Leveraging Latent Factors and Machine Learning to Forecast Internalizing Psychopathology in Emerging Adulthood

项目摘要 情绪障碍和焦虑症是常见的高度并存的疾病，发病率最高的是新兴疾病 成年期(~18-23岁)。发展精神病理学模型表明，内在化的脆弱性 新出现的成年人的障碍是由仍在成熟的自我调节能力(AS)之间的相互作用推动的 执行功能[EF]持续成熟到成年期)，以及奖赏和 威胁敏感度。总而言之，这突出了复杂的神经认知特征的重要性，包括 这三个RDoC结构中的异常是内在化障碍的。然而，先前的研究已经 很大程度上研究了这些结构单独，与个体障碍或症状维度的关系。 鉴于精神病理学内在化的高度同现性和复杂的多重因果关系，关键的下一步 是建立一个框架来理解这些神经认知维度是如何相互作用来预测 跨诊断特定人的症状轨迹。拟议的研究旨在提高这一精确度。 医学目标，通过评估EF、奖励和威胁敏感性的神经认知维度如何相互作用 产生风险表型；并通过使用机器学习技术来识别最简约的风险集 预测精神病的标记物(跨分析单位)。这项纵向研究将招募一名最终的 在压力和精神病理风险增加的大学过渡期，480名初出茅庐的成年人的样本 测试跨诊断(在内在性症状中常见)和特异性(快感缺乏， 焦虑唤醒、躁狂)内化维度，使用方法上严格的潜变量方法。 我们的第一个目标是测试执行功能、威胁敏感性的神经认知维度之间的相互作用 和奖励敏感性作为跨诊断和特定内化症状特征的风险机制 轨迹。我们假设低EF是一个具有特定症状的跨诊断风险因素 取决于威胁(导致焦虑的唤醒)和奖励(导致快感缺失或躁狂) 敏感性，以及不同的适应不良行为(例如，社交退缩与危险行为)。我们的第二个目标是 执行自动风险分析，使用机器学习来确定最节俭的单元集 预测结果-未来临床翻译的一个关键目标，用于内化精神病理学的筛查 风险。这种方法的优点包括认知控制的模型驱动维度结构、负面和 正价，跨越分析单位(生理、行为、自我报告)，处于严重的发育风险 句号。稳健的样本量实现了严格的统计建模方法和调节测试 影响(例如，性)。通过阐明神经认知风险和特定生物行为之间的相互作用 机制，我们可以产生新的影响，这将是开发翻译工具的关键， 预测特定于个人的症状轨迹，通知未来的诊断系统(RDoC优先级)和 个性化有希望的干预措施(哪些风险机制是干预的目标，针对谁)。

Project Summary Mood and anxiety disorders are common and highly comorbid conditions with peak incidence in emerging adulthood (~ages 18-23). Developmental psychopathology models suggest that vulnerability to internalizing disorders in emerging adults is driven by interactions between still maturing self-regulatory abilities (as executive function [EF] continues to mature into young adulthood), and individual differences in reward and threat sensitivity. Together, this highlights the importance of complex neurocognitive profiles consisting of abnormalities across these three RDoC constructs for internalizing disorders. However, prior research has largely investigated these constructs individually, in relation to individual disorders or symptom dimensions. Given the high co-occurrence and complex multi-causality of internalizing psychopathology, the critical next step is to build a framework for understanding how these neurocognitive dimensions interact to predict transdiagnostic person-specific symptom trajectories. The proposed study aims to advance this precision medicine goal, by evaluating how the neurocognitive dimensions of EF, reward and threat sensitivity interact to produce risk phenotypes; and by using machine learning techniques to identify the most parsimonious set of risk markers (across units of analysis) that forecast psychopathology. This longitudinal study will recruit a final sample of 480 emerging adults during the transition to college, when stress and psychopathology risk increase, to test risk pathways for transdiagnostic (common across internalizing symptoms) and specific (anhedonia, anxious arousal, mania) internalizing dimensions, using a methodologically rigorous latent variable approach. Our first aim is to test interactions among the neurocognitive dimensions of executive function, threat sensitivity and reward sensitivity as risk mechanisms for transdiagnostic and specific internalizing symptom profiles and trajectories. We hypothesize that poor EF is a transdiagnostic risk factor, with specific symptom profile depending on threat (contributing to anxious arousal) and reward (contributing to anhedonia or mania) sensitivity, and different maladaptive behaviors (e.g., social withdrawal vs. risky behavior). Our second aim is to perform automated risk profiling, using machine learning to determine most parsimonious set of units that predict outcome– a key objective for future clinical translation for screening for internalizing psychopathology risk. Strengths of this approach include model-driven dimensional constructs of cognitive control, negative and positive valence, spanning units of analysis (physiology, behavior, self-report) at a critical developmental risk period. The robust sample size enables a rigorous statistical modeling approach and testing of moderating influences (e.g., sex). By elucidating interactions between neurocognitive risks and the specific biobehavioral mechanisms involved, we can make a novel impact that will be critical for developing translational tools that predict person-specific symptom trajectories, informing future diagnostic systems (RDoC priority) and personalizing promising interventions (which risk mechanisms to target for intervention, and for whom).