| Literature DB >> 34807353 |
Kelly Rootes-Murdy1,2, Harshvardhan Gazula3, Eric Verner2, Ross Kelly2, Thomas DeRamus2, Sergey Plis2, Anand Sarwate4, Jessica Turner1,2, Vince Calhoun5,6.
Abstract
The field of neuroimaging has embraced sharing data to collaboratively advance our understanding of the brain. However, data sharing, especially across sites with large amounts of protected health information (PHI), can be cumbersome and time intensive. Recently, there has been a greater push towards collaborative frameworks that enable large-scale federated analysis of neuroimaging data without the data having to leave its original location. However, there still remains a need for a standardized federated approach that not only allows for data sharing adhering to the FAIR (Findability, Accessibility, Interoperability, Reusability) data principles, but also streamlines analyses and communication while maintaining subject privacy. In this paper, we review a non-exhaustive list of neuroimaging analytic tools and frameworks currently in use. We then provide an update on our federated neuroimaging analysis software system, the Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation (COINSTAC). In the end, we share insights on future research directions for federated analysis of neuroimaging data.Entities:
Keywords: COINSTAC; Federated learning; Neuroimaging
Mesh:
Year: 2021 PMID: 34807353 PMCID: PMC9124226 DOI: 10.1007/s12021-021-09550-7
Source DB: PubMed Journal: Neuroinformatics ISSN: 1539-2791