TY - JOUR
T1 - COINSTAC
T2 - A privacy enabled model and prototype for leveraging and processing decentralized brain imaging data
AU - Plis, Sergey M.
AU - Sarwate, Anand D.
AU - Wood, Dylan
AU - Dieringer, Christopher
AU - Landis, Drew
AU - Reed, Cory
AU - Panta, Sandeep R.
AU - Turner, Jessica A.
AU - Shoemaker, Jody M.
AU - Carter, Kim W.
AU - Thompson, Paul
AU - Hutchison, Kent
AU - Calhoun, Vince D.
N1 - Publisher Copyright:
© 2016 Plis, Sarwate, Wood, Dieringer, Landis, Reed, Panta, Turner, Shoemaker, Carter, Thompson, Hutchison and Calhoun.
PY - 2016/8/19
Y1 - 2016/8/19
N2 - The field of neuroimaging has embraced the need for sharing and collaboration. Data sharing mandates from public funding agencies and major journal publishers have spurred the development of data repositories and neuroinformatics consortia. However, efficient and effective data sharing still faces several hurdles. For example, open data sharing is on the rise but is not suitable for sensitive data that are not easily shared, such as genetics. Current approaches can be cumbersome (such as negotiating multiple data sharing agreements). There are also significant data transfer, organization and computational challenges. Centralized repositories only partially address the issues. We propose a dynamic, decentralized platform for large scale analyses called the Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation (COINSTAC). The COINSTAC solution can include data missing from central repositories, allows pooling of both open and "closed" repositories by developing privacy-preserving versions of widely-used algorithms, and incorporates the tools within an easy-to-use platform enabling distributed computation. We present an initial prototype system which we demonstrate on two multi-site data sets, without aggregating the data. In addition, by iterating across sites, the COINSTAC model enables meta-analytic solutions to converge to "pooled-data" solutions (i.e., as if the entire data were in hand). More advanced approaches such as feature generation, matrix factorization models, and preprocessing can be incorporated into such a model. In sum, COINSTAC enables access to the many currently unavailable data sets, a user friendly privacy enabled interface for decentralized analysis, and a powerful solution that complements existing data sharing solutions.
AB - The field of neuroimaging has embraced the need for sharing and collaboration. Data sharing mandates from public funding agencies and major journal publishers have spurred the development of data repositories and neuroinformatics consortia. However, efficient and effective data sharing still faces several hurdles. For example, open data sharing is on the rise but is not suitable for sensitive data that are not easily shared, such as genetics. Current approaches can be cumbersome (such as negotiating multiple data sharing agreements). There are also significant data transfer, organization and computational challenges. Centralized repositories only partially address the issues. We propose a dynamic, decentralized platform for large scale analyses called the Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation (COINSTAC). The COINSTAC solution can include data missing from central repositories, allows pooling of both open and "closed" repositories by developing privacy-preserving versions of widely-used algorithms, and incorporates the tools within an easy-to-use platform enabling distributed computation. We present an initial prototype system which we demonstrate on two multi-site data sets, without aggregating the data. In addition, by iterating across sites, the COINSTAC model enables meta-analytic solutions to converge to "pooled-data" solutions (i.e., as if the entire data were in hand). More advanced approaches such as feature generation, matrix factorization models, and preprocessing can be incorporated into such a model. In sum, COINSTAC enables access to the many currently unavailable data sets, a user friendly privacy enabled interface for decentralized analysis, and a powerful solution that complements existing data sharing solutions.
KW - Brain imaging
KW - Data sharing
KW - Decentralized algorithms
KW - Decentralized processing
KW - Privacy
UR - http://www.scopus.com/inward/record.url?scp=84988358471&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84988358471&partnerID=8YFLogxK
U2 - 10.3389/fnins.2016.00365
DO - 10.3389/fnins.2016.00365
M3 - Article
C2 - 27594820
AN - SCOPUS:84988358471
SN - 1662-4548
VL - 10
JO - Frontiers in Neuroscience
JF - Frontiers in Neuroscience
IS - AUG
M1 - 365
ER -