Two-Stage Multiscale Adaptive Regression Methods for Twin Neuroimaging Data

  • Yimei Li
  • John H. Gilmore
  • Jiaping Wang
  • Martin Styner
  • Weili Lin
  • Hongtu Zhu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7012)

Abstract

Twin imaging studies have been valuable for understanding the contribution of the environment and genes on brain structure and function. The conventional analyses are limited due to the same amount of smoothing throughout the whole image, the arbitrary choice of smoothing extent, and the decreased power in detecting environmental and genetic effects introduced by smoothing raw images. The goal of this article is to develop a two-stage multiscale adaptive regression method (TwinMARM) for spatial and adaptive analysis of twin neuroimaging and behavioral data. The first stage is to establish the relationship between twin imaging data and a set of covariates of interest, such as age and gender. The second stage is to disentangle the environmental and genetic influences on brain structure and function. Simulation studies and real data analysis show that TwinMARM significantly outperforms the conventional analyses.

Keywords

Twin Pair Ground Truth Image Wald Test Statistic Adaptive Analysis Twin Imaging 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Yimei Li
    • 1
  • John H. Gilmore
    • 2
  • Jiaping Wang
    • 2
  • Martin Styner
    • 2
  • Weili Lin
    • 2
  • Hongtu Zhu
    • 2
  1. 1.St. Jude Children’s Research HospitalMemphis
  2. 2.University of North Carolina at Chapel HillChapel Hill

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