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    <title>Visualizing anatomically registered data with brainrender</title>
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      <article itemscope="" itemtype="http://schema.org/Article" data-itemscope="root">
        <h1 itemprop="headline">Visualizing anatomically registered data with brainrender</h1>
        <meta itemprop="image"
          content="https://via.placeholder.com/1200x714/dbdbdb/4a4a4a.png?text=Visualizing%20anatomically%20registered%20data%20with%20brainrender">
        <ol data-itemprop="authors">
          <li itemscope="" itemtype="http://schema.org/Person" itemprop="author">
            <meta itemprop="name" content="Federico Claudi"><span data-itemprop="givenNames"><span
                itemprop="givenName">Federico</span></span><span data-itemprop="familyNames"><span
                itemprop="familyName">Claudi</span></span><span data-itemprop="emails"><a
                itemprop="email"
                href="mailto:federico.claudi.17@ucl.ac.uk">federico.claudi.17@ucl.ac.uk</a></span><span
              data-itemprop="affiliations"><a itemprop="affiliation"
                href="#author-organization-1">1</a></span>
          </li>
          <li itemscope="" itemtype="http://schema.org/Person" itemprop="author">
            <meta itemprop="name" content="Adam L Tyson"><span data-itemprop="givenNames"><span
                itemprop="givenName">Adam</span><span itemprop="givenName">L</span></span><span
              data-itemprop="familyNames"><span itemprop="familyName">Tyson</span></span><span
              data-itemprop="affiliations"><a itemprop="affiliation"
                href="#author-organization-1">1</a><a itemprop="affiliation"
                href="#author-organization-2">2</a><a itemprop="affiliation"
                href="#author-organization-3">3</a><a itemprop="affiliation"
                href="#author-organization-4">4</a></span>
          </li>
          <li itemscope="" itemtype="http://schema.org/Person" itemprop="author">
            <meta itemprop="name" content="Luigi Petrucco"><span data-itemprop="givenNames"><span
                itemprop="givenName">Luigi</span></span><span data-itemprop="familyNames"><span
                itemprop="familyName">Petrucco</span></span><span data-itemprop="affiliations"><a
                itemprop="affiliation" href="#author-organization-1">1</a><a itemprop="affiliation"
                href="#author-organization-2">2</a><a itemprop="affiliation"
                href="#author-organization-3">3</a><a itemprop="affiliation"
                href="#author-organization-4">4</a></span>
          </li>
          <li itemscope="" itemtype="http://schema.org/Person" itemprop="author">
            <meta itemprop="name" content="Troy W Margrie"><span data-itemprop="givenNames"><span
                itemprop="givenName">Troy</span><span itemprop="givenName">W</span></span><span
              data-itemprop="familyNames"><span itemprop="familyName">Margrie</span></span><span
              data-itemprop="affiliations"><a itemprop="affiliation"
                href="#author-organization-1">1</a><a itemprop="affiliation"
                href="#author-organization-2">2</a><a itemprop="affiliation"
                href="#author-organization-3">3</a><a itemprop="affiliation"
                href="#author-organization-4">4</a></span>
          </li>
          <li itemscope="" itemtype="http://schema.org/Person" itemprop="author">
            <meta itemprop="name" content="Ruben Portugues"><span data-itemprop="givenNames"><span
                itemprop="givenName">Ruben</span></span><span data-itemprop="familyNames"><span
                itemprop="familyName">Portugues</span></span><span data-itemprop="affiliations"><a
                itemprop="affiliation" href="#author-organization-1">1</a><a itemprop="affiliation"
                href="#author-organization-2">2</a><a itemprop="affiliation"
                href="#author-organization-3">3</a><a itemprop="affiliation"
                href="#author-organization-4">4</a></span>
          </li>
          <li itemscope="" itemtype="http://schema.org/Person" itemprop="author">
            <meta itemprop="name" content="Tiago Branco"><span data-itemprop="givenNames"><span
                itemprop="givenName">Tiago</span></span><span data-itemprop="familyNames"><span
                itemprop="familyName">Branco</span></span><span data-itemprop="emails"><a
                itemprop="email" href="mailto:t.branco@ucl.ac.uk">t.branco@ucl.ac.uk</a></span><span
              data-itemprop="affiliations"><a itemprop="affiliation"
                href="#author-organization-1">1</a></span>
          </li>
        </ol>
        <ol data-itemprop="affiliations">
          <li itemscope="" itemtype="http://schema.org/Organization" itemid="#author-organization-1"
            id="author-organization-1"><span itemprop="name">UCL Sainsbury Wellcome
              Centre</span><address itemscope="" itemtype="http://schema.org/PostalAddress"
              itemprop="address"><span itemprop="addressLocality">London</span><span
                itemprop="addressCountry">United Kingdom</span></address></li>
          <li itemscope="" itemtype="http://schema.org/Organization" itemid="#author-organization-2"
            id="author-organization-2"><span itemprop="name">Institute of Neuroscience, Technical
              University of Munich</span><address itemscope=""
              itemtype="http://schema.org/PostalAddress" itemprop="address"><span
                itemprop="addressLocality">Munich</span><span
                itemprop="addressCountry">Germany</span></address></li>
          <li itemscope="" itemtype="http://schema.org/Organization" itemid="#author-organization-3"
            id="author-organization-3"><span itemprop="name">Max Planck Institute of Neurobiology,
              Research Group of Sensorimotor Control</span><address itemscope=""
              itemtype="http://schema.org/PostalAddress" itemprop="address"><span
                itemprop="addressLocality">Martinsried</span><span
                itemprop="addressCountry">Germany</span></address></li>
          <li itemscope="" itemtype="http://schema.org/Organization" itemid="#author-organization-4"
            id="author-organization-4"><span itemprop="name">Munich Cluster for Systems Neurology
              (SyNergy)</span><address itemscope="" itemtype="http://schema.org/PostalAddress"
              itemprop="address"><span itemprop="addressLocality">Munich</span><span
                itemprop="addressCountry">Germany</span></address></li>
        </ol><span itemscope="" itemtype="http://schema.org/Organization" itemprop="publisher">
          <meta itemprop="name" content="Unknown"><span itemscope=""
            itemtype="http://schema.org/ImageObject" itemprop="logo">
            <meta itemprop="url"
              content="https://via.placeholder.com/600x60/dbdbdb/4a4a4a.png?text=Unknown">
          </span>
        </span><time itemprop="datePublished" datetime="2021-03-19">2021-03-19</time>
        <ul data-itemprop="genre">
          <li itemprop="genre">Tools and Resources</li>
        </ul>
        <ul data-itemprop="about">
          <li itemscope="" itemtype="http://schema.org/DefinedTerm" itemprop="about"><span
              itemprop="name">Neuroscience</span></li>
        </ul>
        <ul data-itemprop="keywords">
          <li itemprop="keywords">software</li>
          <li itemprop="keywords">data visualization</li>
          <li itemprop="keywords">open source</li>
          <li itemprop="keywords">anatomy</li>
          <li itemprop="keywords">None</li>
        </ul>
        <ul data-itemprop="identifiers">
          <li itemscope="" itemtype="http://schema.org/PropertyValue" itemprop="identifier">
            <meta itemprop="propertyID"
              content="https://registry.identifiers.org/registry/publisher-id"><span
              itemprop="name">publisher-id</span><span itemprop="value"
              data-itemtype="http://schema.org/Number">65751</span>
          </li>
          <li itemscope="" itemtype="http://schema.org/PropertyValue" itemprop="identifier">
            <meta itemprop="propertyID" content="https://registry.identifiers.org/registry/doi">
            <span itemprop="name">doi</span><span itemprop="value">10.7554/eLife.65751</span>
          </li>
          <li itemscope="" itemtype="http://schema.org/PropertyValue" itemprop="identifier">
            <meta itemprop="propertyID"
              content="https://registry.identifiers.org/registry/elocation-id"><span
              itemprop="name">elocation-id</span><span itemprop="value">e65751</span>
          </li>
        </ul>
        <section data-itemprop="description">
          <h2 data-itemtype="http://schema.stenci.la/Heading">Abstract</h2>
          <meta itemprop="description"
            content="Three-dimensional (3D) digital brain atlases and high-throughput brain-wide imaging techniques generate large multidimensional datasets that can be registered to a common reference frame. Generating insights from such datasets depends critically on visualization and interactive data exploration, but this a challenging task. Currently available software is dedicated to single atlases, model species or data types, and generating 3D renderings that merge anatomically registered data from diverse sources requires extensive development and programming skills. Here, we present brainrender: an open-source Python package for interactive visualization of multidimensional datasets registered to brain atlases. Brainrender facilitates the creation of complex renderings with different data types in the same visualization and enables seamless use of different atlas sources. High-quality visualizations can be used interactively and exported as high-resolution figures and animated videos. By facilitating the visualization of anatomically registered data, brainrender should accelerate the analysis, interpretation, and dissemination of brain-wide multidimensional data.">
          <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Three-dimensional (3D)
            digital brain atlases and high-throughput brain-wide imaging techniques generate large
            multidimensional datasets that can be registered to a common reference frame. Generating
            insights from such datasets depends critically on visualization and interactive data
            exploration, but this a challenging task. Currently available software is dedicated to
            single atlases, model species or data types, and generating 3D renderings that merge
            anatomically registered data from diverse sources requires extensive development and
            programming skills. Here, we present brainrender: an open-source Python package for
            interactive visualization of multidimensional datasets registered to brain atlases.
            Brainrender facilitates the creation of complex renderings with different data types in
            the same visualization and enables seamless use of different atlas sources. High-quality
            visualizations can be used interactively and exported as high-resolution figures and
            animated videos. By facilitating the visualization of anatomically registered data,
            brainrender should accelerate the analysis, interpretation, and dissemination of
            brain-wide multidimensional data.</p>
        </section>
        <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
          data-execution_count="1" data-programminglanguage="python">
          <pre class="language-python" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
            slot="text"><code># Import to make brainrender embed scenes in jupyter notebooks
from brainrender.scene import Scene
from brainrender import __version__, settings
from rich import print
from vedo import embedWindow

