Multiple testing for neuroimaging via hidden Markov random field.
Multiple testing for neuroimaging via hidden Markov random field.
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DOI：
10.1111/biom.12329
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发表时间：
2015-09
期刊：
影响因子：
1.9
通讯作者：
Koeppe R
中科院分区：
文献类型：
--
作者：
Shu H;Nan B;Koeppe R
传统的体素级多重检验方法，主要是基于p值的，往往忽略了相邻体素之间的空间相关性，从而遭受大量的功率损失。我们扩展的本地显着性指数为基础的程序最初开发的隐马尔可夫链模型，其目的是尽量减少错误的非发现率受到约束的错误发现率，三维神经影像数据使用隐马尔可夫随机场模型。提出了一种最大化惩罚似然的广义期望最大化算法来估计模型参数。大量的仿真结果表明，该方法是更强大的比传统的错误发现率的程序。我们将该方法应用于轻度认知功能障碍，阿尔茨海默病或其他痴呆症风险增加的疾病状态与阿尔茨海默病神经成像倡议的FDG-PET成像研究中的正常对照之间的比较。
Traditional voxel-level multiple testing procedures in neuroimaging, mostly p-value based, often ignore the spatial correlations among neighboring voxels and thus suffer from substantial loss of power. We extend the local-significance-index based procedure originally developed for the hidden Markov chain models, which aims to minimize the false nondiscovery rate subject to a constraint on the false discovery rate, to three-dimensional neuroimaging data using a hidden Markov random field model. A generalized expectation-maximization algorithm for maximizing the penalized likelihood is proposed for estimating the model parameters. Extensive simulations show that the proposed approach is more powerful than conventional false discovery rate procedures. We apply the method to the comparison between mild cognitive impairment, a disease status with increased risk of developing Alzheimer’s or another dementia, and normal controls in the FDG-PET imaging study of the Alzheimer’s Disease Neuroimaging Initiative.