基于分数投影的可解释高维推理及其在神经影像学中的应用
Interpretable High-Dimensional Inference Via Score Projection with an Application in Neuroimaging
摘要：
在神经影像学和遗传学领域，一个关键的目标是测试一个单一的结果与一个非常高维的成像或遗传变量的关联。通常，创建高维变量的汇总度量以顺序地测试和定位与结果的关联。在某些情况下，总结措施的结果是显着的，但随后的测试用于本地化的差异是不够的，并没有确定与结果相关的区域。在这里，我们提出了一个推广的Rao的分数测试的基础上投影的分数统计到一个高维参数空间的线性子空间。此外，我们提供的方法来本地化的信号在高维空间中的分数投影到子空间的分数测试进行。这允许在与得分测试相同的自由度上执行高维空间中的推断，有效地减少了比较的数量。仿真结果表明，该测试相对于其他常用的测试具有竞争力。我们通过分析阿尔茨海默病神经成像倡议数据集的一个子集来说明该方法。结果表明，额叶和颞叶的皮质变薄可能是阿尔茨海默氏症风险的一个有用的生物标志。
英文摘要：
In the fields of neuroimaging and genetics, a key goal is testing the association of a single outcome with a very high-dimensional imaging or genetic variable. Often, summary measures of the high-dimensional variable are created to sequentially test and localize the association with the outcome. In some cases, the results for summary measures are significant, but subsequent tests used to localize differences are underpowered and do not identify regions associated with the outcome. Here, we propose a generalization of Rao's score test based on projecting the score statistic onto a linear subspace of a high-dimensional parameter space. In addition, we provide methods to localize signal in the high-dimensional space by projecting the scores to the subspace where the score test was performed. This allows for inference in the high-dimensional space to be performed on the same degrees of freedom as the score test, effectively reducing the number of comparisons. Simulation results demonstrate the test has competitive power relative to others commonly used. We illustrate the method by analyzing a subset of the Alzheimer's Disease Neuroimaging Initiative dataset. Results suggest cortical thinning of the frontal and temporal lobes may be a useful biological marker of Alzheimer's risk.
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