RT Journal Article
SR Electronic
T1 <em>Brainrender</em>: a python-based software for visualizing anatomically registered data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.02.23.961748
DO 10.1101/2020.02.23.961748
A1 F. Claudi
A1 A. L. Tyson
A1 L. Petrucco
A1 T.W. Margrie
A1 R. Portugues
A1 T. Branco
YR 2020
UL http://biorxiv.org/content/early/2020/12/11/2020.02.23.961748.abstract
AB The recent development of high-resolution three-dimensional (3D) digital brain atlases and high-throughput brain wide imaging techniques has fueled the generation of large datasets that can be registered to a common reference frame. This registration facilitates integrating data from different sources and resolutions to assemble rich multidimensional datasets. Generating insights from these new types of datasets depends critically on the ability to easily visualize and explore the data in an interactive manner. This is, however, 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. To address this challenge, we have developed brainrender: a generic, open-source Python package for simultaneous and interactive visualization of multidimensional datasets registered to brain atlases. Brainrender has been designed to facilitate the creation of complex custom renderings and can be used programmatically or through a graphical user interface. It can easily render different data types in the same visualization, including user-generated data, and enables seamless use of different brain atlases using the same code base. In addition, brainrender generates high-quality visualizations that 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.Competing Interest StatementThe authors have declared no competing interest.