Studying the neurological, genetic and evolutionary basis of human vocal communication mechanisms is an important field of neuroscience. In the absence of high quality data on humans, mouse vocalization experiments in laboratory settings have been proven to be useful in providing valuable insights into mammalian vocal development and evolution, including especially the impact of certain genetic mutations. Data sets from mouse vocalization experiments usually consist of categorical syllable sequences along with continuous inter-syllable interval times for mice of different genotypes vocalizing under various contexts. Few statistical models have considered the inference for both transition probabilities and inter-state intervals. The latter is of particular importance as increased inter-state intervals can be an indication of possible vocal impairment. In this paper, we propose a class of novel Markov renewal mixed models that capture the stochastic dynamics of both state transitions and inter-state interval times. Specifically, we model the transition dynamics and the inter-state intervals 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 inter-state interval times but, compared to previous analyses, the influences of genotype and social context on the syllable transition dynamics are weaker.
翻译:研究人类声学交流机制的神经学、遗传学和进化基础是神经科学的一个重要领域。由于缺乏高质量的人类数据,实验室环境中的老鼠声学实验已证明对提供哺乳动物声学发育和演化的宝贵洞见非常有用,特别是某些基因突变的影响。鼠声学实验的数据集通常包括绝对可听的序列以及在不同情况下对不同基因型小鼠发声的连续可读间隔时间。很少有统计模型考虑到过渡概率和州际间隔的推论。后者特别重要,因为增加州际间隔可以表明可能的声障。在本论文中,我们建议了一组新颖的马尔科夫更新混合模型,该模型可以捕捉到状态转变和州际间间隔时间的变异性动态。具体地说,我们用迪里特和伽玛混合物分别模拟过渡动态和州际间间隔的间隔时间。两种情况下的混合物变异性都能够与固定的变异性效应以及随机的个别具体影响。我们用新的马科夫复变混合模型分析了州间变异性结构的变异性,我们用这种模型分析了州际变异性结构的变异性模型,从而分析了州际变异性分析了先变式的基因结构。我们对州际的变异性模型,并对了先变式分析了先变式的变式分析。我们对州间变式的变式的变式的变式的变式分析,并对了先变式的变式的变式分析,对了先变。我们的变式的基因的变式分析,对了先变式分析,对了先变式分析,对了先变式的变式的变式分析,对了基的变式的变式的变式的变式的变式的变式,对了先变式,对了先变式的变式的变式的变式的变式的变式的变式,对了。对了。对了。对了基的变式分析对了。