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

项目摘要 研究还没有弄清楚为什么一些有精神病样经历的人（PLEs;精神病的早期标志物） 风险）转变为精神病谱系障碍，而其他人仅报告了短暂的PLEs。这些信息将 对于理解精神病谱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PLEs的人报告精神健康状况 成年后的诊断）。根据扩展的倾向-持续性-损害（PPI）模型， 暂时性PLEs和那些向精神障碍转变的人之间的区别因素是他们是否 持续的和令人痛苦的（即，持续dPLEs）。根据NIMH战略目标2，K23 应用将通过描述关键风险因素和临床特征来填补文献中的关键缺失空白。 早期持续性dPLEs的意义。该应用程序将重点关注来自青少年大脑的约11，800名儿童 和认知发展研究最初年龄为9-11岁，在获奖期间每年进行一次。 分析将测试PPI模型假设，包括调查区分 从短暂的dPLEs持续，检查神经生物学相关性（例如，静息状态功能连接， 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（不良童年经历、大麻使用;目标1）。模型还将测试 认知、神经预测因子和环境风险因素的纵向变化是否能区分持续性 对比瞬态dPLEs（目标2）。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dPLEs的意义，检查社会和教育功能障碍，寻求治疗 行为，并在青少年年龄为16至18岁时进行额外的数据收集，以评估基本比率 在支持持续性dPLEs的青年中减少精神病综合征（APS）（目的3）。评估 APS、申请人和一名硕士级临床医生将对ABCD参与者的一个子集（n=500）及其 使用前驱综合征的结构化访谈的父母/照顾者。总的来说，申请人将实施 严格的做法，包括对三分之二的 数据，然后在未触及的三分之一数据上复制完全相同的模型。在一位 多元化的发育性精神病谱系精神病理学、纵向分析、机器专家团队 学习和神经成像分析，这项科学严谨的建议将测试关于交叉的假设， 持续性与短暂性dPLEs的截面和纵向预测因子，用于未来早期 识别和预防干预。该申请解决了申请人培训中的几个空白 作为一名独立的临床研究者，这是成功的关键，包括需要进一步培训， 先进的统计技术（例如，机器学习、纵向分析、神经成像分析）以及 在发展性精神病谱系精神病理学领域的暴露增加。

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.