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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_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/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/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_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>
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<li class="toctree-l3"><a class="reference internal" href="../auto_examples/04_glm_first_level/plot_design_matrix.html">Examples of design matrices</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../auto_examples/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_two_runs_model.html">Simple example of two-runs fMRI model fitting</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>
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<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-l2 has-children"><a class="reference internal" href="../auto_examples/08_experimental/index.html">Examples for experimental modules</a><input 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 navigation of Examples for experimental modules</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/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 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"><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 current has-children"><a class="reference internal" href="index.html">8. Analyzing fMRI using GLMs</a><input checked="" 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 class="current">
<li class="toctree-l3"><a class="reference internal" href="glm_intro.html">8.1. An introduction to GLMs in fMRI statistical analysis</a></li>
<li class="toctree-l3 current current-page"><a class="current reference internal" href="#">8.2. First level models</a></li>
<li class="toctree-l3"><a class="reference internal" href="second_level_model.html">8.3. Second level models</a></li>
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<li class="toctree-l2 has-children"><a class="reference internal" href="../manipulating_images/index.html">9. Manipulation brain volumes with nilearn</a><input class="toctree-checkbox" id="toctree-checkbox-16" name="toctree-checkbox-16" role="switch" type="checkbox"/><label for="toctree-checkbox-16"><div class="visually-hidden">Toggle navigation of 9. Manipulation brain volumes with nilearn</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="../manipulating_images/input_output.html">9.1. Input and output: neuroimaging data representation</a></li>
<li class="toctree-l3"><a class="reference internal" href="../manipulating_images/manipulating_images.html">9.2. Manipulating images: resampling, smoothing, masking, ROIs…</a></li>
<li class="toctree-l3"><a class="reference internal" href="../manipulating_images/masker_objects.html">9.3. From neuroimaging volumes to data matrices: the masker objects</a></li>
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<li class="toctree-l2 has-children"><a class="reference internal" href="../building_blocks/index.html">10. Advanced usage: manual pipelines and scaling up</a><input class="toctree-checkbox" id="toctree-checkbox-17" name="toctree-checkbox-17" role="switch" type="checkbox"/><label for="toctree-checkbox-17"><div class="visually-hidden">Toggle navigation of 10. Advanced usage: manual pipelines and scaling up</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="../building_blocks/manual_pipeline.html">10.1. Building your own neuroimaging machine-learning pipeline</a></li>
<li class="toctree-l3"><a class="reference internal" href="../building_blocks/neurovault.html">10.2. Downloading statistical maps from the Neurovault repository</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../modules/description/neurovault.html">Neurovault statistical maps</a></li>
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<article role="main">
<section id="first-level-models">
<span id="first-level-model"></span><h1><span class="section-number">8.2. </span>First level models<a class="headerlink" href="#first-level-models" title="Link to this heading">#</a></h1>
<aside class="topic">
<p class="topic-title"><strong>Page summary</strong></p>
<p>First level models are, in essence, linear regression models run at the level of a single
run or single subject. The model is applied on a voxel-wise basis, either on the whole
brain or within a region of interest. The timecourse of each <a class="reference internal" href="../glossary.html#term-voxel"><span class="xref std std-term">voxel</span></a> is regressed against a
predicted <a class="reference internal" href="../glossary.html#term-BOLD"><span class="xref std std-term">BOLD</span></a> response created by convolving the haemodynamic response function (HRF) with
a set of predictors defined within the design matrix.</p>
</aside>
<section id="hrf-models">
<h2><span class="section-number">8.2.1. </span>HRF models<a class="headerlink" href="#hrf-models" title="Link to this heading">#</a></h2>
<p>Nilearn offers a few different <a class="reference internal" href="../glossary.html#term-HRF"><span class="xref std std-term">HRF</span></a> models including the commonly used double-gamma <a class="reference internal" href="../glossary.html#term-SPM"><span class="xref std std-term">SPM</span></a> model (‘spm’)
and the model shape proposed by G. Glover (‘glover’), both allowing the option of adding time and
dispersion derivatives. The addition of these derivatives allows to better model any uncertainty in
timing information. In addition, an <a class="reference internal" href="../glossary.html#term-FIR"><span class="xref std std-term">FIR</span></a> (finite impulse response, ‘fir’) model of the <a class="reference internal" href="../glossary.html#term-HRF"><span class="xref std std-term">HRF</span></a> is also available.</p>
