Cross-sectional and longitudinal predictors of distressing psychotic-like experiences in childhood and adolescence

童年和青春期痛苦的精神病样经历的横截面和纵向预测因素

• 批准号: 10054751
• 负责人: Nicole R. Karcher
• 金额: $ 16.3万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10054751
• 关键词: 11 year old; Address; Adolescence; Adolescent; Adult; Age; Alcohol or Other Drugs use; Area; Award; Behavior; Brain; Caregivers; Child; Childhood; Clinical; Clinical Investigator; Cognition; Cognitive; Cognitive deficits; DSM-V; Data; Data Collection; Data Set; Detection; Development; Developmental Delay Disorders; Diagnosis; Disease; Distress; Early Intervention; Early identification; Environmental Risk Factor; Epidemiology; Etiology; Exposure to; Family; Family history of; Functional disorder; Future; Home environment; Impairment; Individual; Institution; Interview; Knowledge; Literature; Machine Learning; Magnetic Resonance Imaging; Measures; Mental Health; Mental disorders; Mentors; Mentorship; Modeling; Motor; National Institute of Mental Health; Nature; Neurobiology; Neuropsychological Tests; Neurosciences; Occupational; Parents; Participant; Preventive Intervention; Psychopathology; Psychotic Disorders; Public Health; Recording of previous events; Reporting; Research; Research Project Summaries; Rest; Risk; Risk Assessment; Risk Factors; Risk Marker; Running; Schizophrenia; Schools; Site; Speech; Structure; Sustainable Development; Symptoms; Syndrome; Techniques; Testing; Thalamic structure; Thick; Time; Training; Youth; adverse childhood events; aged; attenuated psychosis syndrome; base; career; clinically significant; cognitive development; cognitive function; data modeling; experience; functional disability; improved; large scale data; longitudinal analysis; machine learning method; marijuana use; neuroimaging; novel; relating to nervous system; skills; social; structured data; success; theories

Project Summary Research has yet to understand why some with psychotic-like experiences (PLEs; early markers of psychosis risk) transition to psychosis spectrum disorders whereas others report only transient PLEs. This information will be critical for understanding the etiology of psychosis spectrum symptoms and for prevention and intervention efforts for this major public health concern (~90% of individuals with significant PLEs report mental health diagnoses in adulthood). According to the expanded proneness-persistence-impairment (PPI) model, potential distinguishing factors between transient PLEs and those transitioning to psychotic disorders is whether they are sustained and distressing (i.e., sustained dPLEs). Consistent with NIMH Strategic Objective 2, this K23 application will fill critical missing gaps in the literature by characterizing the key risk factors and clinical significance of early sustained dPLEs. The application will focus on ~11,800 children from the Adolescent Brain and Cognitive Development study initially aged 9-11-years-old followed annually over the course of the award. The analyses will test PPI model hypotheses, including investigating the most important factors distinguishing sustained from transient dPLEs, examining neurobiological correlates (e.g., resting state functional connectivity, cortical thickness, cognitive functioning), family history of psychosis, motor and speech developmental delays, and environmental predictors (adverse childhood experiences, cannabis use; Aim 1). Models will also test whether longitudinal changes in cognitive, neural predictors, and environmental risk factors distinguish sustained versus transient dPLEs (Aim 2). Lastly, the application will also fill a critical research gap by examining the clinical significance of sustained dPLEs, examining the social and educational functional impairments, treatment seeking behavior, and conducting additional data collection when the youth are ages 16 to 18 to assess the base rates of attenuated psychosis syndrome (APS) among youth endorsing sustained dPLEs (Aim 3). To assess rates of APS, the applicant and a masters-level clinician will interview a subset of ABCD participants (n=500) and their parents/caregivers using the Structured Interview of Prodromal Syndromes. Overall, the applicant will implement rigorous practices, including running all analyses for the aims and hypotheses outlined below on two-thirds of data and then replicating the exact same models on an untouched one-third of data. Under the mentorship of a diverse team of experts of developmental psychosis spectrum psychopathology, longitudinal analyses, machine learning, and neuroimaging analyses, this scientifically rigorous proposal will test hypotheses regarding cross- sectional and longitudinal predictors of sustained versus transient dPLEs for the future application of early identification and preventative interventions. The application addresses several gaps in the applicant’s training that are critical for success as an independent clinical investigator, including the need for further training in advanced statistical techniques (e.g., machine learning, longitudinal analyses, neuroimaging analyses) and increased exposure in the area of developmental psychosis spectrum psychopathology.

项目摘要 研究还没有弄清楚为什么有些人有类似精神病的经历(PLE；精神病的早期标志 风险)转变为精神病谱系障碍，而其他人只报告一过性的PLE。此信息将 对了解精神病谱系症状的病因以及预防和干预至关重要 为这一重大公共卫生问题所做的努力(约90%患有严重精神障碍的人报告精神健康 成年后的诊断)。根据扩展的倾向性-持久性-损伤(PPI)模型，潜在的 一过性PLE和那些过渡到精神障碍的PLE之间的区别因素是它们是否 持续的和令人痛苦的(即持续的dPLE)。与NIMH战略目标2一致，此K23 应用程序将通过描述关键风险因素和临床特征来填补文献中缺失的关键空白 早期持续性dPLE的意义。该应用程序将重点关注来自青春期大脑的约11,800名儿童 认知发展研究最初年龄为9-11岁，在颁奖过程中每年进行一次。 这些分析将检验PPI模型假设，包括调查区分最重要的因素 从瞬时dPLE持续，检查神经生物学相关性(例如，静息状态功能连接性， 皮质厚度，认知功能)，精神病家族史，运动和语言发育迟缓， 和环境预测因素(儿童不良经历、大麻使用；目标1)。模型也将测试 认知、神经预测因子和环境风险因素的纵向变化是否区分持续性 与瞬变dPLE(目标2)的对比。最后，该应用程序还将通过检查临床应用程序来填补关键的研究空白 持续性dPLE的意义，检查社会和教育功能障碍，寻求治疗 行为，并在16至18岁的年轻人进行额外的数据收集，以评估基本费率 在支持持续DPLE的青年中发现减退精神病综合症(APS)(目标3)。评估的比率 APS，申请者和一名硕士水平的临床医生将采访ABCD参与者的子集(n=500)和他们的 使用前驱症状结构化访谈的父母/照顾者。总体来说，申请者将实施 严格的做法，包括对以下三分之二的目标和假设进行所有分析 数据，然后在三分之一的原始数据上复制完全相同的模型。在一个人的指导下 发展性精神病谱系精神病病理学、纵向分析、机器的不同专家团队 学习和神经成像分析，这项科学上严格的建议将测试关于交叉- 持续和瞬时dPLE的分段和纵向预测因子在未来早期应用中的应用 识别和预防性干预。该申请解决了申请者培训中的几个空白 对于作为一名独立的临床研究人员的成功至关重要，包括需要进一步培训 高级统计技术(例如，机器学习、纵向分析、神经成像分析)和 在发育性精神病谱系精神病理学领域的曝光率增加。