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Big Data and Neuroimaging

Author

Listed:
  • Yenny Webb-Vargas

    (Johns Hopkins Bloomberg School of Public Health)

  • Shaojie Chen

    (Johns Hopkins Bloomberg School of Public Health)

  • Aaron Fisher

    (Johns Hopkins Bloomberg School of Public Health)

  • Amanda Mejia

    (Johns Hopkins Bloomberg School of Public Health)

  • Yuting Xu

    (Johns Hopkins Bloomberg School of Public Health)

  • Ciprian Crainiceanu

    (Johns Hopkins Bloomberg School of Public Health)

  • Brian Caffo

    (Johns Hopkins Bloomberg School of Public Health)

  • Martin A. Lindquist

    (Johns Hopkins Bloomberg School of Public Health)

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.

Suggested Citation

  • Yenny Webb-Vargas & Shaojie Chen & Aaron Fisher & Amanda Mejia & Yuting Xu & Ciprian Crainiceanu & Brian Caffo & Martin A. Lindquist, 2017. "Big Data and Neuroimaging," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(2), pages 543-558, December.
    • Handle: RePEc:spr:stabio:v:9:y:2017:i:2:d:10.1007_s12561-017-9195-y
    DOI: 10.1007/s12561-017-9195-y
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    References listed on IDEAS

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    1. Shaojie Chen & Lei Huang & Huitong Qiu & Mary Beth Nebel & Stewart H Mostofsky & James J Pekar & Martin A Lindquist & Ani Eloyan & Brian S Caffo, 2017. "Parallel group independent component analysis for massive fMRI data sets," PLOS ONE, Public Library of Science, vol. 12(3), pages 1-17, March.
    2. Michael E. Sobel & Martin A. Lindquist, 2014. "Causal Inference for fMRI Time Series Data With Systematic Errors of Measurement in a Balanced On/Off Study of Social Evaluative Threat," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 967-976, September.
    3. Martin A. Lindquist, 2012. "Functional Causal Mediation Analysis With an Application to Brain Connectivity," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(500), pages 1297-1309, December.
    4. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
    5. Xi Luo & Dylan S. Small & Chiang-Shan R. Li & Paul R. Rosenbaum, 2012. "Inference With Interference Between Units in an fMRI Experiment of Motor Inhibition," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(498), pages 530-541, June.
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