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</style><div class="section" id="decoding-and-mvpa-predicting-from-brain-images">
<span id="decoding"></span><h1><span class="section-number">2. </span>Decoding and MVPA: predicting from brain images<a class="headerlink" href="#decoding-and-mvpa-predicting-from-brain-images" title="Permalink to this headline">¶</a></h1>
<p>Decoding consists in predicting external variables such as behavioral or
phenotypic variables from brain image. It can be useful for diagnostic of
prognosis, or to probe the information content of brain activity images.</p>
<p>These are <a class="reference external" href="http://en.wikipedia.org/wiki/Supervised_learning">Supervised learning</a> tasks, focused on
predicting an output value.</p>
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<ul>
<li class="toctree-l1"><a class="reference internal" href="decoding_intro.html">2.1. An introduction to decoding</a><ul>
<li class="toctree-l2"><a class="reference internal" href="decoding_intro.html#loading-and-preparing-the-data">2.1.1. Loading and preparing the data</a><ul>
<li class="toctree-l3"><a class="reference internal" href="decoding_intro.html#the-haxby-2001-experiment">2.1.1.1. The Haxby 2001 experiment</a></li>
<li class="toctree-l3"><a class="reference internal" href="decoding_intro.html#loading-the-data-into-nilearn">2.1.1.2. Loading the data into nilearn</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="decoding_intro.html#performing-a-simple-decoding-analysis">2.1.2. Performing a simple decoding analysis</a><ul>
<li class="toctree-l3"><a class="reference internal" href="decoding_intro.html#a-few-definitions">2.1.2.1. A few definitions</a></li>
<li class="toctree-l3"><a class="reference internal" href="decoding_intro.html#a-first-estimator">2.1.2.2. A first estimator</a></li>
<li class="toctree-l3"><a class="reference internal" href="decoding_intro.html#decoding-made-easy">2.1.2.3. Decoding made easy</a></li>
<li class="toctree-l3"><a class="reference internal" href="decoding_intro.html#measuring-prediction-performance">2.1.2.4. Measuring prediction performance</a><ul>
<li class="toctree-l4"><a class="reference internal" href="decoding_intro.html#cross-validation">2.1.2.4.1. Cross-validation</a></li>
<li class="toctree-l4"><a class="reference internal" href="decoding_intro.html#choosing-a-good-cross-validation-strategy">2.1.2.4.2. Choosing a good cross-validation strategy</a></li>
<li class="toctree-l4"><a class="reference internal" href="decoding_intro.html#choice-of-the-prediction-accuracy-measure">2.1.2.4.3. Choice of the prediction accuracy measure</a></li>
<li class="toctree-l4"><a class="reference internal" href="decoding_intro.html#prediction-accuracy-at-chance-using-simple-strategies">2.1.2.4.4. Prediction accuracy at chance using simple strategies</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="decoding_intro.html#visualizing-the-decoder-s-weights">2.1.2.5. Visualizing the decoder’s weights</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="decoding_intro.html#decoding-without-a-mask-anova-svm">2.1.3. Decoding without a mask: Anova-SVM</a><ul>
<li class="toctree-l3"><a class="reference internal" href="decoding_intro.html#dimension-reduction-with-feature-selection">2.1.3.1. Dimension reduction with feature selection</a></li>
<li class="toctree-l3"><a class="reference internal" href="decoding_intro.html#visualizing-the-results">2.1.3.2. Visualizing the results</a></li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="estimator_choice.html">2.2. Choosing the right predictive model for neuroimaging</a><ul>
<li class="toctree-l2"><a class="reference internal" href="estimator_choice.html#predictions-regression-classification-and-multi-class">2.2.1. Predictions: regression, classification and multi-class</a><ul>
<li class="toctree-l3"><a class="reference internal" href="estimator_choice.html#regression">2.2.1.1. Regression</a></li>
<li class="toctree-l3"><a class="reference internal" href="estimator_choice.html#classification-two-classes-or-multi-class">2.2.1.2. Classification: two classes or multi-class</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="estimator_choice.html#different-linear-models">2.2.2. Different linear models</a></li>
<li class="toctree-l2"><a class="reference internal" href="estimator_choice.html#setting-estimator-parameters">2.2.3. Setting estimator parameters</a></li>
<li class="toctree-l2"><a class="reference internal" href="estimator_choice.html#bagging-several-models">2.2.4. Bagging several models</a></li>
