choice.html">Choosing the right predictive model for neuroimaging</a></li>
<li class="toctree-l3"><a class="reference internal" href="decoding/frem.html">FREM: fast ensembling of regularized models for robust decoding</a></li>
<li class="toctree-l3"><a class="reference internal" href="decoding/space_net.html">SpaceNet: decoding with spatial structure for better maps</a></li>
<li class="toctree-l3"><a class="reference internal" href="decoding/searchlight.html">Searchlight : finding voxels containing information</a></li>
<li class="toctree-l3"><a class="reference internal" href="decoding/going_further.html">Running scikit-learn functions for more control on the analysis</a></li>
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<li class="toctree-l3"><a class="reference internal" href="connectivity/functional_connectomes.html">Extracting times series to build a functional connectome</a></li>
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<li class="toctree-l3"><a class="reference internal" href="connectivity/parcellating.html">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">Plotting brain images</a></li>
<li class="toctree-l2 has-children"><a class="reference internal" href="glm/index.html">Analyzing fMRI using GLMs</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 child pages in navigation</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">An introduction to GLMs in fMRI statistical analysis</a></li>
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<li class="toctree-l3"><a class="reference internal" href="manipulating_images/input_output.html">Input and output: neuroimaging data representation</a></li>
<li class="toctree-l3"><a class="reference internal" href="manipulating_images/manipulating_images.html">Manipulating images: resampling, smoothing, masking, ROIs…</a></li>
<li class="toctree-l3"><a class="reference internal" href="manipulating_images/masker_objects.html">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">Building your own neuroimaging machine-learning pipeline</a></li>
<li class="toctree-l3"><a class="reference internal" href="building_blocks/neurovault.html">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-17" name="toctree-checkbox-17" role="switch" type="checkbox"/><label for="toctree-checkbox-17"><div class="visually-hidden">Toggle child pages in navigation</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-18" name="toctree-checkbox-18" role="switch" type="checkbox"/><label for="toctree-checkbox-18"><div class="visually-hidden">Toggle child pages in navigation</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="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>
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<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>
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<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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<li class="toctree-l2 has-children"><a class="reference internal" href="modules/datasets.html"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.datasets</span></code>: Automatic Dataset Fetching</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 child pages in navigation</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
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<section id="introduction">
<h1>Introduction<a class="headerlink" href="#introduction" title="Permalink to this heading">#</a></h1>
<section id="what-is-nilearn-mvpa-decoding-predictive-models-functional-connectivity">
<h2>What is nilearn: MVPA, decoding, predictive models, functional connectivity<a class="headerlink" href="#what-is-nilearn-mvpa-decoding-predictive-models-functional-connectivity" title="Permalink to this heading">#</a></h2>
<div class="topic">
<p class="topic-title"><strong>Why use nilearn?</strong></p>
<p>Nilearn makes it easy to use many advanced <strong>machine learning</strong>,
<strong>pattern recognition</strong> and <strong>multivariate statistical</strong> techniques on
neuroimaging data for applications such as <strong>MVPA</strong> (Mutli-Voxel
Pattern Analysis),
<a class="reference internal" href="decoding/index.html#decoding"><span class="std std-ref">decoding</span></a>,
<a class="reference internal" href="decoding/index.html#decoding"><span class="std std-ref">predictive modelling</span></a>,
<a class="reference internal" href="connectivity/functional_connectomes.html#functional-connectomes"><span class="std std-ref">functional connectivity</span></a>,
<a class="reference internal" href="connectivity/parcellating.html#parcellating-brain"><span class="std std-ref">brain parcellations</span></a>,
<a class="reference internal" href="connectivity/functional_connectomes.html#functional-connectomes"><span class="std std-ref">connectomes</span></a>.</p>
<p>Nilearn can readily be used on <a class="reference internal" href="decoding/decoding_intro.html#decoding-intro"><span class="std std-ref">task fMRI</span></a>,
<a class="reference internal" href="connectivity/functional_connectomes.html#functional-connectomes"><span class="std std-ref">resting-state</span></a>, or
<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">VBM</span></a> data.</p>
<p>For a machine-learning expert, the value of nilearn can be seen as
domain-specific <strong>feature engineering</strong> construction, that is, shaping
neuroimaging data into a feature matrix well suited to statistical
learning, or vice versa.</p>
</div>
<section id="why-is-machine-learning-relevant-to-neuroimaging-a-few-examples">
<h3>Why is machine learning relevant to NeuroImaging? A few examples!<a class="headerlink" href="#why-is-machine-learning-relevant-to-neuroimaging-a-few-examples" title="Permalink to this heading">#</a></h3>
<dl class="field-list simple">
<dt class="field-odd">Diagnosis and prognosis</dt>
<dd class="field-odd"><p>Predicting a clinical score or even treatment response
from brain imaging with <a class="reference internal" href="decoding/index.html#decoding"><span class="std std-ref">supervised
learning</span></a> e.g. <a class="reference external" href="http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0029482">[Mourao-Miranda 2012]</a></p>
</dd>
<dt class="field-even">Measuring generalization scores</dt>
<dd class="field-even"><ul class="simple">
<li><p><strong>Information mapping</strong>: using the prediction accuracy of a classifier
to characterize relationships between brain images and stimuli. (e.g.
