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Recent rapid advances in neuroscience and neuroimaging techniques have prompted the need for new statistical methodology and analysis. Neuroimaging data are often non-standard in that they may be high or infinite dimensional, may not lie in a Euclidean space, and may have complex geometry. While such data entail challenges for statistical modeling, they also bring opportunities to advance science and the field of statistics as they stimulate the development of innovative theory, methodology and computation.

This track provides a glimpse of the recent developments of statistical methods for neuroimaging data with the ultimate goal to quantify and understand the workings of the human brain.

Jane-Ling Wang, University of California Davis, United States
Organized Sessions associated with this Track
  • EO142: Statistical advances in neuroimaging
    Organizers: Timothy Johnson
  • EO144: Statistics in neuroscience
    Organizers: Jeff Goldsmith
  • EO146: Recent development in neuroimaging research
    Organizers: Tingting Zhang
  • EO486: Multi-dimensional modeling techniques for brain imaging data
    Organizers: Damla Senturk
  • EO599: Statistical inference for fMRI data
    Organizers: Armin Schwartzman
  • EO672: Understanding brain functional connectivity
    Organizers: Jane-Ling Wang