Using Neuroimaging Markers to Understand Risk Factors and Consequences of Cannabis on Brain Structure and Function

使用神经影像标记物了解大麻对大脑结构和功能的危险因素和后果

• 批准号: 10468167
• 负责人: Jodi Gilman
• 金额: $ 12.83万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10468167
• 关键词: Address; Adolescence; Adolescent; Age; Anxiety; Area; Award; Base of the Brain; Behavioral; Behavioral Research; Big Data; Brain; Cannabis; Child; Childhood; Chronic; Clinical Trials; Cognition; Data; Data Set; Data Storage and Retrieval; Development; Drug usage; Educational process of instructing; Factor Analysis; Foundations; Funding; Future; Goals; Growth; Image; Impulsivity; Independent Scientist Award; Knowledge; Laboratories; Lasso; Learning; Light; Longevity; Measures; Medical Marijuana; Mental Depression; Mentors; Methods; Modeling; Neurosciences; Pain; Parents; Participant; Patient Self-Report; Patients; Phase; Population; Reporting; Reproducibility; Research; Research Project Grants; Research Training; Risk; Risk Factors; Running; Sampling; Scanning; Scientist; Solid; Statistical Data Interpretation; Structure; Substance Use Disorder; Techniques; Testing; Thinness; Time; Training; Validation; Visit; Wages; Work; addiction; behavioral outcome; career; career development; cognitive development; detection method; disorder risk; experimental study; improved; indexing; large datasets; longitudinal dataset; machine learning prediction; marijuana use disorder; neural correlate; neuroimaging; neuroimaging marker; parallel computer; predictive modeling; programs; relating to nervous system; skills; substance use; theories; tool; vector

Project Summary This application for an Independent Scientist Award (K02) requests support to develop a program of brain-based and behavioral research on substance use disorders (SUD), especially cannabis use disorder (CUD). My laboratory is supported in part by a funded R01 (DA039135) that seeks to understand the effects of medical marijuana on the brain, cognition, and escalation to CUD. In the next phase of my career, I propose to learn enhanced analytical techniques (e.g. machine learning and predictive modeling) that can capitalize on newly-available, large, longitudinal datasets that can answer fundamental questions about the development of SUD that cannot be answered in small populations. In addition, I propose a secondary training goal of gaining expertise in developmental neuroscience, to broaden the populations I can study to include children and adolescents at-risk for SUD. Together, these skillsets will allow me to greatly expand the types of questions I can ask, by allowing me to answer questions about SUD using both large observational data-sets, and also targeted experimental manipulations (such as my R01). My goal at the end of this K02 to have the tools to not only use big data to better understand risk factors and consequences of addiction in large samples, but also to use this knowledge to inform future experimental studies and clinical trials that I can conduct in my lab. The scientific focus of this application is a research project, using the publicly-available Adolescent Brain and Cognitive Development (ABCD) dataset of 11,877 children scanned longitudinally, to understand how trajectories of change in impulsivity associate with brain-based abnormalities and SUD risk. The aims for the ABCD analysis project are to (1) define the factorial structure of impulsivity in the ABCD dataset as it may portend SUD risk (using a confirmatory factor model which will be applied to the array of behavioral tasks, self- report measures, and parent reports), (2) determine structural and functional brain measures that correlate with impulsivity, and (3) examine how longitudinal changes in brain signatures of impulsivity are associated with risk for SUD. To accomplish these aims, I will participate in courses and train with collaborators with expertise in developmental neuroscience and in statistical analyses of big data. The skills I learn in the K02 period will be used not only to query the ABCD dataset, but also to enhance the sophistication of neuroimaging analyses of my R01 data and any future data collected in my laboratory. This training will enhance my laboratory by significantly expanding the questions I can address, improving my analytic skills, furthering my ability to collaborate, and providing a foundation for mentoring junior scientists. In the absence of K02 support, I will need to cover my salary through teaching and administrative work, which would detract from my research, training, and mentoring activities. This K02 award will provide protected time to develop my research program and receive this training that will be critical for my career development as an addiction neuroscientist.

项目摘要 本独立科学家奖(K02)申请书请求支持制定一项 物质使用障碍，特别是大麻使用障碍的脑部和行为学研究 (CUD)。我的实验室部分得到了R01(DA039135)的资助，该基金试图了解 医用大麻对大脑、认知的影响，并升级为CUD。在我职业生涯的下一个阶段，我建议 学习可充分利用的增强分析技术(例如机器学习和预测建模) 新获得的大型纵向数据集，可以回答有关开发的基本问题 在少数人群中无法回答的疾病。此外，我提出了一个次要的培训目标，即获得 发展神经科学方面的专业知识，以扩大我可以研究的人群，包括儿童和 青少年有患上自闭症的风险。总而言之，这些技能组合将使我能够极大地扩展我的问题类型 通过允许我使用大型观测数据集回答有关SUD的问题，以及 有针对性的实验操作(如我的R01)。我在K02课程结束时的目标是拥有工具，而不是 仅使用大数据在大样本中更好地了解成瘾的风险因素和后果，还可以 利用这些知识为我未来的实验研究和临床试验提供信息，我可以在我的实验室进行这些研究。 这个应用程序的科学焦点是一个研究项目，使用可公开获得的青少年 对11877名儿童的大脑和认知发展(ABCD)数据集进行了纵向扫描，以了解 冲动的变化轨迹如何与基于大脑的异常和SUD风险相关联。目标是 ABCD分析项目的目的是(1)定义ABCD数据集中冲动性的阶乘结构 预示SUD风险(使用验证性因素模型，该模型将应用于一系列行为任务、自我 报告测量和父报告)，(2)确定与以下各项相关的结构和功能大脑测量 冲动，以及(3)研究大脑冲动信号的纵向变化是如何与风险相关的 对苏德来说。为了实现这些目标，我将参加课程，并与具有以下专业知识的合作者一起培训 发展神经科学和大数据的统计分析。我在K02期间学到的技能将是 不仅用于查询ABCD数据集，而且还用于增强神经成像分析的复杂性 我的R01数据和在我的实验室中收集的任何未来数据。这次培训将加强我的实验室 大大扩展我可以解决的问题，提高我的分析技能，进一步提高我的能力 合作，并为指导初级科学家提供基础。在没有K02支持的情况下，我将 我需要通过教学和行政工作来支付工资，这会影响我的研究， 培训和辅导活动。这项K02奖将为我的研究计划提供有保障的时间 并接受这种培训，这对我作为一名上瘾神经学家的职业发展至关重要。