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Multi-modal MRI data fusion to assess neurobiological effects of marijuana use

多模态 MRI 数据融合评估大麻使用的神经生物学影响

基本信息

批准号：
9095283
负责人：
LISA D NICKERSON
金额：
$ 23.46万
依托单位：
MCLEAN HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-07-15 至 2018-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9095283
关键词：
Address Affect Behavior Biological Markers Brain Brain imaging Brain region Chronic Cognitive Data Data Analyses Data Set Detection Diffusion Magnetic Resonance Imaging Disease Drug abuse Drug usage Functional Magnetic Resonance Imaging Funding Future Genetic Goals Health Hour Image Impairment Individual Lead Life Style Link Magnetic Resonance Imaging Marijuana Marijuana Smoking Measurable Measurement Measures Medical Marijuana Methods Monitor Morphology Movement National Institute of Drug Abuse Neurobiology Nicotine Pattern Predictive Value Predictive Value of Tests Prevention Prevention strategy Rage Research Research Personnel Sampling Scanning Statistical Methods Structure Surveys Task Performances Teenagers Testing Time Validation addiction brain shape cognitive control cognitive function cognitive task drug of abuse executive function gray matter imaging biomarker imaging modality innovation marijuana legalization marijuana use marijuana use disorder marijuana user neuroimaging novel novel diagnostics novel marker response sex social treatment strategy trend twelfth grade white matter

项目摘要

DESCRIPTION (provided by applicant): The 2012 Monitoring the Future survey found that 6.5% of high school seniors smoke marijuana daily, up from 5.1% from five years ago. At the same time, only 44.1% of older teens see regular marijuana use as harmful (the lowest since 1979). Because perceived harm is a key indicator of use and movements to legalize marijuana (MJ) are succeeding in the US, these trends will continue and may lead to a new health crisis. While the debate as to the harms of MJ rages, neuroimaging studies have not realized their full potential to inform the national dialogue on this key social issue. For example, magnetic resonance imaging (MRI) studies suggest some differences in brain structure and function associated with MJ use, however, the findings are equivocal. One possible reason for this is that the effects of MJ use are so intertwined with specific drug use patterns in the individual that it becomes difficult to identify differences between MJ users and controls using conventional statistical methods (because there is such large variability in MJ users). To address this gap, this project combines MRI data from five different NIDA-funded studies of chronic MJ use to derive benefit from having a much larger sample of MJ users. A new statistical method suitable only with large datasets, called data fusion, will then be applied to the combined dataset. Data fusion capitalizes on the wide range of effects that MJ use may engender, rather than being confounded by it. A further strength of this method is that it integrates information across multiple MRI measurements to link together, for example, gray and white matter structure with circuit-level behavior of brain networks. Analyses of extant data will focus on investigating the effects of MJ use on executive function and cognitive control in MJ users by: 1) assessing interactions between large-scale brain networks that coordinate together during cognitive tasks, 2) applying the data fusion approach to link cognitive network function with brain structure and, in turn, disentangle the impact of chronic MJ use, and 3) most importantly, testing the predictive value of these findings in another large dataset that will be available in the third year of this study. Validating relationships between structure-function patterns and MJ use in a new sample is a significant advancement to addiction neuroimaging that will lead to novel biomarkers to target the detection of vulnerable individuals and to suggest new diagnostic, prevention and treatment strategies. Consistent with NIDA's strategic goals of treatment and prevention, the proposed research will: 1) Advance our understanding of the neurobiological effects of chronic marijuana use on brain structure and network circuitry, 2) Use novel statistical analyses of extant data combined with validation in a different dataset to identify biomarkers to characterize vulnerable individuals and suggest new diagnostic, prevention, and treatment strategies.

描述（由申请人提供）：2012 年“监测未来”调查发现，6.5% 的高中生每天吸食大麻，高于五年前的 5.1%。与此同时，只有 44.1% 的老年青少年认为经常吸食大麻有害（这是 1979 年以来的最低水平）。由于感知危害是大麻使用的关键指标，并且大麻合法化 (MJ) 运动在美国取得了成功，因此这些趋势将持续下去，并可能导致新的健康危机。尽管关于 MJ 危害的争论十分激烈，但神经影像学研究尚未充分发挥其潜力，为有关这一关键社会问题的全国对话提供信息。例如，磁共振成像 (MRI) 研究表明，与使用 MJ 相关的大脑结构和功能存在一些差异，但研究结果是模棱两可的。造成这种情况的一个可能原因是，MJ 使用的影响与个体的特定药物使用模式密切相关，以至于使用传统统计方法很难识别 MJ 使用者和对照组之间的差异（因为 MJ 使用者之间存在很大的变异性）。为了解决这一差距，该项目结合了 NIDA 资助的五项不同的慢性 MJ 使用研究的 MRI 数据，以便从更大的 MJ 用户样本中获益。然后，一种仅适用于大型数据集的新统计方法（称为数据融合）将应用于组合数据集。数据融合利用了 MJ 使用可能产生的广泛影响，而不是被它所混淆。这种方法的另一个优点是，它整合了多个 MRI 测量的信息，将其链接在一起，例如灰质和白质结构与大脑网络的电路级行为。对现有数据的分析将侧重于调查 MJ 使用对 MJ 用户的执行功能和认知控制的影响，方法是：1）评估在认知任务期间相互协调的大规模大脑网络之间的相互作用，2）应用数据融合方法将认知网络功能与大脑结构联系起来，进而理清长期使用 MJ 的影响，3）最重要的是，在另一个大型数据集中测试这些发现的预测价值将在本研究的第三年推出。在新样本中验证结构功能模式与 MJ 使用之间的关系是成瘾神经影像学的重大进步，这将导致新的生物标志物能够针对易受伤害的个体进行检测，并提出新的诊断、预防和治疗策略。与 NIDA 的治疗和预防战略目标相一致，拟议的研究将：1）加深我们对长期吸食大麻对大脑结构和网络电路的神经生物学影响的理解，2）利用现有数据的新颖统计分析结合不同数据集中的验证来识别生物标志物来表征易感个体，并提出新的诊断、预防和治疗策略。