The SPARC DRC: Building a Resource for the Autonomic Nervous System Community

Characterization, number, and spatial organization of nerve fibers in the human cervical vagus nerve and its superior cardiac branch

BACKGROUND

Electrical stimulation of the vagus nerve (VN) is a therapy for epilepsy, obesity, depression, and heart diseases. However, whole nerve stimulation leads to side effects. We examined the neuroanatomy of the mid-cervical segment of the human VN and its superior cardiac branch to gain insight into the side effects of VN stimulation and aid in developing targeted stimulation strategies.

METHODS

Nerve specimens were harvested from eight human body donors, then subjected to immunofluorescence and semiautomated quantification to determine the signature, quantity, and spatial distribution of different axonal categories.

RESULTS

The right and left cervical VN (cVN) contained a total of 25,489 ± 2781 and 23,286 ± 3164 fibers, respectively. Two-thirds of the fibers were unmyelinated and one-third were myelinated. About three-quarters of the fibers in the right and left cVN were sensory (73.9 ± 7.5 % versus 72.4 ± 5.6 %), while 13.2 ± 1.8 % versus 13.3 ± 3.0 % were special visceromotor and parasympathetic, and 13 ± 5.9 % versus 14.3 ± 4.0 % were sympathetic. Special visceromotor and parasympathetic fibers formed clusters. The superior cardiac branches comprised parasympathetic, vagal sensory, and sympathetic fibers with the left cardiac branch containing more sympathetic fibers than the right (62.7 ± 5.4 % versus 19.8 ± 13.3 %), and 50 % of the left branch contained sensory and sympathetic fibers only.

CONCLUSION

The study indicates that selective stimulation of vagal sensory and motor fibers is possible. However, it also highlights the potential risk of activating sympathetic fibers in the superior cardiac branch, especially on the left side.

A roadmap for delivering a human musculoskeletal cell atlas

Advances in single-cell technologies have transformed the ability to identify the individual cell types present within tissues and organs. The musculoskeletal bionetwork, part of the wider Human Cell Atlas project, aims to create a detailed map of the healthy musculoskeletal system at a single-cell resolution throughout tissue development and across the human lifespan, with complementary generation of data from diseased tissues. Given the prevalence of musculoskeletal disorders, this detailed reference dataset will be critical to understanding normal musculoskeletal function in growth, homeostasis and ageing. The endeavour will also help to identify the cellular basis for disease and lay the foundations for novel therapeutic approaches to treating diseases of the joints, soft tissues and bone. Here, we present a Roadmap delineating the critical steps required to construct the first draft of a human musculoskeletal cell atlas. We describe the key challenges involved in mapping the extracellular matrix-rich, but cell-poor, tissues of the musculoskeletal system, outline early milestones that have been achieved and describe the vision and directions for a comprehensive musculoskeletal cell atlas. By embracing cutting-edge technologies, integrating diverse datasets and fostering international collaborations, this endeavour has the potential to drive transformative changes in musculoskeletal medicine.

Waxholm Space atlas of the rat brain: a 3D atlas supporting data analysis and integration

Volumetric brain atlases are increasingly used to integrate and analyze diverse experimental neuroscience data acquired from animal models, but until recently a publicly available digital atlas with complete coverage of the rat brain has been missing. Here we present an update of the Waxholm Space rat brain atlas, a comprehensive open-access volumetric atlas resource. This brain atlas features annotations of 222 structures, of which 112 are new and 57 revised compared to previous versions. It provides a detailed map of the cerebral cortex, hippocampal region, striatopallidal areas, midbrain dopaminergic system, thalamic cell groups, the auditory system and main fiber tracts. We document the criteria underlying the annotations and demonstrate how the atlas with related tools and workflows can be used to support interpretation, integration, analysis and dissemination of experimental rat brain data.

