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. 2008 Sep;6(3):241-52.
doi: 10.1007/s12021-008-9030-1. Epub 2008 Oct 24.

NeuroMorpho.Org implementation of digital neuroscience: dense coverage and integration with the NIF

Maryam Halavi  1 , Sridevi Polavaram, Duncan E Donohue, Gail Hamilton, Jeffrey Hoyt, Kenneth P Smith, Giorgio A Ascoli
Affiliations

NeuroMorpho.Org implementation of digital neuroscience: dense coverage and integration with the NIF

Maryam Halavi et al. Neuroinformatics. 2008 Sep.

Abstract

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.

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Figures

Figure 1
Figure 1
Screen shot of the main page of NeuroMorpho.Org, the starting point for navigating through the site with a complete list of options on the left.
Figure 2
Figure 2
Structure and information flow among the components of NeuroMorpho.Org. (A) Organization of the processing pipeline. (B) Role of MRALD in the three-tier data retrieval architecture.
Figure 3
Figure 3
Representative search through the Metadata page. Filter criteria can be combined and fine tuned through cascades of drop-down menus to increasing levels of detail. In this example, Monkey is selected as species, Macaque as strain, Lucifer Yellow as staining method, and Local projecting Pyramidal cell as the cell type and subtype of the principal cell class. These selections returned 20 neurons as the results set.
Figure 4
Figure 4
Data content diversity in NeuroMorpho.Org: pie chart representations of different species (top) and brain regions (bottom).
Figure 5
Figure 5
A schema representation of cell type mapping between NeuroMorpho.Org and three relevant ontologies: NIF Standard (NIFSTD), Open Biomedical Ontologies (OBO), and Subcellular Anatomy Ontology (SAO). Each NeuroMorpho.Org cell type is represented as a tree node in the hierarchy. Rectangular nodes are mapped onto equivalent terms in NIFSTD (prefix “nif_cell”), OBO (prefix “CL”), or SAO (prefix “sao”). The nodes in oval shape are not mapped. Arrows represent “is-a” relationships (e.g. “Chandelier cells are interneurons”).
Figure 6
Figure 6
Examples of morphological irregularities in the original data before processing for inclusion in NeuroMorpho.Org.
Figure 7
Figure 7
Usage of NeuroMorpho.Org. (A) A chronological illustration of the number of site access and file downloads on a logarithmic scale. (B) Distribution of session profiles based on the relative proportion of downloads per hit.
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