title: A unified kernel regression for diffusion wavelets on manifolds detects aging-related changes in the amygdala and hippocampus.
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.
authors.forename: Moo K
authors.initials: MK
authors.lastname: Chung
authors.forename: Stacey M
authors.initials: SM
authors.lastname: Schaefer
authors.forename: Carien M
authors.initials: CM
authors.lastname: Van Reekum
authors.forename: Lara
authors.initials: L
authors.lastname: Peschke-Schmitz
authors.forename: Mattew J
authors.initials: MJ
authors.lastname: Sutterer
authors.forename: Richard J
authors.initials: RJ
authors.lastname: Davidson
mesh_terms: D000375:Aging / Q000473:pathology*; D000465:Algorithms; D000679:Amygdala / Q000033:anatomy & histology / Q000473:pathology*; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006624:Hippocampus / Q000033:anatomy & histology / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D058067:Wavelet Analysis