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<li class="toctree-l3"><a class="reference internal" href="../auto_examples/00_tutorials/plot_single_subject_single_run.html">Intro to GLM Analysis: a single-run, single-subject fMRI dataset</a></li>
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<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>
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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>
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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>
<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>
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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_multi_subject_connectome.html">Group Sparse inverse covariance for multi-subject connectome</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/03_connectivity/plot_seed_to_voxel_correlation.html">Producing single subject maps of seed-to-voxel correlation</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/03_connectivity/plot_compare_decomposition.html">Deriving spatial maps from group fMRI data using ICA and Dictionary Learning</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/03_connectivity/plot_extract_regions_dictlearning_maps.html">Regions extraction using dictionary learning and functional connectomes</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/03_connectivity/plot_atlas_comparison.html">Comparing connectomes on different reference atlases</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/03_connectivity/plot_group_level_connectivity.html">Classification of age groups using functional connectivity</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/03_connectivity/plot_signal_extraction.html">Extracting signals from a brain parcellation</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/03_connectivity/plot_sphere_based_connectome.html">Extract signals on spheres and plot a connectome</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../auto_examples/04_glm_first_level/plot_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-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_extract_rois_smith_atlas.html">Regions Extraction of Default Mode Networks using Smith Atlas</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/06_manipulating_images/plot_nifti_simple.html">Simple example of NiftiMasker use</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/06_manipulating_images/plot_extract_rois_statistical_maps.html">Region Extraction using a t-statistical map (3D)</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/06_manipulating_images/plot_nifti_labels_simple.html">Extracting signals from brain regions using the NiftiLabelsMasker</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/06_manipulating_images/plot_mask_computation.html">Understanding NiftiMasker and mask computation</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/06_manipulating_images/plot_affine_transformation.html">Visualization of affine resamplings</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/06_manipulating_images/plot_roi_extraction.html">Computing a Region of Interest (ROI) mask manually</a></li>
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<li class="toctree-l2 has-children"><a class="reference internal" href="../auto_examples/07_advanced/index.html">Advanced statistical analysis of brain images</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" role="switch" type="checkbox"/><label for="toctree-checkbox-9"><div class="visually-hidden">Toggle navigation of Advanced statistical analysis of brain images</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/07_advanced/plot_ica_resting_state.html">Multivariate decompositions: Independent component analysis of fMRI</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/07_advanced/plot_localizer_simple_analysis.html">Massively univariate analysis of a calculation task from the Localizer dataset</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/07_advanced/plot_bids_analysis.html">BIDS dataset first and second level analysis</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/07_advanced/plot_neurovault_meta_analysis.html">NeuroVault meta-analysis of stop-go paradigm studies</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/07_advanced/plot_age_group_prediction_cross_val.html">Functional connectivity predicts age group</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/07_advanced/plot_surface_bids_analysis.html">Surface-based dataset first and second level analysis of a dataset</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/07_advanced/plot_localizer_mass_univariate_methods.html">Massively univariate analysis of a motor task from the Localizer dataset</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/07_advanced/plot_ica_neurovault.html">NeuroVault cross-study ICA maps</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/07_advanced/plot_haxby_mass_univariate.html">Massively univariate analysis of face vs house recognition</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/07_advanced/plot_advanced_decoding_scikit.html">Advanced decoding using scikit learn</a></li>
<li class="toctree-l3"><a class="reference internal" href="../auto_examples/07_advanced/plot_beta_series.html">Beta-Series Modeling for Task-Based Functional Connectivity and Decoding</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../auto_examples/08_experimental/plot_surface_image_and_maskers.html">A short demo of the surface images & maskers</a></li>
