Abstract
Efforts to expand sharing of neuroimaging data have been growing exponentially in recent years. There are several different types of data sharing which can be considered to fall along a spectrum, ranging from simpler and less informative to more complex and more informative. In this paper we consider this spectrum for three domains: data capture, data density, and data analysis. Here the focus is on the right end of the spectrum, that is, how to maximize the information content while addressing the challenges. A summary of associated challenges of and possible solutions is presented in this review and includes: 1) a discussion of tools to monitor quality of data as it is collected and encourage adoption of data mapping standards; 2) sharing of time-series data (not just summary maps or regions); and 3) the use of analytic approaches which maximize sharing potential as much as possible. Examples of existing solutions for each of these points, which we developed in our lab, are also discussed including the use of a comprehensive beginning-to-end neuroinformatics platform and the use of flexible analytic approaches, such as independent component analysis and multivariate classification approaches, such as deep learning.
Highlights
Efforts to expand sharing of neuroimaging data have been growing exponentially in recent years
collaborative informatics and neuroimaging suite (COINS) provides a multitude of tools to maximize the efficiency and minimize the errors associated with collecting both assessment and neuroimaging data [11,12], and provides tools to enable the sharing of data from within this framework [13]
In particular ENIGMA [46], which work with more sensitive genetic data, do not require data to be shared, but instead work with individual investigators to have them run scripts on their data to provide desired summary measures for meta-analysis. This is more private, though not in a quantifiable way. Another solution to address the above concerns about privacy is to provide tools to share data in a way that protects privacy while still enabling maximal information extraction via analytic approaches, such as multivariate classification [17,47]
Summary
Efforts to expand sharing of neuroimaging data have been growing exponentially in recent years. COINS provides a multitude of tools to maximize the efficiency and minimize the errors associated with collecting both assessment and neuroimaging data [11,12], and provides tools to enable the sharing of data from within this framework [13].
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