from myterial import blue_light, salmon

settings.SHOW_AXES = False
settings.WHOLE_SCREEN = False

embedWindow(None)
if not __version__==&#39;2.0.2.9&#39;:
    raise ValueError(f&#39;This executable is meant to work with brainrender 2.0.2.9, not: {__version__}&#39;)

def make_scene(species=&#39;mouse&#39;):
    if species == &#39;mouse&#39;:
        scene = Scene()
    elif species == &#39;zfish&#39;:
        scene = Scene(atlas_name=&quot;mpin_zfish_1um&quot;,)
    else:
        raise ValueError(f&#39;Species not supported: {species}&#39;)
    scene.root._needs_silhouette = True
    scene.root._silhouette_kwargs[&#39;lw&#39;] = 1
    return scene
    
def render_scene(scene, **kwargs):
    print(f&#39;[{blue_light}]Rendering scene, press &quot;[{salmon} b]q[/{salmon} b]&quot; to close&#39;)
    scene.render(**kwargs)

    # close when done
    scene.plotter.close()</code></pre>
        </stencila-code-chunk>
        <h2 itemscope="" itemtype="http://schema.stenci.la/Heading" id="introduction">Introduction
        </h2>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Understanding how nervous
          systems generate behavior benefits from gathering multidimensional data from different
          individual animals. These data range from neural activity recordings and anatomical
          connectivity, to cellular and subcellular information such as morphology and gene
          expression profiles. These different types of data should ideally all be in register so
          that, for example, neural activity in one brain region can be interpreted in light of the
          connectivity of that region or the cell types it contains. Such registration, however, is
          challenging. Often it is not technically feasible to obtain multidimensional data in a
          single experiment, and registration to a common reference frame must be performed post
          hoc. Even for the same experiment type, registration is necessary to allow comparisons
          across individual animals <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib25"><span>25</span><span>Simmons and
                Swanson</span><span>2009</span></a></cite>.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">While different types of
          references can in principle be used, neuroanatomical location is a natural and most
          commonly used reference frame <span itemscope=""
            itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib6"><span>6</span><span>Chon et
                  al.</span><span>2019</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib21"><span>21</span><span>Oh et
                  al.</span><span>2014</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a
                href="#bib2"><span>2</span><span>Arganda-Carreras et
                  al.</span><span>2018</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib15"><span>15</span><span>Kunst et
                  al.</span><span>2019</span></a></cite></span>. In recent years, several
          high-resolution three-dimensional (3D) digital brain atlases have been generated for model
          species commonly used in neuroscience <span itemscope=""
            itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib32"><span>32</span><span>Wang et
                  al.</span><span>2020</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib21"><span>21</span><span>Oh et
                  al.</span><span>2014</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a
                href="#bib2"><span>2</span><span>Arganda-Carreras et
                  al.</span><span>2018</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib15"><span>15</span><span>Kunst et
                  al.</span><span>2019</span></a></cite></span>. These atlases provide a framework
          for registering different types of data across macro- and microscopic scales. A key output
          of this process is the visualization of all datasets in register. Given the intrinsically
          3D geometry of brain structures and individual neurons, 3D renderings are more readily
          understandable and can provide more information when compared to two dimensional images.
          Exploring interactive 3D visualizations of the brain gives an overview of the relationship
          between datasets and brain regions and helps generating intuitive insights about these
          relationships. This is particularly important for large-scale datasets such as the ones
          generated by open-science projects like MouseLight <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib33"><span>33</span><span>Winnubst
                et al.</span><span>2019</span></a></cite> and the Allen Mouse Connectome <cite
            itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib21"><span>21</span><span>Oh et al.</span><span>2014</span></a></cite>. In
          addition, high-quality 3D visualizations facilitate the communication of experimental
          results registered to brain anatomy.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Generating custom 3D
          visualizations of atlas data requires programmatic access to the atlas. While some of the
          recently developed atlases provide an API (Application Programming Interface) for
          accessing atlas data <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite
              itemscope="" itemtype="http://schema.stenci.la/Cite"><a
                href="#bib32"><span>32</span><span>Wang et
                  al.</span><span>2020</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib15"><span>15</span><span>Kunst et
                  al.</span><span>2019</span></a></cite></span>, rendering these data in 3D remains
          a demanding and time-consuming task that requires significant programming skills.
          Moreover, visualization of user-generated data registered onto the atlas requires an
          interface between the user data and the atlas data, which further requires advanced
          programming knowledge and extensive development. There is therefore the need for software
          that can simplify the process of visualizing 3D anatomical data from available atlases and
          from new experimental datasets.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Currently, existing software
          packages such as cocoframer <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib16"><span>16</span><span>Lein et al.</span><span>2007</span></a></cite>,
          BrainMesh <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib34"><span>34</span><span>Yaoyao</span><span>2020</span></a></cite>, and
          SHARPTRACK <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib24"><span>24</span><span>Shamash et al.</span><span>2018</span></a></cite>
          provide some functionality for 3D rendering of anatomical data. These packages, however,
          are only compatible with a single atlas and cannot be used to render data from different
          atlases or different animal species. Achieving this requires adapting the existing
          software to the different atlases datasets or developing new dedicated software all
          together, at the cost of significant additional efforts, often duplicated. An important
          limitation of the currently available software is that it frequently does not support
          rendering of non-atlas data, such as data from publicly available datasets (e.g.
          MouseLight) or produced by individual laboratories. This capability is essential for
          easily mapping newly generated data onto brain anatomy at high resolution and produce
          visualizations of multidimensional datasets. More advanced software such as natverse <cite
            itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib5"><span>5</span><span>Bates et al.</span><span>2020</span></a></cite>
          offers extensive data visualization and analysis functionality, but currently, it is
          mostly restricted to data obtained from the <em itemscope=""
            itemtype="http://schema.stenci.la/Emphasis">Drosophila</em> brain. Simple Neurite Tracer
          <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib3"><span>3</span><span>Arshadi et al.</span><span>2020</span></a></cite>, an
          ImageJ-based software, can render neuronal morphological data from public and
          user-generated datasets and is compatible with several reference atlases. However, this
          software does not support visualization of data other than neuronal morphological
          reconstructions nor can it be easily adapted to work with different or new atlases beyond
          the ones already supported. Finally, software such as MagellanMapper <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib35"><span>35</span><span>Young et
                al.</span><span>2020</span></a></cite> can be used to visualize and analyze large 3D
          brain imaging datasets, but the visualization is restricted to one data item (i.e. images
          from one individual brain). It is therefore not possible to combine data from different
          sources into a single visualization. Ideally, a rendering software should work with 3D
          mesh data instead of 3D voxel image data to allow the creation of high-quality renderings
          and facilitate the integration of data from different sources.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">An additional consideration is
          that existing software tools for programmatic neuroanatomical renderings have been
          developed in programming languages such as R and Matlab, and there is currently no
          available alternative in Python. The popularity of Python within the neuroscientific
          community has grown tremendously in recent years <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib19"><span>19</span><span>Muller et
                al.</span><span>2015</span></a></cite>. Building on Python’s simple syntax and free,
          high-quality data processing and analysis packages, several open-source tools directly
          aimed at neuroscientists have been written in Python and are increasingly used (e.g.,
          <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib18"><span>18</span><span>Mathis et al.</span><span>2018</span></a></cite>;
          <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib22"><span>22</span><span>Pachitariu et
                al.</span><span>2017</span></a></cite>; <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib31"><span>31</span><span>Tyson and
                Rousseau</span><span>2020</span></a></cite>). Developing a python-based software for
          universal generation of 3D renderings of anatomically registered data can therefore take
          advantage of the increasing strength and depth of the python neuroscience community for
          testing and further development.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">For these reasons, we have
          developed brainrender: an open-source python package for creating high-resolution,
          interactive 3D renderings of anatomically registered data. Brainrender is written in
          Python and integrated with BrainGlobe’s AtlasAPI <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib7"><span>7</span><span>Claudi et
                al.</span><span>2020</span></a></cite> to interface natively with different atlases
          without need for modification. Brainrender supports the visualization of data acquired
          with different techniques and at different scales. Data from multiple sources can be
          combined in a single rendering to produce rich and informative visualizations of
          multidimensional data. Brainrender can also be used to create high-resolution,
          publication-ready images and videos (see <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib31"><span>31</span><span>Tyson and
                Rousseau</span><span>2020</span></a></cite>; <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib1"><span>1</span><span>Adkins et
                al.</span><span>2020</span></a></cite>), as well as interactive online
          visualizations to facilitate the dissemination of anatomically registered data. Finally,
          using brainrender requires minimal programming skills, which should accelerate the
          adoption of this new software by the research community. All brainrender code is available
          at the GitHub repository together with extensive online documentation and examples.</p>
        <h2 itemscope="" itemtype="http://schema.stenci.la/Heading" id="results">Results</h2>
        <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
          id="design-principles-and-implementation">Design principles and implementation</h3>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">A core design goal for
          brainrender was to generate a visualization software compatible with any reference atlas,
          thus providing a generic and flexible tool (<a href="#fig1" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 1A</a>). To achieve this goal,
          brainrender has been developed as part of the BrainGlobe’s computational neuroanatomy
          software suite. In particular, we integrated brainrender directly with BrainGlobe’s
          AtlasAPI <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib7"><span>7</span><span>Claudi et al.</span><span>2020</span></a></cite>. The
          AtlasAPI can download and access atlas data from several supported atlases in an unified
          format. Brainrender uses the AtlasAPI to access 3D mesh data from individual brain regions
          as well as metadata about the hierarchical organization of the brain’s structures (<a
            href="#fig1" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 1B</a>). Thus,
          the same programming interface can be used to access data from any atlas (see code
          examples in <a href="#fig2" itemscope="" itemtype="http://schema.stenci.la/Link">Figure
            2</a>), including recently developed ones (e.g. the enhanced and unified mouse brain
