COINSTAC: Decentralizing the future of brain imaging analysis

Jing Ming, Eric Verner, Anand Sarwate, Ross Kelly, Cory Reed, Torran Kahleck, Rogers Silva, Sandeep Panta, Jessica Turner, Sergey Plis, Vince Calhoun
2017 F1000Research  
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
more » ... in 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. PubMed Abstract | Publisher Full Text Calhoun VD, Adali T, Pearlson GD, et al.: A method for making group inferences from functional MRI data using independent component analysis. Hum Brain Mapp. 2001; 14(3): 140-151. PubMed Abstract | Publisher Full Text De Silva V, Tenenbaum JB: Sparse multidimensional scaling using landmark points. Technical report, Stanford University. 2004. Reference Source Di Martino A, Yan CG, Li Q, et al.: The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol Psychiatry. 2014; 19(6): 659-67. PubMed Abstract | Publisher Full Text | Free Full Text Dwork C, McSherry F, Nissim K, et al.: Calibrating noise to sensitivity in private data analysis. Theory of Cryptography Conference. TCC, Springer, 2006; 265-284. Publisher Full Text Engle RF: Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica. 1982; 50(4): 987-1007. Publisher Full Text Erhardt EB, Allen EA, Wei Y, et al.: SimTB, a simulation toolbox for fMRI data under a model of spatiotemporal separability. Neuroimage. 2012; 59(4): 4160-4167. PubMed Abstract | Publisher Full Text | Free Full Text Heatherton TF, Kozlowski L: Nicotine addiction and its assessment. Ear Nose Throat J. 1992; 69(11): 763-767. Hibar DP, Westlye LT, Doan NT, et al.: Cortical abnormalities in bipolar disorder: an MRI analysis of 6503 individuals from the ENIGMA Bipolar Disorder Working Group. Mol Psychiatry. 2017. PubMed Abstract | Publisher Full Text
doi:10.12688/f1000research.12353.1 pmid:29123643 pmcid:PMC5657031 fatcat:drf45koyazgytkhwoxdjgdl4mu