Multiscale adaptive marginal analysis of longitudinal neuroimaging data with time-varying covariates.
Multiscale adaptive marginal analysis of longitudinal neuroimaging data with time-varying covariates.
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DOI：
10.1111/j.1541-0420.2012.01767.x
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发表时间：
2012-12
期刊：
影响因子：
1.9
通讯作者：
Zhang H
中科院分区：
文献类型：
--
作者：
Skup M;Zhu H;Zhang H
反复收集的神经影像学数据越来越受到神经影像学界的关注，因为它们有可能回答有关大脑发育、衰老和神经变性的问题。这些数据集庞大而复杂，其特点是每个响应图像的空间依赖结构复杂，每个受试者有多个响应图像，协变量可能随时间变化。我们提出了一种多尺度自适应广义矩法（MA-GMM）方法来估计包含时变、空间相关响应和一些时变协变量的成像数据集的边际回归模型。我们的方法将协变量分类为不同的类型，以确定在估计过程中要组合的有效矩条件。此外，与当前许多神经成像分析技术所做的体素（构成每个受试者在每个时间点的反应图像的组成部分）独立的假设不同，该方法“自适应平滑”神经成像反应数据，通过迭代地在每个体素周围构建球体并将球体内的观察结果与权重相结合来计算参数估计。MA-GMM的开发增加了用于纵向成像数据分析的少数可用建模方法。模拟研究和对阿尔茨海默病神经影像学倡议的真实纵向成像数据集的分析用于评估MA-GMM的性能。
Neuroimaging data collected at repeated occasions are gaining increasing attention in the neuroimaging community due to their potential in answering questions regarding brain development, aging, and neurodegeneration. These datasets are large and complicated, characterized by the intricate spatial dependence structure of each response image, multiple response images per subject, and covariates that may vary with time. We propose a multiscale adaptive generalized method of moments (MA-GMM) approach to estimate marginal regression models for imaging datasets that contain time-varying, spatially-related responses and some time-varying covariates. Our method categorizes covariates into types to determine the valid moment conditions to combine during estimation. Further, instead of assuming independence of voxels (the components that make up each subject’s response image at each time point) as many current neuroimaging analysis techniques do, this method “adaptively smoothes” neuroimaging response data, computing parameter estimates by iteratively building spheres around each voxel and combining observations within the spheres with weights. MA-GMM’s development adds to the few available modeling approaches intended for longitudinal imaging data analysis. Simulation studies and an analysis of a real longitudinal imaging dataset from the Alzheimer’s Disease Neuroimaging Initiative are used to assess the performance of MA-GMM.
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影响因子：
6.1
作者：
HANSEN, LP
通讯作者：
HANSEN, LP
影响因子：
5.7
作者：
Tabelow, Karsten;Polzehl, Joerg;Spokoiny, Vladimir
通讯作者：
Spokoiny, Vladimir
影响因子：
5.7
作者：
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通讯作者：
Crainiceanu, Ciprian
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
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通讯作者：
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影响因子：
2.7
作者：
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通讯作者：
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