A neuroscientist’s guide to using murine brain atlases for efficient analysis and transparent reporting

Brain atlases are important reference resources for accurate anatomical description of neuroscience data. Open access, three-dimensional atlases serve as spatial frameworks for integrating experimental data and defining regions-of-interest in analytic workflows. However, naming conventions, parcellation criteria, area definitions, and underlying mapping methodologies differ considerably between atlases and across atlas versions. This lack of standardized description impedes use of atlases in analytic tools and registration of data to different atlases. To establish a machine-readable standard for representing brain atlases, we identified four fundamental atlas elements, defined their relations, and created an ontology model. Here we present our Atlas Ontology Model (AtOM) and exemplify its use by applying it to mouse, rat, and human brain atlases. We discuss how AtOM can facilitate atlas interoperability and data integration, thereby increasing compliance with the FAIR guiding principles. AtOM provides a standardized framework for communication and use of brain atlases to create, use, and refer to specific atlas elements and versions. We argue that AtOM will accelerate analysis, sharing, and reuse of neuroscience data.

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“…The coordinate system of an atlas provides a framework for specifying locations with origin, units and direction of the axes 43 (Fig. 2c ).…”

Section: Resultsmentioning

confidence: 99%

AtOM, an ontology model to standardize use of brain atlases in tools, workflows, and data infrastructures

Kleven

Gillespie

Zehl³

et al. 2023

Sci Data

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“…These annotations fit the current understanding of developmental anatomy with the notable re-organization of the pallium into a concentric ring topology 35,38 . The expert guided 3D digital annotations allow users to examine labels with any desired angle and serve as a neuroinformatic tool to automatically quantify signals across different brain areas in the developing mouse brain 15,[44][45][46] .…”

Section: Discussionmentioning

confidence: 99%

Developmental Mouse Brain Common Coordinate Framework

Kronman,

Liwang,

Betty

et al. 2023

Preprint

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3D standard reference brains serve as key resources to understand the spatial organization of the brain and promote interoperability across different studies. However, unlike the adult mouse brain, the lack of standard 3D reference atlases for developing mouse brains has hindered advancement of our understanding of brain development. Here, we present a multimodal 3D developmental common coordinate framework (DevCCF) spanning mouse embryonic day (E) 11.5, E13.5, E15.5, E18.5, and postnatal day (P) 4, P14, and P56 with anatomical segmentations defined by a developmental ontology. At each age, the DevCCF features undistorted morphologically averaged atlas templates created from Magnetic Resonance Imaging and co-registered high-resolution templates from light sheet fluorescence microscopy. Expert-curated 3D anatomical segmentations at each age adhere to an updated prosomeric model and can be explored via an interactive 3D web-visualizer. As a use case, we employed the DevCCF to unveil the emergence of GABAergic neurons in embryonic brains. Moreover, we integrated the Allen CCFv3 into the P56 template with stereotaxic coordinates and mapped spatial transcriptome cell-type data with the developmental ontology. In summary, the DevCCF is an openly accessible resource that can be used for large-scale data integration to gain a comprehensive understanding of brain development.

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“…A range of image registration software are available (Klein et al, 2010;Niedworok et al, 2016;Fürth et al, 2018;Puchades et al, 2019;Tappan et al, 2019), suitable for different types of data and purposes. Further discussions about the choice and application of such tools are provided in reviews by Tyson and Margrie (2022) and Kleven et al (2023b). Whether or not suitable anatomical landmarks are available for determining the specific anatomical location of a sample should be considered case by case.…”

Section: The Workflow Route For Image Location Documentationmentioning

confidence: 99%

“…This allows researchers to integrate and analyze data from different sources within a common anatomical context more easily. For example, spatial registration procedures allow image data to be directly compared and analyzed based on atlas coordinates or annotated brain structures (Puchades et al, 2019;Tappan et al, 2019;Tyson and Margrie, 2022;Kleven et al, 2023b), e.g., through use of computational analyses of features of interest in atlas-defined regions of interest (Kim et al, 2017;Bjerke et al, 2018bBjerke et al, , 2023Yates et al, 2019;Kleven et al, 2023a,b). For other data types, such as locations of electrode tracts, 3D reconstructed neurons, or other features of interest, procedures and tools have been developed to represent the data as coordinate-based points of interest allowing validation or visualization of locations (Bjerke et al, 2018b;Fiorilli et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

The Locare workflow: representing neuroscience data locations as geometric objects in 3D brain atlases

Blixhavn,

Reiten,

Kleven

et al. 2024

Front. Neuroinform.

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Neuroscientists employ a range of methods and generate increasing amounts of data describing brain structure and function. The anatomical locations from which observations or measurements originate represent a common context for data interpretation, and a starting point for identifying data of interest. However, the multimodality and abundance of brain data pose a challenge for efforts to organize, integrate, and analyze data based on anatomical locations. While structured metadata allow faceted data queries, different types of data are not easily represented in a standardized and machine-readable way that allow comparison, analysis, and queries related to anatomical relevance. To this end, three-dimensional (3D) digital brain atlases provide frameworks in which disparate multimodal and multilevel neuroscience data can be spatially represented. We propose to represent the locations of different neuroscience data as geometric objects in 3D brain atlases. Such geometric objects can be specified in a standardized file format and stored as location metadata for use with different computational tools. We here present the Locare workflow developed for defining the anatomical location of data elements from rodent brains as geometric objects. We demonstrate how the workflow can be used to define geometric objects representing multimodal and multilevel experimental neuroscience in rat or mouse brain atlases. We further propose a collection of JSON schemas (LocareJSON) for specifying geometric objects by atlas coordinates, suitable as a starting point for co-visualization of different data in an anatomical context and for enabling spatial data queries.

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A neuroscientist’s guide to using murine brain atlases for efficient analysis and transparent reporting

Cited by 6 publications

References 63 publications

AtOM, an ontology model to standardize use of brain atlases in tools, workflows, and data infrastructures

AtOM, an ontology model to standardize use of brain atlases in tools, workflows, and data infrastructures

Developmental Mouse Brain Common Coordinate Framework

The Locare workflow: representing neuroscience data locations as geometric objects in 3D brain atlases

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