<p>In order to visualize the predicted regressor prior to plugging it into the linear model, use the
function <a class="reference internal" href="../modules/generated/nilearn.glm.first_level.compute_regressor.html#nilearn.glm.first_level.compute_regressor" title="nilearn.glm.first_level.compute_regressor"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.glm.first_level.compute_regressor</span></code></a>, or explore the <a class="reference internal" href="../glossary.html#term-HRF"><span class="xref std std-term">HRF</span></a> plotting
example <a class="reference internal" href="../auto_examples/04_glm_first_level/plot_hrf.html#sphx-glr-auto-examples-04-glm-first-level-plot-hrf-py"><span class="std std-ref">Example of MRI response functions</span></a>.</p>
</section>
<section id="design-matrix-event-based-and-time-series-based">
<h2><span class="section-number">8.2.2. </span>Design matrix: event-based and time series-based<a class="headerlink" href="#design-matrix-event-based-and-time-series-based" title="Link to this heading">#</a></h2>
<section id="event-based">
<h3><span class="section-number">8.2.2.1. </span>Event-based<a class="headerlink" href="#event-based" title="Link to this heading">#</a></h3>
<p>To create an event-based design matrix, information about the trial type, onset time and duration of the
events in the experiment are necessary. This can be provided by the user, or be part of the dataset if
using a <a class="reference internal" href="../glossary.html#term-BIDS"><span class="xref std std-term">BIDS</span></a>-compatible dataset or one of the nilearn dataset fetcher functions like
<a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_spm_multimodal_fmri.html#nilearn.datasets.fetch_spm_multimodal_fmri" title="nilearn.datasets.fetch_spm_multimodal_fmri"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.datasets.fetch_spm_multimodal_fmri</span></code></a>,
<a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_language_localizer_demo_dataset.html#nilearn.datasets.fetch_language_localizer_demo_dataset" title="nilearn.datasets.fetch_language_localizer_demo_dataset"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.datasets.fetch_language_localizer_demo_dataset</span></code></a>, etc.</p>
<dl class="simple">
<dt>Refer to the examples below for usage under the different scenarios:</dt><dd><ul class="simple">
<li><p>User-defined: <a class="reference internal" href="../auto_examples/04_glm_first_level/plot_design_matrix.html#sphx-glr-auto-examples-04-glm-first-level-plot-design-matrix-py"><span class="std std-ref">Examples of design matrices</span></a></p></li>
<li><p>Using an OpenNEURO dataset: <a class="reference internal" href="../auto_examples/04_glm_first_level/plot_bids_features.html#sphx-glr-auto-examples-04-glm-first-level-plot-bids-features-py"><span class="std std-ref">First level analysis of a complete BIDS dataset from openneuro</span></a></p></li>
<li><p>Using nilearn fetcher functions: <a class="reference internal" href="../auto_examples/04_glm_first_level/plot_spm_multimodal_faces.html#sphx-glr-auto-examples-04-glm-first-level-plot-spm-multimodal-faces-py"><span class="std std-ref">Single-subject data (two runs) in native space</span></a></p></li>
<li><p>Advanced beta series models: <a class="reference internal" href="../auto_examples/07_advanced/plot_beta_series.html#sphx-glr-auto-examples-07-advanced-plot-beta-series-py"><span class="std std-ref">Beta-Series Modeling for Task-Based Functional Connectivity and Decoding</span></a></p></li>
</ul>
</dd>
</dl>
<p>To ascertain that the sequence of events provided to the first level model is accurate, Nilearn provides an
event visualization function called <a class="reference internal" href="../modules/generated/nilearn.plotting.plot_event.html#nilearn.plotting.plot_event" title="nilearn.plotting.plot_event"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.plot_event</span></code></a>.
Sample usage for this is available in
<a class="reference internal" href="../auto_examples/04_glm_first_level/plot_write_events_file.html#sphx-glr-auto-examples-04-glm-first-level-plot-write-events-file-py"><span class="std std-ref">Generate an events.tsv file for the NeuroSpin localizer task</span></a>.</p>
<p>Once the events are defined, the design matrix is created using the
<a class="reference internal" href="../modules/generated/nilearn.glm.first_level.make_first_level_design_matrix.html#nilearn.glm.first_level.make_first_level_design_matrix" title="nilearn.glm.first_level.make_first_level_design_matrix"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.glm.first_level.make_first_level_design_matrix</span></code></a> function:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">nilearn.glm.first_level</span> <span class="kn">import</span> <span class="n">make_first_level_design_matrix</span>
<span class="n">design_matrices</span> <span class="o">=</span> <span class="n">make_first_level_design_matrix</span><span class="p">(</span><span class="n">frame_times</span><span class="p">,</span> <span class="n">events</span><span class="p">,</span>
<span class="n">drift_model</span><span class="o">=</span><span class="s1">'polynomial'</span><span class="p">,</span> <span class="n">drift_order</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
</pre></div>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Additional predictors, like subject motion, can be specified using the add_reg parameter. Look at the function definition for available arguments.</p>
</div>
<p>A handy function called <a class="reference internal" href="../modules/generated/nilearn.plotting.plot_design_matrix.html#nilearn.plotting.plot_design_matrix" title="nilearn.plotting.plot_design_matrix"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.plot_design_matrix</span></code></a> can be used to visualize the design matrix.