          atlas, <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib6"><span>6</span><span>Chon et al.</span><span>2019</span></a></cite>).</p>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig1" title="Figure 1.">
          <label data-itemprop="label">Figure 1.</label><img src="index.html.media/fig1.jpg" alt=""
            itemscope="" itemtype="http://schema.org/ImageObject">
          <figcaption>
            <h4 itemscope="" itemtype="http://schema.stenci.la/Heading" id="design-principles">
              Design principles.</h4>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">(<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">A</strong>) Schematic illustration of how
              different types of data can be loaded into brainrender using either brainrender’s own
              functions, software packages from the BrainGlobe suite, or custom Python scripts. All
              data loaded into brainrender is converted to a unified format, which simplifies the
              process of visualizing data from different sources. (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">B</strong>) Using brainrender with
              different atlases. Visualization of brain atlas data from three different atlases
              using brainrender. Left, Allen atlas of the mouse brain showing the superficial (SCs)
              and motor (SCm) subdivisions of the superior colliculus and the Zona Incerta (data
              from <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
                  href="#bib32"><span>32</span><span>Wang et
                    al.</span><span>2020</span></a></cite>). Middle, visualization of the cerebellum
              and tectum in the larval zebrafish brain (data from <cite itemscope=""
                itemtype="http://schema.stenci.la/Cite"><a href="#bib15"><span>15</span><span>Kunst
                    et al.</span><span>2019</span></a></cite>). Right, visualization of the
              precentral gyrus, postcentral gyrus, and temporal lobe of the human brain (data from
              <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
                  href="#bib8"><span>8</span><span>Ding et al.</span><span>2016</span></a></cite>).
              (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">C</strong>) The
              brainrender GUI. Mouse, human, and zebrafish larvae drawings from <a
                href="https://scidraw.io/" itemscope=""
                itemtype="http://schema.stenci.la/Link">scidraw.io</a> (<a
                href="http://doi.org/10.5281/zenodo.3925991" itemscope=""
                itemtype="http://schema.stenci.la/Link">doi.org/10.5281/zenodo.3925991</a>, <a
                href="http://doi.org/10.5281/zenodo.3926189" itemscope=""
                itemtype="http://schema.stenci.la/Link">doi.org/10.5281/zenodo.3926189</a>, <a
                href="http://doi.org/10.5281/zenodo.3926123" itemscope=""
                itemtype="http://schema.stenci.la/Link">doi.org/10.5281/zenodo.3926123</a>).</p>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig2" title="Figure 2.">
          <label data-itemprop="label">Figure 2.</label><img src="index.html.media/fig2.jpg" alt=""
            itemscope="" itemtype="http://schema.org/ImageObject">
          <figcaption>
            <h4 itemscope="" itemtype="http://schema.stenci.la/Heading" id="code-examples">Code
              examples.</h4>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Example python code for
              visualizing brain regions in the mouse and larval zebrafish brains. The same commands
              can be used for both atlases and switching between atlases can be done by simply
              specifying which atlas to use when creating the visualization. Further examples can be
              found in brainrender’s GitHub repository.</p>
          </figcaption>
        </figure>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">The second major design
          principle was to enable rendering of any data type that can be registered to a reference
          atlas, either from publicly available datasets or from individual laboratories.
          Brainrender can directly visualize data produced with any analysis software from the
          BrainGlobe suite, including cellfinder <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib30"><span>30</span><span>Tyson et
                al.</span><span>2020</span></a></cite> and brainreg <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib31"><span>31</span><span>Tyson and
                Rousseau</span><span>2020</span></a></cite>. In addition, brainrender provides
          functionality for easily loading and visualizing commonly used data types such as .npy
          files with cell coordinates or image data, .obj, and .stl files with 3D mesh data and
          .json files with streamlines data for mesoscale connectomics. Additional information about
          the file formats accepted by brainrender can be found in the online documentation.
          Brainglobe’s software suite also includes imio which can load data from several file types
          (e.g. tiff and .nii), and additional file formats can be loaded through the numerous
          packages provided by the python ecosystem. Finally, the existing loading functionality can
          be easily expanded to support user-specific needs by directly plugging in custom user code
          into the brainrender interface (<a href="#fig1" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 1A</a>).</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">One of the goals of brainrender
          is to facilitate the creation of high-resolution images, animated videos, and interactive
          online visualizations from any anatomically registered data. Brainrender uses vedo as the
          rendering engine <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib20"><span>20</span><span>Musy et al.</span><span>2019</span></a></cite>, a
          state-of-the-art tool that enables fast, high-quality rendering with minimal hardware
          requirements.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">High-resolution renderings of
          rich 3D scenes can be produced rapidly (e.g. 10,000 cells in less than 2 s) in standard
          laptop or desktop configurations. Benchmarking tests across different operating systems
          and machine configurations show that using a GPU can increase the framerate of interactive
          renderings by a factor of 3.5 (see <a href="#table1" itemscope=""
            itemtype="http://schema.stenci.la/Link">Tables 1</a> and <a href="#table2" itemscope=""
            itemtype="http://schema.stenci.la/Link"><span
              data-itemtype="http://schema.org/Number">2</span></a> in Materials and methods). This
          performance increase, however, depends on the complexity of the pre-processing steps, such
          as data loading and mesh generation, which run on the CPU. As one the main goals of
          brainrender is to produce high-resolution visualizations, we have made the rendering
          quality independent of hardware configuration, which only affects the rendering time.
          Animated videos and online visualizations can be produced with a few lines of code in
          brainrender. Several options are provided for easily customizing the appearance of
          rendered objects, thus enabling high-quality, rich data visualizations that combine
          multiple data sources.</p>
        <table id="table1" itemscope="" itemtype="http://schema.org/Table">
          <caption><label data-itemprop="label">Table 1.</label>
            <div itemprop="caption">
              <h4 itemscope="" itemtype="http://schema.stenci.la/Heading"
                id="machine-configurations-used-for-benchmark-tests">Machine configurations used for
                benchmark tests.</h4>
            </div>
          </caption>
          <thead>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <th itemscope="" itemtype="http://schema.stenci.la/TableCell">N</th>
              <th itemscope="" itemtype="http://schema.stenci.la/TableCell">OS</th>
              <th itemscope="" itemtype="http://schema.stenci.la/TableCell">CPU</th>
              <th itemscope="" itemtype="http://schema.stenci.la/TableCell">GPU</th>
            </tr>
          </thead>
          <tbody>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">1</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Macos Mojave 10.14.6
              </td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">2.3 ghz Intel Core i9
              </td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Radeon Pro 560 × 4 GB
                GPU</td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">2</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Ubuntu 18.04.2 LTS x86
                64</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Intel i7-8565U (x) @ 4.5
                ghz</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">NO GPU</td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">3</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Windows 10</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Intel(R) Core i7-7700HQ
                2.8 ghz</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">NO GPU</td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">4</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Windows 10</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Intel(R) Xeon(R) CPU
                E5-2643 v3 3.4 ghz</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">NVIDIA geforce GTX 1080
                Ti</td>
            </tr>
          </tbody>
        </table>
        <table id="table2" itemscope="" itemtype="http://schema.org/Table">
          <caption><label data-itemprop="label">Table 2.</label>
            <div itemprop="caption">
              <h4 itemscope="" itemtype="http://schema.stenci.la/Heading"
                id="benchmark-tests-results">Benchmark tests results.</h4>
              <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">The number of actors
                refers to the total number of elements rendered, and the number of vertices refers
                to the total number of mesh vertices in the rendering.</p>
            </div>
          </caption>
          <thead>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <th itemscope="" itemtype="http://schema.stenci.la/TableCell">Test</th>
              <th itemscope="" itemtype="http://schema.stenci.la/TableCell">Machine</th>
              <th itemscope="" itemtype="http://schema.stenci.la/TableCell">GPU</th>
              <th itemscope="" itemtype="http://schema.stenci.la/TableCell"># actors</th>
              <th itemscope="" itemtype="http://schema.stenci.la/TableCell"># vertices</th>
              <th itemscope="" itemtype="http://schema.stenci.la/TableCell">FPS</th>
              <th itemscope="" itemtype="http://schema.stenci.la/TableCell">Run duration</th>
            </tr>
          </thead>
          <tbody>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">10 k cells</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">1</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Yes</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">3</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">1,029,324</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">24.76</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">0.81</span></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">2</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">No</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">3</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">1,029,324</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">22.46</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">1.16</span></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">3</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">No</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">3</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">1,029,324</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">20</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">1.41</span></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">4</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Yes</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">3</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">1,029,324</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">100</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">1.34</span></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">100 k cells</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">1</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Yes</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">3</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">9,849,324</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">18.87</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">3.23</span></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">2</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">No</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">3</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">9,849,324</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">14.91</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">4.34</span></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">3</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">No</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">3</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">9,849,324</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">0.43</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">7.94</span></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">4</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Yes</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">3</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">9,849,324</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">1.2</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">1.13</span></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">1 M