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Human neuroimaging as a “Big Data” science

  • SI: Genetic Neuroimaging in Aging and Age-Related Diseases
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Abstract

The maturation of in vivo neuroimaging has led to incredible quantities of digital information about the human brain. While much is made of the data deluge in science, neuroimaging represents the leading edge of this onslaught of “big data”. A range of neuroimaging databasing approaches has streamlined the transmission, storage, and dissemination of data from such brain imaging studies. Yet few, if any, common solutions exist to support the science of neuroimaging. In this article, we discuss how modern neuroimaging research represents a multifactorial and broad ranging data challenge, involving the growing size of the data being acquired; sociological and logistical sharing issues; infrastructural challenges for multi-site, multi-datatype archiving; and the means by which to explore and mine these data. As neuroimaging advances further, e.g. aging, genetics, and age-related disease, new vision is needed to manage and process this information while marshalling of these resources into novel results. Thus, “big data” can become “big” brain science.

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Acknowledgments

This article was supported by a P41 (RR013642) award from the National Institute for General Medicine (NIGMS), through the National Institutes of Health Roadmap for Medical Research (U54 RR021813, The Center for Computational Biology—CCB), and through an National Institute of Mental Health ARRA award (RC1 MH088194). We express our gratitude the faculty and staff of the Laboratory of Neuro Imaging (LONI) at the University of California Los Angeles.

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Correspondence to John Darrell Van Horn.

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Van Horn, J.D., Toga, A.W. Human neuroimaging as a “Big Data” science. Brain Imaging and Behavior 8, 323–331 (2014). https://doi.org/10.1007/s11682-013-9255-y

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