Organization and morphology of calcitonin gene-related peptide-immunoreactive axons in the whole mouse stomach

Nociceptive afferent axons innervate the stomach and send signals to the brain and spinal cord. Peripheral nociceptive afferents can be detected with a variety of markers (e.g., substance P [SP] and calcitonin gene-related peptide [CGRP]). We recently examined the topographical organization and morphology of SP-immunoreactive (SP-IR) axons in the whole mouse stomach muscular layer. However, the distribution and morphological structure of CGRP-IR axons remain unclear. We used immunohistochemistry labeling and applied a combination of imaging techniques, including confocal and Zeiss Imager M2 microscopy, Neurolucida 360 tracing, and integration of axon tracing data into a 3D stomach scaffold to characterize CGRP-IR axons and terminals in the whole mouse stomach muscular layers. We found that: (1) CGRP-IR axons formed extensive terminal networks in both ventral and dorsal stomachs. (2) CGRP-IR axons densely innervated the blood vessels. (3) CGRP-IR axons ran in parallel with the longitudinal and circular muscles. Some axons ran at angles through the muscular layers. (4) They also formed varicose terminal contacts with individual myenteric ganglion neurons. (5) CGRP-IR occurred in DiI-labeled gastric-projecting neurons in the dorsal root and vagal nodose ganglia, indicating CGRP-IR axons were visceral afferent axons. (6) CGRP-IR axons did not colocalize with tyrosine hydroxylase or vesicular acetylcholine transporter axons in the stomach, indicating CGRP-IR axons were not visceral efferent axons. (7) CGRP-IR axons were traced and integrated into a 3D stomach scaffold. For the first time, we provided a topographical distribution map of CGRP-IR axon innervation of the whole stomach muscular layers at the cellular/axonal/varicosity scale.

A Narrative Review of Personalized Musculoskeletal Modeling Using the Physiome and Musculoskeletal Atlas Projects

In this narrative review, we explore developments in the field of computational musculoskeletal model personalization using the Physiome and Musculoskeletal Atlas Projects. Model geometry personalization; statistical shape modeling; and its impact on segmentation, classification, and model creation are explored. Examples include the trapeziometacarpal and tibiofemoral joints, Achilles tendon, gastrocnemius muscle, and pediatric lower limb bones. Finally, a more general approach to model personalization is discussed based on the idea of multiscale personalization called scaffolds.

Toxicology knowledge graph for structural birth defects

BACKGROUND

Birth defects are functional and structural abnormalities that impact about 1 in 33 births in the United States. They have been attributed to genetic and other factors such as drugs, cosmetics, food, and environmental pollutants during pregnancy, but for most birth defects there are no known causes.

METHODS

To further characterize associations between small molecule compounds and their potential to induce specific birth abnormalities, we gathered knowledge from multiple sources to construct a reproductive toxicity Knowledge Graph (ReproTox-KG) with a focus on associations between birth defects, drugs, and genes. Specifically, we gathered data from drug/birth-defect associations from co-mentions in published abstracts, gene/birth-defect associations from genetic studies, drug- and preclinical-compound-induced gene expression changes in cell lines, known drug targets, genetic burden scores for human genes, and placental crossing scores for small molecules.

RESULTS

Using ReproTox-KG and semi-supervised learning (SSL), we scored >30,000 preclinical small molecules for their potential to cross the placenta and induce birth defects, and identified >500 birth-defect/gene/drug cliques that can be used to explain molecular mechanisms for drug-induced birth defects. The ReproTox-KG can be accessed via a web-based user interface available at https://maayanlab.cloud/reprotox-kg . This site enables users to explore the associations between birth defects, approved and preclinical drugs, and all human genes.

CONCLUSIONS

ReproTox-KG provides a resource for exploring knowledge about the molecular mechanisms of birth defects with the potential of predicting the likelihood of genes and preclinical small molecules to induce birth defects.

