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 该申请申请独立科学家奖（K02）请求支持，以制定一个计划 关于药物使用障碍（SUD）的基于大脑的和行为研究，尤其是大麻使用障碍 （cud）。我的实验室得到了资助的R01（DA039135）的部分支持，该R01（DA039135）试图了解 大麻大麻，认知和升级为CUD。在我职业生涯的下一个阶段，我建议 学习增强的分析技术（例如机器学习和预测建模），可以利用 新的可用，大型的纵向数据集，可以回答有关发展的基本问题 在少数人群中无法回答的SUD。此外，我提出了获得的二级培训目标 发育神经科学方面的专业知识，以扩大我可以研究的人群，以包括儿童和 青少年在SUD风险上。这些技能共同使我大大扩大了我的问题类型 可以通过允许我回答有关SUD的问题，也可以问问有关SUD的问题 有针对性的实验操作（例如我的R01）。我在此K02结束时的目标是拥有不可能的工具 仅使用大数据来更好地了解大型样本中成瘾的风险因素和后果，但也 使用这些知识为我可以在实验室中进行的未来实验研究和临床试验提供信息。 该应用程序的科学重点是一个研究项目，使用了公开的青少年 11,877名儿童纵向扫描的大脑和认知发展（ABCD）数据集，以了解 冲动性变化的轨迹如何与基于大脑的异常和SUD风险相关联。目的 ABCD分析项目是（1）定义ABCD数据集中冲动性的阶乘结构 Portend SUD风险（使用将应用于行为任务的验证因素模型，自我 报告措施和父母报告），（2）确定与之相关的结构和功能性脑测量 冲动性和（3）检查冲动性脑签名的纵向变化与风险有关 对于SUD。为了实现这些目标，我将参加课程，并与具有专业知识的合作者一起培训 发育神经科学和大数据的统计分析。我在K02时期学习的技能将是 不仅用于查询ABCD数据集，还用于增强神经影像学分析的复杂性 我的R01数据以及我实验室收集的任何未来数据。这项培训将通过 显着扩大我可以解决的问题，提高了我的分析技能，增进了我的能力 合作，为指导初级科学家提供基础。在没有K02支持的情况下，我会 需要通过教学和行政工作来支付我的工资，这会损害我的研究， 培训和指导活动。该K02奖将为制定我的研究计划提供受保护的时间 并接受这项对我作为成瘾神经科学家职业发展至关重要的培训。