Blixhavn Camilla H, Reiten Ingrid, Kleven Heidi, Øvsthus Martin, Yates Sharon C, Schlegel Ulrike, Puchades Maja A, Schmid Oliver, Bjaalie Jan G, Bjerke Ingvild E, Leergaard Trygve B
Neural Systems Laboratory, Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway.
EBRAINS AISBL, Brussels, Belgium.
Front Neuroinform. 2024 Feb 9;18:1284107. doi: 10.3389/fninf.2024.1284107. eCollection 2024.
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.
神经科学家采用一系列方法,并生成越来越多描述大脑结构和功能的数据。观测或测量所源自的解剖位置是数据解释的共同背景,也是识别感兴趣数据的起点。然而,大脑数据的多模态性和丰富性给基于解剖位置来组织、整合和分析数据的工作带来了挑战。虽然结构化元数据允许进行分面数据查询,但不同类型的数据不容易以标准化且机器可读的方式来表示,以便进行与解剖相关性相关的比较、分析和查询。为此,三维(3D)数字脑图谱提供了框架,在其中可以对不同的多模态和多层次神经科学数据进行空间表示。我们建议将不同神经科学数据的位置表示为3D脑图谱中的几何对象。这样的几何对象可以用标准化文件格式指定,并作为位置元数据存储,以便与不同的计算工具一起使用。我们在此展示为将来自啮齿动物大脑的数据元素的解剖位置定义为几何对象而开发的Locare工作流程。我们演示了如何使用该工作流程来定义表示大鼠或小鼠脑图谱中多模态和多层次实验神经科学的几何对象。我们还提出了一组JSON模式(LocareJSON),用于通过图谱坐标指定几何对象,适合作为在解剖背景下对不同数据进行协同可视化以及实现空间数据查询的起点。