This is generally a good practice to follow before proceeding with the analysis:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">nilearn.plotting</span> <span class="kn">import</span> <span class="n">plot_design_matrix</span>
<span class="n">plot_design_matrix</span><span class="p">(</span><span class="n">design_matrices</span><span class="p">)</span>
</pre></div>
</div>
<a class="reference external image-reference" href="../auto_examples/04_glm_first_level/plot_design_matrix.html#sphx-glr-auto-examples-04-glm-first-level-models-plot-design-matrix-py"><img alt="../_images/sphx_glr_plot_design_matrix_001.png" src="../_images/sphx_glr_plot_design_matrix_001.png" /></a>
</section>
<section id="time-series-based">
<h3><span class="section-number">8.2.2.2. </span>Time series-based<a class="headerlink" href="#time-series-based" title="Link to this heading">#</a></h3>
<p>The time series of a seed region can also be used as the predictor for a first level model. This approach would help
identify brain areas co-activating with the seed region. The time series is extracted using
<a class="reference internal" href="../modules/generated/nilearn.maskers.NiftiSpheresMasker.html#nilearn.maskers.NiftiSpheresMasker" title="nilearn.maskers.NiftiSpheresMasker"><code class="xref py py-class docutils literal notranslate"><span class="pre">nilearn.maskers.NiftiSpheresMasker</span></code></a>. For instance, if the seed region is the posterior
cingulate cortex with coordinate [pcc_coords]:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">nilearn.maskers</span> <span class="kn">import</span> <span class="n">NiftiSpheresMasker</span>
<span class="n">seed_masker</span> <span class="o">=</span> <span class="n">NiftiSpheresMasker</span><span class="p">([</span><span class="n">pcc_coords</span><span class="p">],</span> <span class="n">radius</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
<span class="n">seed_time_series</span> <span class="o">=</span> <span class="n">seed_masker</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">adhd_dataset</span><span class="o">.</span><span class="n">func</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
</pre></div>
</div>
<p>The seed_time_series is then passed into the design matrix using the add_reg argument mentioned in the note
above. Code for this approach is in <a class="reference internal" href="../auto_examples/04_glm_first_level/plot_adhd_dmn.html#sphx-glr-auto-examples-04-glm-first-level-plot-adhd-dmn-py"><span class="std std-ref">Default Mode Network extraction of ADHD dataset</span></a>.</p>
</section>
</section>
<section id="fitting-a-first-level-model">
<h2><span class="section-number">8.2.3. </span>Fitting a first level model<a class="headerlink" href="#fitting-a-first-level-model" title="Link to this heading">#</a></h2>
<p>The <a class="reference internal" href="../modules/generated/nilearn.glm.first_level.FirstLevelModel.html#nilearn.glm.first_level.FirstLevelModel" title="nilearn.glm.first_level.FirstLevelModel"><code class="xref py py-class docutils literal notranslate"><span class="pre">nilearn.glm.first_level.FirstLevelModel</span></code></a> class provides the tools to fit the linear model to
the <a class="reference internal" href="../glossary.html#term-fMRI"><span class="xref std std-term">fMRI</span></a> data. The <a class="reference internal" href="../modules/generated/nilearn.glm.first_level.FirstLevelModel.html#nilearn.glm.first_level.FirstLevelModel.fit" title="nilearn.glm.first_level.FirstLevelModel.fit"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.glm.first_level.FirstLevelModel.fit</span></code></a> function takes the fMRI data
and design matrix as input and fits the GLM. Like other Nilearn functions,
<a class="reference internal" href="../modules/generated/nilearn.glm.first_level.FirstLevelModel.html#nilearn.glm.first_level.FirstLevelModel.fit" title="nilearn.glm.first_level.FirstLevelModel.fit"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.glm.first_level.FirstLevelModel.fit</span></code></a> accepts file names as input, but can also
work with <a class="reference external" href="https://nipy.org/nibabel/nibabel_images.html">NiftiImage objects</a>. More information about
input formats is available <a class="reference internal" href="../manipulating_images/input_output.html#loading-data"><span class="std std-ref">here</span></a></p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">nilearn.glm.first_level</span> <span class="kn">import</span> <span class="n">FirstLevelModel</span>
<span class="n">fmri_glm</span> <span class="o">=</span> <span class="n">FirstLevelModel</span><span class="p">()</span>
<span class="n">fmri_glm</span> <span class="o">=</span> <span class="n">fmri_glm</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">subject_data</span><span class="p">,</span> <span class="n">design_matrices</span><span class="o">=</span><span class="n">design_matrices</span><span class="p">)</span>
</pre></div>
</div>
<section id="computing-contrasts">
<h3><span class="section-number">8.2.3.1. </span>Computing contrasts<a class="headerlink" href="#computing-contrasts" title="Link to this heading">#</a></h3>
<p>To get more interesting results out of the <a class="reference internal" href="../glossary.html#term-GLM"><span class="xref std std-term">GLM</span></a> model, contrasts can be computed between regressors of interest.
