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: 10225618
• 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/10225618
• 关键词: 11 year old; Address; Adolescence; Adolescent; Adult; Age; 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; Impairment; Individual; Institution; Intervention; 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; Prevention; Psychopathology; Psychoses; 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; preventive intervention; relating to nervous system; risk prediction; skills; social; substance use; 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.

项目概要 研究尚未了解为什么有些人会出现类似精神病的经历（PLE；精神病的早期标志） 风险）转变为精神病谱系障碍，而其他人仅报告短暂的 PLE。 对于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 PLE 的人报告心理健康状况） 根据扩展的倾向性-持续性-损伤（PPI）模型，潜在的 短暂性 PLE 和转变为精神障碍的因素之间的区别因素是它们是否 持续且令人痛苦（即持续的 dPLE），本 K23 与 NIMH 战略目标 2 一致。 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的关键缺失空白 该应用程序将重点关注约 11,800 名青少年大脑儿童。 在获奖期间，每年都会对 9 至 11 岁的儿童进行认知发展研究。 分析将测试 PPI 模型假设，包括调查区分的最重要因素 从短暂的 dPLE 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、运动和言语发育迟缓， 和环境预测因素（不良童年经历、大麻使用；目标 1）。 认知、神经预测因素和环境风险因素的纵向变化是否区分持续的 最后，该应用程序还将通过检查临床来填补关键的研究空白。 持续 dPLE 的重要性、检查社会和教育功能障碍、寻求治疗 行为，并在 16 至 18 岁青少年时进行额外数据收集以评估基本费率 青少年中减轻精神病综合征 (APS) 的发生率支持持续 dPLE（目标 3）。 APS、申请人和一名硕士级临床医生将采访 ABCD 参与者的子集 (n=500) 及其 使用前驱综合症结构化访谈的父母/照顾者总体而言，申请人将实施。 严格的实践，包括对三分之二的目标和假设进行所有分析 数据，然后在未受影响的数据的三分之一上复制完全相同的模型。 发展性精神病谱系精神病理学、纵向分析、机器的多元化专家团队 学习和神经影像分析，这个科学严谨的提案将测试有关交叉的假设 持续与短暂 dPLE 的截面和纵向预测因子，用于早期早期应用 该应用程序解决了申请人培训中的几个差距。 这对于作为独立临床研究者的成功至关重要，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、神经影像分析）和 增加对发展性精神病谱系病理学领域的接触。