Terminology for neuroscience data discovery: multi-tree syntax and investigator-derived semantics

A Perspective on Neuroscience Data Standardization with Neurodata Without Borders

Neuroscience research has evolved to generate increasingly large and complex experimental data sets, and advanced data science tools are taking on central roles in neuroscience research. Neurodata Without Borders (NWB), a standard language for neurophysiology data, has recently emerged as a powerful solution for data management, analysis, and sharing. We here discuss our labs' efforts to implement NWB data science pipelines. We describe general principles and specific use cases that illustrate successes, challenges, and non-trivial decisions in software engineering. We hope that our experience can provide guidance for the neuroscience community and help bridge the gap between experimental neuroscience and data science. Key takeaways from this article are that (1) standardization with NWB requires non-trivial design choices; (2) the general practice of standardization in the lab promotes data awareness and literacy, and improves transparency, rigor, and reproducibility in our science; (3) we offer several feature suggestions to ease the extensibility, publishing/sharing, and usability for NWB standard and users of NWB data.

Establishing neuroanatomical correspondences across mouse and marmoset brain structures

Interest in the common marmoset is growing due to evolutionarily proximity to humans compared to laboratory mice, necessitating a comparison of mouse and marmoset brain architectures, including connectivity and cell type distributions. Creating an actionable comparative platform is challenging since these brains have distinct spatial organizations and expert neuroanatomists disagree. We propose a general theoretical framework to relate named atlas compartments across taxa and use it to establish a detailed correspondence between marmoset and mice brains. Contrary to conventional wisdom that brain structures may be easier to relate at higher levels of the atlas hierarchy, we find that finer parcellations at the leaf levels offer greater reconcilability despite naming discrepancies. Utilizing existing atlases and associated literature, we created a list of leaf-level structures for both species and establish five types of correspondence between them. One-to-one relations were found between 43% of the structures in mouse and 47% in marmoset, whereas 25% of mouse and 10% of marmoset structures were not relatable. The remaining structures show a set of more complex mappings which we quantify. Implementing this correspondence with volumetric atlases of the two species, we make available a computational tool for querying and visualizing relationships between the corresponding brains. Our findings provide a foundation for computational comparative analyses of mesoscale connectivity and cell type distributions in the laboratory mouse and the common marmoset.

An open-source framework for neuroscience metadata management applied to digital reconstructions of neuronal morphology

Research advancements in neuroscience entail the production of a substantial amount of data requiring interpretation, analysis, and integration. The complexity and diversity of neuroscience data necessitate the development of specialized databases and associated standards and protocols. NeuroMorpho.Org is an online repository of over one hundred thousand digitally reconstructed neurons and glia shared by hundreds of laboratories worldwide. Every entry of this public resource is associated with essential metadata describing animal species, anatomical region, cell type, experimental condition, and additional information relevant to contextualize the morphological content. Until recently, the lack of a user-friendly, structured metadata annotation system relying on standardized terminologies constituted a major hindrance in this effort, limiting the data release pace. Over the past 2 years, we have transitioned the original spreadsheet-based metadata annotation system of NeuroMorpho.Org to a custom-developed, robust, web-based framework for extracting, structuring, and managing neuroscience information. Here we release the metadata portal publicly and explain its functionality to enable usage by data contributors. This framework facilitates metadata annotation, improves terminology management, and accelerates data sharing. Moreover, its open-source development provides the opportunity of adapting and extending the code base to other related research projects with similar requirements. This metadata portal is a beneficial web companion to NeuroMorpho.Org which saves time, reduces errors, and aims to minimize the barrier for direct knowledge sharing by domain experts. The underlying framework can be progressively augmented with the integration of increasingly autonomous machine intelligence components.

A two-tiered curriculum to improve data management practices for researchers

BACKGROUND

Better research data management (RDM) provides the means to analyze data in new ways, effectively build on another researcher's results, and reproduce the results of an experiment. Librarians are recognized by many as a potential resource for assisting researchers in this area, however this potential has not been fully realized in the biomedical research community. While librarians possess the broad skill set needed to support RDM, they often lack specific knowledge and time to develop an appropriate curriculum for their research community. The goal of this project was to develop and pilot educational modules for librarians to learn RDM and a curriculum for them to subsequently use to train their own research communities.

MATERIALS AND METHODS

We created online modules for librarians that address RDM best practices, resources and regulations, as well as the culture and practice of biomedical research. Data was collected from librarians through questions embedded in the online modules on their self-reported changes in understanding of and comfort level with RDM using a retrospective pre-post design. We also developed a Teaching Toolkit which consists of slides, a script, and an evaluation form for librarians to use to teach an introductory RDM class to researchers at their own institutions. Researchers' satisfaction with the class and intent to use the material they had learned was collected. Actual changes in RDM practices by researchers who attended was assessed with a follow-up survey administered seven months after the class.

