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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/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>
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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/06_manipulating_images/plot_negate_image.html">Negating an image with math_img</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/06_manipulating_images/plot_compare_mean_image.html">Comparing the means of 2 images</a></li>
<li class="toctree-l3"><a class="reference internal" href="auto_examples/06_manipulating_images/plot_smooth_mean_image.html">Smoothing an image</a></li>
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<li class="toctree-l3"><a class="reference internal" href="auto_examples/06_manipulating_images/plot_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>
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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-l1 has-children"><a class="reference internal" href="user_guide.html">User guide</a><input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" role="switch" type="checkbox"/><label for="toctree-checkbox-11"><div class="visually-hidden">Toggle navigation of User guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l2"><a class="reference internal" href="introduction.html">1. Introduction</a></li>
<li class="toctree-l2"><a class="reference internal" href="introduction.html#what-is-nilearn">2. What is <code class="docutils literal notranslate"><span class="pre">nilearn</span></code>?</a></li>
<li class="toctree-l2"><a class="reference internal" href="introduction.html#using-nilearn-for-the-first-time">3. Using <code class="docutils literal notranslate"><span class="pre">nilearn</span></code> for the first time</a></li>
<li class="toctree-l2"><a class="reference internal" href="introduction.html#machine-learning-applications-to-neuroimaging">4. Machine learning applications to Neuroimaging</a></li>
<li class="toctree-l2 has-children"><a class="reference internal" href="decoding/index.html">5. Decoding and MVPA: predicting from brain images</a><input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" role="switch" type="checkbox"/><label for="toctree-checkbox-12"><div class="visually-hidden">Toggle navigation of 5. Decoding and MVPA: predicting from brain images</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="decoding/decoding_intro.html">5.1. An introduction to decoding</a></li>
<li class="toctree-l3"><a class="reference internal" href="decoding/estimator_choice.html">5.2. Choosing the right predictive model for neuroimaging</a></li>
<li class="toctree-l3"><a class="reference internal" href="decoding/frem.html">5.3. FREM: fast ensembling of regularized models for robust decoding</a></li>
<li class="toctree-l3"><a class="reference internal" href="decoding/space_net.html">5.4. SpaceNet: decoding with spatial structure for better maps</a></li>
<li class="toctree-l3"><a class="reference internal" href="decoding/searchlight.html">5.5. Searchlight : finding voxels containing information</a></li>
<li class="toctree-l3"><a class="reference internal" href="decoding/going_further.html">5.6. Running scikit-learn functions for more control on the analysis</a></li>
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<li class="toctree-l2 has-children"><a class="reference internal" href="connectivity/index.html">6. Functional connectivity and resting state</a><input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" role="switch" type="checkbox"/><label for="toctree-checkbox-13"><div class="visually-hidden">Toggle navigation of 6. Functional connectivity and resting state</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="connectivity/functional_connectomes.html">6.1. Extracting times series to build a functional connectome</a></li>
<li class="toctree-l3 has-children"><a class="reference internal" href="connectivity/connectome_extraction.html">6.2. Connectome extraction: inverse covariance for direct connections</a><input class="toctree-checkbox" id="toctree-checkbox-14" name="toctree-checkbox-14" role="switch" type="checkbox"/><label for="toctree-checkbox-14"><div class="visually-hidden">Toggle navigation of 6.2. Connectome extraction: inverse covariance for direct connections</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
