NeuroMorpho.Org Implementation of Digital Neuroscience: Dense Coverage and Integration with the NIF

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 webaccessible 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.

Neuron tracing in perspective

“…3), or contributing lab, and is also searchable by general keywords, or more specifically by morphometry or metadata, including information on the imaging protocol and parameters as well as the method used for reconstruction. Being the largest web-accessible collection of neuronal reconstructions and associated metadata, it is one of the integrated resources in the Neuroscience Information Framework (NIF), a recent initiative sponsored by the National Institutes of Health (NIH) for integrating access to and promoting the use of webbased neuroscience resources (131,132). Moreover, it mirrors many other repositories, in line with its primary goal of achieving and maintaining dense coverage of all publically available neuronal reconstructions.…”

The DIADEM Data Sets: Representative Light Microscopy Images of Neuronal Morphology to Advance Automation of Digital Reconstructions

“…Because Neuromantic measures coordinate distance in pixels for the XY plane and image sequence number within the stack for the Z axis, all manual reconstructions had to be scaled from their original physical unit (microns). Reconstructions were run through the StdSwc error checking software (http://neuromorpho.org/neuroMorpho/StdSwc1.21.jsp), which logs common mistakes found in manual reconstructions (Halavi et al, 2008), such as large Z jumps. This step also ensured the consistent adoption of a common topological convention across data sets.…”

Morphological Neuron Classification Using Machine Learning

“…It often represents the basis of modeling efforts to study the impact of a cell’s morphology on its electrical behavior (Insel et al, 2004; Druckmann et al, 2012; Gidon and Segev, 2012; Bar-Ilan et al, 2013) and on the network it is embedded in (Hill et al, 2012). Many different frameworks, tools and analysis have been developed to contribute to this effort (Schierwagen and Grantyn, 1986; Ascoli et al, 2001, 2007; Ascoli, 2002a,b, 2006; van Pelt and Schierwagen, 2004; Halavi et al, 2008; Scorcioni et al, 2008; Cuntz et al, 2011; Guerra et al, 2011; Schmitz et al, 2011; Hill et al, 2012), such as the Carmen project, framework focusing on neural activity (Jessop et al, 2010), NeuroMorpho.org, repository of digitally reconstructed neurons (Halavi et al, 2008) or the TREES toolbox for morphological modeling (Cuntz et al, 2011). …”