svReg：结构变系数回归分析区分亨廷顿舞蹈病严重程度组的局部脑萎缩如何影响运动障碍
svReg: Structural Varying-coefficient regression to differentiate how regional brain atrophy affects motor impairment for Huntington disease severity groups
摘要：
对于亨廷顿舞蹈症，识别与运动障碍相关的脑区可用于开发干预措施以减轻运动症状，该疾病的主要症状。然而，大脑区域对运动障碍的影响可能因患者的不同而不同。因此，我们的兴趣不仅是识别大脑区域，而且还要了解它们对运动障碍的影响如何因患者群体而异。这可以看作是变系数回归的模型选择问题。然而，当变量之间存在预先指定的组结构时，这是具有挑战性的。我们提出了一种新的变量选择方法的变系数回归这样的结构变量。经验表明，我们的方法可以始终如一地选择相关变量。此外，我们的方法筛选不相关的变量比现有的方法更好。因此，我们的方法导致一个模型具有更高的灵敏度，较低的错误发现率和更高的预测精度比现有的方法。最后，我们发现，从大脑区域运动障碍的影响不同的疾病严重程度的患者。据我们所知，我们的研究是第一个确定疾病严重程度和大脑区域之间的这种相互作用的影响，这表明需要根据疾病严重程度进行定制干预。
英文摘要：
For Huntington disease, identification of brain regions related to motor impairment can be useful for developing interventions to alleviate the motor symptom, the major symptom of the disease. However, the effects from the brain regions to motor impairment may vary for different groups of patients. Hence, our interest is not only to identify the brain regions but also to understand how their effects on motor impairment differ by patient groups. This can be cast as a model selection problem for a varying-coefficient regression. However, this is challenging when there is a pre-specified group structure among variables. We propose a novel variable selection method for a varying-coefficient regression with such structured variables. Our method is empirically shown to select relevant variables consistently. Also, our method screens irrelevant variables better than existing methods. Hence, our method leads to a model with higher sensitivity, lower false discovery rate and higher prediction accuracy than the existing methods. Finally, we found that the effects from the brain regions to motor impairment differ by disease severity of the patients. To the best of our knowledge, our study is the first to identify such interaction effects between the disease severity and brain regions, which indicates the need for customized intervention by disease severity.
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