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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-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>
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<section id="general-bibliography">
<span id="id1"></span><h1>General bibliography<a class="headerlink" href="#general-bibliography" title="Link to this heading">¶</a></h1>
<p>The references below are arranged alphabetically by first author. You can download the bib file <a class="reference download internal" download="" href="_downloads/33e59b5ca9ec342068223d604c3f8d55/references.bib"><code class="xref download docutils literal notranslate"><span class="pre">here</span></code></a>.</p>
<div class="docutils container" id="id2">
<ol class="arabic simple" start="1">
<li id="id3"><p>Hunar Abdulrahman and Richard N Henson. Effect of trial-to-trial variability on optimal event-related fmri design: implications for beta-series correlation and multi-voxel pattern analysis. <em>NeuroImage</em>, 125:756–766, 2016.</p></li>
<li id="id4"><p>Alexandre Abraham, Elvis Dohmatob, Bertrand Thirion, Dimitris Samaras, and Gael Varoquaux. Region segmentation for sparse decompositions: better brain parcellations from rest fMRI. Sparsity Techniques in Medical Imaging, September 2014. URL: <a class="reference external" href="https://hal.inria.fr/hal-01093944">https://hal.inria.fr/hal-01093944</a>.</p></li>
<li id="id6"><p>Elena Allen, Erik Erhardt, Eswar Damaraju, William Gruner, Judith Segall, Rogers Silva, Martin Havlicek, Srinivas Rachakonda, Jill Fries, Ravi Kalyanam, Andrew Michael, Arvind Caprihan, Jessica Turner, Tom Eichele, Steven Adelsheim, Angela Bryan, Juan Bustillo, Vincent Clark, Sarah Feldstein Ewing, Francesca Filbey, Corey Ford, Kent Hutchison, Rex Jung, Kent Kiehl, Piyadasa Kodituwakku, Yuko Komesu, Andrew Mayer, Godfrey Pearlson, John Phillips, Joseph Sadek, Michael Stevens, Ursina Teuscher, Robert Thoma, and Vince Calhoun. A baseline for the multivariate comparison of resting-state networks. <em>Frontiers in Systems Neuroscience</em>, 5:2, 2011. URL: <a class="reference external" href="https://www.frontiersin.org/article/10.3389/fnsys.2011.00002">https://www.frontiersin.org/article/10.3389/fnsys.2011.00002</a>, <a class="reference external" href="https://doi.org/10.3389/fnsys.2011.00002">doi:10.3389/fnsys.2011.00002</a>.</p></li>
<li id="id7"><p>Katrin Amunts, Hartmut Mohlberg, Sebastian Bludau, and Karl Zilles. Julich-Brain: A 3D probabilistic atlas of the human brain’s cytoarchitecture. <em>Science</em>, 369(6506):988–992, August 2020. URL: <a class="reference external" href="https://www.science.org/doi/10.1126/science.abb4588">https://www.science.org/doi/10.1126/science.abb4588</a> (visited on 2024-02-01), <a class="reference external" href="https://doi.org/10.1126/science.abb4588">doi:10.1126/science.abb4588</a>.</p></li>
<li id="id8"><p>Marti J. Anderson and John Robinson. Permutation tests for linear models. <em>Australian & New Zealand Journal of Statistics</em>, 43(1):75–88, 2001. URL: <a class="reference external" href="https://onlinelibrary.wiley.com/doi/abs/10.1111/1467-842X.00156">https://onlinelibrary.wiley.com/doi/abs/10.1111/1467-842X.00156</a>, <a class="reference external" href="https://arxiv.org/abs/https://onlinelibrary.wiley.com/doi/pdf/10.1111/1467-842X.00156">arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1111/1467-842X.00156</a>, <a class="reference external" href="https://doi.org/https://doi.org/10.1111/1467-842X.00156">doi:https://doi.org/10.1111/1467-842X.00156</a>.</p></li>
<li id="id5"><p>Luca Baldassarre, Janaina Mourao-Miranda, and Massimiliano Pontil. The civet image-processing environment: a fully automated comprehensive pipeline for anatomical neuroimaging research. In <em>Proceedings of the 12th Annual Meeting of the Human Brain Mapping Organization</em>. 2006. URL: <a class="reference external" href="https://www.bic.mni.mcgill.ca/users/yaddab/Yasser-HBM2006-Poster.pdf">https://www.bic.mni.mcgill.ca/users/yaddab/Yasser-HBM2006-Poster.pdf</a>.</p></li>
