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

基本信息

批准号：
10449324
负责人：
STEPHEN J SHEINKOPF
金额：
$ 35.63万
依托单位：
UNIVERSITY OF MISSOURI-COLUMBIA
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-03-26 至 2025-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10449324
关键词：
2 year old 3 year old Acoustics Address Affect Age-Months Arousal Autism Diagnosis Birth Characteristics Child Child Development Childhood Classification Complex Data Development Developmental Course Diagnosis Diagnostic Disease Progression Early Intervention Early identification Enrollment Evaluation Family Family member Functional disorder Genetic Hospitals Impairment Infant Life Link Logistics Long-Term Effects Measures Methods Modeling Modernization Movement Neonatal Neurodevelopmental Deficit Newborn Infant Outcome Parents Population Predictive Value Pregnancy Prevention Procedures Process Production Property Public Health Publishing Recording of previous events Registries Regulation Research Rhode Island Risk Risk Marker Sampling Screening procedure Series Siblings Signal Transduction Statistical Models Stress Systems Analysis Technology Testing Validation Woman Work autism spectrum disorder autistic children base biobehavior cohort computerized detection method early childhood early screening follow-up high risk infant improved infancy model building model development neonatal period neurobehavior neurobehavioral novel predictive modeling recruit response screening signal processing success tool development

项目摘要

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.

项目概要/摘要自闭症谱系障碍（自闭症）对儿童及其家人的生活具有重大和长期的影响家庭。自闭症的可靠诊断要到 2 岁或以后才能实现，目前还没有婴儿早期筛查自闭症的可用方法。初步证据表明，患有此病的儿童自闭症的特征可能是婴儿早期的非典型哭泣和神经行为特征。以前的研究发现，年龄较大的自闭症儿童的发声能力存在非典型性，并且声音异常婴儿哭泣的特征与幼儿时期的这些发现是一致的。声学障碍之一对新生儿哭声的分析一直缺乏基于现代技术的计算机化哭声分析系统技术。我们的实验室开发了一种基于最先进的信号处理的新型哭泣分析系统可用于研究可能与自闭症风险相关的哭泣的声学特征。我们组还有开发了 NNNS，这是一种广泛使用且经过充分验证的新生儿神经行为检查时期。初步研究结果表明，这些措施产生了一系列哭泣和神经行为特征。能够将后来诊断为自闭症的婴儿样本与包含在一个样本中的非自闭症婴儿区分开来纵向、异质的儿童发展研究。其他初步结果支持以下假设：后来诊断出自闭症的婴儿的婴儿哭泣和神经行为会受到影响。在拟议的项目中，我们将利用作为独特怀孕和分娩的一部分收集的哭泣和神经行为评估一家大型地区医院的队列。预计将招募和跟踪多达 5,000 名婴儿纵向。两阶段筛查和评估流程将在 24 至 36 个月大时识别出患有自闭症的儿童的年龄。将进行持续的分析，以确定新生儿的哭声和神经行为特征，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对有自闭症风险的婴儿进行早期筛查和早期干预。