SPARC Connectivity Knowledge Base of the Autonomic Nervous System (SCKAN)

The SPARC Connectivity Knowledge Base of the Autonomic Nervous System (SCKAN) is a semantic store modeling the topology of nerve-end organ connectivity and will act as a core component of powerful query and visualization capabilities within SPARC.


Tool / Resource Name

SPARC Connectivity Knowledge Base of the Autonomic Nervous System (SCKAN)




A key component of the SPARC Program is the SPARC Connectivity Knowledge Base of the Autonomic Nervous system, now referred to as SCKAN, a semantic store housing a comprehensive knowledge base of autonomic nervous system (ANS) nerve to end organ connectivity. Connectivity information is derived from SPARC experts, SPARC data, literature and textbooks. SCKAN supports reasoning and offers powerful query and visualization capabilities. Find out how to access SCKAN here (release link) and the types of queries supported here.

How is SCKAN organized?

SCKAN contains statements about neuronal connectivity at the neuron population level, largely in the form of: “Neurons with somas in structure A project to structure B via nerve C.” For some connections, we have detailed connections modeled with a specification of the locations of their somas and dendrites, axon segments and synapses. Connectivity information is available as RDF triples or via SciGraph, an OWL-based graph database.

The SCKAN models connections at two levels of granularity:

Image SCKAN two methods -2

Circuits: A circuit represents a detailed model of ANS connectivity associated with a particular organ (e.g., bladder) or functional circuit (e.g., defensive breathing). These models are created and curated by experts in conjunction with the SPARC ANATOMY WORKING GROUP (SAWG) using information from multiple sources such as data, literature, and ontologies. Each circuit contains detailed representations of neuron populations, including locations of cell bodies and dendrites and the anatomical course of axons as they traverse anatomical structures and nerves and synapse with their targets (example). This knowledge is represented using the ApiNATOMY connectivity model, a sophisticated semantic model that represents multiscale connectivity [Kokash N and de Bono B (2021), Osanlouy M, et al. (2021) - for additional information see this video]. ApiNATOMY provides a method of chaining together axon segments to create a unified representation of tractography.

General knowledge about individual connections of CNS nuclei, ANS ganglia, nerves, and end organs: In order to provide a comprehensive overview of the state of knowledge about ANS connectivity, the circuit-based approach is supplemented with well-known connections of ANS ganglia and nerves derived from the literature and textbooks using a Natural Language Processing (NLP) pipeline [Menke J et al. (2020) - to learn more about this process, see this video]. These connections are generally in the form of: Neuron population with somas in Structure A project to Structure B via Nerve C. These types of statements do not have detailed topological information associated with them and are represented using The Neuron Phenotype Ontology (NPO), [Gillespie TH, et al. (2020)]

For a list of circuits and connections currently in SCKAN, please see the release notes.

Why Create SCKAN?

SCKAN provides a central location to populate, discover, and query ANS connectivity knowledge over multiple scales. SCKAN offers an unprecedented opportunity to aggregate all information regarding ANS connectivity into a machine-readable form. Representing connectivity information using a semantic model allows us to issue queries such as, “what are the locations of neuron somas with processes that pass through spinal cord level C4?” and create a queryable visual atlas of ANS circuitry. Users of the SPARC maps will be able to query SCKAN to find more information about routes, targets and evidence. Conversely, SCKAN will shortly be used to generate SPARC maps automatically, tying a connectivity atlas to a powerful database back end.

SCKAN is a subset of the SPARC Knowledge Graph, which ultimately allows us to link connectivity information to SPARC data, models, flat maps, simulations, and other related information. Information about the types of queries supported can be found here.

Who Created SCKAN?

The SCKAN is a product of the Knowledge Management team of K-Core, a collaboration between the FAIR Data Informatics Lab at UCSD and Whitby et al, Inc. The connectivity knowledge contained in SCKAN has been contributed by a number of SPARC investigators and the SPARC Anatomy Working Group (SAWG).

How do I access SCKAN?

In the near future, SCKAN will be accessible through the SPARC Connectivity FlatMaps on the SPARC Portal and via the ApiNATOMY platform.

Currently, direct access to SCKAN is limited. We have created a few example queries that can be executed via the web. For those who are familiar with the Cypher or SPARQL query language, we’ve created a containerized version of SCKAN that can be downloaded and installed locally along with the necessary documentation to install and query SCKAN. Updates to SCKAN will be released quarterly. See the detailed Help page “How do I access SCKAN.”

In the future, we plan on developing a more user-friendly front end to SCKAN and also making the data available via WikiData.

To submit new queries you would like add to the SPARC Connectivity Knowledge Base, please fill out the form available here.

Contact Info:

For additional information contact us at

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