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<div class="section" id="id1">
<h1>0.7.0<a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h1>
<p><strong>Released November 2020</strong></p>
<div class="section" id="highlights">
<h2>HIGHLIGHTS<a class="headerlink" href="#highlights" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li><p>Nilearn now includes the functionality of <a class="reference external" href="https://nistats.github.io">Nistats</a> as <a class="reference internal" href="../modules/reference.html#module-nilearn.glm" title="nilearn.glm"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.glm</span></code></a>. This module is experimental, hence subject to change in any future release. <a class="reference internal" href="../nistats_migration.html#nistats-migration"><span class="std std-ref">Here’s a guide to replacing Nistats imports to work in Nilearn.</span></a> (<a class="reference external" href="https://github.com/nilearn/nilearn/issues/2299">#2299</a>, <a class="reference external" href="https://github.com/nilearn/nilearn/issues/2304">#2304</a>, and <a class="reference external" href="https://github.com/nilearn/nilearn/issues/2307">#2307</a> by <a class="reference external" href="https://github.com/kchawla-pi">Kshitij Chawla</a>, and <a class="reference external" href="https://github.com/nilearn/nilearn/issues/2509">#2509</a> by <a class="reference external" href="https://www.imo.universite-paris-saclay.fr/~tbnguyen/">Binh Nguyen</a>).</p></li>
<li><p>New classes <a class="reference internal" href="../modules/generated/nilearn.decoding.Decoder.html#nilearn.decoding.Decoder" title="nilearn.decoding.Decoder"><code class="xref py py-class docutils literal notranslate"><span class="pre">nilearn.decoding.Decoder</span></code></a> (for <a class="reference internal" href="../glossary.html#term-classification"><span class="xref std std-term">classification</span></a>) and <a class="reference internal" href="../modules/generated/nilearn.decoding.DecoderRegressor.html#nilearn.decoding.DecoderRegressor" title="nilearn.decoding.DecoderRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">nilearn.decoding.DecoderRegressor</span></code></a> (for <a class="reference internal" href="../glossary.html#term-regression"><span class="xref std std-term">regression</span></a>) implement a model selection scheme that averages the best models within a cross validation loop (<a class="reference external" href="https://github.com/nilearn/nilearn/issues/2000">#2000</a> by <a class="reference external" href="https://www.imo.universite-paris-saclay.fr/~tbnguyen/">Binh Nguyen</a>).</p></li>
<li><p>New classes <a class="reference internal" href="../modules/generated/nilearn.decoding.FREMClassifier.html#nilearn.decoding.FREMClassifier" title="nilearn.decoding.FREMClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">nilearn.decoding.FREMClassifier</span></code></a> (for <a class="reference internal" href="../glossary.html#term-classification"><span class="xref std std-term">classification</span></a>) and <a class="reference internal" href="../modules/generated/nilearn.decoding.FREMRegressor.html#nilearn.decoding.FREMRegressor" title="nilearn.decoding.FREMRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">nilearn.decoding.FREMRegressor</span></code></a> (for <a class="reference internal" href="../glossary.html#term-regression"><span class="xref std std-term">regression</span></a>) extend the <a class="reference internal" href="../modules/generated/nilearn.decoding.Decoder.html#nilearn.decoding.Decoder" title="nilearn.decoding.Decoder"><code class="xref py py-class docutils literal notranslate"><span class="pre">Decoder</span></code></a> object with one fast clustering step at the beginning and aggregates a high number of estimators trained on various splits of the training set (<a class="reference external" href="https://github.com/nilearn/nilearn/issues/2327">#2327</a> by <a class="reference external" href="https://github.com/thomasbazeille">Thomas Bazeille</a>).</p></li>
<li><p>New plotting functions:</p>
<ul>
<li><p><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">plot_event</span></code></a> to visualize events file.</p></li>
<li><p><a class="reference internal" href="../modules/generated/nilearn.plotting.plot_roi.html#nilearn.plotting.plot_roi" title="nilearn.plotting.plot_roi"><code class="xref py py-func docutils literal notranslate"><span class="pre">plot_roi</span></code></a> can now plot ROIs in contours with <code class="docutils literal notranslate"><span class="pre">view_type</span></code> argument.</p></li>
<li><p><a class="reference internal" href="../modules/generated/nilearn.plotting.plot_carpet.html#nilearn.plotting.plot_carpet" title="nilearn.plotting.plot_carpet"><code class="xref py py-func docutils literal notranslate"><span class="pre">plot_carpet</span></code></a> generates a “carpet plot” (also known as a “Power plot” or a “grayplot”)</p></li>
<li><p><a class="reference internal" href="../modules/generated/nilearn.plotting.plot_img_on_surf.html#nilearn.plotting.plot_img_on_surf" title="nilearn.plotting.plot_img_on_surf"><code class="xref py py-func docutils literal notranslate"><span class="pre">plot_img_on_surf</span></code></a> generates multiple views of <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">plot_surf_stat_map</span></code></a> in a single figure.</p></li>
<li><p><a class="reference internal" href="../modules/generated/nilearn.plotting.plot_markers.html#nilearn.plotting.plot_markers" title="nilearn.plotting.plot_markers"><code class="xref py py-func docutils literal notranslate"><span class="pre">plot_markers</span></code></a> shows network nodes (markers) on a glass brain template</p></li>
<li><p><a class="reference internal" href="../modules/generated/nilearn.plotting.plot_surf_contours.html#nilearn.plotting.plot_surf_contours" title="nilearn.plotting.plot_surf_contours"><code class="xref py py-func docutils literal notranslate"><span class="pre">plot_surf_contours</span></code></a> plots the contours of regions of interest on the surface</p></li>
</ul>
</li>
</ul>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>Minimum required version of <code class="docutils literal notranslate"><span class="pre">Joblib</span></code> is now <code class="docutils literal notranslate"><span class="pre">0.12</span></code>.</p>
</div>
</div>
<div class="section" id="new">
<h2>NEW<a class="headerlink" href="#new" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li><p>Nilearn now includes the functionality of <a class="reference external" href="https://nistats.github.io">Nistats</a>.
