Neurocognitive Fingerprints of Substance Use and Misuse in Adolescents

摘要 酒精和毒品的使用率在整个青春期（10-21岁）急剧上升， 成年，然后衰退。在美国，大约10%的青少年报告过去饮酒。 月1.到了八年级，美国大约20%的青少年尝试过大麻，50%的青少年 在10年级时就这样做了。酗酒和非法药物使用率最高的时期是在 青春期2.药物使用和滥用的增加突出了理解的重要性 在青春期与物质使用有关的行为的脆弱性。大量研究链接 神经认知过程，如反应抑制，工作记忆和成本效益决策， 与滥用物质有关的行为（例如，酗酒，共用针头）和物质使用 紊乱前瞻性研究表明，神经认知的个体差异与认知控制有关 特别是情感敏感性，会增加青少年使用物质行为的风险。 然而，这项工作在联合收割机结合多个数据库（如神经成像指标）的能力方面受到限制 和行为数据，据称是挖掘潜在的潜在特征（即，神经认知能力）和提取物 对个人预测有用的“指纹”。这项提案计划使用来自青少年大脑的数据 认知发展研究（ABCD），每年收集大约 12 000名青年，实施一个创新的多层次方法框架，以便（1）制定一个 贝叶斯非参数降维算法工具包，允许减少一个 多维数据集（例如，平均反应时间，多个任务之间的大脑连接测量）转化为 潜在特征的小得多的集合以及（2）使用所提取的潜在特征来预测发作， 维护，和性质的物质使用在发展中的青少年。通过精确识别和指定 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奠定创新的经验基础， 重要的临床终点，如物质使用相关问题。此外，这项研究提供了一个 通过整合促进行为改变的启动、个性化和维持的框架 跨理论和方法论领域的工作。

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.