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年对未来的调查进行的监视发现，有6.5％的高中生每天吸烟大麻，高于五年前的5.1％。同时，只有44.1％的年龄较大的青少年将常规大麻使用视为有害（自1979年以来最低）。由于感知的伤害是使用大麻（MJ）合法化的主要指标，在美国取得了成功，因此这些趋势将继续并可能导致新的健康危机。尽管有关MJ愤怒的危害的辩论，但神经影像学研究并未意识到其有关这一关键社会问题的全国对话的全部潜力。例如，磁共振成像（MRI）研究表明，与MJ使用相关的大脑结构和功能有些差异，但是，发现是模棱两可的。造成这种情况的一个可能原因是，MJ使用的效果与个人中的特定药物使用模式交织在一起，以至于很难使用常规统计方法识别MJ用户和控件之间的差异（因为MJ用户的差异很大）。为了解决这一差距，该项目结合了来自五个不同NIDA资助的慢性MJ使用的MRI数据，从而从更大的MJ用户样本中获得了好处。然后将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效果，而不是被它混淆。该方法的另一个优势在于，它将跨多个MRI测量的信息整合在一起，以将灰质和白质结构与大脑网络的电路级行为联系在一起。现有数据的分析将着重于调查MJ使用对MJ用户中执行功能和认知控制的影响：1）评估在认知任务期间协调的大型大脑网络之间的互动该研究的第三年将提供。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，这将导致新型的生物标志物针对易受伤害的个体的检测，并提出新的诊断，预防和治疗策略。拟议的研究将与NIDA的治疗和预防策略目标一致，将：1）提高我们对慢性大麻使用对大脑结构和网络电路的神经生物学影响的理解，2）使用现有数据的新型统计分析与其他数据集中的验证相结合，以识别弱点的个体，以识别脆弱的个体，以鉴定弱点的人，并提出新诊断的诊断，并提出新的诊断，预先诊断，预先诊断和新诊断策略。