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<!doctype html>
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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_prob_atlas.html">Visualizing 4D probabilistic atlas maps</a></li>
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<li class="toctree-l3"><a class="reference internal" href="auto_examples/01_plotting/plot_overlay.html">Visualizing a probabilistic atlas: the default mode in the MSDL atlas</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/01_plotting/plot_dim_plotting.html">Controlling the contrast of the background when plotting</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/01_plotting/plot_multiscale_parcellations.html">Visualizing multiscale functional brain parcellations</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/01_plotting/plot_colormaps.html">Matplotlib colormaps in Nilearn</a></li>
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<li class="toctree-l3"><a class="reference internal" href="auto_examples/02_decoding/plot_haxby_stimuli.html">Show stimuli of Haxby et al. dataset</a></li>
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<li class="toctree-l3"><a class="reference internal" href="auto_examples/03_connectivity/plot_inverse_covariance_connectome.html">Computing a connectome with sparse inverse covariance</a></li>
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<li class="toctree-l3"><a class="reference internal" href="auto_examples/03_connectivity/plot_compare_decomposition.html">Deriving spatial maps from group fMRI data using ICA and Dictionary Learning</a></li>
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<li class="toctree-l3"><a class="reference internal" href="auto_examples/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-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>
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<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>
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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 current has-children"><a class="reference internal" href="user_guide.html">User guide</a><input checked="" 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 class="current">
<li class="toctree-l2 current current-page"><a class="current reference internal" href="#">1. Introduction</a></li>
<li class="toctree-l2"><a class="reference internal" href="#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="#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="#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 has-children"><a class="reference internal" href="glm/index.html">8. Analyzing fMRI using GLMs</a><input class="toctree-checkbox" id="toctree-checkbox-15" name="toctree-checkbox-15" role="switch" type="checkbox"/><label for="toctree-checkbox-15"><div class="visually-hidden">Toggle navigation of 8. Analyzing fMRI using GLMs</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="glm/glm_intro.html">8.1. An introduction to GLMs in fMRI statistical analysis</a></li>
<li class="toctree-l3"><a class="reference internal" href="glm/first_level_model.html">8.2. First level models</a></li>
<li class="toctree-l3"><a class="reference internal" href="glm/second_level_model.html">8.3. Second level models</a></li>
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<li class="toctree-l3"><a class="reference internal" href="manipulating_images/input_output.html">9.1. Input and output: neuroimaging data representation</a></li>
<li class="toctree-l3"><a class="reference internal" href="manipulating_images/manipulating_images.html">9.2. Manipulating images: resampling, smoothing, masking, ROIs…</a></li>
<li class="toctree-l3"><a class="reference internal" href="manipulating_images/masker_objects.html">9.3. From neuroimaging volumes to data matrices: the masker objects</a></li>
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<li class="toctree-l3"><a class="reference internal" href="building_blocks/manual_pipeline.html">10.1. Building your own neuroimaging machine-learning pipeline</a></li>
<li class="toctree-l3"><a class="reference internal" href="building_blocks/neurovault.html">10.2. Downloading statistical maps from the Neurovault repository</a></li>
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<article role="main">
<section id="introduction">
<h1><span class="section-number">1. </span>Introduction<a class="headerlink" href="#introduction" title="Link to this heading">#</a></h1>
</section>
<section id="what-is-nilearn">
<h1><span class="section-number">2. </span>What is <code class="docutils literal notranslate"><span class="pre">nilearn</span></code>?<a class="headerlink" href="#what-is-nilearn" title="Link to this heading">#</a></h1>
<p><code class="docutils literal notranslate"><span class="pre">nilearn</span></code> is a package that makes it easy to use advanced machine learning techniques to analyze data acquired with MRI machines.