<li id="id9"><p>Luca Baldassarre, Janaina Mourao-Miranda, and Massimiliano Pontil. Structured sparsity models for brain decoding from fmri data. In <em>2012 Second International Workshop on Pattern Recognition in NeuroImaging</em>, volume, 5–8. 2012. URL: <a class="reference external" href="http://www0.cs.ucl.ac.uk/staff/M.Pontil/reading/neurosparse_prni.pdf">http://www0.cs.ucl.ac.uk/staff/M.Pontil/reading/neurosparse_prni.pdf</a>, <a class="reference external" href="https://doi.org/10.1109/PRNI.2012.31">doi:10.1109/PRNI.2012.31</a>.</p></li>
<li id="id10"><p>Yashar Behzadi, Khaled Restom, Joy Liau, and Thomas T. Liu. A component based noise correction method (compcor) for bold and perfusion based fmri. <em>NeuroImage</em>, 37(1):90–101, 2007. URL: <a class="reference external" href="https://www.sciencedirect.com/science/article/pii/S1053811907003837">https://www.sciencedirect.com/science/article/pii/S1053811907003837</a>, <a class="reference external" href="https://doi.org/https://doi.org/10.1016/j.neuroimage.2007.04.042">doi:https://doi.org/10.1016/j.neuroimage.2007.04.042</a>.</p></li>
<li id="id11"><p>Pierre Bellec. Mining the hierarchy of resting-state brain networks: selection of representative clusters in a multiscale structure. In <em>2013 International Workshop on Pattern Recognition in Neuroimaging</em>, volume, 54–57. 06 2013. <a class="reference external" href="https://doi.org/10.1109/PRNI.2013.23">doi:10.1109/PRNI.2013.23</a>.</p></li>
<li id="id12"><p>Pierre Bellec, Pedro Rosa-Neto, Oliver C. Lyttelton, Habib Benali, and Alan C. Evans. Multi-level bootstrap analysis of stable clusters in resting-state fmri. <em>NeuroImage</em>, 51(3):1126–1139, 2010. URL: <a class="reference external" href="https://www.sciencedirect.com/science/article/pii/S1053811910002697">https://www.sciencedirect.com/science/article/pii/S1053811910002697</a>, <a class="reference external" href="https://doi.org/https://doi.org/10.1016/j.neuroimage.2010.02.082">doi:https://doi.org/10.1016/j.neuroimage.2010.02.082</a>.</p></li>
<li id="id13"><p>Yi Chen, Praneeth Namburi, Lloyd T. Elliott, Jakob Heinzle, Chun Siong Soon, Michael W.L. Chee, and John-Dylan Haynes. Cortical surface-based searchlight decoding. <em>NeuroImage</em>, 56(2):582–592, 2011. Multivariate Decoding and Brain Reading. URL: <a class="reference external" href="https://www.sciencedirect.com/science/article/pii/S1053811910010086">https://www.sciencedirect.com/science/article/pii/S1053811910010086</a>, <a class="reference external" href="https://doi.org/https://doi.org/10.1016/j.neuroimage.2010.07.035">doi:https://doi.org/10.1016/j.neuroimage.2010.07.035</a>.</p></li>
<li id="id14"><p>Rastko Ciric, Daniel H. Wolf, Jonathan D. Power, David R. Roalf, Graham L. Baum, Kosha Ruparel, Russell T. Shinohara, Mark A. Elliott, Simon B. Eickhoff, Christos Davatzikos, Ruben C. Gur, Raquel E. Gur, Danielle S. Bassett, and Theodore D. Satterthwaite. Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity. <em>NeuroImage</em>, 154(1):174–187, 2017. <a class="reference external" href="https://doi.org/10.1016/j.neuroimage.2017.03.020">doi:10.1016/j.neuroimage.2017.03.020</a>.</p></li>
<li id="id15"><p>Josh M Cisler, Keith Bush, and J Scott Steele. A comparison of statistical methods for detecting context-modulated functional connectivity in fmri. <em>Neuroimage</em>, 84:1042–1052, 2014.</p></li>
<li id="id16"><p>Alex Clarke and Lorraine K. Tyler. Object-specific semantic coding in human perirhinal cortex. <em>Journal of Neuroscience</em>, 34(14):4766–4775, 2014. URL: <a class="reference external" href="https://www.jneurosci.org/content/34/14/4766">https://www.jneurosci.org/content/34/14/4766</a>, <a class="reference external" href="https://arxiv.org/abs/https://www.jneurosci.org/content/34/14/4766.full.pdf">arXiv:https://www.jneurosci.org/content/34/14/4766.full.pdf</a>, <a class="reference external" href="https://doi.org/10.1523/JNEUROSCI.2828-13.2014">doi:10.1523/JNEUROSCI.2828-13.2014</a>.</p></li>
<li id="id17"><p>D. Louis Collins and Alan C. Evans. Animal: validation and applications of nonlinear registration-based segmentation. <em>International journal of pattern recognition and artificial intelligence</em>, 11(08):1271–1294, 1997.</p></li>