<a class="reference internal" href="../nistats_migration.html#nistats-migration"><span class="std std-ref">Here’s a guide to replacing Nistats imports to work in Nilearn.</span></a></p></li>
<li><p>New decoder object
<a class="reference internal" href="../modules/generated/nilearn.decoding.Decoder.html#nilearn.decoding.Decoder" title="nilearn.decoding.Decoder"><code class="xref py py-class docutils literal notranslate"><span class="pre">nilearn.decoding.Decoder</span></code></a> (for classification) and
<a class="reference internal" href="../modules/generated/nilearn.decoding.DecoderRegressor.html#nilearn.decoding.DecoderRegressor" title="nilearn.decoding.DecoderRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">nilearn.decoding.DecoderRegressor</span></code></a> (for regression) implement a model
selection scheme that averages the best models within a cross validation loop.
The resulting average model is the one used as a classifier or a regressor.
These two objects also leverage the <cite>NiftiMaskers</cite> to provide a direct
interface with the Nifti files on disk.</p></li>
<li><p>New FREM object
<a class="reference internal" href="../modules/generated/nilearn.decoding.FREMClassifier.html#nilearn.decoding.FREMClassifier" title="nilearn.decoding.FREMClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">nilearn.decoding.FREMClassifier</span></code></a> (for classification) and
<a class="reference internal" href="../modules/generated/nilearn.decoding.FREMRegressor.html#nilearn.decoding.FREMRegressor" title="nilearn.decoding.FREMRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">nilearn.decoding.FREMRegressor</span></code></a> (for regression) extend the decoder
object pipeline with one fast clustering step at the beginning (yielding an
implicit spatial regularization) and aggregates a high number of estimators
trained on various splits of the training set. This returns a state-of-the-art
decoding pipeline at a low computational cost.
These two objects also leverage the <cite>NiftiMaskers</cite> to provide a direct
interface with the Nifti files on disk.</p></li>
<li><p>Plot events file
Use <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> to visualize events file.
The function accepts the <a class="reference internal" href="../glossary.html#term-BIDS"><span class="xref std std-term">BIDS</span></a> events file read using <cite>pandas</cite>
utilities.</p></li>
<li><p>Plotting function <a class="reference internal" href="../modules/generated/nilearn.plotting.plot_roi.html#nilearn.plotting.plot_roi" title="nilearn.plotting.plot_roi"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.plot_roi</span></code></a> can now plot ROIs
in contours with <cite>view_type</cite> argument.</p></li>
<li><p>New plotting function
<a class="reference internal" href="../modules/generated/nilearn.plotting.plot_carpet.html#nilearn.plotting.plot_carpet" title="nilearn.plotting.plot_carpet"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.plot_carpet</span></code></a> generates a “carpet plot” (also known
as a “Power plot” or a “grayplot”), for visualizing global patterns in
4D functional data over time.</p></li>
<li><p>New plotting function
<a class="reference internal" href="../modules/generated/nilearn.plotting.plot_img_on_surf.html#nilearn.plotting.plot_img_on_surf" title="nilearn.plotting.plot_img_on_surf"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.plot_img_on_surf</span></code></a> generates multiple views of
<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> in a single figure.</p></li>
<li><p><a class="reference internal" href="../modules/generated/nilearn.plotting.plot_markers.html#nilearn.plotting.plot_markers" title="nilearn.plotting.plot_markers"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.plot_markers</span></code></a> shows network nodes (markers) on a glass
brain template and color code them according to provided nodal measure (i.e.