Vision for the 12 LABOURS Digital Twin Platform. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023;2023:1-4. [PMID: 38083471 DOI: 10.1109/embc40787.2023.10341138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Clinical translation of personalised computational physiology workflows and digital twins can revolutionise healthcare by providing a better understanding of an individual's physiological processes and any changes that could lead to serious health consequences. However, the lack of common infrastructure for developing these workflows and digital twins has hampered the realisation of this vision. The Auckland Bioengineering Institute's 12 LABOURS project aims to address these challenges by developing a Digital Twin Platform to enable researchers to develop and personalise computational physiology models to an individual's health data in clinical workflows. This will allow clinical trials to be more efficiently conducted to demonstrate the efficacy of these personalised clinical workflows. We present a demonstration of the platform's capabilities using publicly available data and an existing automated computational physiology workflow developed to assist clinicians with diagnosing and treating breast cancer. We also demonstrate how the platform facilitates the discovery and exploration of data and the presentation of workflow results as part of clinical reports through a web portal. Future developments will involve integrating the platform with health systems and remote-monitoring devices such as wearables and implantables to support home-based healthcare. Integrating outputs from multiple workflows that are applied to the same individual's health data will also enable the generation of their personalised digital twin.Clinical Relevance- The proposed 12 LABOURS Digital Twin Platform will enable researchers to 1) more efficiently conduct clinical trials to assess the efficacy of their computational physiology workflows and support the clinical translation of their research; 2) reuse primary and derived data from these workflows to generate novel workflows; and 3) generate personalised digital twins by integrating the outputs of different computational physiology workflows.

Mapping the Organization and Morphology of Calcitonin Gene-Related Peptide (CGRP)-IR Axons in the Whole Mouse Stomach

Nociceptive afferent axons innervate the stomach and send signals to the brain and spinal cord. Peripheral nociceptive afferents can be detected with a variety of markers [e.g., substance P (SP) and calcitonin gene-related peptide (CGRP)]. We recently examined the topographical organization and morphology of SP-immunoreactive (SP-IR) axons in the whole mouse stomach muscular layer. However, the distribution and morphological structure of CGRP-IR axons remain unclear. We used immunohistochemistry labeling and applied a combination of imaging techniques, including confocal and Zeiss Imager M2 microscopy, Neurolucida 360 tracing, and integration of axon tracing data into a 3D stomach scaffold to characterize CGRP-IR axons and terminals in the whole mouse stomach muscular layers. We found that: 1) CGRP-IR axons formed extensive terminal networks in both ventral and dorsal stomachs. 2) CGRP-IR axons densely innervated the blood vessels. 3) CGRP-IR axons ran in parallel with the longitudinal and circular muscles. Some axons ran at angles through the muscular layers. 4) They also formed varicose terminal contacts with individual myenteric ganglion neurons. 5) CGRP-IR occurred in DiI-labeled gastric-projecting neurons in the dorsal root and vagal nodose ganglia, indicating CGRP-IR axons were visceral afferent axons. 6) CGRP-IR axons did not colocalize with tyrosine hydroxylase (TH) or vesicular acetylcholine transporter (VAChT) axons in the stomach, indicating CGRP-IR axons were not visceral efferent axons. 7) CGRP-IR axons were traced and integrated into a 3D stomach scaffold. For the first time, we provided a topographical distribution map of CGRP-IR axon innervation of the whole stomach muscular layers at the cellular/axonal/varicosity scale.

Correcting bias in cardiac geometries derived from multimodal images using spatiotemporal mapping

