Connectome-based prediction and neurodevelopmental trajectories of alcohol phenotypes across development

摘要 早年开始饮酒与许多负面后果有关，包括显著增加 在以后的生活中有发展成酒精使用障碍的风险。早期误用和其他有问题的漏洞 酒精使用行为与大脑功能的个体差异有关。然而，很少有研究表明 旨在确定青少年饮酒行为的大脑预测因素(“神经标记物”)。身份识别 基于大脑的青少年饮酒行为预测对于更有效的早期发展是必不可少的 预防和干预努力。该方案结合了机器学习和纵向建模 方法1)识别预测早期饮酒和滥用酒精的神经网络和2)绘制 使用以下数据的大样本青年(N&GT；3000)中这些网络随时间的发展轨迹 三个唯一的、专有的和完整的数据集。神经网络导致酒精使用的脆弱性 青春期的行为将使用基于连接组的预测建模(CPM)来识别。物理服务器是一种 从大脑组织的个体模式生成行为预测的机器学习方法；即， 函数连通性矩阵。与传统的机器学习方法不同，CPM完全是数据驱动的，并且 不需要事先选择大脑区域或网络。因此，CPM既是一种预测工具，也是一种方法 识别构成特定行为基础的网络；即神经标记物。黑石物理服务器已成功用于 预测复杂的行为，包括未来的禁欲和其他与成瘾相关的表型。这项建议 将使用CPM来识别酒精起始的神经标记物，并预测青年时期向高风险饮酒的转变(AIM 1)。量化大脑功能的变化，例如生长曲线轨迹分析，是 对发育现象的表征。对发展轨迹的分析可以用来确定 特别是敏感的生长期，检测可能发出风险信号的变化，定义可修改的目标，并监控 环境和干预措施对发展的影响。虽然现有的数据表明与酒精有关 神经发育的改变，很少有研究评估神经发育之间的相互作用 随时间的轨迹和饮酒行为。已确定的网络与以下方面的发展轨迹 随着时间的推移，酒精使用行为将使用多水平建模(AIM 2)进行评估。这项提案代表了 首次尝试使用完整的数据来识别预测酒精引发和危险饮酒的神经网络- 在大样本青年中采用了驱动、机器学习的方法，并利用现有数据做到了这一点。这是一个关键的 朝着确定青年饮酒开始的可靠预测因素迈出了一步，并将揭示个体差异 代表易被滥用的因素。需要这样的预测者来理解发展中的 酒精表型的轨迹，并为早期风险模型和预防性干预努力提供信息。

Abstract Alcohol initiation at an early age is associated with numerous negative outcomes, including a significant increase in the risk of developing an alcohol-use disorder later in life. Vulnerability for early misuse and other problematic alcohol use behaviors have been linked to individual differences in brain function. However, few studies have sought to identify brain-based predictors (‘neuromarkers’) of alcohol use behaviors in youth. Identification of brain-based predictors of alcohol use behaviors in youth is essential for the development of more effective early prevention and intervention efforts. This proposal combines machine learning and longitudinal modeling approaches to 1) identify neural networks predictive of early alcohol initiation and misuse and 2) chart the developmental trajectories of these networks over time in a large sample of youth (N>3,000) using data from three unique, proprietary and completed datasets. Neural networks conferring vulnerability for alcohol use behaviors during adolescence will be identified using connectome-based predictive modeling (CPM). CPM is a machine-learning method of generating behavioral predictions from individual patterns of brain organization; i.e., functional connectivity matrices. Unlike traditional machine learning approaches, CPM is entirely data-driven and requires no a priori selection of brain regions or networks. As such, CPM is both a predictive tool and a method of identifying networks that underlie specific behaviors; i.e., neuromarkers. CPM has been successfully used to predict complex behaviors including future abstinence and other addiction-relevant phenotypes. This proposal will use CPM to identify neuromarkers of alcohol initiation and predict transitions to risky drinking in youth (AIM 1). Quantification of changes in brain function, e.g., growth curve trajectory analysis, is central to the characterization of developmental phenomena. Analyses of developmental trajectories can be used to identify particularly sensitive growth periods, detect variations that may signal risk, define modifiable targets, and monitor the impact of environment and interventions on development. While extant data indicate alcohol-related alterations in neural development, very few studies have assessed interactions between neurodevelopmental trajectories over time and alcohol-use behaviors. Developmental trajectories of identified networks in relation to alcohol use behaviors over time will be assessed using multilevel modeling (AIM 2). This proposal represents the first attempt to identify neural networks predictive of alcohol-initiation and risky drinking using a wholly data- driven, machine learning approach in a large sample of youth and does so using existing data. This is a critical step toward identifying a reliable predictor of alcohol initiation in youth and will shed light on individual difference factors representing vulnerability for misuse. Such predictors are needed to understand the developmental trajectories of alcohol phenotypes and to inform early risk models and preventative intervention efforts.