connection strength). This function will replace
<code class="docutils literal notranslate"><span class="pre">nilearn.plotting.plot_connectome_strength</span></code>.</p></li>
<li><p>New plotting function
<a class="reference internal" href="../modules/generated/nilearn.plotting.plot_surf_contours.html#nilearn.plotting.plot_surf_contours" title="nilearn.plotting.plot_surf_contours"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.plot_surf_contours</span></code></a> plots the contours of regions of
interest on the surface, optionally overlaid on top of a statistical map.</p></li>
<li><p>The position annotation on the plot methods now implements the <cite>decimals</cite> option
to enable annotation of a slice coordinate position with the float.</p></li>
<li><p>New example in
<a class="reference internal" href="../auto_examples/02_decoding/plot_haxby_searchlight_surface.html#sphx-glr-auto-examples-02-decoding-plot-haxby-searchlight-surface-py"><span class="std std-ref">Cortical surface-based searchlight decoding</span></a>
to demo how to do cortical surface-based searchlight decoding with Nilearn.</p></li>
<li><p>confounds or additional regressors for design matrix can be specified as
numpy arrays or pandas DataFrames interchangeably</p></li>
<li><p>The decomposition estimators will now accept argument <cite>per_component</cite>
with <cite>score</cite> method to explain the variance for each component.</p></li>
</ul>
</div>
<div class="section" id="fixes">
<h2>Fixes<a class="headerlink" href="#fixes" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li><p><a class="reference internal" href="../modules/generated/nilearn.maskers.NiftiLabelsMasker.html#nilearn.maskers.NiftiLabelsMasker" title="nilearn.maskers.NiftiLabelsMasker"><code class="xref py py-class docutils literal notranslate"><span class="pre">nilearn.maskers.NiftiLabelsMasker</span></code></a> no longer ignores its <cite>mask_img</cite></p></li>
<li><p><a class="reference internal" href="../modules/generated/nilearn.masking.compute_brain_mask.html#nilearn.masking.compute_brain_mask" title="nilearn.masking.compute_brain_mask"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.masking.compute_brain_mask</span></code></a> has replaced
nilearn.masking.compute_gray_matter_mask. Features remained the same but
some corrections regarding its description were made in the docstring.</p></li>
<li><p>the default background (MNI template) in plotting functions now has the
correct orientation; before left and right were inverted.</p></li>
<li><p>first level modelling can deal with regressors
having multiple events which share onsets or offsets.
Previously, such cases could lead to an erroneous baseline shift.</p></li>
<li><p><a class="reference internal" href="../modules/generated/nilearn.mass_univariate.permuted_ols.html#nilearn.mass_univariate.permuted_ols" title="nilearn.mass_univariate.permuted_ols"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.mass_univariate.permuted_ols</span></code></a> no longer returns transposed
t-statistic arrays when no permutations are performed.</p></li>
<li><p>Fix decomposition estimators returning explained variance score as 0.
based on all components i.e., when per_component=False.</p></li>
<li><p>Fix readme file of the Destrieux 2009 atlas.</p></li>
</ul>
</div>
<div class="section" id="changes">
<h2>Changes<a class="headerlink" href="#changes" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li><p>Function <code class="docutils literal notranslate"><span class="pre">nilearn.datasets.fetch_cobre</span></code> has been deprecated and will be
removed in release 0.9 .</p></li>
<li><p>Function <code class="docutils literal notranslate"><span class="pre">nilearn.plotting.plot_connectome_strength</span></code> has been deprecated and will
be removed in release 0.9 .</p></li>
<li><p><a class="reference internal" href="../modules/generated/nilearn.connectome.ConnectivityMeasure.html#nilearn.connectome.ConnectivityMeasure" title="nilearn.connectome.ConnectivityMeasure"><code class="xref py py-class docutils literal notranslate"><span class="pre">nilearn.connectome.ConnectivityMeasure</span></code></a> can now remove
confounds in its transform step.</p></li>
<li><p><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> can now sample between two nested surfaces
(eg white matter and pial surfaces) at specific cortical depths</p></li>
<li><p><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> now also downloads white matter
surfaces.</p></li>
</ul>
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<h4> Giving credit </h4>
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<li><p>Please consider <a href="../authors.html#citing">citing the
papers</a>.</p></li>
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<h3><a href="../index.html">Table of Contents</a></h3>
<ul>
<li><a class="reference internal" href="#">0.7.0</a><ul>
<li><a class="reference internal" href="#highlights">HIGHLIGHTS</a></li>
<li><a class="reference internal" href="#new">NEW</a></li>
<li><a class="reference internal" href="#fixes">Fixes</a></li>
<li><a class="reference internal" href="#changes">Changes</a></li>
</ul>
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