title: A spectrum of sharing: maximization of information content for brain imaging data.
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.
authors: The Mind Research Network & LBERI, 1101 Yale Blvd NE, Albuquerque, New Mexico 87106 USA ; Department of ECE, University of New Mexico, Albuquerque, New Mexico USA.
authors: Vince D
authors: VD
authors: Calhoun
mesh_terms: D001921:Brain / Q000502:physiology*; D006801:Humans; D033181:Information Dissemination; D008279:Magnetic Resonance Imaging; D059906:Neuroimaging / Q000379:methods*; D012984:Software
keywords: Classification; Data sharing; Deep learning; Independent component analysis; Multivariate; Neuroinformatics; Privacy
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