Bayesian semiparametric Markov renewal mixed models for vocalization syntax.
Bayesian semiparametric Markov renewal mixed models for vocalization syntax.
复制标题
发声语法的贝叶斯半参数马尔可夫更新混合模型。
DOI：
10.1093/biostatistics/kxac050
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Sarkar,Abhra
中科院分区：
文献类型：
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作者：
Wu,Yutong;Jarvis,ErichD;Sarkar,Abhra
言语和语言在人类的声音交流中起着重要的作用。研究表明，发声障碍可能是遗传因素造成的。在缺乏高质量的人类数据的情况下，实验室环境中的小鼠发声实验已被证明有助于为哺乳动物发声发育提供有价值的见解，特别是某些基因突变的影响。这样的数据集通常包括分类音节序列沿着与连续的音节间间隔（ISI）的时间为不同基因型的小鼠在不同的情况下发声。ISI是特别重要的，因为增加的ISI可能是可能的声音障碍的指示。然而，缺乏用于正确分析ISI沿着转移概率的统计方法。在这篇文章中，我们提出了一类新的马尔可夫更新混合模型，捕获的状态转移和ISI长度的随机动态。具体而言，我们分别使用Dirichlet和Gamma混合物对过渡动力学和ISI进行建模，允许在这两种情况下的混合概率随固定协变量效应以及随机个体特异性效应灵活变化。我们应用我们的模型来分析Foxp2基因突变对小鼠发声行为的影响。我们发现，基因型和社会背景显着影响ISI的长度，但与以前的分析相比，基因型和社会背景的影响音节过渡动态是较弱的。
Speech and language play an important role in human vocal communication. Studies have shown that vocal disorders can result from genetic factors. In the absence of high-quality data on humans, mouse vocalization experiments in laboratory settings have been proven useful in providing valuable insights into mammalian vocal development, including especially the impact of certain genetic mutations. Such data sets usually consist of categorical syllable sequences along with continuous intersyllable interval (ISI) times for mice of different genotypes vocalizing under different contexts. ISIs are of particular importance as increased ISIs can be an indication of possible vocal impairment. Statistical methods for properly analyzing ISIs along with the transition probabilities have however been lacking. In this article, we propose a class of novel Markov renewal mixed models that capture the stochastic dynamics of both state transitions and ISI lengths. Specifically, we model the transition dynamics and the ISIs using Dirichlet and gamma mixtures, respectively, allowing the mixture probabilities in both cases to vary flexibly with fixed covariate effects as well as random individual-specific effects. We apply our model to analyze the impact of a mutation in the Foxp2 gene on mouse vocal behavior. We find that genotypes and social contexts significantly affect the length of ISIs but, compared to previous analyses, the influences of genotype and social context on the syllable transition dynamics are weaker.