<a class="reference internal" href="decoding/searchlight.html#searchlight"><span class="std std-ref">searchlight</span></a>) <a class="reference external" href="http://www.pnas.org/content/103/10/3863.short">[Kriegeskorte 2006]</a></p></li>
<li><p><strong>Transfer learning</strong>: measuring how much an estimator trained on one
specific psychological process/task can predict the neural activity
underlying another specific psychological process/task
(e.g. discriminating left from
right eye movements also discriminates additions from subtractions
<a class="reference external" href="http://www.sciencemag.org/content/324/5934/1583.short">[Knops 2009]</a>)</p></li>
</ul>
</dd>
<dt class="field-odd">High-dimensional multivariate statistics</dt>
<dd class="field-odd"><p>From a statistical point of view, machine learning implements
statistical estimation of models with a large number of parameters.
Tricks pulled in machine learning (e.g. regularization) can
make this estimation possible despite the usually
small number of observations in the neuroimaging domain
<a class="reference external" href="http://icml.cc/2012/papers/688.pdf">[Varoquaux 2012]</a>. This
usage of machine learning requires some understanding of the models.</p>
</dd>
<dt class="field-even">Data mining / exploration</dt>
<dd class="field-even"><p>Data-driven exploration of brain images. This includes the extraction of
the major brain networks from resting-state data (“resting-state networks”)
or movie-watching data as well as the discovery of connectionally coherent
functional modules (“connectivity-based parcellation”).
For example,
<a class="reference internal" href="connectivity/resting_state_networks.html#extracting-rsn"><span class="std std-ref">Extracting functional brain networks: ICA and related</span></a> or <a class="reference internal" href="connectivity/parcellating.html#parcellating-brain"><span class="std std-ref">Clustering to parcellate the brain in regions</span></a> with clustering.</p>
</dd>
</dl>
</section>
</section>
<section id="installing-nilearn">
<span id="installation"></span><h2>Installing <code class="docutils literal notranslate"><span class="pre">nilearn</span></code><a class="headerlink" href="#installing-nilearn" title="Permalink to this heading">#</a></h2>
<div class="sd-tab-set docutils">
<input checked="checked" id="1e4cece7-1c32-4cbe-bca8-d12364e5dd77" name="7a26b7d6-87b6-43ab-8e86-d76b9bb67c95" type="radio">
</input><label class="sd-tab-label" for="1e4cece7-1c32-4cbe-bca8-d12364e5dd77">
Latest release</label><div class="sd-tab-content docutils">
<p><strong>1. Setup a virtual environment</strong></p>
<p>We recommend that you install <code class="docutils literal notranslate"><span class="pre">nilearn</span></code>
in a virtual Python environment, either managed
with the standard library <code class="docutils literal notranslate"><span class="pre">venv</span></code>
or with <code class="docutils literal notranslate"><span class="pre">conda</span></code> (see <a class="reference external" href="https://docs.conda.io/en/latest/miniconda.html">miniconda</a> for instance).</p>
<p>Either way, create and activate a new python environment.</p>
<p>With <code class="docutils literal notranslate"><span class="pre">venv</span></code>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python3</span> <span class="o">-</span><span class="n">m</span> <span class="n">venv</span> <span class="o">/<</span><span class="n">path_to_new_env</span><span class="o">></span>
<span class="n">source</span> <span class="o">/<</span><span class="n">path_to_new_env</span><span class="o">>/</span><span class="nb">bin</span><span class="o">/</span><span class="n">activate</span>
</pre></div>
</div>
<p>Windows users should change the last line to <code class="docutils literal notranslate"><span class="pre">\<path_to_new_env>\Scripts\activate.bat</span></code>
