COINSTAC: A Privacy Enabled Model and Prototype for Leveraging and Processing Decentralized Brain Imaging Data

Abstract

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.

Decentralized Analysis of Brain Imaging Data: Voxel-Based Morphometry and Dynamic Functional Network Connectivity

“…In one approach to solve the decentralized regression problem, termed the single-shot regression (Plis et al, 2016), each site j finds the minimizer ${\widehat{bold\text{w}}}_j$ of the local objective function F j ( w ). This is the same as solving the regression problem at each local site.…”

Crowdsourcing Precision Cerebrovascular Health: Imaging and Cloud Seeding A Million Brains Initiative™

“…Crowdsourcing allows an individual to contribute to aggregated data on a population, while preserving their right to specific information about their own brain health. The use of anonymized or de-identified data may be used for an individual to store their own brain imaging, compare brain health with population-derived data, access a variety of computing modules, and track how these things change over decades (5). Cloud seeding or sharing of imaging with other data may be encouraged through the crowdsourcing model of social media and the Internet.…”

“…The crowdsourcing model of big data would allow for age- and sex-matched comparisons on numerous metrics of brain morphology and even function. High-performance computing algorithms already have the ability to process cloud-based imaging, and future modules could be sequentially deployed to provide enormous information on the cerebrovascular status of individuals within a truly global population (5, 6, 11). Unlike other neuroimaging initiatives, such a vision is fueled by individuals, volunteering and yet, immediately benefiting from the novel information resource simultaneously created.…”

Privacy-preserving quality control of neuroimaging datasets in federated environment

“…Meanwhile, many systems allow virtually pooling datasets located at multiple research sites and analyzing them using algorithms that are able to operate on decentralized datasets [14,15,16]. The importance of operating on sensitive data without pooling it together and thus generating truly large-scale neuroimaging datasets is so high that researchers successfully engage into manually simulating a distributed system [17].…”