NeuroMorpho.Org Implementation of Digital Neuroscience: Dense Coverage and Integration with the NIF
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- Halavi, M., Polavaram, S., Donohue, D.E. et al. Neuroinform (2008) 6: 241. doi:10.1007/s12021-008-9030-1
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Neuronal morphology affects network connectivity, plasticity, and information processing. Uncovering the design principles and functional consequences of dendritic and axonal shape necessitates quantitative analysis and computational modeling of detailed experimental data. Digital reconstructions provide the required neuromorphological descriptions in a parsimonious, comprehensive, and reliable numerical format. NeuroMorpho.Org is the largest web-accessible repository service for digitally reconstructed neurons and one of the integrated resources in the Neuroscience Information Framework (NIF). Here we describe the NeuroMorpho.Org approach as an exemplary experience in designing, creating, populating, and curating a neuroscience digital resource. The simple three-tier architecture of NeuroMorpho.Org (web client, web server, and relational database) encompasses all necessary elements to support a large-scale, integrate-able repository. The data content, while heterogeneous in scientific scope and experimental origin, is unified in format and presentation by an in house standardization protocol. The server application (MRALD) is secure, customizable, and developer-friendly. Centralized processing and expert annotation yields a comprehensive set of metadata that enriches and complements the raw data. The thoroughly tested interface design allows for optimal and effective data search and retrieval. Availability of data in both original and standardized formats ensures compatibility with existing resources and fosters further tool development. Other key functions enable extensive exploration and discovery, including 3D and interactive visualization of branching, frequently measured morphometrics, and reciprocal links to the original PubMed publications. The integration of NeuroMorpho.Org with version-1 of the NIF (NIFv1) provides the opportunity to access morphological data in the context of other relevant resources and diverse subdomains of neuroscience, opening exciting new possibilities in data mining and knowledge discovery. The outcome of such coordination is the rapid and powerful advancement of neuroscience research at both the conceptual and technological level.
KeywordsNeuronal morphology Digital reconstruction Data sharing Neuroscience information framework Axonal arbors Dendritic trees
The ability of the brain to process information, control behavior, and drive consciousness depends substantially on the formation and preservation of proper connections between axons and dendrites from different regions and neuronal classes. Etiological investigations in neurological and psychiatric disorders provide ample evidence for the crucial role of appropriate development and maintenance of neural circuits in healthy brain function. Each neuron must have proper synaptic partners in order to function effectively and accurately. The rich and unique morphological properties that are characteristic of each cell type in the nervous system play an essential role in targeting and invading different regions and layers, and in establishing the proper circuitry. The anatomy of the dendritic tree is a major determinant of synaptic integration (Rall and Rinzel 1973; Segev and London 2000; Gulledge et al. 2005; London and Hausser 2005) as well as cell excitability and the neural firing behavior (Mainen and Sejnowski 1996; Cuntz et al. 2007). Morphological diversity also relates to the intrinsic functional differences between neuron classes (Vetter et al. 2001; Stiefel and Sejnowski 2007). As a result, dendritic morphology influences both neural computation (Eilers and Konnerth 1997; Duch and Levine 2000; Tossit and Stocker 2000; Schierwagen and Claus 2002) and network function (Chklovskii 2004; Chen et al. 2006). Understanding the relationship between structure and function in the nervous system implies investigation of the complexity of and dynamic interactions among numerous neuron types (Bota and Swanson 2007a, b). These studies often combine quantitative analyses and computational models based on synaptic biophysics and realistic neuronal morphology as provided in three-dimensional (3D) digital reconstructions.
NeuroMorphpo.Org also extends data usability and visibility by making morphological reconstructions accessible through the Neuroscience Information Framework (NIF). The NIF is a pioneering initiative that fosters the seamless integration of neuroscience resources from different domains of expertise into a single search engine (Gardner et al. 2008). The close link between NeuroMorpho.Org and NIF was naturally inspired by the wide relevance and applicability of neuronal morphology in many other neuroscience resources. Currently in its third year of existence, NeuroMorpho.Org continues to improve its functionality and expand its data content. The main goal of this paper is to describe the NeuroMorpho.Org approach to digital neuroscience, its integration with other NIF resources, and some of the most instructive challenges and solutions in this process. A more coincise and less technical description for the general neuroscience readership, aimed at users rather than developers, was recently published (Ascoli et al. 2007). Interested readers are also invited to browse and download the data and a wealth of additional information and explanation online at NeuroMorpho.Org, and to provide feedback.
Design of NeuroMorpho.Org
to store the various data types according to a pre-defined structured schema;
to support different reconstruction formats, while providing a standardized output;
to facilitate complex queries and data retrieval;
to enable rapid development and deployment while remaining user-friendly;
to be interoperable with other neuroscience resources;
to optimize reliability, security, robustness, and scalability.
