Computational examination of RDoC threat and reward constructs in a representative, predominantly low-income, longitudinal sample at increased risk for internalizing disorders

在具有代表性的、主要是低收入的、内化障碍风险增加的纵向样本中对 RDoC 威胁和奖励结构进行计算检查

• 批准号: 10657487
• 负责人: Luke Williamson Hyde
• 金额: $ 102.46万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-15 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10657487
• 关键词: Affect; African American; African American population; Age; Amygdaloid structure; Anhedonia; Anxiety; Behavior; Behavioral; Big Data; Birth; Brain; Child; Child Welfare; Cities; Clinical; Clinical Psychology; Consensus; Copy Number Polymorphism; Corpus striatum structure; Data; Data Analyses; Data Set; Demography; Depressed mood; Development; Diffusion Magnetic Resonance Imaging; Disease; Dissociation; Distress; Epigenetic Process; Exposure to; Family; Functional Magnetic Resonance Imaging; Genetic; Genomics; Income; Individual; Intervention; Interview; Link; Longitudinal cohort; Low income; Magnetic Resonance Imaging; Maps; Mental Depression; Modeling; Nature; Neurosciences; Participant; Patient Self-Report; Persons; Phenotype; Population; Positioning Attribute; Poverty; Process; Protocols documentation; Psychopathology; Research; Research Domain Criteria; Rewards; Risk; Sampling; Source; Structure; Symptoms; Testing; Underrepresented Populations; Validation; Visualization; anxiety symptoms; anxious; behavior prediction; biotypes; cohort; data structure; data-driven model; depressive symptoms; deprivation; design; emerging adult; epigenome-wide association studies; genome wide association study; longitudinal analysis; lower income families; multidimensional data; multimodal data; multimodality; neural; neural circuit; neurodevelopment; neuroimaging; rare variant; recruit; social; social deprivation; violence exposure; young adult

Summary: Depression and anxiety are prevalent, debilitating, and poorly understood disorders. RDoC charts the nature of these conditions across multiple units, but the domains are based on expert consensus, permitting bias and missed opportunities. Moreover, little is known about how adversity affects RDoC constructs and contributes to psychopathology. Thus, there is a critical need to rigorously evaluate RDoC domains in developmental samples from diverse backgrounds at increased risk for exposure to adversity and later psychopathology. We will use data-driven analytics to design, apply and validate multilevel-multimodal models of Threat and Reward constructs in an existing longitudinal cohort at risk for psychopathology. To predict internalizing symptoms, we will identify biotypes cross-sectionally and examine the longitudinal plasticity of RDoC-informed biotypes. Harsh social-ecological conditions will be deeply assessed and used to forecast the onset/intensification of internalizing symptoms at multiple units. We will assess 1,000 young adults from The Fragile Families and Child Wellbeing Study (FFCWS), an ongoing study of children born to predominantly low-income families. Attributes of the FFCWS are: 1) children were assessed at birth, 1, 3, 5, 9, 15 years; 2) the sample is representative of people born in cities and, thus, unlike almost all other neuroimaging research, findings are generalizable; 3) Although a full range of incomes are represented, there is substantial enrichment for low-income and African-American families, populations often under-represented in research; and 4) participants are entering early adulthood, a period of heightened risk for psychopathology. We will assess Threat and Reward at four units of analysis: symptoms, task-based behaviors, brain, and genomics and link these units to exposure to adversity. The central hypothesis is that the RDoC Threat and Reward constructs will each cluster across individuals and units, are distinct from each other, and have specific socio- ecologic predictors. We will examine multisource/multimodal data structure in 1000 participants cross- sectionally and 213 participants longitudinally. Our transdisciplinary team of experts positions us well to elucidate the structure of the Threat and Reward constructs and map risk for internalizing biotypes. We will dramatically expand our established protocol to harmonize, aggregate, cross-sectionally and longitudinally analyze, cluster, and visualize the high-dimensional datasets. Using data-driven validation approaches at four units of analysis, we will examine three aims: Aim 1 will examine RDoC Threat construct cross-sectionally, developmentally, and ecologically. Aim 2 will test RDoC Reward construct cross-sectionally, developmentally, and ecologically. Aim 3 will assess the degree to which Threat and Reward dissociate cross-sectionally, developmentally and ecologically. By deeply phenotyping a large cohort enriched for low income and African American participants, we will determine the validity of Threat/Reward and findings will generalize to a population underrepresented in research and disproportionately affected by adversity.

摘要：抑郁症和焦虑症是普遍存在、使人衰弱且人们对其知之甚少的疾病。 RDoC 图表 这些条件的性质跨多个单位，但这些领域是基于专家共识， 允许偏见和错失机会。此外，人们对逆境如何影响 RDoC 知之甚少 构建并贡献了精神病理学。因此，迫切需要严格评估 RDoC 来自不同背景的发育样本中的领域面临逆境的风险增加，并且 后来的精神病理学。我们将使用数据驱动的分析来设计、应用和验证多级多模式 在现有的处于精神病理学风险的纵向队列中构建威胁和奖励模型。到 预测内化症状，我们将横断面识别生物型并检查纵向 RDoC 信息生物型的可塑性。深入评估恶劣社会生态条件 预测多个单位内化症状的发作/加剧。我们将评估 1,000 名年轻人 来自脆弱家庭和儿童福祉研究 (FFCWS)，这是一项针对出生于以下儿童的持续研究 主要是低收入家庭。 FFCWS 的属性是：1) 儿童在出生时、1、3、5、9 岁时接受评估， 15 年； 2）样本代表了出生在城市的人，因此与几乎所有其他城市的人不同 神经影像学研究，结果具有普遍性； 3）虽然收入范围很广，但 为低收入家庭和非裔美国家庭带来了巨大的财富，但这些家庭的代表性往往不足 研究; 4) 参与者正进入成年早期，这是精神病理学风险较高的时期。我们 将评估四个分析单元的威胁和奖励：症状、基于任务的行为、大脑和基因组学 并将这些单位与逆境联系起来。中心假设是 RDoC 威胁和奖励 构建体将每个集群跨越个人和单位，彼此不同，并且具有特定的社会- 生态预测因子。我们将检查 1000 名参与者的多源/多模式数据结构 分段和纵向参与者 213 名。我们的跨学科专家团队使我们能够 阐明威胁和奖励结构的结构，并绘制内化生物型的风险图。我们将 极大地扩展我们既定的协议，以协调、聚合、横向和纵向 分析、聚类和可视化高维数据集。在四点使用数据驱动的验证方法 作为分析单元，我们将研究三个目标：目标 1 将横断面研究 RDoC 威胁结构， 发展上和生态上。目标 2 将测试 RDoC 奖励结构的横截面、发展、 和生态上。目标 3 将评估威胁和奖励的横截面分离程度， 发展和生态方面。通过对大量低收入人群和非洲人进行深入表型分析 美国参与者，我们将确定威胁/奖励的有效性，结果将概括为 人口在研究中的代表性不足，并且受到逆境的影响尤为严重。