Infant Vocalizations as Early Markers of Autism Spectrum Disorder

摘要 本R03的目标是描述患有自闭症和自闭症的婴幼儿的纵向发声发育。 使用这些指标来预测后续诊断和多维社交/语言能力。有一个关键的问题 需要可靠的自闭症谱系障碍(ASD)标志物，可以用来检测婴儿期的状况 并加快开展早期干预服务。我们团队的初步数据显示，婴儿 发声特征区分了从出生第一年开始的群体，并解释了 晚期诊断状态和社会/语言表型。例如，我们发现后来被诊断出患有 ASD会产生更少的类似说话的发声，更少的针对他人的发声，更多的哭泣，以及 改变了发声声学。然而，这些早期出现的差异的纵向发展已经 没有被完全描述，虽然它们作为早期标记有希望，但它们预测个体的能力 临床结果还没有在大样本中进行直接测试。因此，这些早期发声的力量 改善临床检测的差异尚未得到充分利用。 为了解决这一差距，我们利用通过婴儿脑成像研究(IBIS； NICHD R01HD055741，PI：Joseph Piven博士)。在这项成功的纵向多点研究中，高峰期和高峰期的婴儿 ASD的低家庭风险在6、12和24个月龄时完成了视频记录的行为评估， 以及同一时间点的神经成像。我们的研究计划包括对婴儿发声进行注释 使用我们已经开发的编码方案的扩展版本在这些交互期间产生， 对注释员进行了手工操作和培训，以可靠地实施和测量每个注释器的声学特性 带注释的发声。在目标1中，我们将使用混合模型来分析 发声特征，决定哪些品质区分不同的群体和何时。在目标2中，我们将使用机器 学习测试在出生第一年产生的发声特征是否能准确地预测 2岁时的诊断和社会/语言结果。这项工作将为婴儿的临床实用提供证据 发声作为诊断前的行为生物标志物，为后续的研究奠定了基础 早期发声行为和大脑发育之间的关系(使用先前收集的数据进行测量 来自相同参与者的神经成像数据)。 这个

Abstract The goal of this R03 is to characterize longitudinal vocal development in infants and toddlers at risk for ASD and use these metrics to predict subsequent diagnosis and dimensional social/language abilities. There is a critical need for reliable markers of autism spectrum disorder (ASD) that can be used to detect the condition in infancy and hasten the onset of early intervention services. Preliminary data from our team indicates that infant vocalization features distinguish groups beginning in the first year of life, and account for significant variance in later diagnostic status and social/language phenotype. For example, we found that infants later diagnosed with ASD produce fewer speech-like vocalizations, fewer vocalizations directed toward others, more crying, and altered vocalization acoustics. However, the longitudinal development of these early-emerging differences has not been completely described, and while they hold promise as early markers, their ability to predict individual clinical outcomes has not been directly tested in a large sample. Therefore, the power of these early vocalization differences to improve clinical detection has not been fully harnessed. To address this gap, we leverage a large existing dataset collected through the Infant Brain Imaging Study (IBIS; NICHD R01HD055741, PI: Dr. Joseph Piven). In this successful longitudinal multi-site study, infants at high and low familial risk for ASD completed video-recorded behavioral assessments at 6, 12, and 24 months of age, as well as neuroimaging at the same time points. Our research plan includes annotating infant vocalizations produced during these interactions using an expanded version of a coding scheme we have already developed, manualized, and trained annotators to reliably implement, and measuring the acoustic properties of each annotated vocalization. In Aim 1, we will use mixed models to analyze developmental group differences in vocalization features, determining which qualities distinguish groups and when. In Aim 2, we will use machine learning to test whether vocalization features produced during the first year of life can accurately predict diagnostic and social/language outcomes at age 2. This work will provide evidence of the clinical utility of infant vocalizations as pre-diagnostic behavioral biomarkers, setting the stage for subsequent studies of the relationship between early vocal behavior and brain development (measured using previously collected neuroimaging data from the same participants). The