Neonatal Cry Acoustics and Neurobehavioral Characteristics as Early Markers of Risk for Autism Spectrum Disorder

新生儿哭声和神经行为特征作为自闭症谱系障碍风险的早期标志

• 批准号: 10597406
• 负责人: STEPHEN J SHEINKOPF
• 金额: $ 86.02万
• 依托单位: UNIVERSITY OF MISSOURI-COLUMBIA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-26 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10597406
• 关键词: Neonatal; Cry; Acoustics; Neurobehavioral; Characteristics

PROJECT SUMMARY/ABSTRACT Autism Spectrum Disorders (autism) has significant and long term effects on the lives of children and their families. Reliable diagnosis of autism is not possible until 2 years of age or later, and there are no currently available methods to screen for autism in early infancy. Preliminary evidence suggests that children with autism may be characterized by atypical features of cry and neurobehavior during early infancy. Previous research has identified atypicalities in vocal production in older children with autism, and unusual acoustic features of infant cry would be consistent with such findings from early childhood. One barrier to the acoustic analysis of newborn cry has been the lack of a computerized cry analysis system based on modern technology. Our lab has developed a new cry analysis system based on state-of-the-art signal processing that can be used to study acoustic characteristics of cry that could relate to risk for autism. Our group has also developed the NNNS, a widely used and well validated neurobehavioral exam for infants in the newborn period. Preliminary findings point to a set of cry and neurobehavioral features from these measures that was able to differentiate a sample of infants later diagnosed with autism from non-autistic infants included in a longitudinal, heterogeneous, child development study. Other preliminary findings support the hypothesis that infant cry and neurobehavior are affected in infants with later autism diagnoses. In the proposed project, we will utilize cry and neurobehavioral assessments that are being collected as part of unique pregnancy and birth cohort at a large regional hospital. It is anticipated that up to up to 5,000 infants will be enrolled and followed longitudinally. A 2-stage screening and evaluation process will identify children with autism by 24 to 36 months of age. Ongoing analyses will be conducted to identify neonatal cry and neurobehavioral characteristics that are associated with risk for autism using signal detection methods and complex conditional statistical models. This project is of high significance and has the potential to have substantial impact on public health by identifying potential indicators of risk for autism. The long term impact of this research would be on the development of early screening and early interventions for infants at risk for autism.

项目总结/摘要 自闭症谱系障碍（自闭症）对儿童的生活和他们的生活有重大和长期的影响。 家庭自闭症的可靠诊断要到2岁或更大才可能，目前还没有 在婴儿早期筛查自闭症的方法。初步证据表明， 孤独症的特征可能是婴儿早期的非典型的哭泣和神经行为。先前 研究发现，年龄较大的自闭症儿童在发声方面存在缺陷， 婴儿哭声的特征与儿童早期的这些发现一致。一个隔音屏障 新生儿哭声的分析一直缺乏一个计算机化的哭声分析系统， 技术.我们的实验室开发了一种基于最先进的信号处理的新的哭泣分析系统， 可以用来研究哭泣的声音特征，这可能与自闭症的风险有关。我们小组还 开发了NNNS，这是一种广泛使用且经过验证的新生儿神经行为检查 期初步研究结果指出，这些测量结果具有一系列哭泣和神经行为特征， 能够区分后来被诊断患有自闭症的婴儿样本和包括在一个 纵向异质性儿童发展研究其他的初步发现也支持这一假设， 婴儿的哭声和神经行为在后来被诊断为自闭症的婴儿中受到影响。在项目中，我们 将利用哭泣和神经行为评估，作为独特的怀孕和分娩的一部分， 在一家大型区域医院工作。预计将有多达5，000名婴儿入组并接受随访 纵向。一个2阶段的筛选和评估过程将确定自闭症儿童的24至36个月 年龄。将进行持续的分析，以确定新生儿哭泣和神经行为特征， 使用信号检测方法和复杂的条件统计模型与自闭症风险相关。 该项目意义重大，有可能对公众健康产生重大影响， 识别自闭症风险的潜在指标。这项研究的长期影响将是 为有自闭症风险的婴儿开展早期筛查和早期干预。