A spectrum of sharing: maximization of information content for brain imaging data by Vince Calhoun

Author:Vince Calhoun [Calhoun, Vince]
Language: eng
Format: azw3
Tags: Data sharing, Privacy, Classification, Multivariate, Neuroinformatics, Deep learning, Independent component analysis
Published: 2015-05-17T04:00:00+00:00

Conclusions

Data sharing in neuroimaging is alive and well. This review has focused upon the concept of maximization of information, which is extremely important if we are to move our understanding of the brain forward. Consider the fact that we are still finding new information within very complex fMRI data sets that was not initially revealed (such as the recent focus on time-varying connectivity [34]). Current approaches are taking a variety of practical shortcuts to push data sharing forward, such as focusing only on meta-analytic approaches or sharing of only contrast images. While such approaches have their place and are extremely useful, we must not lose sight of the goal of making all collected data available to the community. Within the domains of data capture, data density, and data analysis I have tried to provide some examples, challenges, and solutions in order to foster this ongoing discussion. I look forward to the future and believe the combination of 1) technological advances and tools to assist investigators in collection of high quality data in a way that can be easily shared; 2) approaches to confront storage and computational barriers associated with sharing of the most raw form of the data; and 3) advanced algorithms to enable data-mining of rich data sets even in the context of possible constraints, such as privacy concerns, will move the field ahead at a rapid pace to help fill in the huge gaps in knowledge we have about human brain function and ultimately may help improve the lives of those with devastating brain disease.

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