in order to activate their virtual environment.</p>
<p>With <code class="docutils literal notranslate"><span class="pre">conda</span></code>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">conda</span> <span class="n">create</span> <span class="o">-</span><span class="n">n</span> <span class="n">nilearn</span> <span class="n">python</span><span class="o">=</span><span class="mf">3.9</span>
<span class="n">conda</span> <span class="n">activate</span> <span class="n">nilearn</span>
</pre></div>
</div>
<p><strong>2. Install nilearn with pip</strong></p>
<p>Execute the following command in the command prompt / terminal
in the proper python environment:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">pip</span> <span class="n">install</span> <span class="o">-</span><span class="n">U</span> <span class="n">nilearn</span>
</pre></div>
</div>
<p><strong>3. Check installation</strong></p>
<p>Try importing nilearn in a python / iPython session:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">nilearn</span>
</pre></div>
</div>
<p>If no error is raised, you have installed nilearn correctly.</p>
</div>
<input id="d85b0b36-c816-44ad-ab70-5b7c30d3bf9e" name="7a26b7d6-87b6-43ab-8e86-d76b9bb67c95" type="radio">
</input><label class="sd-tab-label" for="d85b0b36-c816-44ad-ab70-5b7c30d3bf9e">
Development version</label><div class="sd-tab-content docutils">
<p>In order to access the development version of nilearn,
simply clone and go to the repo:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">git</span> <span class="n">clone</span> <span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">github</span><span class="o">.</span><span class="n">com</span><span class="o">/</span><span class="n">nilearn</span><span class="o">/</span><span class="n">nilearn</span><span class="o">.</span><span class="n">git</span>
<span class="n">cd</span> <span class="n">nilearn</span>
</pre></div>
</div>
<p>Install the package in the proper conda environment with</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">pip</span> <span class="n">install</span> <span class="o">-</span><span class="n">r</span> <span class="n">requirements</span><span class="o">-</span><span class="n">dev</span><span class="o">.</span><span class="n">txt</span>
<span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">pip</span> <span class="n">install</span> <span class="o">-</span><span class="n">e</span> <span class="o">.</span>
</pre></div>
</div>
<p>To check your install, try importing nilearn in a python session:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">nilearn</span>
</pre></div>
</div>
<p>If no error is raised, you have installed nilearn correctly.</p>
</div>
</div>
</section>
<section id="python-for-neuroimaging-a-quick-start">
<span id="quick-start"></span><h2>Python for NeuroImaging, a quick start<a class="headerlink" href="#python-for-neuroimaging-a-quick-start" title="Permalink to this heading">#</a></h2>
<p>If you don’t know Python, <strong>Don’t panic. Python is easy</strong>. It is important
to realize that most things you will do in nilearn require only a few or a
few dozen lines of Python code.
Here, we give
the basics to help you get started. For a very quick start into the programming
language, you can <a class="reference external" href="http://www.learnpython.org/">learn it online</a>.
For a full-blown introduction to
using Python for science, see the <a class="reference external" href="http://scipy-lectures.github.io/">scipy lecture notes</a>.</p>
<p>We will be using <a class="reference external" href="http://jupyter.org">Jupyter</a> for notebooks, or
<a class="reference external" href="http://ipython.org">IPython</a>, which provides an interactive scientific
environment that facilitates many everyday data-manipulation steps (e.g.