cells</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">1</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Yes</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">3</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">98,049,324</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">2.65</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">31.01</span></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">2</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">No</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">3</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">98,049,324</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">2.55</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">96.49</span></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">3</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">No</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">3</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">98,049,324</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">0.03</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">86.75</span></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">4</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Yes</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">3</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">9,8049,324</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">0.13</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">36.57</span></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Slicing 10 k cells</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">1</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Yes</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">3</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">237,751</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">37.64</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">0.96</span></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">2</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">No</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">3</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">237,751</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">39.1</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">1.25</span></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">3</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">No</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">3</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">237,751</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">26.32</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">1.88</span></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">4</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Yes</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">3</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">237,751</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">200</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">1.34</span></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Slicing 100 k cells</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">1</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Yes</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">3</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">276,092</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">31.79</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">7.77</span></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">2</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">No</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">3</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">276,092</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">25.98</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">9.09</span></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">3</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">No</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">3</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">276,092</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">21.28</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">16.88</span></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">4</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Yes</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">3</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">276,092</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">111.11</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">9.65</span></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Slicing 1 M cells</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">1</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Yes</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">3</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">275,069</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">11.23</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">91.31</span></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">2</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">No</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">3</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">275,069</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">5.39</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">104.79</span></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">3</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">No</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">3</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">275,069</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">5.03</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">158.99</span></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">4</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Yes</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">3</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">275,069</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">37.04</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">97.43</span></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Brain regions</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">1</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Yes</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">1678</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">1,864,388</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">9.38</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">11.78</span></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">2</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">No</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">1678</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">1,864,388</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">7.61</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">27.4</span></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">3</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">No</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">1678</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">1,864,388</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">6.49</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">46.79</span></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">4</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Yes</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">1678</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">1,864,388</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">11.9</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">35.83</span></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Animation</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">1</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Yes</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">8</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">96,615</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">9.91</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">18.98</span></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">2</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">No</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">8</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">96,615</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">22.12</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">12.63</span></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">3</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">No</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">8</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">96,615</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">15.15</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">11.92</span></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">4</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Yes</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">8</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">96,615</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">47.62</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">12.29</span></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Volume</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">1</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Yes</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">12</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">49,324</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">1.79</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">2.31</span></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">2</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">No</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">12</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">49,324</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">1.66</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">1.95</span></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">3</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">No</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">12</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">49,324</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">3.55</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">2.15</span></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">4</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Yes</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">12</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">49,324</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">23.26</span></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><span
                  data-itemtype="http://schema.org/Number">1.21</span></td>
            </tr>
          </tbody>
        </table>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Finally, we aimed for
          brainrender to empower scientists with little or no programming experience to generate
          advanced visualizations of their anatomically registered data. To make brainrender as
          user-friendly as possible we have produced extensive documentation, tutorials and examples
          for installing and using the software. We have also developed a graphic user interface
          (GUI) to access most of brainrender’s core functionality. This GUI can be used to perform
          actions such as rendering of brain regions and labeled cells (e.g. from cellfinder) and
          creating images of the rendered data, without writing custom python code (<a href="#fig1"
            itemscope="" itemtype="http://schema.stenci.la/Link">Figure 1C</a>), (<a href="#video1"
            itemscope="" itemtype="http://schema.stenci.la/Link">Video 1</a>).</p>
        <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
          id="visualizing-brain-regions-and-other-structures">Visualizing brain regions and other
          structures</h3>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">A key element of any
          neuroanatomical visualization is the rendering of the entire outline of the brain as well
          as the borders of brain regions of interest. In brainrender, this can easily be achieved
          by specifying which brain regions to include in the rendering. The software will then use
          BrainGlobe’s AtlasAPI to load the 3D data and subsequently renders them (<a href="#fig1"
            itemscope="" itemtype="http://schema.stenci.la/Link">Figure 1B</a>).</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Brainrender can also render
          brain areas defined by factors other than anatomical location, such as gene expression
          levels or functional properties. These can be loaded either directly as 3D mesh data after
          processing with dedicated software (e.g., <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib30"><span>30</span><span>Tyson et
                al.</span><span>2020</span></a></cite>; <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib27"><span>27</span><span>Song et
                al.</span><span>2020</span></a></cite>; <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib13"><span>13</span><span>Jin et
                al.</span><span>2019</span></a></cite>; <a href="#fig3" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 3A</a>) or as 3D volumetric data (<a
            href="#fig3" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 3E</a>). For
          the latter, brainrender takes care of the conversion of voxels into a 3D mesh for
          rendering. Furthermore, custom 3D meshes can be created to visualize different types of
          data. For example, brainrender can import JSON files with tractography connectivity data
          and create ‘streamlines’ to visualize efferent projections from a brain region of
          interested (<a href="#fig3" itemscope="" itemtype="http://schema.stenci.la/Link">Figure
            3B</a>).</p>
        <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
          data-execution_count="2" data-programminglanguage="python">
          <pre class="language-python" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
            slot="text"><code># Figure 3
&#39;&#39;&#39;