The <a class="reference internal" href="../modules/generated/nilearn.glm.first_level.FirstLevelModel.html#nilearn.glm.first_level.FirstLevelModel.compute_contrast" title="nilearn.glm.first_level.FirstLevelModel.compute_contrast"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.glm.first_level.FirstLevelModel.compute_contrast</span></code></a> function can be used for that. First,
the contrasts of interest must be defined. In the spm_multimodal_fmri dataset referenced above, subjects are
presented with ‘normal’ and ‘scrambled’ faces. The basic contrasts that can be constructed are the main effects
of ‘normal faces’ and ‘scrambled faces’. Once the basic_contrasts have been set up, we can construct more
interesting contrasts like ‘normal faces - scrambled faces’.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>The compute_contrast function can work with both numeric and symbolic arguments. See <a class="reference internal" href="../modules/generated/nilearn.glm.first_level.FirstLevelModel.html#nilearn.glm.first_level.FirstLevelModel.compute_contrast" title="nilearn.glm.first_level.FirstLevelModel.compute_contrast"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.glm.first_level.FirstLevelModel.compute_contrast</span></code></a> for more information.</p>
</div>
<p>And finally we can compute the contrasts using the compute_contrast function.
Refer to <a class="reference internal" href="../auto_examples/04_glm_first_level/plot_spm_multimodal_faces.html#sphx-glr-auto-examples-04-glm-first-level-plot-spm-multimodal-faces-py"><span class="std std-ref">Single-subject data (two runs) in native space</span></a> for the full example.</p>
<p>The activation maps from these 3 contrasts is presented below:</p>
<a class="reference external image-reference" href="../auto_examples/04_glm_first_level/plot_spm_multimodal_faces.html"><img alt="../_images/sphx_glr_plot_spm_multimodal_faces_001.png" src="../_images/sphx_glr_plot_spm_multimodal_faces_001.png" style="width: 438.0px; height: 192.0px;" /></a>
<a class="reference external image-reference" href="../auto_examples/04_glm_first_level/plot_spm_multimodal_faces.html"><img alt="../_images/sphx_glr_plot_spm_multimodal_faces_002.png" src="../_images/sphx_glr_plot_spm_multimodal_faces_002.png" style="width: 438.0px; height: 192.0px;" /></a>
<a class="reference external image-reference" href="../auto_examples/04_glm_first_level/plot_spm_multimodal_faces.html"><img alt="../_images/sphx_glr_plot_spm_multimodal_faces_003.png" src="../_images/sphx_glr_plot_spm_multimodal_faces_003.png" style="width: 438.0px; height: 192.0px;" /></a>
<p>Additional example: <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>
</section>
</section>
<section id="extracting-predicted-time-series-and-residuals">
<h2><span class="section-number">8.2.4. </span>Extracting predicted time series and residuals<a class="headerlink" href="#extracting-predicted-time-series-and-residuals" title="Link to this heading">#</a></h2>
<p>One way to assess the quality of the fit is to compare the observed and predicted time series of voxels.
