The use of large multidimensional datasets such as those produced by brain-wide imaging techniques and brain atlases demands tools that can meet the challenge of interaction with, and visualization of, this data easily and effectively. Current solutions for generating 3D visualizations of largescale neural data are limited to using particular atlases or species and are often unable to incorporate user-generated data. To accommodate these limitations, Federico Claudi and colleagues developed Brainrender, a Python package for visualizing and interacting with datasets registered to brain atlases.
Brainrender is integrated with BrainGlobe’s Atlas API, allowing for access to data and metadata from several atlases without modification. Brainrender is also compatible with a large number of commonly used data types, is cross-platform, and can be operated by researchers with little to no programming experience thanks to a user-friendly, versatile GUI. The package uses the vedo rendering engine that allows for the creation of high-resolution images, videos, and interactive online visualizations with minimal hardware requirements. Potential visualizations include gene expression levels, single neuron morphology, electrode/fiber optic implant locations, virus injection spread, projection streamlines, and more.
The group has also provided extensive documentation and tutorials for installing and using Brainrender, making it easy to get started. Brainrender is a powerful tool making large multidimensional data visualization more accessible and adaptable than ever.