figure: Figure 3.
:::
![](article.ipynb.media/fig3.jpg)

### Visualizing different types of data in brainrender.

(**A**) Spread of fluorescence labeling following viral injection of AAV2-CRE-eGPF in the superior colliculus of two FLEX-TdTomato mice. 3D objects showing the injection sites were created using custom python scripts following acquisition of a 3D image of the entire brain with serial two-photon tomography and registration of the image data to the atlas’ template (with brainreg, [@bib30]). (**B**) Streamlines visualization of efferent projections from the mouse primary motor cortex following injection of an anterogradely transported virus expressing fluorescent proteins (original data from [@bib21]), downloaded from (Neuroinformatics NL with brainrender). (**C**) Visualization of the location of several implanted neuropixel probes from multiple mice (data from [@bib28]). Dark salmon colored tracks show probes going through both primary/anterior visual cortex (VISp/VISa) and the dorsal lateral geniculate nucleus of the thalamus. (**D**) Single periaqueductal gray (PAG) neuron. The PAG and superior colliculus are also shown. The neuron’s morphology was reconstructed by targeting the expression of fluorescent proteins in excitatory neurons in the PAG via an intersectional viral strategy, followed by imaging of cleared tissue and manual reconstruction of the neuron’s morphology with Vaa3D software. Data were registered to the Allen atlas with SHARPTRACK [@bib24]. The 3D data was saved as a .stl file and loaded directly into brainrender. (**E**) Gene expression data. Left, expression of genes ‘brn3c’ and ‘nk1688CGt’ in the tectum of the larval zebrafish brain (gene expression data from fishatlas.neuro.mpg.de, 3D objects created with custom python scripts). Right, expression of gene ‘Gpr161’ in the mouse hippocampus (gene expression data from [@bib32]), downloaded with brainrender (3D objects created with brainrender). Colored voxels show voxels with high gene expressions. The CA1 field of the hippocampus is also shown.
:::
{#fig3}
&#39;&#39;&#39;
from myterial import indigo as scmcol
from myterial import indigo_dark as scscol
from myterial import blue_darker as zicol



# Panel (A)
scene = make_scene()
for reg, col in zip((&quot;SCm&quot;, &quot;SCs&quot;, &quot;ZI&quot;), (scmcol, scscol, zicol)):
    scene.add_brain_region(reg, color=col, silhouette=True)
render_scene(scene)
</code></pre>
          <figure slot="outputs"><span data-itemtype="http://schema.stenci.la/Null">null</span>
          </figure>
        </stencila-code-chunk>
        <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
          data-execution_count="3" data-programminglanguage="python">
          <pre class="language-python" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
            slot="text"><code># Panel (B)
from myterial import blue_grey
from myterial import salmon_dark as streamlinescol

from brainrender.atlas_specific import get_streamlines_for_region
from brainrender.actors.streamlines import Streamlines

scene = make_scene()

# get streamlines data
streams = get_streamlines_for_region(&quot;MOp&quot;)

# add Streamlines actors
s = scene.add(Streamlines(streams[0], color=streamlinescol, alpha=1))

# add brain regions
th = scene.add_brain_region(
    &quot;TH&quot;, alpha=0.45, silhouette=False, color=blue_grey
)


render_scene(scene)</code></pre>
          <figure slot="outputs"><span data-itemtype="http://schema.stenci.la/Null">null</span><span
              data-itemtype="http://schema.stenci.la/Null">null</span></figure>
        </stencila-code-chunk>
        <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
          data-execution_count="4" data-programminglanguage="python">
          <pre class="language-python" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
            slot="text"><code># Panel (C)
from brainrender.actors import Points

try:
    from oneibl.onelight import ONE
except ImportError:
    %pip install ibllib
    from oneibl.onelight import ONE

from myterial import blue_grey, blue_grey_dark, salmon_light, salmon_darker

scene = make_scene()


# download probe data from ONE
one = ONE()
one.set_figshare_url(&quot;https://figshare.com/articles/steinmetz/9974357&quot;)

# select sessions with trials
sessions = one.search([&quot;trials&quot;])

# get probe locations
probes_locs = []
for sess in sessions:
    probes_locs.append(one.load_dataset(sess, &quot;channels.brainLocation&quot;))

# get single probe tracks
for locs in probes_locs:
    k = int(len(locs) / 374.0)

    for i in range(k):
        points = locs[i * 374 : (i + 1) * 374]
        regs = points.allen_ontology.values

        # color based on if probes go through selected regions
        if &quot;LGd&quot; in regs and (&quot;VISa&quot; in regs or &quot;VISp&quot; in regs):
            color = salmon_darker
            alpha = 1
            sil = 1
        elif &quot;VISa&quot; in regs:
            color = salmon_light
            alpha = 1
            sil = 0.5
        else:
            continue

        # render channels as points
        spheres = Points(
            points[[&quot;ccf_ap&quot;, &quot;ccf_dv&quot;, &quot;ccf_lr&quot;]].values,
            colors=color,
            alpha=alpha,
            radius=30,
        )
        spheres = scene.add(spheres)
        scene.add_silhouette(spheres, lw=sil)


# Add brain regions
visp, lgd = scene.add_brain_region(
    &quot;VISp&quot;,
    &quot;LGd&quot;,
    hemisphere=&quot;right&quot;,
    alpha=0.3,
    silhouette=False,
    color=blue_grey_dark,
)
visa = scene.add_brain_region(
    &quot;VISa&quot;,
    hemisphere=&quot;right&quot;,
    alpha=0.2,
    silhouette=False,
    color=blue_grey,
)
th = scene.add_brain_region(
    &quot;TH&quot;, alpha=0.3, silhouette=False, color=blue_grey_dark
)
th.wireframe()
scene.add_silhouette(lgd, visp, lw=2)

render_scene(scene)</code></pre>
          <figure slot="outputs"><span data-itemtype="http://schema.stenci.la/Null">null</span>
          </figure>
        </stencila-code-chunk>
        <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
          data-execution_count="16" data-programminglanguage="python">
          <pre class="language-python" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
            slot="text"><code># Panel (D)
from brainrender.actors import Point

from myterial import blue_grey_light as scmcol
from myterial import blue_grey as pagcol
from myterial import salmon_dark as neuroncol

cam = {
    &quot;pos&quot;: (-16954, 2456, -3961),
    &quot;viewup&quot;: (0, -1, 0),
    &quot;clippingRange&quot;: (22401, 34813),
    &quot;focalPoint&quot;: (7265, 2199, -5258),
    &quot;distance&quot;: 24256,
}

scene = make_scene()

# add brain regions
pag = scene.add_brain_region(&quot;PAG&quot;, alpha=0.4, silhouette=False, color=pagcol)
scm = scene.add_brain_region(&quot;SCm&quot;, alpha=0.3, silhouette=False, color=scmcol)

# add neuron mesh
neuron = scene.add(r&quot;C:\Users\Federico\Documents\GitHub\BrainRender\paper\data\yulins_neuron.stl&quot;)
neuron.c(neuroncol)

# add sphere at soma location
soma_pos = [9350.51912036, 2344.33986638, 5311.18297796]
point = scene.add(Point(soma_pos, color=neuroncol, radius=25))
scene.add_silhouette(point, lw=1, color=&quot;k&quot;)
scene.add_silhouette(neuron, lw=1, color=&quot;k&quot;)

# slice scene repeatedly to cut out region of interest
p = [9700, 1, 800]
plane = scene.atlas.get_plane(pos=p, plane=&quot;frontal&quot;)
scene.slice(plane, actors=[scm, pag, scene.root])

p = [11010, 5000, 5705]
plane = scene.atlas.get_plane(pos=p, norm=[0, -1, 0])
scene.slice(plane, actors=[scene.root])

