Application of a Bayesian strategy to ABCD: Identification of substance use risk and COVID-19 effects on neurodevelopment

贝叶斯策略在 ABCD 中的应用：识别物质使用风险和 COVID-19 对神经发育的影响

• 批准号: 10599090
• 负责人: Danilo Bzdok
• 金额: $ 31.8万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2027-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10599090
• 关键词: Acceleration; Address; Adolescence; Adolescent; Adoption; Age; Bayesian Modeling; Bayesian Prediction; Bayesian learning; Behavioral; Brain; COVID-19; COVID-19 impact; COVID-19 pandemic; COVID-19 pandemic effects; Cessation of life; Child; Child Development; Child Health; Cities; Collaborations; Communities; Complex; Corpus striatum structure; Data; Data Aggregation; Data Collection; Data Set; Development; Dietary Fiber; Economics; Event; Exhibits; Face; Family history of; Fingerprint; Functional Magnetic Resonance Imaging; Future; Generations; Geographic Locations; Growth; Healthcare Systems; Height; Home environment; Hospitalization; Individual; Individual Differences; Infection; Joints; Laboratories; Life; Life Style; Linear Regressions; Logistics; Longitudinal Studies; Measurable; Measures; Modeling; Neurobiology; Neurosciences; Outcome; Participant; Patients; Phenotype; Population; Probability; Process; Psychiatry; Psychosocial Stress; Public Health; Recording of previous events; Recreation; Reproducibility; Research; Rewards; Risk; Role; Series; Site; Social Distance; Source; Statistical Models; Structure; Substance Use Disorder; System; Thalamic structure; Time; Uncertainty; Unemployment; United States; Variant; Youth; clinical practice; cognitive development; cohort; complex data; course development; early onset substance use; experience; flexibility; insight; interest; neural; neurodevelopment; novel; pandemic disease; pandemic impact; perceived stress; predictive modeling; preventive intervention; psychosocial; repository; response; school closure; social; social media; substance use; theories; tool

Abstract Substance use initiation at an early age is associated with numerous negative outcomes, including increased likelihood of substance use disorders later in life. Multiple lines of evidence indicate that the risk for early substance use initiation is influenced by individual differences in neural development. The precise neural developmental mechanisms that give rise to heighted substance-use vulnerability remain poorly understood and The Adolescent Brain and Cognitive Development (ABCD) study provides an unprecedented opportunity to elucidate these mechanisms. However, children in this cohort now face a unique developmental challenge: entering adolescence during the COVID-19 pandemic. In direct response to PAR-19-162 (‘Accelerating the Pace of Child Health Research Using Existing Data from the ABCD Study’), this application aims to characterize neurodevelopmental trajectories of substance use risk with specific consideration of the societal and individual effects of COVID-19. Specifically, using Bayesian machine learning and hierarchical time-series modeling of longitudinal ABCD data, this proposal will establish, refine, and deploy models of normative trajectories in brain development and quantify deviations related to substance-use risk (AIM 1). Further, this effort will carefully contextualize the effects of the COVID-19 crisis as a US-wide event with deep consequences for child development (AIM 2). As a primary research product of this proposal, all derived models and functional connectivity metrics will be shared via ABCD’s central repository (AIM 3). This will include (i) complete neural ‘fingerprints’ or functional connectivity matrices for all task-based data from ages 10-14; (ii) derived normative ‘growth curves’, and (iii) the full generative probabilistic models for reuse by other laboratories. This key data contribution will relieve logistic burdens for a large number of research labs and further promote widespread use of ABCD data, propelling comparability and reproducibility of single-subject prediction studies towards identifying a reliable predictor of substance-use initiation in youth. This is a critical step toward precision psychiatry and will shed light on individual difference factors that contribute to vulnerability in the exigent context of the evolving COVID-19 pandemic. Such predictors are needed to understand the developmental trajectories of substance- use phenotypes and to inform early risk models and preventative intervention efforts.

摘要 早期开始使用药物与许多负面结果有关，包括增加 物质使用障碍的可能性以后的生活。多项证据表明， 物质使用起始受神经发育的个体差异的影响。精确的神经 对导致吸毒脆弱性加剧的发展机制仍然知之甚少， 青少年大脑和认知发展（ABCD）研究提供了前所未有的机会， 阐明这些机制。然而，这一群体的儿童现在面临着一个独特的发展挑战： 在新冠肺炎大流行期间进入青春期。直接响应PAR-19-162（“加快步伐 使用ABCD研究的现有数据进行儿童健康研究”），该应用程序旨在表征 物质使用风险的神经发育轨迹，特别考虑社会和个人 COVID-19的影响。具体而言，使用贝叶斯机器学习和分层时间序列建模， 纵向ABCD数据，该提案将建立，完善和部署大脑中的规范轨迹模型 开发和量化与物质使用风险相关的偏离（AIM 1）。此外，这一努力将仔细 将COVID-19危机的影响作为一个对儿童产生深刻影响的美国范围内的事件 开发（AIM 2）。作为本提案的主要研究成果，所有衍生模型和功能 连通性指标将通过ABCD的中央存储库（AIM 3）共享。这将包括（i）完整的神经系统 （二）10-14岁所有基于任务的数据的“指纹”或功能连接矩阵; “增长曲线”，以及（iii）供其他实验室重复使用的完整生成概率模型。这些关键数据 捐款将减轻大量研究实验室的后勤负担，并进一步促进广泛使用 ABCD数据，推动可比性和重复性的单一主题的预测研究，以确定 青年开始使用药物的可靠预测因素。这是迈向精确精神病学的关键一步， 阐明在不断变化的经济和社会发展的紧迫背景下造成脆弱性的个人差异因素， 2019冠状病毒病大流行。需要这些预测因子来理解物质的发展轨迹- 使用表型并为早期风险模型和预防性干预工作提供信息。