Federated Analysis of Neuroimaging Data: A Review of the Field

• Type of Material: Article
• Authors: Kelly Rootes-Murdy, Georgia State University; Harshvardhan Gazula, Princeton Neuroscience Institute, Princeton, NJ, USA; Eric Verner, Emory University; Ross Kelly, Emory University; Thomas DeRamus, Emory University; Sergey Plis, Emory University; Anand Sarwate, Rutgers State University; Jessica Turner, Georgia State University; Vince Calhoun, Emory University
• Language: English
• Date: 2021-11-22
• Publisher: HUMANA PRESS INC
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2021, The Author(s), under exclusive licence to Springer Science Business Media, LLC, part of Springer Nature
• Final Published Version (URL): http://dx.doi.org/10.1007/s12021-021-09550-7
• Title of Journal or Parent Work: NEUROINFORMATICS
• Volume: 20
• Issue: 2
• Start Page: 377
• End Page: 390
• Grant/Funding Information: This work was funded by the National Institutes of Health grants: R01DA040487, R01DA049238, R01MH121246.
• Keywords: Neurosciences; Computer Science; Neurosciences & Neurology; Life Sciences & Biomedicine; Computer Science, Interdisciplinary Applications; PLATFORM; Federated learning; COINSTAC; Science & Technology; Technology; Neuroimaging

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.