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.
Highlights
While sharing neuroimaging data either prior to or subsequent to a study’s completion is becoming more commonplace (Jack et al, 2008; Potkin and Ford, 2009; Poldrack et al, 2013; Eickhoff et al, 2016), two key challenges are emerging
The enhancing neuroimaging genetics through meta analysis (ENIGMA) approach has led to a number of innovative findings through massive international collaborations, there are some challenges that can be addressed by the approach we present in this paper
Several algorithms with required properties already exist, including our own decentralized joint ICA (djICA) and multilevel classification results (Sarwate et al, 2014; Baker et al, 2015), more development is needed to support many of the popular tools used for neuroimaging analysis
Summary
While sharing neuroimaging data either prior to or subsequent to a study’s completion is becoming more commonplace (Jack et al, 2008; Potkin and Ford, 2009; Poldrack et al, 2013; Eickhoff et al, 2016), two key challenges are emerging. In the most widely used computational model (centralized sharing), all shared data are downloaded and processed locally. This entails significant computational and storage requirements, which become barriers to access as data sets increase in size—many groups lack sufficient infrastructure for processing. For various reasons, there may be policy restrictions on openly sharing the data. In some cases the data can be properly anonymized and be categorized as no longer meeting the strict definition of human subjects research, but in other cases institutional or IRB policies may still prevent such data from ever being shared for secondary use. The data may be subject to governmental or proprietary restrictions or there may be prima facie reasons why the data may not ever be de-identifiable. While DUAs enable sharing, virtually every shared data set has its own DUA, and many of these are approved manually by a committee, so the timeframe to access data that are already shareable can be days, weeks, or even months
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