Neurocognitive Fingerprints of Substance Use and Misuse in Adolescents

摘要 酒精和毒品使用率在整个青春期(10-21岁)急剧上升，在年轻人中达到顶峰 成年了，然后就没落了。约10%的美国青少年称过去曾饮酒 第一个月。到了八年级，美国大约20%的年轻人尝试过大麻，50%的年轻人尝试过大麻 已经在10年级1完成了这项工作。重酒和非法药物使用率最高发生在#年。 青春期2。物质使用和误用的增加突显了理解 青春期易受药物使用相关行为的影响。实质性的研究环节 神经认知过程，如反应抑制、工作记忆和成本效益决策，以 与滥用物质有关的行为(例如，酗酒、共用针头)和物质使用 精神错乱。前瞻性研究表明，神经认知的个体差异与认知控制有关 尤其是情感敏感，会增加青春期药物使用行为的风险。 然而，这项工作在组合多种数据类型的能力方面受到了限制，例如神经成像指标 和行为数据，据称挖掘潜在的特征(即神经认知能力)并提取 对个体预测有用的“指纹”。该提案计划使用来自青少年大脑的数据 认知发展℠(ABCD)研究，收集每年大约 12,000名青年，以实施创新的多层次方法框架，以便(1)制定 贝叶斯非参数降维算法工具包，允许降维 多维数据集(例如，平均反应时间、跨多个任务的大脑连通性测量)转换为 潜在特征的集合小得多，以及(2)使用所提取的潜在特征来预测发病， 发育期青少年物质使用的维持和性质。通过准确地识别和指定 潜在的认知-情感、行为和神经生物学机制的变化，这一建议提供了 为制定预防和治疗方法提供创新的经验基础 重要的临床终点，如与物质使用有关的问题。此外，本研究还提供了一种 通过整合来促进行为改变的发起、个性化和维护的框架 在理论和方法领域开展工作。

ABSTRACT Rates of alcohol and drug use rise sharply throughout adolescence (ages 10-21), peak in young adulthood, and then decline. Approximately 10% of adolescents in the US report consuming alcohol in the past month1. By the eighth grade, approximately 20% of youth in the US have tried cannabis and 50% of youth have done so by the 10th grade1. The highest rates of heavy alcohol and illicit substance use occur during adolescent years2. This increase in substance use and misuse underscores the importance of understanding vulnerability for substance use-related behaviors during adolescence. Substantial research links neurocognitive processes, such as response inhibition, working memory, and cost-benefit decision-making, to behaviors related to the misuse of substances (e.g., binge drinking, sharing needles) and substance use disorders. Prospective studies suggest that individual differences in neurocognition related to cognitive control and affective sensitivity, in particular, contribute to the risk for substance use behavior in adolescence. However, this work has been limited in its ability to combine multiple datatypes, such as neuroimaging metrics and behavioral data, purportedly tapping an underlying latent feature (i.e., neurocognitive capacity) and extract `fingerprints' that are useful for individual prediction. This proposal plans to use data from the Adolescent Brain Cognitive Development℠ (ABCD) Study, which collects annual measures of neurocognition for approximately 12,000 youth, to implement an innovative multilevel methodological framework in order to (1) develop a Bayesian non-parametric dimensionality reduction algorithm toolkit that allows for the reduction of a multidimensional dataset (e.g., mean reaction times, brain connectivity measures across multiple tasks) into a much smaller collection of latent features and (2) use the extracted latent features to predict the onset, maintenance, and nature of substance use in developing adolescents. By precisely identifying and specifying the variation in underlying cognitive-affective, behavioral, and neurobiological mechanisms, this proposal offers an innovative empirical foundation for the development of prevention and treatment methods that address important clinical endpoints such as substance use-related problems. Moreover, this research provides a framework for promoting the initiation, personalization, and maintenance of behavior change by integrating work across theoretical and methodological domains.