RESULTS AND DISCUSSION

The online curriculum increased librarians' self-reported understanding of and comfort level with RDM. The Teaching Toolkit, when employed by librarians to teach researchers in person, resulted in improved RDM practices. This two-tiered curriculum provides concise training and a ready-made curriculum that allows working librarians to quickly gain an understanding of RDM, and translate this knowledge to researchers through training at their own institutions.

Neurodata Without Borders: Creating a Common Data Format for Neurophysiology

The Neurodata Without Borders (NWB) initiative promotes data standardization in neuroscience to increase research reproducibility and opportunities. In the first NWB pilot project, neurophysiologists and software developers produced a common data format for recordings and metadata of cellular electrophysiology and optical imaging experiments. The format specification, application programming interfaces, and sample datasets have been released.

A survey of the neuroscience resource landscape: perspectives from the neuroscience information framework

The number of available neuroscience resources (databases, tools, materials, and networks) available via the Web continues to expand, particularly in light of newly implemented data sharing policies required by funding agencies and journals. However, the nature of dense, multifaceted neuroscience data and the design of classic search engine systems make efficient, reliable, and relevant discovery of such resources a significant challenge. This challenge is especially pertinent for online databases, whose dynamic content is largely opaque to contemporary search engines. The Neuroscience Information Framework was initiated to address this problem of finding and utilizing neuroscience-relevant resources. Since its first production release in 2008, NIF has been surveying the resource landscape for the neurosciences, identifying relevant resources and working to make them easily discoverable by the neuroscience community. In this chapter, we provide a survey of the resource landscape for neuroscience: what types of resources are available, how many there are, what they contain, and most importantly, ways in which these resources can be utilized by the research community to advance neuroscience research.

Development and use of Ontologies Inside the Neuroscience Information Framework: A Practical Approach

An initiative of the NIH Blueprint for neuroscience research, the Neuroscience Information Framework (NIF) project advances neuroscience by enabling discovery and access to public research data and tools worldwide through an open source, semantically enhanced search portal. One of the critical components for the overall NIF system, the NIF Standardized Ontologies (NIFSTD), provides an extensive collection of standard neuroscience concepts along with their synonyms and relationships. The knowledge models defined in the NIFSTD ontologies enable an effective concept-based search over heterogeneous types of web-accessible information entities in NIF’s production system. NIFSTD covers major domains in neuroscience, including diseases, brain anatomy, cell types, sub-cellular anatomy, small molecules, techniques, and resource descriptors. Since the first production release in 2008, NIF has grown significantly in content and functionality, particularly with respect to the ontologies and ontology-based services that drive the NIF system. We present here on the structure, design principles, community engagement, and the current state of NIFSTD ontologies.

A Bottom-up Approach to Data Annotation in Neurophysiology

Metadata providing information about the stimulus, data acquisition, and experimental conditions are indispensable for the analysis and management of experimental data within a lab. However, only rarely are metadata available in a structured, comprehensive, and machine-readable form. This poses a severe problem for finding and retrieving data, both in the laboratory and on the various emerging public data bases. Here, we propose a simple format, the "open metaData Markup Language" (odML), for collecting and exchanging metadata in an automated, computer-based fashion. In odML arbitrary metadata information is stored as extended key-value pairs in a hierarchical structure. Central to odML is a clear separation of format and content, i.e., neither keys nor values are defined by the format. This makes odML flexible enough for storing all available metadata instantly without the necessity to submit new keys to an ontology or controlled terminology. Common standard keys can be defined in odML-terminologies for guaranteeing interoperability. We started to define such terminologies for neurophysiological data, but aim at a community driven extension and refinement of the proposed definitions. By customized terminologies that map to these standard terminologies, metadata can be named and organized as required or preferred without softening the standard. Together with the respective libraries provided for common programming languages, the odML format can be integrated into the laboratory workflow, facilitating automated collection of metadata information where it becomes available. The flexibility of odML also encourages a community driven collection and definition of terms used for annotating data in the neurosciences.

The NeuroDevNet Neuroinformatics Core

The field of neuroinformatics has expanded dramatically during the past decade building on the development of new technologies in brain research as well as in computing. The activities are diverse, from data management and standardization that has become essential due to the large amount of data generated and the needs to share it, to the development of sophisticated software necessary for the analyses and visualization of such data. NeuroDevNet is a Canadian initiative, funded by the Networks of Centres of Excellence, devoted to the study of brain development with the goal to translate this knowledge into improved diagnosis, prevention and treatment of neurodevelopmental disorders. The NeuroDevNet Neuroinformatics Core is dedicated to helping researchers across the network with their data management, standardization and sharing, as well as to implement innovative solutions to facilitate their research. It is an essential component to NeuroDevNet, enabling active collaboration across the country and optimizing this unique endeavor.

