UC San Diego Clinical and Translational Research Institute

摘要 物质使用与自杀想法和行为（STB;自杀意念，自杀）的共同发生 在青少年中，这是一种既常见又致命的疾病。报告共病药物使用的青少年， 有自杀倾向人自杀的风险很高。不幸的是，临床医生预测两者出现的能力仍然存在， 接近偶然，目前的护理标准依赖于非生物学措施，如患者自我报告 临床印象 在这项提案中，我们利用机器学习来更好地了解神经健康对人类健康的预测能力。 使用青少年大脑的数据，物质使用，自杀倾向及其共同发生的出现 认知发育（ABCD）研究（n = 11，878）。我们将研究一套结构性的大脑和神经认知 样本在三个时间点的特征：基线（9 - 10岁）、第2年（11 - 12岁）和第4年（13 - 14岁）。 14）。我们的主要目的是识别与纵向运动相关的脑结构和神经认知特征。 （1）物质使用，（2）自杀倾向，（3）共病物质使用和自杀倾向的变化。很少 研究已经检查了可能在自杀之前的神经因素，没有研究已经检查了这些因素是如何发生的。 在一个大的纵向样本中，一些因素可能预测青少年前的共病物质使用和自杀倾向 和青少年。 通过在不同导师团队的指导下完成这项研究所获得的培训将为 作为一名具有机器学习专业知识的跨学科临床科学家， 神经科学最后，这项研究的结果将为神经科学的评估提供建议 以及开发一种针对可改变的神经生物学的连续性认知补救干预， 与青春期前和青少年同时发生的物质使用和自杀倾向相关的危险因素。 这项研究有可能产生很大的公共卫生影响，并有望帮助改善生活质量， 自杀风险高的青少年和青少年。

ABSTRACT The co-occurrence of substance use and suicidal thoughts and behaviors (STBs; suicidal ideation, suicide attempt) in adolescents is both common and deadly. Adolescents who report comorbid substance use and suicidality are at high risk of suicide. Unfortunately, clinicians’ ability to predict the emergence of both remains near-chance, with the current standard of care relying on non-biological measures, such as patient self-report and clinical impression. In this proposal, we leverage machine learning to better understand the predictive power of neural health on the emergence of substance use, suicidality, and their co-occurrence, using data from the Adolescent Brain Cognitive Development (ABCD) study (n=11,878). We will examine a set of structural brain and neurocognitive features of the sample at three-time points: Baseline (ages 9-10), Year 2 (ages 11-12), and Year 4 (ages 13- 14). Our primary aim is to identify structural brain and neurocognitive features associated with longitudinal changes in (1) substance use, (2) suicidality, and (3) comorbid substance use and suicidality. Very little research has examined neural factors that may precede suicidality, and no research has examined how these factors might predict comorbid substance use and suicidality in a large, longitudinal sample of preadolescents and adolescents. The training gained by completing this research with guidance from a diverse team of mentors will pave the way for career independence as an interdisciplinary clinical scientist with expertise in machine learning and neuroscience. Finally, the results of this study will suggest avenues for neuroscience-informed assessments and the development of an adjunctive cognitive remediation intervention targeting modifiable neurobiological risk factors associated with co-occurring substance use and suicidality for preadolescents and adolescents. This study has the potential for high public health impact and promise to help improve the quality of life for preadolescents and adolescents at high risk for suicide.