Visualizing anatomically registered data with brainrender.
| Autor: | Claudi F; UCL Sainsbury Wellcome Centre, London, United Kingdom., Tyson AL; UCL Sainsbury Wellcome Centre, London, United Kingdom., Petrucco L; Institute of Neuroscience, Technical University of Munich, Munich, Germany.; Max Planck Institute of Neurobiology, Research Group of Sensorimotor Control, Martinsried, Germany., Margrie TW; UCL Sainsbury Wellcome Centre, London, United Kingdom., Portugues R; Institute of Neuroscience, Technical University of Munich, Munich, Germany.; Max Planck Institute of Neurobiology, Research Group of Sensorimotor Control, Martinsried, Germany.; Munich Cluster for Systems Neurology (SyNergy), Munich, Germany., Branco T; UCL Sainsbury Wellcome Centre, London, United Kingdom. |
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| Jazyk: | angličtina |
| Zdroj: | ELife [Elife] 2021 Mar 19; Vol. 10. Date of Electronic Publication: 2021 Mar 19. |
| DOI: | 10.7554/eLife.65751 |
| Abstrakt: | 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. Competing Interests: FC, AT, LP, TM, RP, TB No competing interests declared (© 2021, Claudi et al.) |
| Databáze: | MEDLINE |
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