Event Abstract Back to Event Mining retinal connectomes Robert Marc1* 1 University of Utah, Moran Eye Center, United States Robert E. Marc and James R. Anderson, John A. Moran Eye Center, University of Utah Automated transmission electron microscope (TEM) allows extensive capture of contiguous 2D and 3D imagery, posing challenges for data storage, access, viewing, annotation, tracking and analysis. Such datasets quickly transcend a userʼs capacity for analysis. And, as annotated anatomical data sets represent significant investment of resources, we argue they should follow Open Data concepts for access and reuse. The Viking application (Anderson et al., 2010, J Microscopy) was our solution to view and annotate RC1, a 16.5 TB ultrastructural retinal connectome volume. Viking is HTTP-compliant, supports concurrent authenticated users, and collaborative annotation strategies, including mining, graphing and rendering neural networks. It demarcates viewing and analysis from capture and hosting and permits applying image transforms in real-time. It also permits the fusion of registered thin-section optical molecular data with TEM image data, augmenting the collection of cell classification metadata. Connectome dataset RC1 was imaged at 2 nm resolution, balancing competing constraints of large-area sampling and fine-scale cell association maps (subclasses of chemical synapses, gap junctions, adherens junctions, organelle patterning). We use a crowd-sourcing strategy for annotation with Viking. This leads to rapid assembly of directed cyclic network graphs, dynamically visualized via a web-services Viz application that also provides network validation, error discovery and correction. The network graph below illustrates the associations of a single class A-II glycinergic amacrine cell (C467, circled) in the rabbit retina tracked through four synaptic “hops”. Even if automated tracking and annotation were viable, a Viz-like application would still be critical for finding and correcting network errors. Moreover, crowd-sourcing enables the discovery of novelty (connective, associative and ultrastructural), which automated tools have yet to achieve. In a year of analysis, mining connectome RC1 has uncovered new synaptic pathways and topologies, new non-neural activities, and new signaling states. Intensive mining of connectomics datasets provides the unique opportunity to build realistic system models based on complete synaptic partner maps. Support: NEI EY02576, NEI EY015128, P30EY014800, NIH T32DC008553, NSF 0941717, Research to Prevent Blindness. Disclosure: REM is a principal of Signature Immunologics, Inc. Conference: Neuroinformatics 2010 , Kobe, Japan, 30 Aug - 1 Sep, 2010. Presentation Type: Oral Presentation Topic: Workshop 2: Synaptoprojectomes: assembling, using and sharing dense cellular micromaps of brains Citation: Marc R (2010). Mining retinal connectomes. Front. Neurosci. Conference Abstract: Neuroinformatics 2010 . doi: 10.3389/conf.fnins.2010.13.00004 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 08 Jun 2010; Published Online: 08 Jun 2010. * Correspondence: Robert Marc, University of Utah, Moran Eye Center, Salt Lake City, United States, robert.marc@hsc.utah.edu Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Robert Marc Google Robert Marc Google Scholar Robert Marc PubMed Robert Marc Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.