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BrainGlobe Atlas API: a common interface for neuroanatomical atlases

October 2020
The Journal of Open Source Software 5(54):2668

DOI:10.21105/joss.02668

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CC BY 4.0

Authors:

Luigi Petrucco

Max Planck Institute of Neurobiology

Adam L Tyson

University College London

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Neuroscientists routinely perform experiments aimed at recording or manipulating neural activity, uncovering physiological processes underlying brain function or elucidating aspects of brain anatomy. Understanding how the brain generates behaviour ultimately depends on merging the results of these experiments into a unified picture of brain anatomy and function. Brain atlases are crucial in this endeavour: by outlining the organization of brain regions they provide a reference upon which our understanding of brain function can be anchored. More recently, digital high-resolution 3d atlases have been produced for several model organisms providing an invaluable resource for the research community. Effective use of these atlases depends on the availability of an application programming interface (API) that enables researchers to develop software to access and query atlas data. However, while some atlases come with an API, these are generally specific for individual atlases, and this hinders the development and adoption of open-source neuroanatomy software. The BrainGlobe atlas API (BG-Atlas API) overcomes this problem by providing a common interface for programmers to download and process data across a variety of model organisms. By adopting the BG-Atlas API, software can then be developed agnostic to the atlas, increasing adoption and interoperability of packages in neuroscience and enabling direct integration of different experimental modalities and even comparisons across model organisms.

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BrainGlobe Atlas API: a common interface for

neuroanatomical atlases

Federico Claudi∗1, Luigi Petrucco*2, 3, Adam L. Tyson*1, Tiago

Branco1, Troy W. Margrie1, and Ruben Portugues2, 3, 4

1Sainsbury Wellcome Centre, University College London, London, U.K. 2Institute of Neuroscience,

Technical University of Munich, Munich, Germany 3Max Planck Institute of Neurobiology, Research

Group of Sensorimotor Control, Martinsried, Germany 4Munich Cluster for Systems Neurology

(SyNergy), Munich, Germany

DOI: 10.21105/joss.02668

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Submitted: 04 September 2020

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Summary

Neuroscientists routinely perform experiments aimed at recording or manipulating neural activ-

ity, uncovering physiological processes underlying brain function or elucidating aspects of brain

anatomy. Understanding how the brain generates behaviour ultimately depends on merging

the results of these experiments into a unied picture of brain anatomy and function. Brain

atlases are crucial in this endeavour: by outlining the organization of brain regions they provide

a reference upon which our understanding of brain function can be anchored. More recently,

digital high-resolution 3d atlases have been produced for several model organisms providing

an invaluable resource for the research community. Eective use of these atlases depends

on the availability of an application programming interface (API) that enables researchers to

develop software to access and query atlas data. However, while some atlases come with an

API, these are generally specic for individual atlases, and this hinders the development and

adoption of open-source neuroanatomy software. The BrainGlobe atlas API (BG-Atlas API)

overcomes this problem by providing a common interface for programmers to download and

process data across a variety of model organisms. By adopting the BG-Atlas API, software can

then be developed agnostic to the atlas, increasing adoption and interoperability of packages

in neuroscience and enabling direct integration of dierent experimental modalities and even

comparisons across model organisms.

Statement of need

To facilitate the study of neural function, a long-standing approach has been to identify

neuroanatomically dened brain regions: structures with dened function, connectivity and

anatomical location. The study of these brain regions led to the development of a number

of brain atlases for various species. Typically these atlases are made up of a reference image

of a brain, voxel-wise annotations (e.g. a mapping from each voxel to a brain structure) and

additional metadata such as region hierarchy (region A is a subdivision of region B). These

atlases are used throughout neuroscience, for teaching, visualisation of data, and registration

of imaging data to a common coordinate space.

Many excellent and open access atlases exist, such as the Allen Mouse Brain Common Coordi-

nate Framework (Wang et al., 2020) and the Max Planck Larval Zebrash Atlas (Kunst et al.,

2019), from which the neuroscience community benets enormously. These atlases provide

a valuable resource for individual scientists and enabled important open-science projects such

∗Joint rst author, ordered alphabetically

Claudi et al., (2020). BrainGlobe Atlas API: a common interface for neuroanatomical atlases. Journal of Open Source Software, 5(54), 2668.

https://doi.org/10.21105/joss.02668

as Janelia Campus’ Mouse Light project (Winnubst et al., 2019). Furthermore, for several at-

lases stand-alone software is available that can be used to explore the atlas’ data and requires

no coding experience, thus making the atlases accessible to a broader audience. However,

to be used in the context of new software (e.g. new visualization tools, or brain registration

pipelines) it is necessary that atlases expose their data through an API. Several commonly

used atlases come with APIs, but learning how to use each of them is a time-consuming

endavour and can require considerable coding experience. For this reason, often developers

produce software that works only with a specic atlas. A single and well documented API that

worked across atlases would thus lower the cost of developing new software, which can also

be made available for a larger number of scientists. An eort in this direction has been made

in the R ecosystem with the natverse package (Bates et al., 2020), but, to our knowledge,

no such option exists in Python, which is emerging as the programming language of choice in

neuroscience (Muller et al., 2015).

bg-atlasapi was built to address these issues and with two main design goals in mind. The

rst was to simplify the use of atlases for neuroscientists by providing a simple, concise and

well-documented API. The second was to reduce the burden required to develop tools that can

be used across atlases. The majority of neuroanatomical software tools developed currently

are for a single model organism, yet many of these tools could be of great use for many other

neuroscientists.

Developers can use bg-atlasapi to access data from multiple atlases in common formats.

