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The December 2022 release of the SPARC Portal ( https://sparc.science ) included the first significant update to the anatomical connectivity flatmaps since the portal first launched. These flatmaps provide an interactive and visual map for the display and exploration of the autonomic nervous system of Human, rat, mouse, pig, and cat ( https://sparc.science/maps ). In addition to improved anatomical ‘cartoons’ for all species and adding a female Human map, these maps, for the first time, automatically render the connectivity knowledge directly retrieved from the SPARC Connectivity Knowledge Base of the Autonomic Nervous System (SCKAN; https://sparc.science/resources/6eg3VpJbwQR4B84CjrvmyD ). SCKAN contains explicit knowledge about CNS-ANS-end organ circuitry derived from SPARC data and scientific literature, in a form that supports computational reasoning. Each flatmap consists of a manually drawn base layer for the species-specific anatomical cartoon and a layer of manually drawn tracts for the large nerves. By annotating these drawings with standard reference SPARC vocabularies consistent with SCKAN usage, software tools are then able to semantically map connectivity circuits retrieved from SCKAN to the visual representation on each species’ flatmap. Beyond the interactive visual rendering of the circuitry on the SPARC Portal, the semantic consistency between flatmaps and SCKAN powers further user interface components on the SPARC Portal to surface additional knowledge for each rendered connection. For example, comprehensive links to the literature and/or data supporting a connectivity statement can be retrieved from SCKAN and provided to Portal users. Now that tools are in place to support this automated workflow to generate the flatmaps from SCKAN knowledge, we are continuing to improve the SPARC Portal to visualise new knowledge as it becomes available. NIH Common Fund, NIH Office of the Director, Awards OT3OD025349 and OT2 OD030541 This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

The Stimulating Peripheral Activity to Relieve Conditions (SPARC) program is a NIH-funded consortium to improve the understanding of how the autonomic nervous system (ANS) interacts with end organs and the central nervous system. A major goal of SPARC is to use this knowledge to develop the next generation of neuromodulator devices as effective disease therapies. To support neuromodulation planning, the routes of ANS neuron populations (as well as those populations conveying visceral sensing) are to be mapped in terms of the anatomical structures where they pass through or terminate. Such a map would also serve as an organizing scaffold for (i) a growing SPARC repository of electrophysiology recordings and molecular assay data, as well as (ii) the computational simulation of the effect of neural stimulation on any relevant point on the CNS or ANS. The resulting SPARC map represents connectivity as a network of conduits, and is known as the SPARC Connectivity Knowledge Base of the Autonomic Nervous System (SCKAN). SCKAN contains explicit knowledge about CNS-ANS-end organ circuitry derived from SPARC data and scientific literature, in a form that supports computational reasoning. All connections are annotated with standard reference SPARC vocabularies allowing us to integrate datasets that are annotated to the same annotation standard. Circuits on the map represent details of ANS connectivity associated with a particular organ, e.g., bladder control, defensive breathing, modulation of peristalsis or cardiac inotropy/chronotropy. These circuits are created through interviews with SPARC investigators, anatomical experts and the scientific literature. They contain detailed representations of neuron populations giving rise to ANS connections, including mappings of the locations of cell bodies, dendrites, axon segments and synaptic endings. Circuits are modeled using ApiNATOMY, a knowledge model and tool suite specifically created to represent biological connectivity. Furthermore, these detailed circuits are supplemented with general knowledge on connectivity between CNS nuclei, ANS ganglia, nerves, and end organs derived from the scientific literature. To facilitate extracting this knowledge from the literature and maintaining relevance over time, we developed a Natural Language Processing (NLP) pipeline, which extracts connectivity relationships between unique anatomical structures within a sentence from the scientific literature. SCKAN is being used to create a queryable visual atlas of ANS circuitry through the SPARC portal. SPARC Funding: NIH Common Fund, NIH Office of the Director, Award OT2 OD030541 This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Characterizing cellular diversity at different levels of biological organization across data modalities is a prerequisite to understanding the function of cell types in the brain. Classification of neurons is also required to manipulate cell types in controlled ways, and to understand their variation and vulnerability in brain disorders. TheBRAIN Initiative Cell Census Network (BICCN)is an integrated network of data generating centers, data archives and data standards developers, with the goal of systematic multimodal brain cell type profiling and characterization. Emphasis of the BICCN is on the whole mouse brain and demonstration of prototypes for human and non-human primate (NHP) brains. Here, we provide a guide to the cellular and spatial approaches employed, and to accessing and using the BICCN data and its extensive resources, including theBRAIN Cell Data Center (BCDC)which serves to manage and integrate data across the ecosystem. We illustrate the power of the BICCN data ecosystem through vignettes highlighting several BICCN analysis and visualization tools. Finally, we present emerging standards that have been developed or adopted by the BICCN toward FAIR (Wilkinson et al. 2016a) neuroscience. The combined BICCN ecosystem provides a comprehensive resource for the exploration and analysis of cell types in the brain.

