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While some of the recently developed atlases provide an API (Application Programming Interface) for accessing atlas data ( Wang et al., 2020 Kunst et al., 2019), rendering these data in 3D remains a demanding and time-consuming task that requires significant programming skills. Generating custom 3D visualizations of atlas data requires programmatic access to the atlas. In addition, high-quality 3D visualizations facilitate the communication of experimental results registered to brain anatomy. This is particularly important for large-scale datasets such as the ones generated by open-science projects like MouseLight ( Winnubst et al., 2019) and the Allen Mouse Connectome ( Oh et al., 2014). 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. 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. A key output of this process is the visualization of all datasets in register. These atlases provide a framework for registering different types of data across macro- and microscopic scales. In recent years, several high-resolution three-dimensional (3D) digital brain atlases have been generated for model species commonly used in neuroscience ( Wang et al., 2020 Oh et al., 2014 Arganda-Carreras et al., 2018 Kunst et al., 2019). While different types of references can in principle be used, neuroanatomical location is a natural and most commonly used reference frame ( Chon et al., 2019 Oh et al., 2014 Arganda-Carreras et al., 2018 Kunst et al., 2019).

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Even for the same experiment type, registration is necessary to allow comparisons across individual animals ( Simmons and Swanson, 2009). 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. Such registration, however, is challenging. 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. These data range from neural activity recordings and anatomical connectivity, to cellular and subcellular information such as morphology and gene expression profiles. Understanding how nervous systems generate behavior benefits from gathering multidimensional data from different individual animals. By facilitating the visualization of anatomically registered data, brainrender should accelerate the analysis, interpretation, and dissemination of brain-wide multidimensional data. High-quality visualizations can be used interactively and exported as high-resolution figures and animated videos.

Brainrender facilitates the creation of complex renderings with different data types in the same visualization and enables seamless use of different atlas sources.

Here, we present brainrender: an open-source Python package for interactive visualization of multidimensional datasets registered to brain atlases.

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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. Generating insights from such datasets depends critically on visualization and interactive data exploration, but this a challenging task. 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.

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