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A Unified Kernel Regression for Diffusion Wavelets on Manifolds Detects Aging-Related Changes in the Amygdala and Hippocampus

  • Moo K. Chung
  • Stacey M. Schaefer
  • Carien M. van Reekum
  • Lara Peschke-Schmitz
  • Mattew J. Sutterer
  • Richard J. Davidson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8674)

Abstract

We present a new unified kernel regression framework on manifolds. Starting with a symmetric positive definite kernel, we formulate a new bivariate kernel regression framework that is related to heat diffusion, kernel smoothing and recently popular diffusion wavelets. Various properties and performance of the proposed kernel regression framework are demonstrated. The method is subsequently applied in investigating the influence of age and gender on the human amygdala and hippocampus shapes. We detected a significant age effect on the posterior regions of hippocampi while there is no gender effect present.

Keywords

Heat Kernel Kernel Regression Mesh Vertex Manifold Data Principal Geodesic Analysis 
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 International Publishing Switzerland 2014

Authors and Affiliations

  • Moo K. Chung
    • 1
  • Stacey M. Schaefer
    • 1
  • Carien M. van Reekum
    • 2
  • Lara Peschke-Schmitz
    • 1
  • Mattew J. Sutterer
    • 3
  • Richard J. Davidson
    • 1
  1. 1.University of Wisconsin-MadisonUSA
  2. 2.University of ReadingUK
  3. 3.University of IowaUSA

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