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

摘要 在幼年开始使用药物与许多负面后果有关，包括增加 在以后的生活中出现物质使用障碍的可能性。多条证据表明，早期死亡的风险 物质使用的启动受到神经发育个体差异的影响。精确的神经 导致物质使用脆弱性增加的发展机制仍然鲜为人知， 青少年大脑和认知发展(ABCD)研究提供了一个前所未有的机会 阐明这些机制。然而，这一群体中的儿童现在面临着一个独特的发展挑战： 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研究的现有数据的儿童健康研究)，该应用程序旨在表征 具体考虑社会和个人的物质使用风险的神经发育轨迹 新冠肺炎的影响。具体来说，使用贝叶斯机器学习和分层时间序列建模 纵向ABCD数据，这项提议将建立、改进和部署大脑中的标准化轨迹模型 制定和量化与物质使用风险有关的偏差(目标1)。此外，这一努力将谨慎地 将新冠肺炎危机的影响背景化为全美范围内的事件，对儿童造成严重后果 发展(目标2)。作为本提案的主要研究产品，所有派生的模型和泛函 连接指标将通过ABCD的中央存储库(AIM 3)共享。这将包括(I)完整的神经 10-14岁所有基于任务的数据的“指纹”或功能连接矩阵；(2)衍生的规范性数据 “生长曲线”，以及(Iii)供其他实验室重复使用的全生成概率模型。此关键数据 贡献将减轻大量研究实验室的后勤负担，进一步促进广泛使用 ABCD数据的可比性和再现性，推动了单一主题预测研究的可比性和可重复性 青年时期开始使用物质的可靠预测指标。这是迈向精确精神病学的关键一步，并将 阐明在不断演变的紧迫背景下导致脆弱性的个体差异因素 新冠肺炎大流行。需要这样的预测者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努力提供信息。

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.