Cardiovascular imaging studies provide a multitude of structural and functional data to better understand disease mechanisms. While pooling data across studies enables more powerful and broader applications, performing quantitative comparisons across datasets with varying acquisition or analysis methods is problematic due to inherent measurement biases specific to each protocol. We show how dynamic time warping and partial least squares regression can be applied to effectively map between left ventricular geometries derived from different imaging modalities and analysis protocols to account for such differences. To demonstrate this method, paired real-time 3D echocardiography (3DE) and cardiac magnetic resonance (CMR) sequences from 138 subjects were used to construct a mapping function between the two modalities to correct for biases in left ventricular clinical cardiac indices, as well as regional shape. Leave-one-out cross-validation revealed a significant reduction in mean bias, narrower limits of agreement, and higher intraclass correlation coefficients for all functional indices between CMR and 3DE geometries after spatiotemporal mapping. Meanwhile, average root mean squared errors between surface coordinates of 3DE and CMR geometries across the cardiac cycle decreased from 7 ± 1 to 4 ± 1 mm for the total study population. Our generalised method for mapping between time-varying cardiac geometries obtained using different acquisition and analysis protocols enables the pooling of data between modalities and the potential for smaller studies to leverage large population databases for quantitative comparisons.

Deep-learning segmentation of fascicles from microCT of the human vagus nerve

Introduction

MicroCT of the three-dimensional fascicular organization of the human vagus nerve provides essential data to inform basic anatomy as well as the development and optimization of neuromodulation therapies. To process the images into usable formats for subsequent analysis and computational modeling, the fascicles must be segmented. Prior segmentations were completed manually due to the complex nature of the images, including variable contrast between tissue types and staining artifacts.

Methods

Here, we developed a U-Net convolutional neural network (CNN) to automate segmentation of fascicles in microCT of human vagus nerve.

Results

The U-Net segmentation of ~500 images spanning one cervical vagus nerve was completed in 24 s, versus ~40 h for manual segmentation, i.e., nearly four orders of magnitude faster. The automated segmentations had a Dice coefficient of 0.87, a measure of pixel-wise accuracy, thus suggesting a rapid and accurate segmentation. While Dice coefficients are a commonly used metric to assess segmentation performance, we also adapted a metric to assess fascicle-wise detection accuracy, which showed that our network accurately detects the majority of fascicles, but may under-detect smaller fascicles.

Discussion

This network and the associated performance metrics set a benchmark, using a standard U-Net CNN, for the application of deep-learning algorithms to segment fascicles from microCT images. The process may be further optimized by refining tissue staining methods, modifying network architecture, and expanding the ground-truth training data. The resulting three-dimensional segmentations of the human vagus nerve will provide unprecedented accuracy to define nerve morphology in computational models for the analysis and design of neuromodulation therapies.

Unpacking the multimodal, multi-scale data of the fast and slow lanes of the cardiac vagus through computational modelling

NEW FINDINGS

What is the topic of this review? The vagus nerve is a crucial regulator of cardiovascular homeostasis, and its activity is linked to heart health. Vagal activity originates from two brainstem nuclei: the nucleus ambiguus (fast lane) and the dorsal motor nucleus of the vagus (slow lane), nicknamed for the time scales that they require to transmit signals. What advances does it highlight? Computational models are powerful tools for organizing multi-scale, multimodal data on the fast and slow lanes in a physiologically meaningful way. A strategy is laid out for how these models can guide experiments aimed at harnessing the cardiovascular health benefits of differential activation of the fast and slow lanes.

ABSTRACT

The vagus nerve is a key mediator of brain-heart signaling, and its activity is necessary for cardiovascular health. Vagal outflow stems from the nucleus ambiguus, responsible primarily for fast, beat-to-beat regulation of heart rate and rhythm, and the dorsal motor nucleus of the vagus, responsible primarily for slow regulation of ventricular contractility. Due to the high-dimensional and multimodal nature of the anatomical, molecular and physiological data on neural regulation of cardiac function, data-derived mechanistic insights have proven elusive. Elucidating insights has been complicated further by the broad distribution of the data across heart, brain and peripheral nervous system circuits. Here we lay out an integrative framework based on computational modelling for combining these disparate and multi-scale data on the two vagal control lanes of the cardiovascular system. Newly available molecular-scale data, particularly single-cell transcriptomic analyses, have augmented our understanding of the heterogeneous neuronal states underlying vagally mediated fast and slow regulation of cardiac physiology. Cellular-scale computational models built from these data sets represent building blocks that can be combined using anatomical and neural circuit connectivity, neuronal electrophysiology, and organ/organismal-scale physiology data to create multi-system, multi-scale models that enable in silico exploration of the fast versus slow lane vagal stimulation. The insights from the computational modelling and analyses will guide new experimental questions on the mechanisms regulating the fast and slow lanes of the cardiac vagus toward exploiting targeted vagal neuromodulatory activity to promote cardiovascular health.