# render
render_scene(scene, camera=cam, zoom=9)
</code></pre>
          <figure slot="outputs"><span data-itemtype="http://schema.stenci.la/Null">null</span>
          </figure>
        </stencila-code-chunk>
        <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
          data-execution_count="5" data-programminglanguage="python">
          <pre class="language-python" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
            slot="text"><code># Panel (E)
from brainrender.actors import Volume
from myterial import purple_dark as gene2_color
from myterial import purple_light as gene1_color

cam = cam = {
    &quot;pos&quot;: (-835, -1346, 1479),
    &quot;viewup&quot;: (0, -1, 0),
    &quot;clippingRange&quot;: (1703, 3984),
    &quot;focalPoint&quot;: (334, 200, -342),
    &quot;distance&quot;: 2660,
}


# shift parameters to adjust meshes position
SHIFT = [-20, 15, 30]  # fine tune pos

scene = make_scene(species=&#39;zfish&#39;)

# add custom meshes from file, but don&#39;t show them (alpha=0)
m = scene.add(r&quot;C:\Users\Federico\Documents\GitHub\BrainRender\paper\data\T_AVG_brn3c_GFP.obj&quot;, color=gene1_color, alpha=0)
m2 = scene.add(r&quot;C:\Users\Federico\Documents\GitHub\BrainRender\paper\data\T_AVG_nk1688CGt_GFP.obj&quot;, color=gene2_color, alpha=0)

# convert meshes to volumetric data showing gene expression density
vol1 = Volume(m.mesh.density(), as_surface=True, min_value=20000, cmap=&quot;Reds&quot;)
vol1.lw(1)
scene.add(vol1)

vol2 = Volume(m2._mesh.density(), as_surface=True, min_value=600, cmap=&quot;Blues&quot;)
vol2.lw(1)
scene.add(vol2)

render_scene(scene, zoom=2.5, camera=cam)</code></pre>
          <figure slot="outputs"><span data-itemtype="http://schema.stenci.la/Null">null</span>
          </figure>
        </stencila-code-chunk>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Brainrender also simplifies
          visualizing the location of devices implanted in the brain for neural activity recordings
          or manipulations, such as electrodes or optical fibers. Post hoc histological images taken
          to confirm the correct placement of the device can be registered to a reference atlas
          using appropriate software, and the registered data can be imported into brainrender (<a
            href="#fig3" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 3C</a>). This
          type of visualization greatly facilitates cross-animal comparisons and helps data
          interpretation within and across research groups.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Finally, brainrender can be
          used to visualize any object represented by the most commonly used file formats for 3D
          design (e.g. .obj, .stl), thus ensuring that brainrender can flexibly adapt to the
          visualization needs of the user (<a href="#fig3" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 3D</a>).</p>
        <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
          id="individual-neurons-and-mesoscale-connectomics">Individual neurons and mesoscale
          connectomics</h3>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Recent advances in large field
          of view and whole-brain imaging allow the generation of brain-wide data at single neuron
          resolution. Having a platform for visualizing these datasets with ease is critical for
          exploratory data analyses. Several open-source software packages are available for
          registering large amounts of such imaging data and automatically identify labeled cells
          (e.g. expressing fluorescent proteins) <span itemscope=""
            itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib30"><span>30</span><span>Tyson et
                  al.</span><span>2020</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib9"><span>9</span><span>Fürth et
                  al.</span><span>2018</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib10"><span>10</span><span>Goubran
                  et al.</span><span>2019</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib23"><span>23</span><span>Renier
                  et al.</span><span>2016</span></a></cite></span>. This processing step outputs a
          table of coordinates for a set of labeled cells, which can be directly imported into
          brainrender to visualize a wealth of anatomical data at cellular resolution (<a
            href="#fig4" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 4A</a>).</p>
        <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
          data-execution_count="null" data-programminglanguage="python">
          <pre class="language-python" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
            slot="text"><code># Figure 4

&#39;&#39;&#39;

figure: Figure 4.
:::
![](article.ipynb.media/fig4.jpg)