<li id="id19"><p>D. Louis Collins, Alex P. Zijdenbos, Wim F. C. Baaré, and Alan C. Evans. Animal+insect: improved cortical structure segmentation. In Attila Kuba, Martin Šáamal, and Andrew Todd-Pokropek, editors, <em>Information Processing in Medical Imaging</em>, 210–223. Berlin, Heidelberg, 1999. Springer Berlin Heidelberg.</p></li>
<li id="id18"><p>D.L. Collins, A.P. Zijdenbos, V. Kollokian, J.G. Sled, N.J. Kabani, C.J. Holmes, and A.C. Evans. Design and construction of a realistic digital brain phantom. <em>IEEE Transactions on Medical Imaging</em>, 17(3):463–468, 1998. <a class="reference external" href="https://doi.org/10.1109/42.712135">doi:10.1109/42.712135</a>.</p></li>
<li id="id20"><p>R. Cameron Craddock, G.Andrew James, Paul E. Holtzheimer III, Xiaoping P. Hu, and Helen S. Mayberg. A whole brain fmri atlas generated via spatially constrained spectral clustering. <em>Human Brain Mapping</em>, 33(8):1914–1928, 2012. URL: <a class="reference external" href="https://onlinelibrary.wiley.com/doi/abs/10.1002/hbm.21333">https://onlinelibrary.wiley.com/doi/abs/10.1002/hbm.21333</a>, <a class="reference external" href="https://arxiv.org/abs/https://onlinelibrary.wiley.com/doi/pdf/10.1002/hbm.21333">arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1002/hbm.21333</a>, <a class="reference external" href="https://doi.org/https://doi.org/10.1002/hbm.21333">doi:https://doi.org/10.1002/hbm.21333</a>.</p></li>
<li id="id21"><p>Kamalaker Dadi, Mehdi Rahim, Alexandre Abraham, Darya Chyzhyk, Michael Milham, Bertrand Thirion, and Gaël Varoquaux. Benchmarking functional connectome-based predictive models for resting-state fmri. <em>NeuroImage</em>, 192:115–134, 2019. URL: <a class="reference external" href="https://www.sciencedirect.com/science/article/pii/S1053811919301594">https://www.sciencedirect.com/science/article/pii/S1053811919301594</a>, <a class="reference external" href="https://doi.org/https://doi.org/10.1016/j.neuroimage.2019.02.062">doi:https://doi.org/10.1016/j.neuroimage.2019.02.062</a>.</p></li>
<li id="id22"><p>Kamalaker Dadi, Gaël Varoquaux, Antonia Machlouzarides-Shalit, Krzysztof J. Gorgolewski, Demian Wassermann, Bertrand Thirion, and Arthur Mensch. Fine-grain atlases of functional modes for fmri analysis. <em>NeuroImage</em>, 221:117126, 2020. URL: <a class="reference external" href="https://hal.inria.fr/hal-02904869">https://hal.inria.fr/hal-02904869</a>, <a class="reference external" href="https://doi.org/https://doi.org/10.1016/j.neuroimage.2020.117126">doi:https://doi.org/10.1016/j.neuroimage.2020.117126</a>.</p></li>
<li id="id23"><p>Anders M. Dale, Bruce Fischl, and Martin I. Sereno. Cortical surface-based analysis: i. segmentation and surface reconstruction. <em>NeuroImage</em>, 9(2):179–194, 1999. URL: <a class="reference external" href="https://www.sciencedirect.com/science/article/pii/S1053811998903950">https://www.sciencedirect.com/science/article/pii/S1053811998903950</a>, <a class="reference external" href="https://doi.org/https://doi.org/10.1006/nimg.1998.0395">doi:https://doi.org/10.1006/nimg.1998.0395</a>.</p></li>
<li id="id24"><p>Russell Davidson and James G. MacKinnon. <em>Econometric theory and methods</em>. Oxford Univ. Press, New York, NY [u.a.], 2004. ISBN 978-0-19-512372-2. URL: <a class="reference external" href="http://gso.gbv.de/DB=2.1/CMD?ACT=SRCHA&SRT=YOP&IKT=1016&TRM=ppn+393847152&sourceid=fbw_bibsonomy">http://gso.gbv.de/DB=2.1/CMD?ACT=SRCHA&SRT=YOP&IKT=1016&TRM=ppn+393847152&sourceid=fbw_bibsonomy</a>.</p></li>
<li id="id25"><p>Ghislaine Dehaene-Lambertz, Stanislas Dehaene, Jean-Luc Anton, Aurelie Campagne, Philippe Ciuciu, Guillaume P Dehaene, Isabelle Denghien, Antoinette Jobert, Denis LeBihan, Mariano Sigman, and others. Functional segregation of cortical language areas by sentence repetition. <em>Human brain mapping</em>, 27(5):360–371, 2006. URL: <a class="reference external" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6871319/">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6871319/</a>.</p></li>
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