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Award Abstract #1131441

CRCNS Data Sharing: An open data repository for cognitive neuroscience: The OpenfMRI Project

NSF Org: ACI
Div Of Advanced Cyberinfrastructure
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Initial Amendment Date: August 27, 2011
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Latest Amendment Date: August 27, 2011
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Award Number: 1131441
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Award Instrument: Standard Grant
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Program Manager: Robert Chadduck
ACI Div Of Advanced Cyberinfrastructure
CSE Direct For Computer & Info Scie & Enginr
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Start Date: September 1, 2011
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End Date: July 31, 2015 (Estimated)
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Awarded Amount to Date: $743,856.00
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Investigator(s): Russell Poldrack poldrack@stanford.edu (Principal Investigator)
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Sponsor: University of Texas at Austin
101 E. 27th Street, Suite 5.300
Austin, TX 78712-1532 (512)471-6424
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NSF Program(s): COGNEURO,
CRCNS,
DATANET
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Program Reference Code(s): 7327
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Program Element Code(s): 1699, 7327, 7726

ABSTRACT

Functional magnetic resonance imaging (fMRI) has become the most common tool for cognitive neuroscience, because it provides a safe, non-invasive, and powerful means to image human brain function. Based on recent rates of publication, there are currently more than 2000 fMRI studies being performed every year worldwide. The aggregation of data across multiple studies can provide the ability to answer questions that cannot be answered based on a single study. For example, using datasets from multiple domains one can start to investigate to what degree a region is selectively engaged in relation to a particular mental process, as opposed to being generally engaged across a broad range of tasks and processes. In addition, it provides the ability to integrate across specific tasks to obtain stronger empirical generalizations about mind-brain relationships, and to better understand the nature of individual variability across different measures. Recent work in neuroimaging analysis has focused on the application of methods such as machine learning techniques to understand the coding of information at the macroscopic level, and network analysis techniques to understand the interactions inherent in large-scale neural systems. The availability of a large testbed of high-quality fMRI data from published studies would also provide an important resource for the development of these and other new analytic techniques for fMRI data. However, sharing of raw fMRI data is challenging due to the large size of the datasets and the complexity of the associated metadata, and there is currently no infrastructure for the open sharing of new fMRI datasets.

This project, OpenfMRI, will provide a new infrastructure for the broad dissemination of raw data within cognitive neuroscience, addressing a critical need by providing an open data sharing resource for neuroimaging. The initial project is already online at http://www.openfmri.org with a limited number of datasets. The full project will greatly expand this repository by providing access to a large number of fMRI datasets from several prominent neuroimaging labs, spanning across a broad range of cognitive domains. Utilizing the substantial computational resources of the Texas Advanced Computing Center, the project will also perform standard fMRI analyses on all data in the repository using a common analysis pipeline, thus providing directly comparable analysis results for all of the studies in the database. The OpenfMRI project will support the development of infrastructural elements to make sharing of data by additional investigators more straightforward.

The repository of data that will be created by the OpenfMRI project will also serve as an important resource for teaching by providing students with the ability to replicate the analyses from published studies using the same data. By providing any researcher in the world with the ability to acquire large fMRI datasets, it will also provide all researchers with the ability to work with the same state-of-the-art datasets, regardless of institution. By creating the infrastructure for open sharing of research data, the project will also enhance the impact of other NSF-funded neuroimaging research projects by providing an infrastructure that can be used to make their data available. The planned work has the potential to benefit society by improving education, health, and human productivity through an increased understanding of mental function and its relationship to brain function.


PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Poldrack RA, Barch DM, Mitchell JP, Wager TD, Wagner AD, Devlin JT, Cumba C, Milham MP. "Towards open sharing of task-based fMRI data: The OpenfMRI project.," Frontiers in Neuroinformatics, v.7, 2013, p. 12. 

Brakewood, Beth and Poldrack, Russell A. "The ethics of secondary data analysis: considering the application of Belmont principles to the sharing of neuroimaging data," Neuroimage, v.82, 2013, p. 671-6. 

Poldrack, Russell A and Barch, Deanna M and Mitchell, Jason P and Wager, Tor D and Wagner, Anthony D and Devlin, Joseph T and Cumba, Chad and Koyejo, Oluwasanmi and Milham, Michael P. "Toward open sharing of task-based fMRI data: the OpenfMRI project," Front Neuroinform, v.7, 2013, p. 12. 

Brakewood B, Poldrack RA. "The Ethics of Secondary Data Analysis: Considering the Application of Belmont Principles to the Sharing of Neuroimaging Data.," Neuroimage, 2013. 

Poldrack RA, Barch DM, Mitchell JP, Wager TD, Wagner AD, Devlin JT, Cumba C, Milham MP. "Towards open sharing of task-based fMRI data: The OpenfMRI project," Frontiers in Neuroinformatics, v.7, 2013. 

 

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