### Visualizing cell location and morphological data.

(**A**) Visualizing the location of labeled cells. Left, visualization of fluorescently labeled cells identified using cellfinder (data from [@bib31]). Right, visualization of functionally defined clusters of regions of interest in the brain of a zebrafish larvae during a visuomotor task (data from [@bib17]). (**B**) Visualizing neuronal morphology data. Left, three secondary motor cortex neurons projecting to the thalamus (data from [@bib33], downloaded with morphapi from [neuromorpho.org](http://neuromorpho.org/), [@bib4]). Right, morphology of cerebellar neurons in larval zebrafish (data from [@bib15]), (downloaded with morphapi). In the left panel of (**A** and **B**), the brain outline was sliced along the midline to expose the data.
:::
{#fig4}
&#39;&#39;&#39;</code></pre>
        </stencila-code-chunk>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Beyond the location of cell
          bodies, visualizing the entire dendritic and axonal arbors of single neurons registered to
          a reference atlas is important for understanding the distribution of neuronal signals
          across the brain. Single-cell morphologies are often complex 3D structures and therefore
          poorly represented in 2D images. Generating 3D interactive renderings is thus important to
          facilitate the exploration of this type of data. Brainrender can be used to parse and
          render .swc files containing morphological data, and it is fully integrated with morphapi,
          a software for downloading morphological data from publicly available datasets (e.g. from
          <a href="http://neuromorpho.org/" itemscope=""
            itemtype="http://schema.stenci.la/Link">neuromorpho.org</a>, <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib4"><span>4</span><span>Ascoli et
                al.</span><span>2007</span></a></cite>; <a href="#fig4" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 4B</a>).</p>
        <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
          id="producing-figures-videos-and-interactive-visualizations-with-brainrender">Producing
          figures, videos, and interactive visualizations with brainrender</h3>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">A core goal of brainrender is
          to facilitate the production of high-quality images, videos, and interactive
          visualizations of anatomical data. Brainrender leverages the functionality provided by
          vedo <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib20"><span>20</span><span>Musy et al.</span><span>2019</span></a></cite> to
          create images directly from the rendered scene. Renderings can also be exported to HTML
          files to create interactive visualizations that can be hosted online. Finally,
          functionality is provided to easily export videos from rendered scenes. Animated videos
          can be created by specifying parameters (e.g. the position of the camera or the
          transparency of a mesh) at selected keyframes. Brainrender then creates a video by
          animating the rendering between the keyframes. This approach facilitates the creation of
          videos while retaining the flexibility necessary to produce richly animated sequences (<a
            href="#video2" itemscope="" itemtype="http://schema.stenci.la/Link">Videos 2</a><a
            href="#video5" itemscope="" itemtype="http://schema.stenci.la/Link"><span
              data-itemtype="http://schema.org/Number">5</span></a>). All example figures and videos
          in this article were generated directly in brainrender, with no further editing.</p>
        <h2 itemscope="" itemtype="http://schema.stenci.la/Heading" id="discussion">Discussion</h2>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">In this article, we have
          presented brainrender, a python software for creating 3D renderings of anatomically
          registered data.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Brainrender addresses the
          current lack of python-based and user-friendly tools for redeanatomical data. Being part
          with BrainGlobe’s suite of software tools for the analysis of anatomical data brainrender
          facilitates the development of integrated analysis pipelines and the re-usability of
          software tools across model species, minimizing the need for additional software
          development. Finally, brainrender promises to improve how anatomically registered data are
          disseminated both in scientific publications and in other media (e.g., hosted online).</p>
        <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
          id="limitations-and-future-directions">Limitations and future directions</h3>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">With brainrender, we aimed to
          make the rendering process as simple as possible. Nevertheless, some more technically
          demanding pre-processing steps of raw image data are necessary before they can be
          visualized in brainrender. In particular, a critical step for visualizing anatomical data
          is the registration to a reference template (e.g., one of the atlases provided by the
          AtlasAPI). While this step can be challenging and time-consuming, the brainglobe suite
          provides software to facilitate this process (e.g., brainreg and bg-space), and
          alternative software tools have been developed before for this purpose (e.g., <cite
            itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib27"><span>27</span><span>Song et al.</span><span>2020</span></a></cite>;
          <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib13"><span>13</span><span>Jin et al.</span><span>2019</span></a></cite>).
          Additional information about data registration can be found in brainglobe’s and
          brainrender’s online documentation, as well as in the examples in brainrender’s GitHub
          repository. A related challenge is integrating new anatomical atlases into the AtlasAPI.
          While we anticipate that most users will not have this need, it is a non-trivial task that
          requires considerable programming skills. We believe that brainglobe’s AtlasAPI greatly
          facilitates this process, which is presented in <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib7"><span>7</span><span>Claudi et
                al.</span><span>2020</span></a></cite> and has extensive online documentation.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Brainrender has been optimized
          for rendering quality instead of rendering performance. Other commonly used software tools
          like napari <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib26"><span>26</span><span>Sofroniew and
                Lambert</span><span>2020</span></a></cite> and ImageJ are dedicated to visualizing
          N-dimensional image data and perform very well even on large datasets. When comparing
          brainrender with other software, it is important to note brainrender is intended to work
          primarily with mesh data and not 3D image data. Although it can display image data (e.g.,
          with the Volume actor), this functionality is not as fully developed as that using mesh
          data. A direct benchmarking comparison between brainrender and napari shows that
          brainrender is 5× slower than napari at visualizing image data, but 20× faster at
          visualizing mesh data. In both cases, however, brainrender achieves superior rendering
          quality. Other software packages dedicated to high-performance rendering, such as Blender,
          can handle mesh data with a performance that surpasses brainrender. Their use, however,
          comes with the large overhead of learning a very complex software to generate what most
          often will be simple renderings. It also requires that the users themselves take care of
          downloading, storing, and accessing mesh data from the anatomical atlases. Nevertheless,
          the rendering performance of brainrender could be a target for improvement in future
          versions, both for images and for mesh data, through optimizing the Actor classes. While
          we have designed brainrender usage to require minimal programming expertise, installing
          python and brainrender may still prove challenging for some users. In the future, we aim
          to make brainrender a stand-alone application that can be simply downloaded and locally
          installed, either through Docker containers or through executable files. Further possible
          improvements include the development of plug-ins for loading of data from file formats
          other than those already supported, and improvements to the GUI functionality. Moreover,
          in addition to images and videos, brainrender can be used to export renderings as HTML
          files and generate online 3D interactive renderings. Currently, however, embedding
          renderings into a web page remains far from a trivial task. Further developments on this
          front should make it possible to easily host interactive renderings online, therefore
          improving how anatomically registered data are disseminated both in scientific
          publications and in other media. While we plan to continue developing brainrender in the
          future, we welcome contributions from the community. Users should feel encouraged to
          contribute irrespective of their programming experience, and we note that the programming
          ability of many biologists is often better than what they perceive it to be. We especially
          welcome contributions aimed at improving the user-experience of brainrender, at any level
          of interaction. Contributions can involve active development of brainrender’s code base,
          but they can also be bug reports, features request, improvements with the online
          documentation, and help answering users’ questions.</p>
        <h2 itemscope="" itemtype="http://schema.stenci.la/Heading" id="materials-and-methods">
          Materials and methods</h2>
        <table id="keyresource" itemscope="" itemtype="http://schema.org/Table">
          <caption><label data-itemprop="label">Key resources table</label></caption>
          <thead>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <th itemscope="" itemtype="http://schema.stenci.la/TableCell">Reagent type (species)
                or resource</th>
              <th itemscope="" itemtype="http://schema.stenci.la/TableCell">Designation</th>
              <th itemscope="" itemtype="http://schema.stenci.la/TableCell">Source or reference</th>
              <th itemscope="" itemtype="http://schema.stenci.la/TableCell">Identifiers</th>
              <th itemscope="" itemtype="http://schema.stenci.la/TableCell">Additional information
              </th>
            </tr>
          </thead>
          <tbody>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Software, algorithm</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Numpy</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><a
                  href="https://doi.org/10.1038/s41586-020-2649-2" itemscope=""
                  itemtype="http://schema.stenci.la/Link">https://doi.org/10.1038/s41586-020-2649-2</a>
              </td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">RRID:<a
                  href="https://scicrunch.org/resolver/RRID:SCR_008633" itemscope=""
                  itemtype="http://schema.stenci.la/Link">SCR_008633</a></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Software, algorithm</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Vtk</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><a
                  href="https://doi.org/10.1016/j.softx.2015.04.001" itemscope=""
                  itemtype="http://schema.stenci.la/Link">https://doi.org/10.1016/j.softx.2015.04.001</a>
              </td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">RRID:<a
                  href="https://scicrunch.org/resolver/RRID:SCR_015013" itemscope=""
                  itemtype="http://schema.stenci.la/Link">SCR_015013</a></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Software, algorithm</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Vedo</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><a
                  href="https://zenodo.org/record/4287635" itemscope=""
                  itemtype="http://schema.stenci.la/Link">https://zenodo.org/record/4287635</a></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Software, algorithm</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">BrainGlobe Atlas API
              </td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><a
                  href="https://doi.org/10.21105/joss.02668" itemscope=""
                  itemtype="http://schema.stenci.la/Link">https://doi.org/10.21105/joss.02668</a>
              </td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Software, algorithm</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Pandas</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"><a
                  href="https://doi.org/10.5281/zenodo.3509134" itemscope=""
                  itemtype="http://schema.stenci.la/Link">https://doi.org/10.5281/zenodo.3509134</a>
              </td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Software, algorithm</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Matplotlib</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">doi: <a
                  href="https://doi.org/10.1109/MCSE.2007.55" itemscope=""
                  itemtype="http://schema.stenci.la/Link">10.1109/MCSE.2007.55</a></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">RRID:<a
                  href="https://scicrunch.org/resolver/RRID:SCR_008624" itemscope=""
                  itemtype="http://schema.stenci.la/Link">SCR_008624</a></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
            </tr>
            <tr itemscope="" itemtype="http://schema.stenci.la/TableRow">
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Software, algorithm</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">Jupyter</td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">doi:<a
                  href="https://doi.org/10.3233/978-1-61499-649-1-87" itemscope=""
                  itemtype="http://schema.stenci.la/Link">10.3233/978-1-61499-649-1-87</a></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell">RRID:<a
                  href="https://scicrunch.org/resolver/RRID:SCR_018416" itemscope=""
                  itemtype="http://schema.stenci.la/Link">SCR_018416</a></td>