Nilearn makes the predicted time series easily accessible via a parameter called <code class="docutils literal notranslate"><span class="pre">predicted</span></code> that is part
of the <a class="reference internal" href="../modules/generated/nilearn.glm.first_level.FirstLevelModel.html#nilearn.glm.first_level.FirstLevelModel" title="nilearn.glm.first_level.FirstLevelModel"><code class="xref py py-class docutils literal notranslate"><span class="pre">nilearn.glm.first_level.FirstLevelModel</span></code></a>. This parameter is populated when
FistLevelModel is initialized with the <code class="docutils literal notranslate"><span class="pre">minimize_memory</span></code> flag set to <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">observed_timeseries</span> <span class="o">=</span> <span class="n">masker</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">fmri_img</span><span class="p">)</span>
<span class="n">predicted_timeseries</span> <span class="o">=</span> <span class="n">masker</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">fmri_glm</span><span class="o">.</span><span class="n">predicted</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
</pre></div>
</div>
<p>Here, masker is an object of <a class="reference internal" href="../modules/generated/nilearn.maskers.NiftiSpheresMasker.html#nilearn.maskers.NiftiSpheresMasker" title="nilearn.maskers.NiftiSpheresMasker"><code class="xref py py-class docutils literal notranslate"><span class="pre">nilearn.maskers.NiftiSpheresMasker</span></code></a>. In the figure below,
predicted (red) and observed (not red) timecourses of 6 voxels are shown.</p>
<a class="reference external image-reference" href="../auto_examples/04_glm_first_level/plot_predictions_residuals.html"><img alt="../_images/sphx_glr_plot_predictions_residuals_002.png" src="../_images/sphx_glr_plot_predictions_residuals_002.png" /></a>
<p>In addition to the predicted timecourses, this flag also yields the residuals of the GLM. The residuals are
useful to calculate the F and R-squared statistic. For more information refer to
<a class="reference internal" href="../auto_examples/04_glm_first_level/plot_predictions_residuals.html#sphx-glr-auto-examples-04-glm-first-level-plot-predictions-residuals-py"><span class="std std-ref">Predicted time series and residuals</span></a></p>
</section>
<section id="surface-based-analysis">
<h2><span class="section-number">8.2.5. </span>Surface-based analysis<a class="headerlink" href="#surface-based-analysis" title="Link to this heading">#</a></h2>
<p><a class="reference internal" href="../glossary.html#term-fMRI"><span class="xref std std-term">fMRI</span></a> analyses can also be performed on the cortical surface instead of a volumetric brain. Nilearn
provides functions to map subject brains on to a cortical mesh, which can be either a standard surface as
provided by, for e.g. Freesurfer, or a user-defined one. Freesurfer meshes can be accessed using
<a class="reference internal" href="../modules/generated/nilearn.datasets.fetch_surf_fsaverage.html#nilearn.datasets.fetch_surf_fsaverage" title="nilearn.datasets.fetch_surf_fsaverage"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.datasets.fetch_surf_fsaverage</span></code></a>, while the function <a class="reference internal" href="../modules/generated/nilearn.surface.vol_to_surf.html#nilearn.surface.vol_to_surf" title="nilearn.surface.vol_to_surf"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.surface.vol_to_surf</span></code></a>
does the projection from volumetric to surface space. Surface plotting functions like <a class="reference internal" href="../modules/generated/nilearn.plotting.plot_surf.html#nilearn.plotting.plot_surf" title="nilearn.plotting.plot_surf"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.plot_surf</span></code></a>
and <a class="reference internal" href="../modules/generated/nilearn.plotting.plot_surf_stat_map.html#nilearn.plotting.plot_surf_stat_map" title="nilearn.plotting.plot_surf_stat_map"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.plot_surf_stat_map</span></code></a> allow for easy visualization of surface-based data.</p>
<p>For a complete example refer to <a class="reference internal" href="../auto_examples/04_glm_first_level/plot_localizer_surface_analysis.html#sphx-glr-auto-examples-04-glm-first-level-plot-localizer-surface-analysis-py"><span class="std std-ref">Example of surface-based first-level analysis</span></a></p>
</section>
</section>
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<ul>
<li><a class="reference internal" href="#">8.2. First level models</a><ul>
<li><a class="reference internal" href="#hrf-models">8.2.1. HRF models</a></li>
<li><a class="reference internal" href="#design-matrix-event-based-and-time-series-based">8.2.2. Design matrix: event-based and time series-based</a><ul>
<li><a class="reference internal" href="#event-based">8.2.2.1. Event-based</a></li>
<li><a class="reference internal" href="#time-series-based">8.2.2.2. Time series-based</a></li>
</ul>
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<li><a class="reference internal" href="#fitting-a-first-level-model">8.2.3. Fitting a first level model</a><ul>
<li><a class="reference internal" href="#computing-contrasts">8.2.3.1. Computing contrasts</a></li>
</ul>
</li>
<li><a class="reference internal" href="#extracting-predicted-time-series-and-residuals">8.2.4. Extracting predicted time series and residuals</a></li>
<li><a class="reference internal" href="#surface-based-analysis">8.2.5. Surface-based analysis</a></li>
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