In particular, underlying machine learning problems include
<a class="reference internal" href="decoding/index.html#decoding"><span class="std std-ref">decoding brain data</span></a>,
computing <a class="reference internal" href="connectivity/parcellating.html#parcellating-brain"><span class="std std-ref">brain parcellations</span></a>,
analyzing <a class="reference internal" href="connectivity/functional_connectomes.html#functional-connectomes"><span class="std std-ref">functional connectivity</span></a> and <a class="reference internal" href="connectivity/functional_connectomes.html#functional-connectomes"><span class="std std-ref">connectomes</span></a>,
doing multi-voxel pattern analysis (MVPA) or <a class="reference internal" href="decoding/index.html#decoding"><span class="std std-ref">predictive modelling</span></a>.</p>
<p><code class="docutils literal notranslate"><span class="pre">nilearn</span></code> 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">voxel-based morphometry (VBM)</span></a> data.</p>
<p>For machine learning experts, the value of <code class="docutils literal notranslate"><span class="pre">nilearn</span></code> can be seen as
domain-specific <strong>feature engineering</strong> construction, that is, shaping
neuroimaging data into a feature matrix well suited for statistical learning.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>It is ok if these terms don’t make sense to you yet:
this guide will walk you through them in a comprehensive manner.</p>
</div>
</section>
<section id="using-nilearn-for-the-first-time">
<span id="quick-start"></span><h1><span class="section-number">3. </span>Using <code class="docutils literal notranslate"><span class="pre">nilearn</span></code> for the first time<a class="headerlink" href="#using-nilearn-for-the-first-time" title="Link to this heading">#</a></h1>
<p><code class="docutils literal notranslate"><span class="pre">nilearn</span></code> is a Python library. If you have never used Python before,
you should probably have a look at a <a class="reference external" href="https://www.learnpython.org/">general introduction about Python</a>
as well as to <a class="reference external" href="https://lectures.scientific-python.org/">Scientific Python Lectures</a> before diving into <code class="docutils literal notranslate"><span class="pre">nilearn</span></code>.</p>
<section id="first-steps-with-nilearn">
<h2><span class="section-number">3.1. </span>First steps with nilearn<a class="headerlink" href="#first-steps-with-nilearn" title="Link to this heading">#</a></h2>
<p>At this stage, you should have <a class="reference internal" href="quickstart.html#quickstart"><span class="std std-ref">installed</span></a> <code class="docutils literal notranslate"><span class="pre">nilearn</span></code> and opened a Jupyter notebook
or an IPython / Python session. First, load <code class="docutils literal notranslate"><span class="pre">nilearn</span></code> with</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">nilearn</span>
</pre></div>
</div>
<p><code class="docutils literal notranslate"><span class="pre">nilearn</span></code> comes in with some data that are commonly used in neuroimaging.
For instance, it comes with volumic template images of brains such as MNI:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">nilearn</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">MNI152_FILE_PATH</span><span class="p">)</span>
</pre></div>
</div>
<p>Output:</p>
<div class="highlight-primary highlight-text notranslate"><div class="highlight"><pre><span></span>'/home/yasmin/nilearn/nilearn/nilearn/datasets/data/mni_icbm152_t1_tal_nlin_sym_09a_converted.nii.gz'
</pre></div>
</div>
<p>Let’s have a look at this image:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">nilearn</span><span class="o">.</span><span class="n">plotting</span><span class="o">.</span><span class="n">plot_img</span><span class="p">(</span><span class="n">nilearn</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">MNI152_FILE_PATH</span><span class="p">)</span>
</pre></div>
</div>
<a class="reference external image-reference" href="auto_examples/00_tutorials/images/sphx_glr_plot_nilearn_101_001.png"><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: 210.0px;" /></a>
</section>
<section id="learning-with-the-api-references">
<h2><span class="section-number">3.2. </span>Learning with the API references<a class="headerlink" href="#learning-with-the-api-references" title="Link to this heading">#</a></h2>
<p>In the last command, you just made use of 2 <code class="docutils literal notranslate"><span class="pre">nilearn</span></code> modules: <a class="reference internal" href="modules/datasets.html#module-nilearn.datasets" title="nilearn.datasets"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.datasets</span></code></a>
and <a class="reference internal" href="modules/plotting.html#module-nilearn.plotting" title="nilearn.plotting"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.plotting</span></code></a>.
