Resilient Emotion Regulation Development in a South African Birth Cohort

项目摘要 低/中等收入国家(LMIC)的青少年因暴露于 早期逆境，估计有2.5亿儿童面临无法发挥其发展潜力的风险。 这种人类潜能的灾难性丧失发生在10/90的背景下--我们目前科学研究的10% 知识是由占世界人口90%的低收入国家创造的。这个歪曲的证据基础 导致对绝大多数人大脑发育中的风险和弹性模式的了解有限 世界上的青少年。情绪调节(ER)，即改变体验的内部和外部过程 或情感的表达，是早期逆境和结果之间的关键中介，然而，缺乏数据 鉴于有证据表明环境塑造了青年的社会化，世界上大多数国家的青年的ER问题令人担忧 情感。拟议中的研究通过结合)复杂的 数据分析技术和b)适用于纵向多式联运的社区指导/参与式研究 脑成像、高维行为评估和对环境的全面定义 暴露(包括有害的和保护性的)来自已确立的和深具特征的早期出生队列 南非开普敦的青少年。本工作的目标是创建一个解释模型 保护因素对内质网复原力潜在的神经发育轨迹的影响。前提是 早期的逆境是异质性的、强大的事件，显著增加了急诊室不良的风险，但也 在青春期早期经历的复原力因素可以减轻/改善这些风险。因此， ER结果的预测需要尖端、复杂的数据分析方法。我们假设数据- 驱动型方法将1)更准确地定义生活在LMIC中的青少年的ER行为特征 暴露在不同的逆境中，以及2)为复原力之间的联系提供了一个强有力的解释模型 因素和ER神经行为轨迹。目的1对525名12-13岁青少年进行亚型调查 基于早期环境暴露的LMIC出生队列，然后测试ER行为的神经相关性 恢复力(基于功能连通性、基于任务的活动和形态测量的MRI测量)核算 为早期逆境亚型。目标2确定神经生物学的变化，这些变化是青少年进步的基础 并开发了一个用于预测2年(Time2-Time1)纵向ER弹性轨迹的解释性模型 基于并发环境暴露(防护性和不良)的亚型，占 早年逆境 子类型。将参与者的性别和青春期状态包括在内，将确定以下路径的潜在差异 青春期早期。我们将前瞻性方法与机器学习方法结合使用，目标是 提高认识和表征社会经济、结构性、 健康，以及人际关系的逆境。

Project Summary Adolescents in low-/middle-income countries (LMICs) lose substantial developmental potential from exposure to early adversities, with an estimated 250 million children at risk of not reaching their developmental potential. Such catastrophic loss of human potential occurs in the context of the 10/90 divide – 10% of our current scientific knowledge is produced in or by LMICs that comprise 90% of the world’s population. This skewed evidence base has led to a limited understanding of patterns of risk and resilience in brain development for the vast majority of the world’s adolescents. Emotion regulation (ER), the internal and external process that modifies the experience or expression of an emotion, is a key mediator between early adversity and outcomes, and yet, the lack of data on ER in youth from most of the world is of concern given evidence that context shapes the socialization of emotion. The proposed research addresses this significant mental health problem by combining a) sophisticated data analytic techniques and b) community-guided/-participatory research applied to longitudinal multimodal brain imaging, high-dimensional behavioral assessments, and comprehensive defining of environmental exposures (both adverse and protective) from a well-established and deeply-characterized birth cohort of early adolescents in Cape Town, South Africa. The goal of the present work is to create an explanatory model for the impact of protective factors on neurodevelopmental trajectories underlying ER resilience. The premise is that early adversities are heterogeneous, powerful events that significantly increase the risk of poor ER, but also that resilience factors experienced during early adolescence can mitigate/ameliorate these risks. Therefore, prediction of ER outcomes requires cutting-edge, sophisticated data analytic methods. We hypothesize that data- driven approaches will 1) more precisely define ER behavioral profiles for adolescents living in LMICs who are exposed to heterogeneous adversities, and 2) provide a robust explanatory model for links between resilience factors and ER neurobehavioral trajectories. Aim 1 subtypes 525 12-13 year-old adolescents in an established LMIC birth cohort based on early environmental exposures and then tests for neural correlates of ER behavioral resilience (based on MRI measures of functional connectivity, task-based activity, and morphometry) accounting for early adversity subtype. Aim 2 identifies changes in neurobiology that underlie improvements in adolescent ER and develops an explanatory model to predict 2-year (Time2-Time1) longitudinal ER resilience trajectory subtypes based on concurrent environmental exposures (protective and adverse), accounting for early adversity subtype. The inclusion of participant-sex and pubertal status will identify potential divergence in pathways across early adolescence. We use a prospective approach together with machine learning methods with the goal of improving precision and inclusivity in recognizing and characterizing resilience to socio-economic, structural, health, and interpersonal adversities.