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.

抽象的 过早开始使用药物会带来许多负面后果，包括增加 多种证据表明，晚年出现物质使用障碍的可能性。 物质使用的开始受到神经发育的个体差异的影响。 导致药物使用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 然而，该群体的儿童现在面临着独特的发展挑战： 在 COVID-19 大流行期间进入青春期。 直接响应 PAR-19-162（“加快步伐”） 儿童健康研究使用 ABCD 研究的现有数据”），该应用程序旨在表征 物质使用风险的神经发育轨迹，具体考虑社会和个人 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，该提案将建立、完善和部署大脑规范轨迹模型 此外，这项工作将仔细研究与物质使用风险相关的发展和定量偏差。 将 COVID-19 危机的影响视为一场对儿童产生深远影响的美国范围内的事件 作为本提案的主要研究产品，所有衍生模型和功能。 连接性指标将通过 ABCD 的中央存储库（AIM 3）共享，这将包括 (i) 完整的神经网络。 10-14 岁所有基于任务的数据的“指纹”或功能连接矩阵 (ii) 导出的规范； “增长曲线”，以及（iii）可供其他实验室重复使用的完整生成概率模型。 贡献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，推动单受试者预测研究的可比性和可重复性，以识别 这是青少年开始物质使用的可靠预测因素，这是迈向精准精神病学和意志的关键一步。 揭示了在不断变化的紧急背景下导致脆弱性的个体差异因素 COVID-19 大流行需要这样的预测因子来了解物质的发展轨迹。 使用表型并为早期风险模型和预防性干预工作提供信息。