Tissue registration and exploration user interfaces in support of a human reference atlas

Seventeen international consortia are collaborating on a human reference atlas (HRA), a comprehensive, high-resolution, three-dimensional atlas of all the cells in the healthy human body. Laboratories around the world are collecting tissue specimens from donors varying in sex, age, ethnicity, and body mass index. However, harmonizing tissue data across 25 organs and more than 15 bulk and spatial single-cell assay types poses challenges. Here, we present software tools and user interfaces developed to spatially and semantically annotate ("register") and explore the tissue data and the evolving HRA. A key part of these tools is a common coordinate framework, providing standard terminologies and data structures for describing specimen, biological structure, and spatial data linked to existing ontologies. As of April 22, 2022, the "registration" user interface has been used to harmonize and publish data on 5,909 tissue blocks collected by the Human Biomolecular Atlas Program (HuBMAP), the Stimulating Peripheral Activity to Relieve Conditions program (SPARC), the Human Cell Atlas (HCA), the Kidney Precision Medicine Project (KPMP), and the Genotype Tissue Expression project (GTEx). Further, 5,856 tissue sections were derived from 506 HuBMAP tissue blocks. The second "exploration" user interface enables consortia to evaluate data quality, explore tissue data spatially within the context of the HRA, and guide data acquisition. A companion website is at https://cns-iu.github.io/HRA-supporting-information/ .

Addressing barriers in comprehensiveness, accessibility, reusability, interoperability and reproducibility of computational models in systems biology

Computational models are often employed in systems biology to study the dynamic behaviours of complex systems. With the rise in the number of computational models, finding ways to improve the reusability of these models and their ability to reproduce virtual experiments becomes critical. Correct and effective model annotation in community-supported and standardised formats is necessary for this improvement. Here, we present recent efforts toward a common framework for annotated, accessible, reproducible and interoperable computational models in biology, and discuss key challenges of the field.

An Open-source Computational Model of Neurostimulation of the Spinal Pudendo-Vesical Reflex for the Recovery of Bladder Control After Spinal Cord Injury

Spinal cord stimulation (SCS) could be used to restore control of the bladder after spinal cord injury, but substantial development is still required to tailor this technology for bladder function. Computational models could be utilized to accelerate these efforts enabling in-silico optimization of stimulation parameters. However, no model of the spinal pudendo-vesical reflex can simulate the effect of stimulation amplitude on neuron recruitment. This limitation hinders accurate prediction of bladder pressure changes for different stimulation configurations. Here., we implemented an open-source realistic spiking neural network model of the pudendo-vesical reflex enabling exploration of the impact of stimulation amplitude and frequency on bladder pressure changes. We used the o2S2 PARC platform to design a parallel implementation of the bladder reflex circuits with NEURON. Our model successfully reproduced and expanded previous studies., producing a decrease in bladder pressure at low stimulation frequency (10 Hz) and excitation at high stimulation frequency (≥33 Hz) in isovolumetric experiments. We then explored the effect of mixed nerve recruitment., simulating a common case of poorly selective spinal cord stimulation. We found that high recruitments of pudendal nerve axons are necessary to maintain this bi-modal behavior., regardless of stimulation specificity. Our framework is fully open-source and can be used to simulate any type of axon stimulations such as SCS and peripheral nerve stimulation.

