Predicting Heterogeneous Neurodevelopmental Outcomes in School-age Children with Early Caregiving Adversities

项目摘要 患有严重的早期照顾障碍（ECAs）的儿童最容易受到精神病理学的影响， 长期忽视，虐待和护理中断的结果，影响神经发育。目前 估计解决ECA将导致全球精神疾病减少29.8%。现有 研究，包括这次更新的原始拨款的发现，表明有一个非常强大的 ECA暴露与精神病理学风险增加和神经发育改变之间的联系 然而，鉴于非洲经委会人口的异质性， 关于出口信贷机构如何增加任何具体的风险，以个别儿童。拟议的研究解决了这一问题 显著的心理健康问题相结合的sophisitcated数据分析方法，使用经验和 学龄期表型异质性以及纵向神经影像学和行为评估 孩子在将幼儿保育评估与儿童成果联系起来时，这种方法将提高准确性， 负价和认知控制系统（NVS/CCS）的研究领域标准结构。的 目前工作的首要目标是建立一个解释模型，解释ECA的异质性影响 NVS/CCS的神经发育轨迹。该项目的前提是，接触ECA的儿童 高度异质性的发展历史以及异质性的结果;因此， 非洲经委会的成果需要尖端、复杂的数据分析方法。我们假设数据驱动 方法将1）更精确地定义患有ECA的学龄儿童的NVS/CCS结果，以及2） 为ECA和NVS/CCS轨迹之间的联系提供了更可靠的解释模型。Aim 1A亚型 根据NVS/CCS的2.5年发育轨迹，有ECA病史的儿童。300 6-8岁 儿童（250名来自以前的机构和寄养; 50个社区比较）将提供 神经影像学、行为学和自我/照顾者报告，每15个月一次，持续2.5年。双聚类方法将 应用于基线和随访数据，以识别同质NVS/CCS最终结局群， 孩子目的1B建立一个解释性模型，预测儿童的发展轨迹亚型， ECA，来自入组时的早期生活概况和大脑/行为表型。机器学习 应用于早期生命特征、基线NVS/CCS特征和性别的方法将预测发育轨迹 亚型目标2确定了2.5年评估期内的不良和保护性生活事件， 预测ECA儿童的2.5年随访结果。包括儿童性别和目前的生活- 活动将确定在整个童年中期的途径的潜在分歧。这种前瞻性的设计 暴露于各种ECA的儿童旨在开发ECA轨迹亚型的预测模型， 结果，这可以告知我们对风险和保护因素的理解，与以下目标雅阁： 精准医疗

Project Summary Children with severe Early Caregiving Adversities (ECAs) are the most vulnerable to psychopathology as a result of prolonged neglect, abuse, and care disruptions that impact neurodevelopment. It is currently estimated that addressing ECAs would lead to a 29.8% reduction in worldwide psychiatric illness. Existing research, including findings from the original grant of this renewal, has demonstrated that there is a very strong link between ECA exposures and increased risk for psychopathology and altered neurodevelopment at the population level; and yet, given the heterogeneity in ECA populations, there is a critical gap in knowledge regarding how ECAs increase any specific risks to an individual child. The proposed research addresses this significant mental health problem by combining sophisitcated data-analysis methods that use experiential and phenotypic heterogeneity together with longitudinal neuroimaging and behavioral assessments in school-age children. This approach will increase precision when linking ECAs and child outcomes associated with the Research Domain Criteria constructs of Negative Valence and Cognitive Control Systems (NVS/CCS). The overarching goal of the present work is to create an explanatory model for the heterogeneous impact of ECAs on neurodevelopmental trajectories of NVS/CCS. This project's premise is that children exposed to ECAs have highly heterogeneous developmental histories as well as heterogeneous outcomes; therefore, prediction of ECA outcomes requires cutting-edge, sophisticated data analytic methods. We hypothesize that data-driven approaches will 1) more precisely define NVS/CCS outcomes for school-aged children with ECAs, and 2) provide a more robust explanatory model for links between ECAs and NVS/CCS trajectories. Aim 1A subtypes children with a history of ECAs based on 2.5-year developmental trajectories of NVS/CCS. 300 6-8 year old children (250 sampled from previous institutional and foster care; 50 community comparisons) will provide neuroimaging, behavioral, and self/caregiver reports every 15 months for 2.5 years. Biclustering methods will be applied to the baseline and follow-up data to identify homogeneous NVS/CCS final outcome clusters of children. Aim 1B develops an explanatory model to predict developmental trajectory subtypes for children with ECAs, from early life profiles and brain/behavior phenotypes at the time of enrollment. Machine learning methods applied to early life profiles, baseline NVS/CCS profiles, and sex, will predict developmental trajectory subtypes. Aim 2 identifies adverse and protective life events during the 2.5-year assessment period that are predictive of 2.5-year follow-up outcomes for children with ECAs. The inclusion of child-sex and current life- events will identify potential divergence in pathways across middle childhood. This prospective design of children exposed to various ECAs is designed to develop predictive models for ECA trajectory subtypes and outcomes, which can inform our understanding of risk and protective factors in accord with the goals of precision medicine.