Neuroscience has undergone a significant transformation over the past decade, becoming an increasingly open and FAIR discipline. I provide personal perspectives on the importance of two community organizations, FORCE11: The Future of Research Communications and e-Scholarship and INCF: The International Neuroinformatics Coordinating Facility in providing the intellectual and community environment where ideas and open sharing of data and code were incubated and tried.

Current information technology practices in neuroscience make it difficult to understand the organization of the brain across spatial scales. Subcellular junctional connectivity, cytoarchitectural local connectivity, and long-range topographical connectivity are just a few of the relevant data domains that must be synthesized in order to make sense of the brain. However, due to the heterogeneity of the data produced within these domains, the landscape of multiscale neuroscience data is fragmented. A standard framework for neuroscience data is needed to bridge existing digital data resources and to help in the conceptual unification of the multiple disciplines of neuroscience. Using our efforts in building ontologies for neuroscience as an example, we examine the benefits and limits of ontologies as a solution for this data integration problem. We provide several examples of their application to problems of image annotation, content-based retrieval of structural data, and integration of data across scales and researchers.

Data interoperability between well-defined domains is currently performed by leveraging Web services. In the biosciences, more specifically in neuroscience, robust data interoperability is more difficult to achieve due to data heterogeneity, continuous domain changes, and the constant creation of new semantic data models (Nadkarni et al., J Am Med Inform Assoc 6, 478-93, 1999; Miller et al., J Am Med Inform Assoc 8, 34-48, 2001; Gardner et al., J Am Med Inform Assoc 8, 17-33, 2001). Data heterogeneity in neurosciences is primarily due to its multidisciplinary nature. This results in a compelling need to integrate all available neuroscience information to improve our understanding of the brain. Researchers associated with neuroscience initiatives such as the human brain project (HBP) (Koslow and Huerta, Neuroinformatics: An Overview of the Human Brain Project, 1997), the Bioinformatics Research Network (BIRN), and the Neuroinformatics Information Framework (NIF) are exploring mechanisms to allow robust interoperability between these continuously evolving neuroscience databases. To accomplish this goal, it is crucial to orchestrate technologies such as database mediators, metadata repositories, semantic metadata annotations, and ontological services. This chapter introduces the importance of database interoperability in neurosciences. We also describe current data sharing and integration mechanisms in genera. We conclude with data integration in bioscience and present approaches on neuroscience data sharing.

The Neuroimaging Informatics Tools and Resources Clearinghouse, NITRC is a newly established Web site for organizing knowledge about the resources publicly available functional MRI and related structural imaging analysis. Based on GForge, it provides a common environment for downloading, discussion, education, rating, and documentation for a growing number of resources. Its design and current status is presented.

We report a new Web-based resource (LAMHDI) integrating information about animal models for human disease research. This NCRR supported resource provides a simple interface to existing animal model information for the biomedical research community. It provides more efficient access to animal model information, better categorizes and organizes existing data, facilitates interactions between researchers and resource providers, and promotes broader and more applicable use of animal models for human disease research. Approach Phase 1: A first-generation prototype is available at www.LAMHDI.org. This portal provides searchable access to existing mouse and zebrafish databases through an integrated search engine, as well as linked access to information about a variety of other species including nonhuman primates and rats. The portal also allows researchers to contribute data on animal models, thereby extending the impact of existing information through direct posting by the research community. Phase 2: The second generation will add extensive new information for additional animal model species and will increase the depth and connectivity of the data through development of semantic datastores. A sophisticated software architecture will support intelligent relational searches across diverse datasets to highlight associations between diseases, animal models, and biologic pathways.

International audience