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

利用潜在因素和机器学习来预测成年初期的内化精神病理学

基本信息

批准号：
10366892
负责人：
ROSELINDE H KAISER
金额：
$ 76.73万
依托单位：
UNIVERSITY OF COLORADO
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-06-10 至 2027-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10366892
关键词：
Address Age Anhedonia Anxiety Anxiety Disorders Arousal Behavior Behavioral Clinical Cognition Complex Counseling Data Set Development Diagnosis Dimensions Disease Dropout Enrollment Environment Etiology Evaluation Event Exposure to Future Goals Health Hypersensitivity Impairment Incidence Individual Individual Differences Intervention Investigation Life Link Longitudinal Studies Machine Learning Manic Measures Mental Depression Mental Health Methodology Modeling Mood Disorders Negative Valence Neurocognition Neurocognitive Outcome Participant Pathology Pathway interactions Patient Self-Report Performance Persons Phenotype Physiological Physiology Positive Valence Psychopathology Public Health Recording of previous events Research Research Domain Criteria Rewards Risk Risk Behaviors Risk Factors Risk Marker Sample Size Sampling Sex Differences Statistical Models Stress Students Symptoms Targeted Research Techniques Technology Testing Time Translating Withdrawal anxious approach behavior biobehavior clinical practice clinical translation clinically significant cognitive control cognitive function college comorbidity diagnostic platform diaries emerging adult emerging adulthood emotion regulation executive function experience high reward in vivo maladaptive behavior mobile application novel outcome prediction precision medicine predictive modeling predictive tools preventive intervention recruit response screening sex social stressor tool university student young adult

项目摘要

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).

项目摘要心境障碍和焦虑障碍是常见的高度共病的条件与高峰发病率在新兴成年期（~18 - 23岁）。发展性精神病理学模型表明，新兴成年人的疾病是由仍然成熟的自我调节能力（如执行功能[EF]继续成熟到年轻的成年期），以及奖励和威胁敏感度总之，这突出了复杂的神经认知特征的重要性，这三种RDoC构建体之间的异常用于内化病症。然而，先前的研究已经在很大程度上研究了这些结构单独，与个别疾病或症状的维度。考虑到内化精神病理学的高度共现性和复杂的多重因果关系，关键的下一步是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 transdiagnosis个人特异性症状轨迹。拟议的研究旨在提高这种精度医学目标，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理学的标记（跨分析单元）。这项纵向研究将招募一名最终在向大学过渡期间，当压力和精神病理学风险增加时，为了测试转诊断（在内化症状中常见）和特异性（快感缺乏，焦虑，躁狂）内化维度，使用方法学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，和奖励敏感性作为转诊断和特定内化症状特征的风险机制，轨迹我们假设EF差是一个跨诊断的危险因素，具有特定的症状特征取决于威胁（导致焦虑唤醒）和奖励（导致快感缺乏或躁狂）敏感性，以及不同的适应不良行为（例如，社会退缩与危险行为）。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简约的单位集，预测结果-内化精神病理学筛查的未来临床转化的关键目标风险这种方法的优势包括模型驱动的认知控制维度结构，消极的和消极的，积极效价，跨越分析单元（生理学、行为、自我报告），处于关键发育风险期强大的样本量使严格的统计建模方法和调节测试成为可能。影响（例如，性别）。通过阐明神经认知风险和特定生物行为之间的相互作用，我们可以产生一种新的影响，这对开发翻译工具至关重要，预测个人特定的症状轨迹，为未来的诊断系统提供信息（RDoC优先级），个性化有希望的干预措施（干预的目标风险机制，以及为谁）。