The When to Worry about Language Study (W2W-L): Joint consideration of developmental patterning and neural substrates for enhancing earlyidentification of language impairment

项目总结 初级语言障碍(PLI)开始于儿童早期，影响到6%-8%的儿童。语言干预 交付得越早，效果就越好。然而，语言习得中的规范差异 蹒跚学步(在这里是24-36个月)导致了很高的假阳性率，阻碍了准确的识别 在学龄前晚期的PLI。这项拟议的研究引入了一种新的、有理论基础的、 神经发育框架，旨在产生一个敏感和特定的儿童PLI模型 风险。在这种对发展敏感的翻译方法中引入的创新包括：(1) 使用最新方法表征多种特征和生长的发育前驱模型 在一个大的社区样本中，幼儿涌现语言模式的模式；(2)结合脑电/事件相关电位 将语言和事务同步性的神经生物标志物纳入PLI预测模型；以及(3)考虑 突发的心理健康风险。心理健康风险是通过多方法测量易怒来捕捉的， 对发展有意义的风险标记，用于将常见问题内在化和外部化 PLI的相关因素。建议的何时担心语言学习(W2W-L)将利用 该团队对350名婴儿(50%易怒和50%不应激)进行的现有资助研究(R01MH107652，Wakschlag， PI)，并通过招募200名说话迟缓的幼儿作为新的亚样本来丰富它。这将产生一个巨大的和 对550名24个月大的婴儿进行了不同的抽样，跟踪调查至54个月大(可以可靠地评估PLI)。这个 关键的预测因素将是通过语言技能、语言处理、 以及相应的神经生物标记物。主要结果是初级语言障碍(PLI)状态为 学龄前年龄，通过临床黄金标准测量进行评估。关键的风险修饰符是远端和近端 交易性语言环境的特征，以及易怒的纵向模式。 具体目标：目标1.具体说明语言技能、处理、神经生物标记物和 他们的增长是对PLI的早期预测。假设：1.语言技能、处理能力和神经生物标志物将 每一项都对PLI预测做出了递增的贡献。1B.考虑纵向模式将加强预测。 目标2.确定交易语言环境的远端风险和近端保护特征 为PLI中的个体差异提供最大的解释力。假设2：家族史与贫穷 父母的语言能力会增加PLI的风险，增加父母的输入特征，以及行为和神经 同步性将降低PLI风险。目的3.考察幼儿易怒、近距离语言的相互影响 环境，以及PLI路径上的新兴语言模式。假设3：一个指定这些的模型 随着时间的推移，相互影响将使PLI预测变得敏锐，超出其个人解释的方差 影响。目的4.评估临床算法用于早期PLI风险识别的可行性。我们将使用 机器学习方法，以生成一个敏感/特定的，可行的临床模型建立在目标1-3。

PROJECT SUMMARY Primary language impairment (PLI) begins early in life and affects 6-8% of children. Language intervention is maximally effective the earlier it is delivered. However, normative variation in language acquisition across toddlerhood (here, 24-36 months) contributes to a high rate of false positives, impeding accurate identification of PLI prior to late preschool age. The proposed study introduces a novel, theoretically- grounded, neurodevelopmental framework designed to generate a sensitive and specific model of toddler PLI risk. Innovations introduced in this developmentally-sensitive, translational approach include: (1) a developmental precursor model using state-of-the-art methods to characterize multiple features and growth patterns of toddler emergent language patterns, within a large community sample; (2) incorporating EEG/ERP neural biomarkers of language and transactional synchrony into PLI predictive models; and (3) considering emergent mental health risk. Mental health risk is captured via multi-method measures of irritability, a developmentally meaningful marker of risk for internalizing and externalizing problems that are common correlates of PLI. The proposed When to Worry about Language Study (W2W-L) will capitalize on the team's existed funded study of 350 infants (50% irritable and 50% non irritable) (R01MH107652, Wakschlag, PI) and enrich it via recruitment of a new sub-sample of 200 late talking toddlers. This will yield a large and diverse sample of 550 24 month olds, followed to age 54 months (when PLI can be reliably evaluated). The key predictor will be toddler emergent language patterns measured via language skill, language processing, and corollary neural biomarkers. The central outcome is primary language impairment (PLI) status at preschool age, assessed via clinical gold standard measures. Key risk modifiers are distal and proximal features of the transactional language environment, and longitudinal patterns of irritability. SPECIFIC AIMS: Aim 1. Specify the contribution of language skills, processing, neural biomarkers, and their growth to early PLI prediction. Hypotheses: 1a. Language skills, processing, and neural biomarkers will each contribute incrementally to PLI prediction. 1b. Considering longitudinal patterns will enhance prediction. Aim 2. Identify the distal risk- and proximal protective- features of the transactional language environment that provide greatest explanatory power for individual differences in PLI. Hypothesis 2: Family history and poor parental language ability will increase PLI risk, and features of parental input, and behavioral and neural synchrony will decrease PLI risk. Aim 3. Examine the mutual influences of toddler irritability, proximal language environment, and emergent language patterns on PLI pathways. Hypothesis 3: A model specifying these reciprocal influences over time will sharpen PLI prediction beyond variance explained by their individual influences. Aim 4. Evaluate feasibility of a clinical algorithm for earlier PLI risk identification. We will use machine learning approaches to generate a sensitive/specific, feasible clinical model building on Aims 1-3.