interactive debugging, easy visualization). You can choose notebooks or
terminal:</p>
<dl class="field-list">
<dt class="field-odd">Notebooks</dt>
<dd class="field-odd"><p>Start the Jupyter notebook either with the application menu, or by
typing:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">jupyter</span> <span class="n">notebook</span>
</pre></div>
</div>
</dd>
<dt class="field-even">Terminal</dt>
<dd class="field-even"><p>Start iPython by typing:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">ipython</span> <span class="o">--</span><span class="n">matplotlib</span>
</pre></div>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>The <code class="docutils literal notranslate"><span class="pre">--matplotlib</span></code> flag, which configures matplotlib for
interactive use inside IPython.</p>
</div>
</dd>
</dl>
<p>These will give you a <em>prompt</em> in which you can execute commands:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">In</span> <span class="p">[</span><span class="mi">1</span><span class="p">]:</span> <span class="mi">1</span> <span class="o">+</span> <span class="mi">2</span> <span class="o">*</span> <span class="mi">3</span>
<span class="n">Out</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span> <span class="mi">7</span>
</pre></div>
</div>
<div class="topic">
<p class="topic-title"><cite>>>></cite> <strong>Prompt</strong></p>
<p>Below we’ll be using <cite>>>></cite> to indicate input lines. If you wish to copy and
paste these input lines directly, click on the <cite>>>></cite> located
at the top right of the code block to toggle these prompt signs</p>
</div>
<section id="your-first-steps-with-nilearn">
<h3>Your first steps with nilearn<a class="headerlink" href="#your-first-steps-with-nilearn" title="Permalink to this heading">#</a></h3>
<p>First things first, nilearn does not have a graphical user interface.
But you will soon realize that you don’t really need one.
It is typically used interactively in IPython or in an automated way by Python
code.
Most importantly, nilearn functions that process neuroimaging data accept
either a filename (i.e., a string variable) or a
<a class="reference external" href="https://nipy.org/nibabel/nibabel_images.html">NiftiImage object</a>. We call the latter
“niimg-like”.</p>
<p>Suppose for instance that you have a Tmap image saved in the Nifti file
“t_map000.nii” in the directory “/home/user”. To visualize that image, you will
first have to import the <a class="reference internal" href="plotting/index.html#plotting"><span class="std std-ref">plotting</span></a> functionality by:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">nilearn</span> <span class="kn">import</span> <span class="n">plotting</span>
</pre></div>
</div>
<p>Then you can call the function that creates a “glass brain” by giving it
the file name:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">plotting</span><span class="o">.</span><span class="n">plot_glass_brain</span><span class="p">(</span><span class="s2">"/home/user/t_map000.nii"</span><span class="p">)</span>
</pre></div>
</div>
<aside class="sidebar">
<p class="sidebar-title">File name matchings</p>
<p>The filename could be given as “~/t_map000.nii’ as nilearn expands “~” to
the home directory.
<a class="reference internal" href="manipulating_images/input_output.html#filename-matching"><span class="std std-ref">See more on file name matchings</span></a>.</p>
</aside>
<a class="reference external image-reference" href="auto_examples/01_plotting/plot_demo_glass_brain.html"><img alt="_images/sphx_glr_plot_demo_glass_brain_001.png" class="align-center" src="_images/sphx_glr_plot_demo_glass_brain_001.png" style="width: 396.0px; height: 156.0px;" /></a>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>There are many other plotting functions. Take your time to have a look
at the <a class="reference internal" href="plotting/index.html#plotting"><span class="std std-ref">different options</span></a>.</p>
</div>
<div class="line-block">
<div class="line"><br /></div>
</div>
<p>For simple functions/operations on images, many functions exist, such as in
the <a class="reference internal" href="modules/image.html#module-nilearn.image" title="nilearn.image"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.image</span></code></a> module for image manipulation, e.g.
