Promoting reproducible neuroimaging computation: from data discovery to replicable results
Creators: name: Kennedy, David
Publisher: name: F1000Research
Year: 2016

description: SOBP 2016 Thursday poster section. Entry #290. Presents vision for the Center for Reproducible Neuroimaging Computation (CRNC).<br><br>Over the last two decades a vast technological, computational and societal infrastructure has emerged and transformed how information is collected and knowledge is gathered in all facets of science. In the scientific process, methods should be reproducible. In the economics of science, data and methods should be maximally reusable in order to maximize the scientific return on the data acquisition investment.<br><br>Data and analytic processing methods reuse have become a focal point in a growing concern about the replicability and power of many of today’s scientific studies. Given a neuroimaging study, it is exceedingly difficult to discern between false positive and true positive findings as data is hard to aggregate, and exact methods are hard to replicate. Here we present a vision for the development of technology that supports a comprehensive set of data management, analysis and utilization frameworks in order to promote both basic research and clinical activities.<br><br>Given the complex data, software and computational ecosystem of neuroimaging research (right), it is difficult, using traditional methods, to fully document an imaging experiment in a human readable and machine re-executable fashion. We demonstrate an end-to-end solution. The envisioned system starts with data discovery (supported through the Neuroscience Information Framework (NIF) and SciCrunch). “NeuroBLAST”, a tool that allows users to find matching/similar studies based on a combination of task, analysis, and activation patterns. This novel environment will utilize all information about a study enabling researchers to find similar studies utilizing a number of different similarity metrics. Processing utilizes a complete provenance aware representation of data, workflow and results (through the NeuroImaging Data Model, NIDM). “BrainVerse”, a decentralized and peer-to-peer end-user application that will provide the user interface for workflow deliverables that facilitates interactions between datastores and provenance. Finally, execution of these workflows on large datasets using high performance computing (using NeuroDebian and the NITRC Computational Environment). “NICEMAN” is a web service (public online and available for local deployments) which, using the platform and base environments provided developed by the CRNC, will allow users to (re)create complete computation environments given user’s possibly loose specification of software and dataset requirements. Together, these systems promote integrated data analysis across data sources and workflows.<br><br>Together, these technologies support a vision of a neuroimaging research landscape where the generation of knowledge is accomplished in a reproducible fashion (in terms of data, analysis and computation) coupled with the ability to reuse and extend these studies by others in the community.<br><br>This project is made possible through the efforts of the Center for Reproducible Neuroimaging Computation (CRNC). The CRNC is staffed by the following individuals: Resource Discovery (TR&amp;D1): Maryann Martone, Jeff Grethe, Robert Buccigrossi; Data Modeling (TR&amp;D2): Satra Ghosh, David Keator, JB Poline, Christian Haselgrove; Execution Environments (TR&amp;D3): Yaroslav Halchenko, Robert Buccigrossi, Christian Haselgrove; Training and Dissemination: JB Poline, Satra Ghosh, Yaroslav Halchenko, Nina Preuss.<br><br>Funding for this project was provided by NIH grants: NIBIB P41 EB019936 “Center for Reproducible Neuroimaging Computation – CRNC” PI: Kennedy; and  NIMH R01 MH083320 “A Knowledge Environment for Neuroimaging in Child Psychiatry” PI(s): Frazier/Kennedy.<br><br><br><br><br>