Broader Impacts: The project develops open-source software and publicly-accessible infrastructure for the neuroscience community to collect, curate, and analyze electron microscopy (EM) connectomes on data-intensive clusters. Public data-intensive clusters, such as our Open Connectome Project, ease the storage management burden for the experimental biologists that collect data. High-throughput imaging is already producing massive data sets that overwhelm the infrastructure and expertise of their labs. Public clusters also facilitate data sharing for secondary data studies, verification and reanalysis of existing results, and multilevel models that integrate and differentiate multiple connectomes collected from different subjects, researchers, and instruments. Data-intensive storage and analysis will transform the scientific process for EM connectome imaging. At present, experimental biologists in the life sciences collect and analyze individual, private data sets usin proprietary analysis tools. In an Open Science approach, EM connectome data are also stored remotely on a data-intensive compute cluster designed specifically for the curation and analysis of massive EM connectome data. An open-source software pipeline automatically builds data products, including spatial databases, annotations, graphs, and graph statistics. Researchers explore multiple connectomes. Innovative analysis techniques are contributed back to the community as open-source software. In the EM Open Connectome, we define frameworks to engage an interdisciplinary community of life scientists, computer scientists, and statisticians in solving two fundamental problems in EM connectomes: (1) image segmentation, annotation, and tracking and (2) graph analysis. Our approach develops the concept of alg-sourcing (algorithmic outsourcing) in which researchers can easily deploy, run, evaluate, and visualize the efficiency and accuracy of algorithms against connectome databases. The EM Open Connectome provides access to data sets and an execution framework so that researchers simply upload a script or program for one of the algorithmic tasks. Then, they get instant feedback and can visualize and analyze results remotely on the data-intensive cluster, e.g., from a laptop in a cafe. Intellectual Merit: The primary project goal is to transform the process of extracting anatomical structure from image data. Currently, this is a manual process in which few researchers explore tens of neurons [6]. The EM Open Connectome will support high-throughput, machine annotation over the largest data sets being collected. Obstacles include the accuracy and performance of computational vision algorithms, the quality of the image data, and access to software that execute these analyses. We will explore computational vision based on multi-scale aggregates with anatomical priors. We will develop image processing techniques that improve data quality prior to computational vision. We will also build a systems engineering framework to run vision algorithms that allows for rapid deployment, testing, and evaluation. The project will also enhance knowledge and understanding of the functional and computational capabilities of the brain through data-intensive analysis. Given the spatially registered machine annotations, the team will construct statistical models for brain-graphs that provide insight into neural computation. All tools and data products are publicly accessible to an Open-Science community of researchers in order to accelerate discovery through collaboration and by engaging scientists across disciplinary boundaries. Education and Outreach: Our education mission promotes data-analysis in the K-12 curriculum consistent with national benchmarks for math and sciences. We will provide online lesson plans and activities using the EM Open Connectome that directly support the materials that teachers are required to teach. We will also develop resources for the Center for Talented Youth pre-collegiate summer program. Outreach in the form of museum exhibits and a booth at the National Science Fair support our education materials and public data sets.

Public Health Relevance

There is little detailed information about how the brain works. Thus, a large number of brain disorders, Including childhood learning problems and major psychiatric diseases In adults, have no underiying physical trace. It Is likely that connectional abnormalities (connectopathles) underiie some of these diseases. Until means of mapping neural connections in normal and disordered brains is possible, the underiying proximate causes of many abnormal brain functions will remain poorly understood

National Institute of Health (NIH)
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Research Project (R01)
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Special Emphasis Panel (ZRG1-IFCN-B (55))
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Pai, Vinay Manjunath
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Johns Hopkins University
Biostatistics & Other Math Sci
Schools of Engineering
United States
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Harris, Kristen M; Spacek, Josef; Bell, Maria Elizabeth et al. (2015) A resource from 3D electron microscopy of hippocampal neuropil for user training and tool development. Sci Data 2:150046
Kasthuri, Narayanan; Hayworth, Kenneth Jeffrey; Berger, Daniel Raimund et al. (2015) Saturated Reconstruction of a Volume of Neocortex. Cell 162:648-61
Tomassy, Giulio Srubek; Berger, Daniel R; Chen, Hsu-Hsin et al. (2014) Distinct profiles of myelin distribution along single axons of pyramidal neurons in the neocortex. Science 344:319-24
Burns, Randal; Vogelstein, Joshua T; Szalay, Alexander S (2014) From cosmos to connectomes: the evolution of data-intensive science. Neuron 83:1249-52
Beyer, Johanna; Hadwiger, Markus; Al-Awami, Ali et al. (2013) Exploring the connectome: petascale volume visualization of microscopy data streams. IEEE Comput Graph Appl 33:50-61
Zheng, Da; Burns, Randal; Szalay, Alexander S (2013) Toward Millions of File System IOPS on Low-Cost, Commodity Hardware. ICS :
Terasaki, Mark; Shemesh, Tom; Kasthuri, Narayanan et al. (2013) Stacked endoplasmic reticulum sheets are connected by helicoidal membrane motifs. Cell 154:285-96
Burns, Randal; Roncal, William Gray; Kleissas, Dean et al. (2013) The Open Connectome Project Data Cluster: Scalable Analysis and Vision for High-Throughput Neuroscience. Sci Stat Database Manag :
Morgan, Joshua L; Lichtman, Jeff W (2013) Why not connectomics? Nat Methods 10:494-500
Beyer, Johanna; Al-Awami, Ali; Kasthuri, Narayanan et al. (2013) ConnectomeExplorer: query-guided visual analysis of large volumetric neuroscience data. IEEE Trans Vis Comput Graph 19:2868-77