Visualizing anatomically registered data with Brainrender

  1. Federico Claudi  Is a corresponding author
  2. Adam L Tyson
  3. Luigi Petrucco
  4. Troy W Margrie
  5. Ruben Portugues
  6. Tiago Branco  Is a corresponding author
  1. UCL, United Kingdom
  2. Technical University of Munich, Germany

Abstract

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.

Data availability

All code has been deposited on GitHub and is freely accessible.

The following previously published data sets were used

Article and author information

Author details

  1. Federico Claudi

    Sainsbury Wellcome Centre, UCL, London, United Kingdom
    For correspondence
    federico.claudi.17@ucl.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
  2. Adam L Tyson

    Sainsbury Wellcome Centre, UCL, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3225-1130
  3. Luigi Petrucco

    Institute of Neuroscience, Technical University of Munich, Munich, Germany
    Competing interests
    The authors declare that no competing interests exist.
  4. Troy W Margrie

    Sainsbury Wellcome Centre, UCL, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5526-4578
  5. Ruben Portugues

    Institute of Neuroscience, Technical University of Munich, Munich, Germany
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1495-9314
  6. Tiago Branco

    Sainsbury Wellcome Centre, UCL, London, United Kingdom
    For correspondence
    t.branco@ucl.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5087-3465

Funding

Gatsby Charitable Foundation (GAT3361)

  • Troy W Margrie
  • Tiago Branco

Wellcome (214333/Z/18/Z)

  • Troy W Margrie

Wellcome (214352/Z/18/Z)

  • Tiago Branco

Wellcome (090843/F/09/Z)

  • Troy W Margrie
  • Tiago Branco

Deutsche Forschungsgemeinschaft (390857198)

  • Ruben Portugues

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

© 2021, Claudi et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Federico Claudi
  2. Adam L Tyson
  3. Luigi Petrucco
  4. Troy W Margrie
  5. Ruben Portugues
  6. Tiago Branco
(2021)
Visualizing anatomically registered data with Brainrender
eLife 10:e65751.
https://doi.org/10.7554/eLife.65751

Share this article

https://doi.org/10.7554/eLife.65751

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