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<li class="toctree-l1 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 current-page"><a class="current reference internal" href="#">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>
<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"><a class="reference internal" href="first_level_model.html">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-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-l2 has-children"><a class="reference internal" href="../modules/connectome.html"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.connectome</span></code>: Functional Connectivity</a><input class="toctree-checkbox" id="toctree-checkbox-19" name="toctree-checkbox-19" role="switch" type="checkbox"/><label for="toctree-checkbox-19"><div class="visually-hidden">Toggle navigation of nilearn.connectome: Functional Connectivity</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.connectome.ConnectivityMeasure.html">nilearn.connectome.ConnectivityMeasure</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.connectome.GroupSparseCovariance.html">nilearn.connectome.GroupSparseCovariance</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.connectome.GroupSparseCovarianceCV.html">nilearn.connectome.GroupSparseCovarianceCV</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.connectome.sym_matrix_to_vec.html">nilearn.connectome.sym_matrix_to_vec</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.connectome.vec_to_sym_matrix.html">nilearn.connectome.vec_to_sym_matrix</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.connectome.group_sparse_covariance.html">nilearn.connectome.group_sparse_covariance</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.connectome.cov_to_corr.html">nilearn.connectome.cov_to_corr</a></li>
<li class="toctree-l3"><a class="reference internal" href="../modules/generated/nilearn.connectome.prec_to_partial.html">nilearn.connectome.prec_to_partial</a></li>
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<section id="analyzing-fmri-using-glms">
<span id="glm"></span><h1><span class="section-number">8. </span>Analyzing fMRI using GLMs<a class="headerlink" href="#analyzing-fmri-using-glms" title="Link to this heading">#</a></h1>
<p>Nilearn’s stats module uses Generalized Linear Model to analyse functional MRI data. Nilearn
offers functions to set up first- and second-level models, perform t-tests, multiple-comparisons
correction and thresholding, and allows for the generation of html reports that detail the
analyses performed and present thresholded results.</p>
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<li class="toctree-l1"><a class="reference internal" href="glm_intro.html">8.1. An introduction to GLMs in fMRI statistical analysis</a><ul>
<li class="toctree-l2"><a class="reference internal" href="glm_intro.html#a-primer-on-bold-fmri-data-analysis">8.1.1. A primer on BOLD-fMRI data analysis</a><ul>
<li class="toctree-l3"><a class="reference internal" href="glm_intro.html#what-is-fmri">8.1.1.1. What is fMRI ?</a></li>
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<li class="toctree-l3"><a class="reference internal" href="glm_intro.html#multiple-comparisons">8.1.1.4. Multiple Comparisons</a></li>
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<li class="toctree-l1"><a class="reference internal" href="first_level_model.html">8.2. First level models</a><ul>
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<li class="toctree-l2"><a class="reference internal" href="first_level_model.html#design-matrix-event-based-and-time-series-based">8.2.2. Design matrix: event-based and time series-based</a><ul>
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<li class="toctree-l2"><a class="reference internal" href="first_level_model.html#fitting-a-first-level-model">8.2.3. Fitting a first level model</a><ul>
<li class="toctree-l3"><a class="reference internal" href="first_level_model.html#computing-contrasts">8.2.3.1. Computing contrasts</a></li>
</ul>
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<li class="toctree-l2"><a class="reference internal" href="first_level_model.html#extracting-predicted-time-series-and-residuals">8.2.4. Extracting predicted time series and residuals</a></li>
<li class="toctree-l2"><a class="reference internal" href="first_level_model.html#surface-based-analysis">8.2.5. Surface-based analysis</a></li>
</ul>
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<li class="toctree-l1"><a class="reference internal" href="second_level_model.html">8.3. Second level models</a><ul>
<li class="toctree-l2"><a class="reference internal" href="second_level_model.html#fitting-a-second-level-model">8.3.1. Fitting a second level model</a></li>
<li class="toctree-l2"><a class="reference internal" href="second_level_model.html#thresholding-statistical-maps">8.3.2. Thresholding statistical maps</a></li>
<li class="toctree-l2"><a class="reference internal" href="second_level_model.html#multiple-comparisons-correction">8.3.3. Multiple comparisons correction</a></li>
<li class="toctree-l2"><a class="reference internal" href="second_level_model.html#voxel-based-morphometry">8.3.4. Voxel based morphometry</a></li>
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