Open Access
June 2014 Regularized 3D functional regression for brain image data via Haar wavelets
Xuejing Wang, Bin Nan, Ji Zhu, Robert Koeppe
Ann. Appl. Stat. 8(2): 1045-1064 (June 2014). DOI: 10.1214/14-AOAS736


The primary motivation and application in this article come from brain imaging studies on cognitive impairment in elderly subjects with brain disorders. We propose a regularized Haar wavelet-based approach for the analysis of three-dimensional brain image data in the framework of functional data analysis, which automatically takes into account the spatial information among neighboring voxels. We conduct extensive simulation studies to evaluate the prediction performance of the proposed approach and its ability to identify related regions to the outcome of interest, with the underlying assumption that only few relatively small subregions are truly predictive of the outcome of interest. We then apply the proposed approach to searching for brain subregions that are associated with cognition using PET images of patients with Alzheimer’s disease, patients with mild cognitive impairment and normal controls.


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Xuejing Wang. Bin Nan. Ji Zhu. Robert Koeppe. "Regularized 3D functional regression for brain image data via Haar wavelets." Ann. Appl. Stat. 8 (2) 1045 - 1064, June 2014.


Published: June 2014
First available in Project Euclid: 1 July 2014

zbMATH: 06333787
MathSciNet: MR3262545
Digital Object Identifier: 10.1214/14-AOAS736

Keywords: Alzheimer’s disease , brain imaging , Functional data analysis , Haar wavelet , Lasso , PET image , Variable selection

Rights: Copyright © 2014 Institute of Mathematical Statistics

Vol.8 • No. 2 • June 2014
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