<a class="reference internal" href="modules/generated/nilearn.image.smooth_img.html#nilearn.image.smooth_img" title="nilearn.image.smooth_img"><code class="xref py py-func docutils literal notranslate"><span class="pre">image.smooth_img</span></code></a> for smoothing:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">nilearn</span> <span class="kn">import</span> <span class="n">image</span>
<span class="gp">>>> </span><span class="n">smoothed_img</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">smooth_img</span><span class="p">(</span><span class="s2">"/home/user/t_map000.nii"</span><span class="p">,</span> <span class="n">fwhm</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
</pre></div>
</div>
<p>The returned value <cite>smoothed_img</cite> is a
<a class="reference external" href="https://nipy.org/nibabel/nibabel_images.html">NiftiImage object</a>. It can either be
passed to other nilearn functions operating on niimgs (neuroimaging images) or
saved to disk with:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">smoothed_img</span><span class="o">.</span><span class="n">to_filename</span><span class="p">(</span><span class="s2">"/home/user/t_map000_smoothed.nii"</span><span class="p">)</span>
</pre></div>
</div>
<p>Finally, nilearn deals with Nifti images that come in two flavors: 3D
images, which represent a brain volume, and 4D images, which represent a
series of brain volumes. To extract the n-th 3D image from a 4D image, you can
use the <a class="reference internal" href="modules/generated/nilearn.image.index_img.html#nilearn.image.index_img" title="nilearn.image.index_img"><code class="xref py py-func docutils literal notranslate"><span class="pre">image.index_img</span></code></a> function (keep in mind that array indexing
always starts at 0 in the Python language):</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">first_volume</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">index_img</span><span class="p">(</span><span class="s2">"/home/user/fmri_volumes.nii"</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
</pre></div>
</div>
<p>To loop over each individual volume of a 4D image, use <a class="reference internal" href="modules/generated/nilearn.image.iter_img.html#nilearn.image.iter_img" title="nilearn.image.iter_img"><code class="xref py py-func docutils literal notranslate"><span class="pre">image.iter_img</span></code></a>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">for</span> <span class="n">volume</span> <span class="ow">in</span> <span class="n">image</span><span class="o">.</span><span class="n">iter_img</span><span class="p">(</span><span class="s2">"/home/user/fmri_volumes.nii"</span><span class="p">):</span>
<span class="gp">... </span> <span class="n">smoothed_img</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">smooth_img</span><span class="p">(</span><span class="n">volume</span><span class="p">,</span> <span class="n">fwhm</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
</pre></div>
</div>
<div class="green topic">
<p class="topic-title"><strong>Exercise: varying the amount of smoothing</strong></p>
<p>Want to sharpen your skills with nilearn?
Compute the mean <a class="reference internal" href="glossary.html#term-EPI"><span class="xref std std-term">EPI</span></a> for first subject of the brain development
dataset downloaded with <a class="reference internal" href="modules/generated/nilearn.datasets.fetch_development_fmri.html#nilearn.datasets.fetch_development_fmri" title="nilearn.datasets.fetch_development_fmri"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.datasets.fetch_development_fmri</span></code></a> and
smooth it with an <a class="reference internal" href="glossary.html#term-FWHM"><span class="xref std std-term">FWHM</span></a> varying from 0mm to 20mm in increments of 5mm</p>
<p><strong>Hints:</strong></p>
<blockquote>
<div><ul class="simple">
<li><p>Inspect the ‘.keys()’ of the object returned by
<a class="reference internal" href="modules/generated/nilearn.datasets.fetch_development_fmri.html#nilearn.datasets.fetch_development_fmri" title="nilearn.datasets.fetch_development_fmri"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.datasets.fetch_development_fmri</span></code></a></p></li>
<li><p>Look at the “reference” section of the documentation: there is a
function to compute the mean of a 4D image</p></li>
<li><p>To perform a for loop in Python, you can use the “range” function</p></li>
<li><p>The solution can be found <a class="reference internal" href="auto_examples/06_manipulating_images/plot_smooth_mean_image.html#sphx-glr-auto-examples-06-manipulating-images-plot-smooth-mean-image-py"><span class="std std-ref">here</span></a></p></li>
</ul>
</div></blockquote>
</div>
<div class="line-block">
<div class="line"><br /></div>
</div>
<div class="topic">
<p class="topic-title"><strong>Tutorials to learn the basics</strong></p>
<p>The two following tutorials may be useful to get familiar with data
representation in nilearn:</p>
<ul class="simple">
<li><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></p></li>
<li><p><a class="reference internal" href="auto_examples/00_tutorials/plot_3d_and_4d_niimg.html#sphx-glr-auto-examples-00-tutorials-plot-3d-and-4d-niimg-py"><span class="std std-ref">3D and 4D niimgs: handling and visualizing</span></a></p></li>
</ul>
<p>More tutorials can be found <a class="reference internal" href="auto_examples/00_tutorials/index.html#tutorial-examples"><span class="std std-ref">here</span></a></p>
</div>
<hr class="docutils" />
<p>Now, if you want out-of-the-box methods to process neuroimaging data, jump
directly to the section you need:</p>
<ul class="simple">
<li><p><a class="reference internal" href="decoding/index.html#decoding"><span class="std std-ref">Decoding and MVPA: predicting from brain images</span></a></p></li>
<li><p><a class="reference internal" href="connectivity/index.html#functional-connectivity"><span class="std std-ref">Functional connectivity and resting state</span></a></p></li>
</ul>
<div class="line-block">
<div class="line"><br /></div>
</div>
</section>
<section id="scientific-computing-with-python">
<h3>Scientific computing with Python<a class="headerlink" href="#scientific-computing-with-python" title="Permalink to this heading">#</a></h3>
<p>In case you plan to become a casual nilearn user, note that you will not need
to deal with number and array manipulation directly in Python.
