论文标题
SVREG：结构性变化的回归，以区分区域脑萎缩如何影响亨廷顿疾病严重性组的运动障碍
svReg: Structural Varying-coefficient regression to differentiate how regional brain atrophy affects motor impairment for Huntington disease severity groups
论文作者
论文摘要
对于亨廷顿疾病，鉴定与运动障碍有关的大脑区域对于制定干预措施以减轻运动症状，这是该疾病的主要症状。但是，大脑区域到运动障碍的影响可能会因不同的患者组而有所不同。因此，我们的兴趣不仅是确定大脑区域，而且还了解其对运动障碍的影响如何因患者群体而异。对于不同的回归，这可以作为模型选择问题。但是，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，用于使用这种结构化变量进行变化的回归。我们的方法在经验上被证明是始终如一地选择相关变量。同样，我们的方法筛选不相关的变量比现有方法更好。因此，与现有方法相比，我们的方法导致具有更高灵敏度，较低的错误发现率和更高预测准确性的模型。最后，我们发现大脑区域到运动障碍的影响因患者的疾病严重程度而有所不同。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这种相互作用效应的研究，这表明需要通过疾病严重程度进行定制干预。
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.
