Interpretable Deep Forecasting of Hazardous Substance Use during High School

高中期间有害物质使用的可解释深度预测

• 批准号: 10706556
• 负责人: Kilian Maria Pohl
• 金额: $ 46.18万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-30 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10706556
• 关键词: 16 year old; Accounting; Adolescence; Adolescent; Adverse effects; Affect; Age; Alcohols; American; Attenuated; Behavior; Behavior Control; Brain; Brain imaging; COVID-19 pandemic; Cannabis; Cellular Phone; Cognition; Cognitive; Complex Analysis; Data; Data Set; Development; Developmental Delay Disorders; Dimensions; Emotions; Enrollment; Environment; Environmental Risk Factor; Family history of; Future; Hazardous Substances; Health; Heterogeneity; High School Student; Impulsivity; Individual; Link; Machine Learning; Magnetic Resonance Imaging; Maps; Measurement; Measures; Mediating; Mental Health; Modeling; Motor; Negative Reinforcements; Participant; Patient Self-Report; Pattern; Peer Pressure; Population; Positive Reinforcements; Prevention; Prevention program; Preventive care; Process; Publishing; Rewards; Risk; Risk Assessment; Risk Factors; Risk Taking; Site; Sleep; Structure; Substance Use Disorder; Surveys; Symptoms; Technology; Testing; Thinness; Time; Tobacco; Trauma; Update; Visit; Youth; aged; boys; brain volume; cognitive control; cognitive development; cognitive testing; cohort; cost; demographics; experience; follow-up; frontal lobe; girls; high school; improved; learning strategy; modifiable behavior; morphometry; neural circuit; neuroadaptation; neurodevelopment; novel; personalized approach; poor sleep; precision medicine; resilience; risk mitigation; risk prediction; sex; sleep behavior; substance use; substance user; supervised learning

ABSTRACT Substance use disorder (SUD) affects over 20 million Americans, causing personal strife and cost. A major SUD risk factor is hazardous use (HU) of substances during high school (HUSH), when the brain continues to develop, rendering it especially sensitive to environmental factors. Identifying the effects of and risk for HUSH generally focuses on selected interactions between fixed (i.e., trauma, demographics) and modifiable (i.e., mental health, emotion, environment, behavior, sleep) factors, and occasionally brain development features differentiating substance using and non-using cohorts. Results have yielded limited improvements to risk assessment. Thus, we propose a paradigm shift in the study of HUSH, replacing measurement selection and population splitting with mapping individuals to comprehensive multi-dimensional measures. The objective of our novel data-driven process is to identify constellations of fixed and modifiable factors forecasting HUSH in individuals. As our analysis is based on public data sets that include brain imaging, we will document interactions of those constellations with neural circuits to determine neuromechanistic underpinnings of HUSH. Myriad factors influence hazardous substance use, such as the fixed contributors of sex and family history of SUD; the modifiable factors of unhealthy sleep habits, peer pressure, and risk-taking propensity; and brain development characterized by an atypical imbalance between emotion and control network. We will model this heterogeneity via machine (deep) learning technology identifying constellations of measurements in line with our hypotheses regarding prevention, i.e., modifiable behaviors interacting with anomalies in neuroadaptation forecasting HUSH initiation. Aim 1 will forecast initiation of HUSH in the last years of high school based on the closest visit before turning age 16 years for no/low substance users in National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA, N =350) and confirm findings on the larger Adolescent Brain Cognitive Developmental cohort (ABCD, N>11K). HUSH will be defined by substance use criteria recorded through annual self-reports and refined by weekly surveys administered via cell phones. Aim 2 will create a self-supervised learning model explicitly tracking over time interactions across modifiable behaviors, fixed factors, and brain circuits important for hypothesis testing. We will cross-validate the model by identifying HUSH for each high school year and forecast based on data collected prior to high school. We will explore dynamically updating the model as data are acquired to predict resilience, i.e., youth who abstain from hazardous substance use during high school, despite having risk factors such as traumatic and untoward COVID pandemic experiences. Each aim is linked to hypothesis testing concerning factors that can be altered to mitigate the risk of HUSH. This project will be the first to provide patterns accurately forecasting the risk of HUSH on an individual level. Accurately computing this risk would be foundational for enhancing prevention efforts.

摘要 物质使用障碍(SUD)影响着2000多万美国人，导致个人冲突和成本。一位少校 SUD的危险因素是在高中期间危险地使用物质(HU)，此时大脑继续 发展，使其对环境因素特别敏感。确定封口的影响和风险 通常集中在固定(即，创伤、人口统计)和可修改(即， 心理健康、情绪、环境、行为、睡眠)因素，偶尔还有大脑发育的特点 区分使用和不使用物质的队列。结果对风险的改善是有限的 评估。因此，我们提出了一种研究沉默的范式转变，取代了测量选择和 通过将个体映射到综合的多维措施来进行种群分裂。的目标是 我们的新的数据驱动的过程是识别预测沉默的固定和可修改因素的星座 个人。由于我们的分析是基于包括脑成像在内的公共数据集，我们将记录 这些星座与神经回路的相互作用决定了沉默的神经机制基础。 影响有害物质使用的因素有很多，比如性行为和家族史的固定因素 不健康的睡眠习惯、同伴压力和冒险倾向等可改变因素；以及大脑 以情绪和控制网络之间的非典型失衡为特征的发展。我们会做模特 通过机器(深度)学习技术识别直线上的测量星座的这种异质性 与我们关于预防的假设相一致，即可修改的行为与 神经适应预测沉默的开始。目标1将预测在HIGH的最后几年开始沉默 根据全国戒毒/低吸毒者年满16周岁前最近访问的学校 酒精与青春期神经发育联合会(NCANDA，N=350)，并确认关于 较大的青少年大脑认知发展队列(ABCD，N&GT；11K)。闭嘴将由定义 通过年度自我报告记录的物质使用标准，并通过执行的每周调查进行改进 手机。目标2将创建一个自我监督的学习模型，明确跟踪跨时间的交互 可修改的行为、固定的因素和对假设检验很重要的大脑回路。我们会交叉验证 该模型通过确定每个高中学年的沉默并基于高中之前收集的数据进行预测 学校。我们将探索随着数据的获取而动态更新模型，以预测复原力，即青年 尽管有创伤等危险因素，但他们在高中期间不使用危险物质 以及令人不快的COVID大流行经历。每个目标都与关于以下因素的假设检验相关联 可以改变以降低保密风险。 这个项目将是第一个在个人层面上准确预测保密风险的模式。 准确计算这一风险将是加强预防工作的基础。