Representing connectivity circuits in autonomic physiology: the SPARC map of organ innervation

Abstract

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.