) The goal of this application is to develop, test, and disseminate multi-context (command line, desktop, server, and web applications) software for robust and reproducible neuroscience image analysis from data across mul- tiple scales (two photon microscopy, mesoscale Ca2+ optical imaging, small animal fMRI, and human fMRI) and species (mice, rats, and humans). In particular, we will provide tools for computation and visualization of con- nectomes (connectivity matrices) for such image data sets that will include both modality speci?c preprocessing (e.g. motion correction, nonlinear registration, noise removal) and species speci?c atlases and parcellations to create regions of interests for the computation of connectomes. Our goal is to provide software that can be used by end-users of different technical skill levels ranging from: (i) a limited technical background, whereby an investigator may simply put the data in a Dropbox folder and then use a web-browser to run the software from any computer (or high end tablet), to (ii) a more technically sophisticated background, whereby an investigator may use aspects of our software as command line scripts mixed with other custom tools to create a customized processing pipeline of their own design. In the ?rst scenario, we eliminate any need to download, con?gure, and install software. Only a modern web browser (e.g. Chrome, Safari, or Firefox) and a reasonably powerful computer (as all processing will be done locally) is required. A critical component of the proposed work is that the software will be designed from the ground up to enable robust and reproducible processing. As part of this design, this work will create output ?le formats that will store not only the results but also all necessary meta- data (software version, operating system, input data and parameters) to enable a different researcher to cleanly reproduce the results of someone else. We propose four speci?c aims: (1) Extend current algorithms to accept data from multiple modalities and species, (2) Design and implement multi-context desktop and web applica- tions, (3) Validate the translated algorithms and test the overall software, and (4) Document and distribute the software and train and engage end-users. The signi?cance of this proposal is that it will create software to ro- bustly and reproducibly analyze complex neuroscience imaging data across scales and species. The innovation lies both in the functionality proposed and in the multi-context design that will make the software accessible to end-users of different skill levels in different contexts (command line, server, desktop, and web applications) with robust cloud integration and output formats designed explicitly for reproducibility.

Public Health Relevance

Investigation of the functional organization of the brain through neuroscience imaging is a rapidly growing and rich area of research. Nevertheless, the software infrastructure to integrate across multiple sources, scales, and species is lagging behind the development of new modalities and algorithms and often requires signi?cant computational expertise. We propose to create software that allows both robust and reproducible analysis and that can be be used by both less advanced users (in a browser) as well as computational experts (desktop, command line) to advance research in this promising area.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Resource-Related Research Projects (R24)
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Special Emphasis Panel (ZMH1)
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Zhan, Ming
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Yale University
Schools of Medicine
New Haven
United States
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Johnson, Matthew B; Sun, Xingshen; Kodani, Andrew et al. (2018) Aspm knockout ferret reveals an evolutionary mechanism governing cerebral cortical size. Nature 556:370-375