However, if you plan to go beyond that, here are a few pointers.</p>
<section id="basic-numerics">
<h4>Basic numerics<a class="headerlink" href="#basic-numerics" title="Permalink to this heading">#</a></h4>
<dl class="field-list">
<dt class="field-odd">Numerical arrays</dt>
<dd class="field-odd"><p>The numerical data (e.g. matrices) are stored in numpy arrays:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="gp">>>> </span><span class="n">t</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">2000</span><span class="p">)</span> <span class="c1"># 2000 points between 1 and 10</span>
<span class="gp">>>> </span><span class="n">t</span>
<span class="go">array([ 1. , 1.00450225, 1.0090045 , ..., 9.9909955 ,</span>
<span class="go"> 9.99549775, 10. ])</span>
<span class="gp">>>> </span><span class="n">t</span> <span class="o">/</span> <span class="mi">2</span>
<span class="go">array([ 0.5 , 0.50225113, 0.50450225, ..., 4.99549775,</span>
<span class="go"> 4.99774887, 5. ])</span>
<span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">t</span><span class="p">)</span> <span class="c1"># Operations on arrays are defined in the numpy module</span>
<span class="go">array([ 0.54030231, 0.53650833, 0.53270348, ..., -0.84393609,</span>
<span class="go"> -0.84151234, -0.83907153])</span>
<span class="gp">>>> </span><span class="n">t</span><span class="p">[:</span><span class="mi">3</span><span class="p">]</span> <span class="c1"># In Python indexing is done with [] and starts at zero</span>
<span class="go">array([ 1. , 1.00450225, 1.0090045 ])</span>
</pre></div>
</div>
<p><a class="reference external" href="http://scipy-lectures.github.io/intro/numpy/index.html">More documentation …</a></p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>the default integer type used by Numpy is (signed) 64-bit. Several
popular neuroimaging tools do not handle int64 Nifti images, so if
you build Nifti images directly from Numpy arrays it is recommended
to specify a smaller integer type, for example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2000</span><span class="p">,</span> <span class="mi">7</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s2">"int32"</span><span class="p">)</span>
</pre></div>
</div>
</div>
</dd>
<dt class="field-even">Plotting and figures</dt>
<dd class="field-even"><figure class="align-right">
<a class="reference external image-reference" href="auto_examples/00_tutorials/plot_python_101.html"><img alt="_images/sphx_glr_plot_python_101_001.png" src="_images/sphx_glr_plot_python_101_001.png" style="width: 192.0px; height: 144.0px;" /></a>
</figure>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="gp">>>> </span><span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">t</span><span class="p">))</span>
<span class="go">[<matplotlib.lines.Line2D object at ...>]</span>
</pre></div>
</div>
<p><a class="reference external" href="http://scipy-lectures.github.io/intro/matplotlib/matplotlib.html">More documentation …</a></p>
</dd>
<dt class="field-odd">Image processing</dt>
<dd class="field-odd"><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">scipy.ndimage</span> <span class="kn">import</span> <span class="n">gaussian_filter</span>
<span class="gp">>>> </span><span class="n">t_smooth</span> <span class="o">=</span> <span class="n">gaussian_filter</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">sigma</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
</pre></div>
</div>
<p><a class="reference external" href="http://scipy-lectures.github.io/advanced/image_processing/index.html">More documentation …</a></p>
</dd>
<dt class="field-even">Signal processing</dt>
<dd class="field-even"><div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">signal</span>
<span class="gp">>>> </span><span class="n">t_detrended</span> <span class="o">=</span> <span class="n">signal</span><span class="o">.</span><span class="n">detrend</span><span class="p">(</span><span class="n">t</span><span class="p">)</span>
</pre></div>
</div>
<p><a class="reference external" href="http://scipy-lectures.github.io/intro/scipy.html#signal-processing-scipy-signal">More documentation …</a></p>
</dd>
<dt class="field-odd">Much more</dt>