Representing Normal and Abnormal Physiology as Routes of Flow in ApiNATOMY

We present (i) the ApiNATOMY workflow to build knowledge models of biological connectivity, as well as (ii) the ApiNATOMY TOO map, a topological scaffold to organize and visually inspect these connectivity models in the context of a canonical architecture of body compartments. In this work, we outline the implementation of ApiNATOMY’s knowledge representation in the context of a large-scale effort, SPARC, to map the autonomic nervous system. Within SPARC, the ApiNATOMY modeling effort has generated the SCKAN knowledge graph that combines connectivity models and TOO map. This knowledge graph models flow routes for a number of normal and disease scenarios in physiology. Calculations over SCKAN to infer routes are being leveraged to classify, navigate and search for semantically-linked metadata of multimodal experimental datasets for a number of cross-scale, cross-disciplinary projects.

Biomechanical constitutive modeling of the gastrointestinal tissues: a systematic review

The gastrointestinal (GI) tract is a continuous channel through the body that consists of the esophagus, the stomach, the small intestine, the large intestine, and the rectum. Its primary functions are to move the intake of food for digestion before storing and ultimately expulsion of feces. The mechanical behavior of GI tissues thus plays a crucial role for GI function in health and disease. The mechanical properties are characterized by a biomechanical constitutive model, which is a mathematical representation of the relation between load and deformation in a tissue. Hence, validated biomechanical constitutive models are essential to characterize and simulate the mechanical behavior of the GI tract. Here, a systematic review of these constitutive models is provided. This review is limited to studies where a model of the strain energy function is proposed to characterize the stress-strain relation of a GI tissue. Several needs are identified for more advanced modeling including: 1) Microstructural models that provide actual structure-function relations; 2) Validation of coupled electro-mechanical models accounting for active muscle contractions; 3) Human data to develop and validate models. The findings from this review provide guidelines for using existing constitutive models as well as perspective and directions for future studies.

Studying Cardiac Neural Network Dynamics: Challenges and Opportunities for Scientific Computing

Neural control of the heart involves continuous modulation of cardiac mechanical and electrical activity to meet the organism's demand for blood flow. The closed-loop control scheme consists of interconnected neural networks with central and peripheral components working cooperatively with each other. These components have evolved to cooperate control of various aspects of cardiac function, which produce measurable "functional" outputs such as heart rate and blood pressure. In this review, we will outline fundamental studies probing the cardiac neural control hierarchy. We will discuss how computational methods can guide improved experimental design and be used to probe how information is processed while closed-loop control is operational. These experimental designs generate large cardio-neural datasets that require sophisticated strategies for signal processing and time series analysis, while presenting the usual large-scale computational challenges surrounding data sharing and reproducibility. These challenges provide unique opportunities for the development and validation of novel techniques to enhance understanding of mechanisms of cardiac pathologies required for clinical implementation.

Is Neuroscience FAIR? A Call for Collaborative Standardisation of Neuroscience Data

In this perspective article, we consider the critical issue of data and other research object standardisation and, specifically, how international collaboration, and organizations such as the International Neuroinformatics Coordinating Facility (INCF) can encourage that emerging neuroscience data be Findable, Accessible, Interoperable, and Reusable (FAIR). As neuroscientists engaged in the sharing and integration of multi-modal and multiscale data, we see the current insufficiency of standards as a major impediment in the Interoperability and Reusability of research results. We call for increased international collaborative standardisation of neuroscience data to foster integration and efficient reuse of research objects.