There have been several previous efforts to create repositories for neuronal reconstructions (reviewed in Ascoli 2006b). An extended list of these existing archives is available at NeuroMorpho.Org under the “Tools and Links” menu. In general, all data available through these databases are mirrored in NeuroMorpho.Org. There are however substantial differences that make NeuroMorpho.Org unique among such resources. The foremost is that data in NeuroMorpho.Org are contributed by a large (and continuously increasing) number of different researchers rather than an individual laboratory. A primary goal of NeuroMorpho.Org is to achieve and maintain dense coverage of all the publicly available digital reconstructions rather than provide a static venue to distribute a particular subset of neuronal morphologies.
Examples of metadata extracted from peer-review publications and their descriptions
The name of the primary laboratory that has performed the study.
The name of the morphological data file as assigned by the providing lab.
Maximum_age and Minimum_age
The age of the animals. This is typically reported as a range. Therefore, a maximum and/or a minimum age are included.
The scale used for reporting animal age. Usually, age is reported either in days, months, or years as indicated by a preceding letter (D, M or Y).
Qualitative classification of age (Young, Adult or Old). In some publications this classification is provided without reporting the precise animal age.
Gender of the animal. It is not always specified. Therefore, it is classified explicitly as: male (M), female (F), either sex (M/F), or not reported (NR).
The different areas of the brain that neurons belong to. We organized the brain regions in three different classification levels, starting from main anatomical regions toward the finer distinction of the layers in the cortex.
The morphological class assigned to the neuron. Each neuron is classified based on three cell type levels. At the first level, reconstructions are distinguished as: Principal cell, Interneuron or Axonal terminal.
Information regarding the original format of each data file.
Classification of the experimental method based on whether the dye injection occurred in the live animal, in a slice, or in neuron culture.
The thickness of the slices cut during tissue processing. It may serve as an important determinant of the reconstruction completeness.
The orientation of the sampled tissue cut.
The type of dye used for staining the neurons.
The magnification used to trace and reconstruct the neurons.
The type of the objective used in the reconstruction process.
The software used for tracing the neurons.
The web address of the data file, if it is available on-line.
Any additional, important information, not stored in other fields.
Web-accessible, secure, and user-friendly interaction with the images and metadata in the back-end database is enabled by MRALD (Blake et al. 2002). MRALD is a platform and database independent application, is easily customized to new domains, and has been deployed in mission critical systems (e.g., aviation) continuously since 2001. MRALD’s form builder enables system designers to rapidly generate intuitive hyper-text markup language (HTML)-based data retrieval forms; forms can be associated with specific users for access control. Data interaction is also possible via custom java server pages (JSPs) and keyword search. Hidden from the user, MRALD translates requests into structured query language (SQL) and interrogates the underlying database via Java database connectivity (JDBC). It can return results in multiple formats, including HTML, extensible markup language (XML), comma or configurable separated values (CSV), tab-delimited text, Excel spreadsheet files, and in other, user-defined, formats. MRALD’s web-based administration features include form update, insertion, and deletion; a schema visualization tool; user account management; and the ability to assign data and users to collaborative communities (Smith et al. 2004). MRALD’s internal workflow processing for translating HTML into SQL is customizable, and can be extended by a developer to insert new steps (e.g., filters) into the normal processing pipeline (Fig. 2b). MRALD is freely available to the academic community at neuroinformatics.mitre.org.
When activated by any of these three interfaces, MRALD processes the queries, generates the results dynamically, and sends them to the web client. The number of cells matching a given set of search criteria can also be requested before visualizing the results. The simplest option to display the search results is in a Summary format, in which each neuron is represented with a thumbnail image and an abridged set of its metadata. Clicking on one of these neuron entries calls the individual page of that reconstruction, with links to all raw and processed data, metadata, and related files (described below).
The data presentations by brain regions, cell types, and animal species have clear biological meaning, to be organized according to the NIF standard ontology (Bug et al. 2008). The view by laboratory name can be useful for data contributors to demonstrate to funding agencies and promotion committees that they have followed through with an effective data sharing plan.
NeuroMorpho.Org does not require any user registration or login to search and download data. For each neuron in the database, both graphic representations and flat files are made available through direct links for visualization and download. Flat files include the original reconstruction file as provided by the laboratory of origin, the version converted into a standardized format, the log detailing all modifications, and a document listing any remaining notes or irregularities (see Standardization process section below). Users may choose to download one or all of these four files for any number of neurons as a single compressed archive. Each neuron is illustrated with a static two-dimensional image as well as a 3D animation of the extending arborization while it rotates around the cell central axis. Moreover, to allow for interactive 3D manipulation of neurons, the Cell Viewer Application Cvapp (Cannon et al. 1998) was custom modified, streamlining functionality and enabling automatic online deployment through the Java Network Launching Protocol (JNLP).