All modules are described in the <a class="reference internal" href="modules/index.html#modules"><span class="std std-ref">API references</span></a>.</p>
<p>Oftentimes, if you are already familiar with the problems and vocabulary of MRI analysis,
the module and function names are explicit enough that you should understand what <code class="docutils literal notranslate"><span class="pre">nilearn</span></code> does.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p><strong>Exercise: Varying the amount of smoothing in an image</strong></p>
<p>Compute the mean <a class="reference internal" href="glossary.html#term-EPI"><span class="xref std std-term">EPI</span></a> for one individual 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>Intermediate steps:</strong></p>
<ol class="arabic simple">
<li><p>Run <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 inspect the <code class="docutils literal notranslate"><span class="pre">.keys()</span></code> of the returned object</p></li>
<li><p>Check 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 in the documentation to find a function to compute the mean of a 4D image</p></li>
<li><p>Check 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 again to find a function which smoothes images</p></li>
<li><p>Plot the computed image for each smoothing value</p></li>
</ol>
<p>A 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>
</div>
</section>
<section id="learning-with-examples">
<h2><span class="section-number">3.3. </span>Learning with examples<a class="headerlink" href="#learning-with-examples" title="Link to this heading">#</a></h2>
<p><code class="docutils literal notranslate"><span class="pre">nilearn</span></code> comes with a lot of <a class="reference internal" href="auto_examples/00_tutorials/index.html#tutorial-examples"><span class="std std-ref">examples/tutorials</span></a>.
Going through them should give you a precise overview of what you can achieve with this package.</p>
<p>For new-comers, we recommend going through the following examples in the suggested order:</p>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="A simple example showing how to load an existing Nifti file and use basic nilearn functiona..."><img alt="Basic nilearn example: manipulating and looking at data" src="_images/sphx_glr_plot_nilearn_101_thumb.png" />
<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>
<div class="sphx-glr-thumbnail-title">Basic nilearn example: manipulating and looking at data</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Here we discover how to work with 3D and 4D niimgs."><img alt="3D and 4D niimgs: handling and visualizing" src="_images/sphx_glr_plot_3d_and_4d_niimg_thumb.png" />
<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>
<div class="sphx-glr-thumbnail-title">3D and 4D niimgs: handling and visualizing</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Here is a simple tutorial on decoding with nilearn. It reproduces the Haxby 2001 study on a fac..."><img alt="A introduction tutorial to fMRI decoding" src="_images/sphx_glr_plot_decoding_tutorial_thumb.png" />
<p><a class="reference internal" href="auto_examples/00_tutorials/plot_decoding_tutorial.html#sphx-glr-auto-examples-00-tutorials-plot-decoding-tutorial-py"><span class="std std-ref">A introduction tutorial to fMRI decoding</span></a></p>
<div class="sphx-glr-thumbnail-title">A introduction tutorial to fMRI decoding</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="In this tutorial, we use a General Linear Model (:term:`GLM`) to compare the fMRI signal during..."><img alt="Intro to GLM Analysis: a single-run, single-subject fMRI dataset" src="_images/sphx_glr_plot_single_subject_single_run_thumb.png" />
<p><a class="reference internal" href="auto_examples/00_tutorials/plot_single_subject_single_run.html#sphx-glr-auto-examples-00-tutorials-plot-single-subject-single-run-py"><span class="std std-ref">Intro to GLM Analysis: a single-run, single-subject fMRI dataset</span></a></p>
<div class="sphx-glr-thumbnail-title">Intro to GLM Analysis: a single-run, single-subject fMRI dataset</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we will project a 3D statistical map onto a cortical mesh using vol_to_surf, d..."><img alt="Making a surface plot of a 3D statistical map" src="_images/sphx_glr_plot_3d_map_to_surface_projection_thumb.png" />
<p><a class="reference internal" href="auto_examples/01_plotting/plot_3d_map_to_surface_projection.html#sphx-glr-auto-examples-01-plotting-plot-3d-map-to-surface-projection-py"><span class="std std-ref">Making a surface plot of a 3D statistical map</span></a></p>
<div class="sphx-glr-thumbnail-title">Making a surface plot of a 3D statistical map</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows manual steps to create and further modify an ROI spatial mask. They represent..."><img alt="Computing a Region of Interest (ROI) mask manually" src="_images/sphx_glr_plot_roi_extraction_thumb.png" />
<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></p>
