Big Data and Neuroimaging

Yenny Webb-Vargas; Shaojie Chen; Aaron Fisher; Amanda Mejia; Yuting Xu; Ciprian Crainiceanu; Brian Caffo; Martin A. Lindquist

doi:10.1007/s12561-017-9195-y

Big Data and Neuroimaging

Yenny Webb-Vargas, Shaojie Chen, Aaron Fisher, Amanda Mejia, Yuting Xu, Ciprian Crainiceanu, Brian Caffo, Martin A. Lindquist

Bloomberg School of Public Health

Research output: Contribution to journal › Article › peer-review

Abstract

Big Data are of increasing importance in a variety of areas, especially in the biosciences. There is an emerging critical need for Big Data tools and methods, because of the potential impact of advancements in these areas. Importantly, statisticians and statistical thinking have a major role to play in creating meaningful progress in this arena. We would like to emphasize this point in this special issue, as it highlights both the dramatic need for statistical input for Big Data analysis and for a greater number of statisticians working on Big Data problems. We use the field of statistical neuroimaging to demonstrate these points. As such, this paper covers several applications and novel methodological developments of Big Data tools applied to neuroimaging data.

Original language	English (US)
Pages (from-to)	543-558
Number of pages	16
Journal	Statistics in Biosciences
Volume	9
Issue number	2
DOIs	https://doi.org/10.1007/s12561-017-9195-y
State	Published - Dec 1 2017

Keywords

Big Data
Data fusion
Dynamic networks
High-dimensional causal inference
High-dimensional computation
High-dimensional inference
Neuroimaging
Shrinkage

ASJC Scopus subject areas

Statistics and Probability
Biochemistry, Genetics and Molecular Biology (miscellaneous)

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title = "Big Data and Neuroimaging",

abstract = "Big Data are of increasing importance in a variety of areas, especially in the biosciences. There is an emerging critical need for Big Data tools and methods, because of the potential impact of advancements in these areas. Importantly, statisticians and statistical thinking have a major role to play in creating meaningful progress in this arena. We would like to emphasize this point in this special issue, as it highlights both the dramatic need for statistical input for Big Data analysis and for a greater number of statisticians working on Big Data problems. We use the field of statistical neuroimaging to demonstrate these points. As such, this paper covers several applications and novel methodological developments of Big Data tools applied to neuroimaging data.",

keywords = "Big Data, Data fusion, Dynamic networks, High-dimensional causal inference, High-dimensional computation, High-dimensional inference, Neuroimaging, Shrinkage",

author = "Yenny Webb-Vargas and Shaojie Chen and Aaron Fisher and Amanda Mejia and Yuting Xu and Ciprian Crainiceanu and Brian Caffo and Lindquist, {Martin A.}",

note = "Funding Information: Acknowledgements The projects described were supported by the NIH Grants R01 EB012547 and R01 EB016061 from the National Institute of Biomedical Imaging And Bioengineering, and R01 NS060910 from the National Institute of Neurological Disorders and Stroke.",

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doi = "10.1007/s12561-017-9195-y",

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AU - Webb-Vargas, Yenny

AU - Chen, Shaojie

AU - Fisher, Aaron

AU - Mejia, Amanda

AU - Xu, Yuting

AU - Crainiceanu, Ciprian

AU - Caffo, Brian

AU - Lindquist, Martin A.

N1 - Funding Information: Acknowledgements The projects described were supported by the NIH Grants R01 EB012547 and R01 EB016061 from the National Institute of Biomedical Imaging And Bioengineering, and R01 NS060910 from the National Institute of Neurological Disorders and Stroke.

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