The database also stores the PubMed identity (PMID) for all referenced papers (see Data model and data management section below), and a corresponding XML file, created through Java Server Pages (JSP), is accessed by the NIF Broker that mediates the Entrez LinkOut functionality service provided by PubMed (Marenco et al. 2008). This architecture design allows direct reciprocal access between the peer-reviewed reference and the raw data. In particular, a link from the individual neuron page to the PubMed abstract of the publication(s) describing the experiment provides the users with a broader perspective on the reconstructions. To access the reconstructions from PubMed, users can follow the LinkOut option on the top right corner Links menu (see Fig. 2 in Marenco et al. 2008), which leads to NeuroMorpho.Org through the Neuroscience Database Gateway, a precursor of the NIF (see Fig. 3 in Marenco et al. 2008).
Integration of NeuroMorpho.Org with the NIF
Neuroscience tools, data, information, and knowledge can often be related to the structure of neurons. The long recognized centrality of neuronal morphology in the neuroscience community led to the early identification of NeuroMorpho.Org as a foundational resource in the development of the NIF. At the same time, integration of NeuroMorpho.Org with the NIF facilitates queries in a contextually rich environment, which would not be otherwise available within the restricted domain of digital neuromorphological reconstructions. Moreover, the integration process itself serves as a useful step for benchmarking the technological standards within the field. NIF has faced and successfully overcome numerous challenges to achieve interoperability among resources with respect to hardware, software, communication protocols, user application, and data compatibility.
Resource Registration, Concept Mapping, and Query Mediator Protocols
As with other resources, NeuroMorpho.Org is catalogued as an external public database in the NIF registry. NIF registration requires basic information about the resource’s setup, such as name, URL, administrative and technical contacts, content type, and data availability. In addition to this “superficial” registration, functional interactions of NeuroMorpho.Org with NIF also required a deeper registration at the level of the database schema design. In particular, an essential element of such deep registration is the consistent mapping of corresponding concepts among resources. This step implied sharing access to the standard Java Database Connectivity (JDBC) driver and a complete documentation of the database tables and attribute definitions.
Deep registration and concept mapping acquire practical utility from the user’s perspective if the corresponding resources have programmatically cross-accessible query interfaces. In particular, this deeper registration allows users to “drill down” into the content of dynamic resources such as NeuroMorpho.Org, as explained in the next paragraph. Typical application generated requests are exemplified in the next section as well. NeuroMorpho.Org is also technically interoperable in that it allows direct external access to the data via URL embedded queries that accept name-value pairs specifying data source, SQL query, and output format.
Advanced Functionality Enabled by Integration
The NIFSTD terminology mapping allows concept based search on federated resources. The NIF search engine retrieves data by interrogating other resources in parallel based on their respective semantics as opposed to the original query string. This potential “drill down” usage of NIF with respect to the research domain of NeuroMorpho.Org can be illustrated with a few examples. At the simplest level, users gain access to data and information from multiple resources at once. For instance, a NIF keyword search for “Purkinje cell” retrieves results from the Cell Centered Database (CCDB: Martone et al. 2002), SenseLab (Shepherd et al. 1998), and NeuroMorpho.Org. Thus, the user is provided at once with subcellular microscopy images, physiological properties, and morphological reconstructions for this cell type.
A more complex situation could be envisioned with a NIF search for “Nucleus accumbens”. NeuroMorpho.Org returns 379 basal forebrain reconstructions: 146 large aspiny cells and 232 medium spiny neurons from the rat, and 1 medium spiny neuron from the mouse. One of the two corresponding reference article reports that nicotine may cause enduring changes in both classes of these cells. The CCDB-retrieved high resolution tomographic images show the distribution of spines on a single dendritic branch. Combination of spine density from CCDB data with the arbor metrics from NeuroMorpho.Org suggests a hypothetical effect of nicotine on subcellular volumes. Conveniently, NIF also returns results from the National Institutes of Health grant database CRISP (Computer Retrieval of Information on Scientific Projects), broadening the context of this search to a related list of ongoing funded research.
In another scenario, a user is designing a research project to understand the kinetics of K+ channels in dentate granule cells of Sprague Dawley rats. A NIF search seamlessly yields 44 digital dendritic reconstructions from NeuroMorpho.Org, 21 compartmental models from SenseLab, available monoclonal antibody for Kv subunits from NeuroMab.org, a set of interacting protein along with the gene kcnip3 from Entrez Protein (ncbi.nlm.nih.gov/Database), and gene expression patterns for KcnK1 from Gensat.org. Combining all of this information gives the user a wider perspective on the topic of interest and the available data. Depending on the goal of the user, such direct capability to mine heterogeneous databases can positively impact various stages of research. As the usage statistics of NIF grows, so does the motivation for sharing and organizing data, which in turn will also require expanding the scope of interoperability of this resource.
