REGULARIZED 3D FUNCTIONAL REGRESSION FOR BRAIN IMAGE DATA VIA HAAR WAVELETS.
REGULARIZED 3D FUNCTIONAL REGRESSION FOR BRAIN IMAGE DATA VIA HAAR WAVELETS.
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DOI：
10.1214/14-aoas736
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发表时间：
2014-06
期刊：
影响因子：
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通讯作者：
Koeppe R
中科院分区：
文献类型：
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作者：
Wang X;Nan B;Zhu J;Koeppe R
这篇文章的主要动机和应用来自于对老年脑疾病患者认知障碍的脑成像研究。在函数数据分析的框架下，提出了一种基于正则化Haar小波的三维脑图像数据分析方法，该方法自动考虑相邻体素之间的空间信息。我们进行了大量的模拟研究，以评估所提出的方法的预测性能及其识别与感兴趣结果相关的区域的能力，潜在的假设是，只有相对较小的子区域才能真正预测感兴趣的结果。然后，我们应用所提出的方法，使用阿尔茨海默病患者、轻度认知障碍患者和正常对照组的PET图像来搜索与认知相关的脑亚区。
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.