              <td itemscope="" itemtype="http://schema.stenci.la/TableCell"></td>
            </tr>
          </tbody>
        </table>
        <h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="brainrenders-workflow">
          Brainrender’s workflow</h3>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Brainrender is written in
          Python three and depends on standard python packages such as numpy, matplotlib, and pandas
          <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib11"><span>11</span><span>Harris
                  et al.</span><span>2020</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a
                href="#bib12"><span>12</span><span>Hunter</span><span>2007</span></a></cite><cite
              itemscope="" itemtype="http://schema.stenci.la/Cite"><a
                href="#bib29"><span>29</span><span>The pandas development
                  team</span><span>2020</span></a></cite></span> and on vedo <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib20"><span>20</span><span>Musy et
                al.</span><span>2019</span></a></cite> and BrainGlobe’s AtlasAPI <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib7"><span>7</span><span>Claudi et
                al.</span><span>2020</span></a></cite>. Extensive documentation on how to install
          and use brainrender can be found at docs.brainrender.info, and we provide here a only
          brief overview of the workflow in brainrender. The GitHub repository also contains
          detailed examples of Python scripts and Jupyter notebooks <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib14"><span>14</span><span>Kluyver et
                al.</span><span>2016</span></a></cite>. All brainrender’s code is open-source and
          has been deposited in full in the GitHub repository and at PyPI (a repository of Python
          software) under a permissive BSD 3-Clause license. We welcome any user to download and
          inspect the source code, modify it as needed, or contribute to brainrender’s development
          directly.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Brainrener can be installed in
          any python environment using python version ≥ 3.60.0. We recommend the creation of an
          anaconda or virtual environment with an appropriate python version for use with
          brainrender. Installing brainrender is then as simple as ‘pip install brainrender’
          although additional optional packages might have to be installed separately (e.g., to
          access data from the Allen Institute).</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">The central element of any
          visualization produced by brainrender is the Scene. A Scene controls which elements
          (Actors) are visualized and coordinates the rendering, the position of the camera’s point
          of view, the generation of screenshots and animations from the rendered scene, and other
          important actions.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Actors can be added to the
          scene in several ways. When loading data directly from a file with 3D mesh information
          (e.g. .obj), an Actor is generated automatically to represent the mesh in the rendering.
          When rendering data from other sources (e.g. from a .swc file with neuronal morphology or
          from a table of coordinates of labeled cells), dedicated functions in brainrender parse
          the input data and generate the corresponding Actors. Actors in brainrender have
          properties, such as color and transparency, that can be used to specify the appearance of
          a rendered actor accordingly to the user’s aesthetic preferences. Brainrender’s Scene and
          Actor functionality use vedo as the rendering engine (GitHub repository; <cite
            itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib20"><span>20</span><span>Musy et al.</span><span>2019</span></a></cite>).
        </p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">In addition to data loaded from
          external files, brainrender can directly load atlas data containing, for example, the 3D
          meshes of individual brain regions. This is done via BrainGlobe’s AtlasAPI to allow the
          same programming interface in brainrender to visualize data from any atlas supported by
          the AtlasAPI. Brainrender also provides additional functionality to interface with data
          available from projects that are part of the Allen Institute Mouse Atlas and Mouse
          Connectome projects <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite
              itemscope="" itemtype="http://schema.stenci.la/Cite"><a
                href="#bib32"><span>32</span><span>Wang et
                  al.</span><span>2020</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib21"><span>21</span><span>Oh et
                  al.</span><span>2014</span></a></cite></span>. These projects provide an SDK
          (Software Development Kit) to directly download data from their database, and brainrender
          provides a simple interface for downloading gene expression and connectomics (streamlines)
          data. All atlas and connectomics data downloaded by brainrender can be loaded directly
          into a Scene as Actors.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Visualizing morphological data
          with reconstructions of individual neurons can be done by loading these type of data
          directly from .swc files or by downloading them in Python using morphapi – software from
          the BrainGlobe suite that provides a simple and unified interface with several databases
          of neuron morphologies (e.g., <a href="http://neuromorpho.org/" itemscope=""
            itemtype="http://schema.stenci.la/Link">neuromorpho.org</a>, <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib4"><span>4</span><span>Ascoli et
                al.</span><span>2007</span></a></cite>). Data downloaded with morphapi can be loaded
          directly into a brainrender scene for visualization.</p>
        <h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="example-code">Example code
        </h3>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">As a demonstration of how
          easily renderings can be created in brainrender, the Python code (<a href="#fig5"
            itemscope="" itemtype="http://schema.stenci.la/Link">Figure 5</a>) illustrates how to
          create a Scene and add Actors by loading 3D data from an .obj file and then adding brain
          regions to the visualization. Brainrender’s GitHub repository provides several simple and
          concise examples about how to use brainrender to load user data, atlas data, to edit
          rendered meshes (e.g., to change color or cut them with a plane), to save screenshots from
          rendered scenes, and to create animated videos.</p>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig5" title="Figure 5.">
          <label data-itemprop="label">Figure 5.</label><img src="index.html.media/fig5.jpg" alt=""
            itemscope="" itemtype="http://schema.org/ImageObject">
          <figcaption>
            <h4 itemscope="" itemtype="http://schema.stenci.la/Heading" id="code-examples-1">Code
              examples.</h4>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">(<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">A</strong>) Example code to visualize a
              set of labeled cells coordinates using the Points actor class. (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">B</strong>) Code example illustrating how
              to override brainrender’s default settings and how to use custom camera settings.
              (<strong itemscope="" itemtype="http://schema.stenci.la/Strong">C</strong>) Code
              example showing how custom mesh objects saved as .obj and .stl files can be visualized
              in brainrender. (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">D</strong>) Example usage of brainrender’s
              Animation class to create custom animations. Further examples can be found in
              brainrender’s GitHub repository.</p>
          </figcaption>
        </figure>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">While brainrender is intended
          to be mainly a visualization tool, simple analyses can be carried out directly by
          leveraging functionality from either vedo or BrainGlobe’s AtlasAPI. For example, Vedo can
          access properties of actors added to a brainrender scene, which could be used to measure
          the distance between two actors or to check if two actors’ meshes intersect (<a
            href="#fig6" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 6A</a>).
          Similarly, BrainGlobe’s AtlasAPI provides methods to, for example, check whether a point
          (defined by a set of coordinates) is contained in a brain region of interest or to
          retrieve brain regions that are above or below a brain region of interest in the atlas’
          hierarchy (<a href="#fig6" itemscope="" itemtype="http://schema.stenci.la/Link">Figure
            6B</a>).</p>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig6" title="Figure 6.">
          <label data-itemprop="label">Figure 6.</label><img src="index.html.media/fig6.jpg" alt=""
            itemscope="" itemtype="http://schema.org/ImageObject">
          <figcaption>
            <h4 itemscope="" itemtype="http://schema.stenci.la/Heading" id="advanced-code-examples">
              Advanced code examples.</h4>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">(<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">A</strong>) Example code to measure the
              distance between actors and if a given actor is contained in a target brain region.
              Left: virus injection volumes (red and gray) reconstructed from virus injections
              targeted at the superior colliculus (magenta). Gray colored injection volumes show
              data from the Allen Mouse Connectome <cite itemscope=""
                itemtype="http://schema.stenci.la/Cite"><a href="#bib21"><span>21</span><span>Oh et
                    al.</span><span>2014</span></a></cite>. Right: example code to measure the
              distance between the center of two brainrender actors and to check if an actor’s
              center is contained in a brain region of interest. (<strong itemscope=""
                itemtype="http://schema.stenci.la/Strong">B</strong>) Code example illustrating how
              check if a point (e.g., representing a labeled cell) is in a brain region of interest.
              Left: visualization of reconstructed probe positions from several individual animals,
              data from <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
                  href="#bib28"><span>28</span><span>Steinmetz et
                    al.</span><span>2019</span></a></cite>. Probe channels located in the thalamus
              (red) are highlighted. Right: example code showing how to use BrainGlobe’s AtlasAPI to
              verify whether a point (here representing a probe channel) is contained in a brain
              region of interest or any of its substructures. Further examples can be found in
              brainrender’s GitHub repository.</p>
          </figcaption>
        </figure>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">The code and data used to
          generate the figures and videos in this article are made freely available at brainrender’s
          GitHub repository and provides examples of more advanced usage of brainrender’s
          functionality.</p>
        <h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="benchmark-tests">Benchmark
          tests</h3>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">We designed a series of
          benchmark tests aimed at evaluating brainrender’s performance with different combinations
          of hardware and operating system. We used five tests designed to cover most aspects of
          brainrender’s functionality:</p>
        <ul itemscope="" itemtype="http://schema.org/ItemList">
          <li itemscope="" itemtype="http://schema.org/ListItem" itemprop="itemListElement">
            <meta itemprop="position" content="1">
            <meta itemprop="url" content="#1">rendering large numbers (1<sup itemscope=""
              itemtype="http://schema.stenci.la/Superscript">4</sup>, 1<sup itemscope=""
              itemtype="http://schema.stenci.la/Superscript">6</sup>, 1<sup itemscope=""
              itemtype="http://schema.stenci.la/Superscript">7</sup>) of cells using the Points
            actor.
          </li>
          <li itemscope="" itemtype="http://schema.org/ListItem" itemprop="itemListElement">
            <meta itemprop="position" content="2">
            <meta itemprop="url" content="#2">using a plane to ‘slice’ the same number of cells
            (using the Scene.slice method).
          </li>
          <li itemscope="" itemtype="http://schema.org/ListItem" itemprop="itemListElement">
            <meta itemprop="position" content="3">
            <meta itemprop="url" content="#3">rendering more than 1000 individual meshes
            representing brain regions from the Allen institute’s mouse brain.
          </li>
          <li itemscope="" itemtype="http://schema.org/ListItem" itemprop="itemListElement">
            <meta itemprop="position" content="4">
            <meta itemprop="url" content="#4">making a short (3 s, 10 fps) animation of a spinning
            brain with several brain regions’ meshes displayed.
          </li>
          <li itemscope="" itemtype="http://schema.org/ListItem" itemprop="itemListElement">
            <meta itemprop="position" content="5">
            <meta itemprop="url" content="#5">rendering (10 times) a 3D image representing the
            voxel-wise expression levels of gene Gpr161 in the mouse brain (data from the Allen
            Institute).
          </li>
        </ul>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">For each test, we estimated the
          time necessary to complete the test script as well as the frame rate of the interactive
          rendering. Four machines were used for benchmark tests (see <a href="#table1" itemscope=""
            itemtype="http://schema.stenci.la/Link">Table 1</a>). The results of the benchmark tests
          (see Key resource table) illustrate that although a GPU improves performance, in the
          absence of a dedicated GPU brainrender can handle rich interactive visualizations (for
          most user cases, the number of rendered mesh vertices is much lower than that used in the
          tests).</p>
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