Data Model and Management
Historic review of version releases to date and their number of reconstructions
Upon receipt of data meeting the above four criteria, each neuron is assigned a unique identification number, which is used as the primary organizational key throughout the application. Data content in NeuroMorpho.Org combines flat files, images, and textual metadata, all in several formats. Flat files consist of raw and processed digital morphologies (see Standardization process below). Images include both static (300 × 240 pixel) and animated illustrations of each cell (see Data presentation above), created with Cvapp and MatLab, respectively, with the aid of in-house scripts to semi-automate the process. Detailed information in text-based format is retrieved from the corresponding journal article publication. A considerable amount of effort is expended reviewing publications to gather and annotate the metadata for insertion in the repository. When the necessary information is missing in the original scientific reports and related online sources, we contact the authors directly. Additional metadata is extracted in the form of morphological measurements with L-Measure, which is freely available (Scorcioni et al. 2008).
The main structure of the underlying relational database is organized around 27 metadata tables. The related images and flat files are loaded into the system and indexed in the database for retrieval in the detailed page dynamically generated for each neuron upon query. Similarly, the abstract of and PubMed link to each paper are inserted in the database for reference. The morphological measurements are also stored for use in the morphometry search. The core table links the primary identification key to all metadata fields as well as to these expanded tables in support of complex data retrieval. This flexible data model allows the seamless addition of new metadata fields and related information as needed or desired.
All fixed and flagged lines are documented in a standardization log file with an alphanumeric code (defined in the help), a text description of the anomaly, and any action taken to remedy it. The numerical code denotes the type of error (e.g. “4.1” indicates a point with zero diameter). Automatically fixed lines are designated as “type A”; flagged lines are designated as “type B1” unless they are corrected by hand, in which case they are changed to “type B2”. Any additional irregularities noted by the operator, but not detected by the program, are designated as “type C”. To facilitate processing of reconstructions with complicated or numerous corrections, the visualization and editing program Neuromantic (also freely available at www.rdg.ac.uk/neuromantic) has been modified to automatically track changes and append corresponding descriptions to the comment section of the SWC file. At the end of this process, the standardization program is run once again on the resulting SWC files to create a log of potential “remaining issues”.
For further quality assurance, all files, images, and data are first uploaded into a temporary website for inspection and approval by the original providers of the raw data. This step also ensures that proper credit is assigned, and minimizes the risk of inaccurate representations. After implementing the eventual changes requested by data owners, the new release is uploaded onto the main site.
Abridged examples of cell types included in NeuroMorpho.Org from different brain regions
Number of neurons
Medium spiny cell
Adapting non-pyramidal cell
Double bouquet cell
Neuropeptide Y (NPY) containing cell
Parvalbumin (PV) containing cell
Somatostatin (SOM) containing cell
Calbindin (CB) containing cell
Calretinin (CR) containing cell
Cholecystokinin (CCK) containing cell
Uniglomerular projection neuron
Lateral horn neuron
Dorsal Spinocerebellar tract (DSCT) cell
Ia inhibitory cell
An even broader awareness of NeuroMorpho.Org is expected to result from the NIF integration, as preliminarily evidenced from the usage statistics after the recent NIF release. Increasing usage of NeuroMorpho.Org and all other NIF-related resources will ultimately prove that integration benefits the whole neuroscience community, adding value to the data, and providing greater opportunity for new discoveries. At the same time, it is important to remember that a critical determinant of the success of NeuroMorpho.Org is the intent to cover densely all the digital reconstructions available for public sharing. In light of the NIF aim and scope, the importance of earning the user’s confidence that, if a data file can be obtained, it will be found in such a database, is a key lesson for those seeking to create similar digital resources.
We are grateful to all of the laboratories and researchers sharing their digital neuromorphological reconstructions. We also thank the developers of all the analysis, visualization, and editing tools for neuronal morphologies described in the manuscript. This project has been funded in part through the NIH Blueprint for Neuroscience Research with Federal funds from the National Institute on Drug Abuse, National Institutes of Health, Department of Health and Human Services, under Contract No. HHSN271200577531C. Additional support was provided by NINDS/NIMH/NSF grant R01 NS39600. Work at MITRE was supported by R01 MH64417.
Information sharing statement
All data, metadata, information and computational infrastructure of NeuroMorpho.Org are publicly available.
This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.