Developing a multiscale neural connectivity knowledgebase of the autonomic nervous system.

IF 2.5 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Frontiers in Neuroinformatics Pub Date : 2025-03-14 eCollection Date: 2025-01-01 DOI:10.3389/fninf.2025.1541184
Fahim T Imam, Thomas H Gillespie, Ilias Ziogas, Monique C Surles-Zeigler, Susan Tappan, Burak I Ozyurt, Jyl Boline, Bernard de Bono, Jeffrey S Grethe, Maryann E Martone
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引用次数: 0

Abstract

The Stimulating Peripheral Activity to Relieve Conditions (SPARC) program is a U.S. National Institutes of Health (NIH) funded effort to enhance our understanding of the neural circuitry responsible for visceral control. SPARC's mission is to identify, extract, and compile our overall existing knowledge and understanding of the autonomic nervous system (ANS) connectivity between the central nervous system and end organs. A major goal of SPARC is to use this knowledge to promote the development of the next generation of neuromodulation devices and bioelectronic medicine for nervous system diseases. As part of the SPARC program, we have been developing the SPARC Connectivity Knowledge Base of the Autonomic Nervous System (SCKAN), a dynamic resource containing information about the origins, terminations, and routing of ANS projections. The distillation of SPARC's connectivity knowledge into this knowledge base involves a rigorous curation process to capture connectivity information provided by experts, published literature, textbooks, and SPARC scientific data. SCKAN is used to automatically generate anatomical and functional connectivity maps on the SPARC portal. In this article, we present the design and functionality of SCKAN, including the detailed knowledge engineering process developed to populate the resource with high quality and accurate data. We discuss the process from both the perspective of SCKAN's ontological representation as well as its practical applications in developing information systems. We share our techniques, strategies, tools and insights for developing a practical knowledgebase of ANS connectivity that supports continual enhancement.

建立自主神经系统的多尺度神经连接知识库。
刺激外周活动以缓解疾病(SPARC)计划是美国国立卫生研究院(NIH)资助的一项努力,旨在加强我们对负责内脏控制的神经回路的理解。SPARC的任务是识别、提取和汇编我们对自主神经系统(ANS)连接中枢神经系统和终末器官的整体现有知识和理解。SPARC的一个主要目标是利用这些知识来促进下一代神经调节装置和神经系统疾病生物电子医学的发展。作为SPARC项目的一部分,我们一直在开发自主神经系统SPARC连接知识库(SCKAN),这是一个包含关于ANS投影的起源、终止和路由信息的动态资源。将SPARC的连接性知识提炼到这个知识库中涉及到一个严格的管理过程,以捕获专家、已发表的文献、教科书和SPARC科学数据提供的连接性信息。SCKAN用于在SPARC门户上自动生成解剖和功能连接图。在本文中,我们介绍了SCKAN的设计和功能,包括开发的详细知识工程过程,以填充高质量和准确的数据资源。我们从SCKAN的本体论表示及其在开发信息系统中的实际应用两方面讨论了这一过程。我们分享我们的技术、策略、工具和见解,以开发支持持续增强的ANS连接的实用知识库。
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来源期刊
Frontiers in Neuroinformatics
Frontiers in Neuroinformatics MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
4.80
自引率
5.70%
发文量
132
审稿时长
14 weeks
期刊介绍: Frontiers in Neuroinformatics publishes rigorously peer-reviewed research on the development and implementation of numerical/computational models and analytical tools used to share, integrate and analyze experimental data and advance theories of the nervous system functions. Specialty Chief Editors Jan G. Bjaalie at the University of Oslo and Sean L. Hill at the École Polytechnique Fédérale de Lausanne are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide. Neuroscience is being propelled into the information age as the volume of information explodes, demanding organization and synthesis. Novel synthesis approaches are opening up a new dimension for the exploration of the components of brain elements and systems and the vast number of variables that underlie their functions. Neural data is highly heterogeneous with complex inter-relations across multiple levels, driving the need for innovative organizing and synthesizing approaches from genes to cognition, and covering a range of species and disease states. Frontiers in Neuroinformatics therefore welcomes submissions on existing neuroscience databases, development of data and knowledge bases for all levels of neuroscience, applications and technologies that can facilitate data sharing (interoperability, formats, terminologies, and ontologies), and novel tools for data acquisition, analyses, visualization, and dissemination of nervous system data. Our journal welcomes submissions on new tools (software and hardware) that support brain modeling, and the merging of neuroscience databases with brain models used for simulation and visualization.
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