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Quantile rank maps: a new tool for understanding individual brain development
NeuroImage (2015)
  • Huaihou Chen, University of Florida
  • Clare Kelly
  • F. Xavier Castellanos
  • Ye He
  • Xi-Nian Zuo, Chinese Academy of Sciences
  • Philip T. Reiss

We propose a novel method for neurodevelopmental brain mapping that displays how an individual’s values for a quantity of interest compare with age-specific norms. By estimating smoothly age-varying distributions at a set of brain regions of interest, we derive age-dependent region-wise quantile ranks for a given individual, which can be presented in the form of a brain map. Such quantile rank maps could potentially be used for clinical screening. Bootstrap-based confidence intervals are proposed for the quantile rank estimates. We also propose a recalibrated Kolmogorov-Smirnov test for detecting group differences in the age-varying distribution. This test is shown to be more robust to model misspecification than a linear regression-based t-test. The proposed methods are applied to brain imaging data from the Nathan Kline Institute Rockland Sample and from the Autism Brain Imaging Data Exchange (ABIDE) sample.

  • Box-Cox transformation,
  • Generalized additive models for location,
  • scale and shape,
  • Quantile rank map,
  • MRI,
  • Resting-state fMRI,
  • Penalized B-splines
Publication Date
Citation Information
Huaihou Chen, Clare Kelly, F. Xavier Castellanos, Ye He, et al.. "Quantile rank maps: a new tool for understanding individual brain development" NeuroImage (2015)
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