TY - JOUR
T1 - COINSTAC
T2 - Decentralizing the future of brain imaging analysis [version 1; referees: 2 approved]
AU - Ming, Jing
AU - Verner, Eric
AU - Sarwate, Anand
AU - Kelly, Ross
AU - Reed, Cory
AU - Kahleck, Torran
AU - Silva, Rogers
AU - Panta, Sandeep
AU - Turner, Jessica
AU - Plis, Sergey
AU - Calhoun, Vince
N1 - Publisher Copyright:
© 2017 Ming J et al.
PY - 2017
Y1 - 2017
N2 - In the era of Big Data, sharing neuroimaging data across multiple sites has become increasingly important. However, researchers who want to engage in centralized, large-scale data sharing and analysis must often contend with problems such as high database cost, long data transfer time, extensive manual effort, and privacy issues for sensitive data. To remove these barriers to enable easier data sharing and analysis, we introduced a new, decentralized, privacy-enabled infrastructure model for brain imaging data called COINSTAC in 2016. We have continued development of COINSTAC since this model was first introduced. One of the challenges with such a model is adapting the required algorithms to function within a decentralized framework. In this paper, we report on how we are solving this problem, along with our progress on several fronts, including additional decentralized algorithms implementation, user interface enhancement, decentralized regression statistic calculation, and complete pipeline specifications.
AB - In the era of Big Data, sharing neuroimaging data across multiple sites has become increasingly important. However, researchers who want to engage in centralized, large-scale data sharing and analysis must often contend with problems such as high database cost, long data transfer time, extensive manual effort, and privacy issues for sensitive data. To remove these barriers to enable easier data sharing and analysis, we introduced a new, decentralized, privacy-enabled infrastructure model for brain imaging data called COINSTAC in 2016. We have continued development of COINSTAC since this model was first introduced. One of the challenges with such a model is adapting the required algorithms to function within a decentralized framework. In this paper, we report on how we are solving this problem, along with our progress on several fronts, including additional decentralized algorithms implementation, user interface enhancement, decentralized regression statistic calculation, and complete pipeline specifications.
UR - http://www.scopus.com/inward/record.url?scp=85032891871&partnerID=8YFLogxK
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U2 - 10.12688/f1000research.12353.1
DO - 10.12688/f1000research.12353.1
M3 - Article
C2 - 29123643
AN - SCOPUS:85032891871
VL - 6
JO - F1000Research
JF - F1000Research
SN - 2046-1402
M1 - 1512
ER -