Predicting ASD and Other Developmental Outcomes in the First Year of Life Using EEG in a Diverse Community-Based Sample

项目摘要 接受早期干预服务的自闭症儿童比没有接受干预服务的儿童有更好的结果。它是 因此降低诊断年龄势在必行。有强有力的证据表明，有可靠的行为 在生命的第二年出现的障碍的迹象/症状。然而，越来越多的证据表明， 我们的实验室发现，早在3个月前就出现的脑电模式确实与 自闭症预后在2-3年内。在这项提案中，我们寻求以两种重要方式扩展我们以前的工作。 首先，我们将在一家大型儿科初级保健诊所部署我们的高维EEG数据收集，因此 展示了脑电作为自闭症风险生物标记物的潜在可扩展性。二是集中力量 关于历来未得到充分服务和研究的婴儿群体：主要是黑人和 在低收入家庭长大的西班牙裔婴儿。我们将在3年内招收720名婴儿(240名/年)，并基于 在之前的工作中，预计保留率为85%。我们将收集4个月、9个月和12个月的静息脑电数据 与他们在诊所看望健康宝宝的情况相结合。在24个月时，将使用 ADOS、发育测量和专家临床判断。除了第一次的脑电评估 寿命年，将包括一般发育筛查(年龄和阶段调查表-3)和指数 与一些与自闭症风险增加相关的非遗传变量有关(例如，婴儿性别， 父母年龄、产前产妇健康等)将从人口统计调查问卷和医学调查中获得 唱片。该项目的具体目标是： 目标1：在种族、民族和社会经济多元化的初选中使用前瞻性研究设计 护理人群，我们将在以下位置确定与ASD相关的测量到的1岁生命的脑电特征 --2岁。 目标2：开发具有脑电生物标志物和其他风险因素的预测模型，以可靠地预测 后来诊断为房间隔缺陷症。 目的3：确定ASD与其他神经发育的预测特征的特异性 结果，如语言或认知延迟。 我们的最终目标是创建一个可扩展的、实用的、基于神经生物学的工具，该工具可以很容易地集成 进入儿科初级保健环境，这样做，极大地提高了我们在第一年识别自闭症的能力。 我们相信我们的方法将使我们能够展示EEG在初级保健环境中的可扩展性，开发 为有最大延迟诊断风险的儿童提供可用的模型，并提高我们对潜在疾病的理解 特发性孤独症的神经机制。

Project Summary Children with autism who receive early intervention services have better outcomes than those who do not. It is therefore imperative to lower the age of diagnosis. There is strong evidence that there are reliable behavioral signs/symptoms of the disorder that emerge in the second year of life. However, there is mounting evidence from our laboratory that there are patterns in the EEG that emerge as early as 3 months that are reliably associated with autism outcomes at 2-3 years. In this proposal we seek to extend our previous work in two important ways. First, we will deploy our high-dimensional EEG data collection in a large pediatric primary care clinic, thus demonstrating the potential scalability of EEG as a biomarker of autism risk. Second, we will focus our efforts on a population of infants who have historically been underserved and understudied: primarily Black and Hispanic infants growing up in low-income homes. We will enroll 720 infants over 3 years (240/year), and based on previous work, anticipate a retention rate of 85%. We will collect resting EEG data at 4, 9 and 12 months in conjunction with their well-baby visits at the clinic. At 24 months diagnostic outcomes will be evaluated using the ADOS, developmental measures, and expert clinical judgement. In addition to the EEG assessment in the first year of life, a general developmental screener will be included (Ages and Stages Questionnaire-3) and indices associated with a number of non-genetic variables associated with increased autism risk (e.g., infant sex, parental age, prenatal maternal health, etc.) will be obtained from a demographic questionnaire and medical records. The specific aims of the project are: Aim 1: Using a prospective study design in a racially, ethnically and socioeconomically diverse primary care population, we will identify EEG features measured <1 year of life that are associated with ASD at 2-years of age. Aim 2: To develop predictive models with EEG biomarkers and other risk factors that reliably predict later diagnosis of ASD. Aim 3: To determine the specificity of predictive features for ASD versus other neurodevelopmental outcomes such as language or cognitive delays. Our ultimate goal is to create a scalable, practical, neurobiologically-based tool that can be readily integrated into a pediatric primary care setting, and in so doing, greatly improve our ability to identify autism in the first year. We believe our approach will allow us to demonstrate scalability of EEG in the primary care setting, develop usable models for children at greatest risk of delayed diagnosis, and improve our understanding of the underlying neural mechanisms of idiopathic autism.