<div class="sphx-glr-thumbnail-title">Computing a Region of Interest (ROI) mask manually</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Here, we will go through a full step-by-step example of fitting a GLM to experimental data and ..."><img alt="Simple example of two-runs fMRI model fitting" src="auto_examples/04_glm_first_level/images/thumb/sphx_glr_plot_fiac_analysis_thumb.png" />
<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></p>
<div class="sphx-glr-thumbnail-title">Simple example of two-runs fMRI model fitting</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example compares different kinds of functional connectivity between regions of interest : ..."><img alt="Classification of age groups using functional connectivity" src="_images/sphx_glr_plot_group_level_connectivity_thumb.png" />
<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></p>
<div class="sphx-glr-thumbnail-title">Classification of age groups using functional connectivity</div>
</div></div></section>
<section id="finding-help">
<h2><span class="section-number">3.4. </span>Finding help<a class="headerlink" href="#finding-help" title="Link to this heading">#</a></h2>
<p>On top of this guide, there is a lot of content available outside of <code class="docutils literal notranslate"><span class="pre">nilearn</span></code>
that could be of interest to new-comers:</p>
<ol class="arabic simple">
<li><p><a class="reference external" href="https://www.cs.mtsu.edu/~xyang/fMRIHandBook.pdf">An introduction to fMRI</a> by Russel Poldrack, Jeanette Mumford and Thomas Nichols.</p></li>
<li><p>(For French readers) <a class="reference external" href="https://psy3018.github.io/intro.html">An introduction to cognitive neuroscience</a> given at the University of Montréal.</p></li>
<li><p>The documentation of <code class="docutils literal notranslate"><span class="pre">scikit-learn</span></code> explains each method with tips on practical use and examples: <a class="reference external" href="https://scikit-learn.org/stable/">https://scikit-learn.org/stable/</a>. While not specific to neuroimaging, it is often a recommended read.</p></li>
</ol>
<p>4. (For Python beginners) A quick and gentle introduction to scientific computing with Python with the <a class="reference external" href="https://lectures.scientific-python.org/">scientififc Python lectures</a>.
Moreover, you can use <code class="docutils literal notranslate"><span class="pre">nilearn</span></code> with <a class="reference external" href="https://jupyter.org/">Jupyter</a> notebooks or
<a class="reference external" href="https://ipython.org/">IPython</a> sessions. They provide an interactive
environment that greatly facilitates debugging and visualisation.</p>
<p>Besides, you can find help on <a class="reference external" href="https://neurostars.org/tag/nilearn/">neurostars</a> for questions
related to <code class="docutils literal notranslate"><span class="pre">nilearn</span></code> and to computational neuroscience in general.
Finally, the <code class="docutils literal notranslate"><span class="pre">nilearn</span></code> team organizes weekly <a class="reference internal" href="quickstart.html#quickstart"><span class="std std-ref">drop-in hours</span></a>.
We can also be reached on <a class="reference external" href="https://github.com/nilearn/nilearn/issues">github</a>
in case you find a bug.</p>
</section>
</section>
<section id="machine-learning-applications-to-neuroimaging">
<h1><span class="section-number">4. </span>Machine learning applications to Neuroimaging<a class="headerlink" href="#machine-learning-applications-to-neuroimaging" title="Link to this heading">#</a></h1>
<p><code class="docutils literal notranslate"><span class="pre">nilearn</span></code> brings easy-to-use machine learning tools that can be leveraged to solve more complex applications.
The interested reader can dive into the following articles for more content.</p>
<p>We give a non-exhaustive list of such important applications.</p>
<p><strong>Diagnosis and prognosis</strong></p>
<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="https://doi.org/10.1371/journal.pone.0029482">[Mourao-Miranda 2012]</a></p>
<p><strong>Information mapping</strong></p>
<p>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="https://doi.org/10.1073/pnas.0600244103">[Kriegeskorte 2006]</a></p>
<p><strong>Transfer learning</strong></p>
<p>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="https://doi.org/10.1126/science.1171599">[Knops 2009]</a>)</p>
<p><strong>High-dimensional multivariate statistics</strong></p>
<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="https://icml.cc/2012/papers/688.pdf">[Varoquaux 2012]</a>. This
usage of machine learning requires some understanding of the models.</p>
<p><strong>Data mining / exploration</strong></p>
<p>Data-driven exploration of brain images. This includes the extraction of
the major brain networks from <a class="reference internal" href="glossary.html#term-resting-state"><span class="xref std std-term">resting-state</span></a> 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>
</section>
</article>
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