<li class="toctree-l2"><a class="reference internal" href="estimator_choice.html#references">2.2.5. References</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="frem.html">2.3. FREM: fast ensembling of regularized models for robust decoding</a><ul>
<li class="toctree-l2"><a class="reference internal" href="frem.html#frem-pipeline">2.3.1. FREM pipeline</a></li>
<li class="toctree-l2"><a class="reference internal" href="frem.html#empirical-comparisons">2.3.2. Empirical comparisons</a><ul>
<li class="toctree-l3"><a class="reference internal" href="frem.html#decoding-performance-increase-on-haxby-dataset">2.3.2.1. Decoding performance increase on Haxby dataset</a></li>
<li class="toctree-l3"><a class="reference internal" href="frem.html#spatial-regularization-of-decoding-maps-on-mixed-gambles-study">2.3.2.2. Spatial regularization of decoding maps on mixed gambles study</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="frem.html#references">2.3.3. References</a></li>
</ul>
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<li class="toctree-l1"><a class="reference internal" href="space_net.html">2.4. SpaceNet: decoding with spatial structure for better maps</a><ul>
<li class="toctree-l2"><a class="reference internal" href="space_net.html#the-spacenet-decoder">2.4.1. The SpaceNet decoder</a></li>
<li class="toctree-l2"><a class="reference internal" href="space_net.html#related-example">2.4.2. Related example</a></li>
<li class="toctree-l2"><a class="reference internal" href="space_net.html#references">2.4.3. References</a></li>
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<li class="toctree-l2"><a class="reference internal" href="searchlight.html#principle-of-the-searchlight">2.5.1. Principle of the Searchlight</a></li>
<li class="toctree-l2"><a class="reference internal" href="searchlight.html#preparing-the-data">2.5.2. Preparing the data</a><ul>
<li class="toctree-l3"><a class="reference internal" href="searchlight.html#masking">2.5.2.1. Masking</a></li>
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<li class="toctree-l2"><a class="reference internal" href="searchlight.html#setting-up-the-searchlight">2.5.3. Setting up the searchlight</a><ul>
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<li class="toctree-l3"><a class="reference internal" href="searchlight.html#score-function">2.5.3.2. Score function</a></li>
<li class="toctree-l3"><a class="reference internal" href="searchlight.html#cross-validation">2.5.3.3. Cross validation</a></li>
<li class="toctree-l3"><a class="reference internal" href="searchlight.html#sphere-radius">2.5.3.4. Sphere radius</a></li>
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<li class="toctree-l2"><a class="reference internal" href="searchlight.html#visualization">2.5.4. Visualization</a><ul>
<li class="toctree-l3"><a class="reference internal" href="searchlight.html#id6">2.5.4.1. Searchlight</a></li>
<li class="toctree-l3"><a class="reference internal" href="searchlight.html#comparing-to-massively-univariate-analysis-f-score-or-spm">2.5.4.2. Comparing to massively univariate analysis: F_score or SPM</a></li>
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<li class="toctree-l1"><a class="reference internal" href="going_further.html">2.6. Running scikit-learn functions for more control on the analysis</a><ul>
<li class="toctree-l2"><a class="reference internal" href="going_further.html#performing-decoding-with-scikit-learn">2.6.1. Performing decoding with scikit-learn</a><ul>
<li class="toctree-l3"><a class="reference internal" href="going_further.html#using-scikit-learn-estimators">2.6.1.1. Using scikit-learn estimators</a></li>
<li class="toctree-l3"><a class="reference internal" href="going_further.html#cross-validation-with-scikit-learn">2.6.1.2. Cross-validation with scikit-learn</a></li>
<li class="toctree-l3"><a class="reference internal" href="going_further.html#measuring-the-chance-level">2.6.1.3. Measuring the chance level</a></li>
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<li class="toctree-l2"><a class="reference internal" href="going_further.html#going-further-with-scikit-learn">2.6.2. Going further with scikit-learn</a><ul>
<li class="toctree-l3"><a class="reference internal" href="going_further.html#decoding-without-a-mask-anova-svm-using-scikit-learn">2.6.2.1. Decoding without a mask: Anova-SVM using scikit-learn</a></li>
<li class="toctree-l3"><a class="reference internal" href="going_further.html#using-any-other-model-in-the-pipeline">2.6.2.2. Using any other model in the pipeline</a></li>
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<li class="toctree-l2"><a class="reference internal" href="going_further.html#setting-estimator-parameters">2.6.3. Setting estimator parameters</a></li>
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