CONTROL-CORE: A Framework for Simulation and Design of Closed-Loop Peripheral Neuromodulation Control Systems

Closed-loop Vagus Nerve Stimulation (VNS) based on physiological feedback signals is a promising approach to regulate organ functions and develop therapeutic devices. Designing closed-loop neurostimulation systems requires simulation environments and computing infrastructures that support i) modeling the physiological responses of organs under neuromodulation, also known as physiological models, and ii) the interaction between the physiological models and the neuromodulation control algorithms. However, existing simulation platforms do not support closed-loop VNS control systems modeling without extensive rewriting of computer code and manual deployment and configuration of programs. The CONTROL-CORE project aims to develop a flexible software platform for designing and implementing closed-loop VNS systems. This paper proposes the software architecture and the elements of the CONTROL-CORE platform that allow the interaction between a controller and a physiological model in feedback. CONTROL-CORE facilitates modular simulation and deployment of closed-loop peripheral neuromodulation control systems, spanning multiple organizations securely and concurrently. CONTROL-CORE allows simulations to run on different operating systems, be developed in various programming languages (such as Matlab, Python, C++, and Verilog), and be run locally, in containers, and in a distributed fashion. The CONTROL-CORE platform allows users to create tools and testbenches to facilitate sophisticated simulation experiments. We tested the CONTROL-CORE platform in the context of closed-loop control of cardiac physiological models, including pulsatile and nonpulsatile rat models. These were tested using various controllers such as Model Predictive Control and Long-Short-Term Memory based controllers. Our wide range of use cases and evaluations show the performance, flexibility, and usability of the CONTROL-CORE platform.

SPARClink: an interactive tool to visualize the impact of the SPARC program

The National Institutes of Health (NIH) Stimulating Peripheral Activity to Relieve Conditions (SPARC) program seeks to accelerate the development of therapeutic devices that modulate electrical activity in nerves to improve organ function. SPARC-funded researchers are generating rich datasets from neuromodulation research that are curated and shared according to FAIR (Findable, Accessible, Interoperable, and Reusable) guidelines and are accessible to the public on the SPARC data portal. Keeping track of the utilization of these datasets within the larger research community is a feature that will benefit data-generating researchers in showcasing the impact of their SPARC outcomes. This will also allow the SPARC program to display the impact of the FAIR data curation and sharing practices that have been implemented. This manuscript provides the methods and outcomes of SPARClink, our web tool for visualizing the impact of SPARC, which won the Second prize at the 2021 SPARC FAIR Codeathon. With SPARClink, we built a system that automatically and continuously finds new published SPARC scientific outputs (datasets, publications, protocols) and the external resources referring to them. SPARC datasets and protocols are queried using publicly accessible REST application programming interfaces (APIs, provided by Pennsieve and Protocols.io) and stored in a publicly accessible database. Citation information for these resources is retrieved using the NIH reporter API and National Center for Biotechnology Information (NCBI) Entrez system. A novel knowledge graph-based structure was created to visualize the results of these queries and showcase the impact that the FAIR data principles can have on the research landscape when they are adopted by a consortium.

The Neuron Phenotype Ontology: A FAIR Approach to Proposing and Classifying Neuronal Types

The challenge of defining and cataloging the building blocks of the brain requires a standardized approach to naming neurons and organizing knowledge about their properties. The US Brain Initiative Cell Census Network, Human Cell Atlas, Blue Brain Project, and others are generating vast amounts of data and characterizing large numbers of neurons throughout the nervous system. The neuroscientific literature contains many neuron names (e.g. parvalbumin-positive interneuron or layer 5 pyramidal cell) that are commonly used and generally accepted. However, it is often unclear how such common usage types relate to many evidence-based types that are proposed based on the results of new techniques. Further, comparing different types across labs remains a significant challenge. Here, we propose an interoperable knowledge representation, the Neuron Phenotype Ontology (NPO), that provides a standardized and automatable approach for naming cell types and normalizing their constituent phenotypes using identifiers from community ontologies as a common language. The NPO provides a framework for systematically organizing knowledge about cellular properties and enables interoperability with existing neuron naming schemes. We evaluate the NPO by populating a knowledge base with three independent cortical neuron classifications derived from published data sets that describe neurons according to molecular, morphological, electrophysiological, and synaptic properties. Competency queries to this knowledge base demonstrate that the NPO knowledge model enables interoperability between the three test cases and neuron names commonly used in the literature.