NeuroML: a language for describing data driven models of neurons and networks with a high degree of biological detail

Biologically detailed single neuron and network models are important for understanding how ion channels, synapses and anatomical connectivity underlie the complex electrical behavior of the brain. While neuronal simulators such as NEURON, GENESIS, MOOSE, NEST, and PSICS facilitate the development of these data-driven neuronal models, the specialized languages they employ are generally not interoperable, limiting model accessibility and preventing reuse of model components and cross-simulator validation. To overcome these problems we have used an Open Source software approach to develop NeuroML, a neuronal model description language based on XML (Extensible Markup Language). This enables these detailed models and their components to be defined in a standalone form, allowing them to be used across multiple simulators and archived in a standardized format. Here we describe the structure of NeuroML and demonstrate its scope by converting into NeuroML models of a number of different voltage- and ligand-gated conductances, models of electrical coupling, synaptic transmission and short-term plasticity, together with morphologically detailed models of individual neurons. We have also used these NeuroML-based components to develop an highly detailed cortical network model. NeuroML-based model descriptions were validated by demonstrating similar model behavior across five independently developed simulators. Although our results confirm that simulations run on different simulators converge, they reveal limits to model interoperability, by showing that for some models convergence only occurs at high levels of spatial and temporal discretisation, when the computational overhead is high. Our development of NeuroML as a common description language for biophysically detailed neuronal and network models enables interoperability across multiple simulation environments, thereby improving model transparency, accessibility and reuse in computational neuroscience.

Spike train analysis toolkit: enabling wider application of information-theoretic techniques to neurophysiology

Conventional methods widely available for the analysis of spike trains and related neural data include various time- and frequency-domain analyses, such as peri-event and interspike interval histograms, spectral measures, and probability distributions. Information theoretic methods are increasingly recognized as significant tools for the analysis of spike train data. However, developing robust implementations of these methods can be time-consuming, and determining applicability to neural recordings can require expertise. In order to facilitate more widespread adoption of these informative methods by the neuroscience community, we have developed the Spike Train Analysis Toolkit. STAToolkit is a software package which implements, documents, and guides application of several information-theoretic spike train analysis techniques, thus minimizing the effort needed to adopt and use them. This implementation behaves like a typical Matlab toolbox, but the underlying computations are coded in C for portability, optimized for efficiency, and interfaced with Matlab via the MEX framework. STAToolkit runs on any of three major platforms: Windows, Mac OS, and Linux. The toolkit reads input from files with an easy-to-generate text-based, platform-independent format. STAToolkit, including full documentation and test cases, is freely available open source via http://neuroanalysis.org , maintained as a resource for the computational neuroscience and neuroinformatics communities. Use cases drawn from somatosensory and gustatory neurophysiology, and community use of STAToolkit, demonstrate its utility and scope.

Ontologies for Neuroscience: What are they and What are they Good for?

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.

The NIFSTD and BIRNLex vocabularies: building comprehensive ontologies for neuroscience

A critical component of the Neuroscience Information Framework (NIF) project is a consistent, flexible terminology for describing and retrieving neuroscience-relevant resources. Although the original NIF specification called for a loosely structured controlled vocabulary for describing neuroscience resources, as the NIF system evolved, the requirement for a formally structured ontology for neuroscience with sufficient granularity to describe and access a diverse collection of information became obvious. This requirement led to the NIF standardized (NIFSTD) ontology, a comprehensive collection of common neuroscience domain terminologies woven into an ontologically consistent, unified representation of the biomedical domains typically used to describe neuroscience data (e.g., anatomy, cell types, techniques), as well as digital resources (tools, databases) being created throughout the neuroscience community. NIFSTD builds upon a structure established by the BIRNLex, a lexicon of concepts covering clinical neuroimaging research developed by the Biomedical Informatics Research Network (BIRN) project. Each distinct domain module is represented using the Web Ontology Language (OWL). As much as has been practical, NIFSTD reuses existing community ontologies that cover the required biomedical domains, building the more specific concepts required to annotate NIF resources. By following this principle, an extensive vocabulary was assembled in a relatively short period of time for NIF information annotation, organization, and retrieval, in a form that promotes easy extension and modification. We report here on the structure of the NIFSTD, and its predecessor BIRNLex, the principles followed in its construction and provide examples of its use within NIF.

The neuroscience information framework: a data and knowledge environment for neuroscience

With support from the Institutes and Centers forming the NIH Blueprint for Neuroscience Research, we have designed and implemented a new initiative for integrating access to and use of Web-based neuroscience resources: the Neuroscience Information Framework. The Framework arises from the expressed need of the neuroscience community for neuroinformatic tools and resources to aid scientific inquiry, builds upon prior development of neuroinformatics by the Human Brain Project and others, and directly derives from the Society for Neuroscience's Neuroscience Database Gateway. Partnered with the Society, its Neuroinformatics Committee, and volunteer consultant-collaborators, our multi-site consortium has developed: (1) a comprehensive, dynamic, inventory of Web-accessible neuroscience resources, (2) an extended and integrated terminology describing resources and contents, and (3) a framework accepting and aiding concept-based queries. Evolving instantiations of the Framework may be viewed at http://nif.nih.gov , http://neurogateway.org , and other sites as they come on line.