Each atlas can be instantiated by passing the atlas name to the BrainGlobeAtlas class. A

number of les are provided as class attributes including a reference (structural) image, an

annotation image (a map of brain regions coded by voxel intensity), meshes for each brain

region, and various metadata such as the authors of the atlas, and the hierarchy of the brain

regions. There are methods for many common tasks such as orienting data and parsing the

region hierarchy.

Currently six atlases across three species (larval zebrash, mouse and human) are available

(Chon, Vanselow, Cheng, & Kim, 2019; Ding et al., 2016; Kunst et al., 2019; Wang et al.,

2020), with work underway to add further atlases (e.g. rat, drosophila). The available atlases

were created by parsing their relative online sources and restructuring the data to a standard

format. The atlases were then made accessible by hosting the data in a GNode respository

(https://gin.g-node.org/brainglobe/atlases). The python code used for generating

the atlases is also made available in a separate repository in the BrainGlobe organization:

bg-atlasgen. The same code can be used for easily developing new atlases in BG-AtlasAPI’s

format and we encourage users to contribute new atlases to the project by submitting new

scripts to bg-atlasgen.

BG-atlasAPI’s exible infrastructure already proved crucial in the development and extension

of two software tools for use in neuroscience: brainreg (Tyson, Rousseau, & Margrie, 2020)

for 3D registration of image data between sample and atlas coordinate space and brainrender

(Claudi, Tyson, & Branco, 2020) for 3D visualisation of both user-generated data and atlas

data. We hope that other developers will use the API, and develop tools that can be used

across neuroscience and other research elds, increasing their reach, and preventing duplication

of eort.

Acknowledgments

We would like to thank Nouwar Mokayes for the assistance in packaging the Max Planck Ze-

brash Brain Atlas within BG-atlasAPI. This work was supported by grants from the Gatsby

Charitable Foundation (GAT3361, T.W.M. and T.B.), Wellcome Trust (090843/F/09/Z,

T.W.M. and T.B.; 214333/Z/18/Z, T.W.M.; 214352/Z/18/Z, T.B.) and by the Deutsche

Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence

Claudi et al., (2020). BrainGlobe Atlas API: a common interface for neuroanatomical atlases. Journal of Open Source Software, 5(54), 2668.

https://doi.org/10.21105/joss.02668

Strategy within the framework of the Munich Cluster for Systems Neurology (EXC 2145 SyN-

ergy – ID 390857198).

References

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Neuronal cell types are the nodes of neural circuits that determine the flow of information within the brain. Neuronal morphology, especially the shape of the axonal arbor, provides an essential descriptor of cell type and reveals how individual neurons route their output across the brain. Despite the importance of morphology, few projection neurons in the mouse brain have been reconstructed in their entirety. Here we present a robust and efficient platform for imaging and reconstructing complete neuronal morphologies, including axonal arbors that span substantial portions of the brain. We used this platform to reconstruct more than 1,000 projection neurons in the motor cortex, thalamus, subiculum, and hypothalamus. Together, the reconstructed neurons constitute more than 85 meters of axonal length and are available in a searchable online database. Axonal shapes revealed previously unknown subtypes of projection neurons and suggest organizational principles of long-range connectivity.

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Article

May 2019

Understanding brain-wide neuronal dynamics requires a detailed map of the underlying circuit architecture. We built an interactive cellular-resolution atlas of the zebrafish brain at 6 days post-fertilization (dpf) based on the reconstructions of over 2,000 individually GFP-labeled neurons. We clustered our dataset in “morphotypes,” establishing a unique database of quantitatively described neuronal morphologies together with their spatial coordinates in vivo. Over 100 transgene expression patterns were imaged separately and co-registered with the single-neuron atlas. By annotating 72 non-overlapping brain regions, we generated from our dataset an inter-areal wiring diagram of the larval brain, which serves as ground truth for synapse-scale, electron microscopic reconstructions. Interrogating our atlas by “virtual tract tracing” has already revealed previously unknown wiring principles in the tectum and the cerebellum. In conclusion, we present here an evolving computational resource and visualization tool, which will be essential to map function to structure in a vertebrate brain. Video Abstract eyJraWQiOiI4ZjUxYWNhY2IzYjhiNjNlNzFlYmIzYWFmYTU5NmZmYyIsImFsZyI6IlJTMjU2In0.eyJzdWIiOiI4OTYzMjc1OGNkMmM2MTc2Yjc4YjY0NTQ4MWI5NWE0MSIsImtpZCI6IjhmNTFhY2FjYjNiOGI2M2U3MWViYjNhYWZhNTk2ZmZjIiwiZXhwIjoxNTkwNTIwMjU5fQ.WWWcYqmCfAylt2GEFvqF8UbIH80NyOKXGZ73alX_MIyynUOwOVH5Zxsf9V125VEN2EAMrtpwnQDkshSR7_QEW5eVQtvRs8xmtSwWmN8sc1P24Uqeb80F05XLe4GNdQFa3qGOq81_z2uvZ3oeej9MN_JZNQfG1vHwL97uj4DqYDt1asv_mAztxGkpFcy5t5O1xVP3ueIXwSGlF3IC9E8eNL35FeFUyXeD4fMecRCNF_a-bfZTt1qmGQrJ8pcOG_ZFxymnFZryElZV34_l4LY4EdizbQ_g-D0KhRJJiDMEKvUteI-TqiTiNRFpr2WMtx-4RT5yrLPWfqtKuD7J5V47_Q (mp4, (47.56 MB) Download video

brainreg: automated 3D brain registration with support for multiple species and atlases

Jan 2020

A L Tyson
C V Rousseau
T W Margrie

Tyson, A. L., Rousseau, C. V., & Margrie, T. W. (2020). brainreg: automated 3D brain registration with support for multiple species and atlases. Zenodo. doi:10.5281/zenodo. 3991718

BrainGlobe Atlas API: a common interface for neuroanatomical atlases

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