Multivariate Machine Learning to Characterize Opioid-induced Alterations in the Brain in Chronic Pain

项目概要/摘要 处方阿片类药物是一类有效的治疗疼痛的药物。然而，越来越多的研究表明， 描述了慢性疼痛患者长期（> 90天）使用阿片类药物的医源性后果，包括 痛觉过敏和执行功能受损。多巴胺是执行功能的重要调节剂。而 虽然已经注意到疼痛和行为的变化，但对大脑的形态、神经和 多巴胺能活性随着长期处方阿片类药物使用而随时间变化。与NIDA一致 战略计划目标1.3，K25提案旨在“确定药物使用、成瘾和康复的影响 关于大脑回路、行为和健康的研究”。具体而言，本研究 结合多层次的研究，包括结构和功能磁共振成像（MRI）， 正电子发射断层扫描（PET），定量感觉测试（QST）和神经心理学 执行功能的评估，并采用机器学习技术进行分析，以确定影响 长期处方阿片类药物对慢性疼痛患者大脑的影响。申请人将使用其先进的 神经影像学数据分析和建模的定量技能，以及QST培训， 认知神经心理学，慢性疼痛和成瘾的流行病学，以制定独立的研究计划 并成功竞争未来的R 01资金。为了实现所需的培训， 在这项研究中，申请人咨询了慢性疼痛研究和阿片类药物治疗专家， 药物滥用专家，神经心理学家，流行病学家，成像科学家和机器 在神经影像学领域的学习领导者，制定创新的学习和培训计划。40例患者 诊断上同质的慢性疼痛病症（即，慢性下背痛 与40例阿片类药物初治CLBP患者相比，将研究治疗以实现以下目的：1） 测量疼痛，认知能力，神经和多巴胺能活动，同时疼痛和执行 功能任务fMRI-PET以表征阿片类药物对CLBP疼痛处理和执行功能的影响; 2)在静息状态fMRI-PET期间测量内在脑活动，以确定与脑内活动相关的内在脑改变。 CLBP中长期使用阿片类药物; 3）应用高分辨率结构MRI测量阿片类药物诱导的 CLBP的形态学变化。这项研究是创新的，在其使用相结合的QST和神经， 心理测量与多模态成像和复杂的统计方法。具有重要意义 这是因为它采取了全面的方法来实现NIDA战略计划的目标。调查结果表明 告知有关疼痛护理和阿片类药物处方以及风险缓解的医疗决策 战略布局研究，培训和取得的成果将为申请人提供长期科学研究的平台 研究目标是成为一个独立的R 01资助，学院级的首席研究员， 翻译疼痛研究，旨在开发神经成像工具，具有临床应用。

PROJECT SUMMARY/ABSTRACT Prescription opioids are a potent class of drugs for treating pain. However, growing body of research has described iatrogenic consequences of long-term (> 90 days) opioid use in patients with chronic pain including hyperalgesia and impaired executive function. Dopamine is a critical modulator of executive function. While changes in pain and behavior have been noted, little is known about the brain’s morphology, neural and dopaminergic activity that change over time with long-term prescription opioid use. Consistent with the NIDA Strategic Plan objective 1.3, this K25 proposal seeks to “establish the effects of drug use, addiction, and recovery on brain circuits, behavior, and health” using neuroimaging-informed tools. Specifically, the present study combines multiple levels of investigation, including structural and functional Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), Quantitative Sensory Testing (QST) and neuropsychological assessments of executive function, and employ machine learning techniques for analysis to identify the effects of long-term prescription opioid use on the brain in chronic pain patients. The applicant will use her advanced quantitative skills in neuroimaging data analysis and modeling to training in QST, and experience in cognitive neuropsychology, epidemiology of chronic pain and addiction to develop an independent research plan in translational pain and successfully compete for future R01 funding. To achieve the training needed to facilitate this investigation, the applicant has consulted with an expert in chronic pain research and opioid therapy, a substance abuse specialist, a neuropsychologist, an epidemiologist, an imaging scientist, and a machine learning leader in neuroimaging field to develop an innovative study and training plan. 40 patients with a diagnostically homogeneous chronic pain condition (i.e., chronic low back pain; CLBP) on long-term opioid therapy, as compared to 40 opioid-naïve CLBP patients, will be studied to achieve the following Aims: 1) Measure pain, cognitive performance, neural and dopaminergic activity during concurrent pain and executive function task fMRI-PET to characterize the effects of opioids on pain processing and executive function in CLBP; 2) measure intrinsic brain activity during resting state fMRI-PET to identify intrinsic brain alterations associated with long-term opioid use in CLBP; and 3) apply high-resolution structural MRI to measure opioid-induced morphological changes in CLBP. This research is innovative in its use of combined QST and neuro- psychological measures with multimodal imaging and sophisticated statistical approaches. It is significant because of its comprehensive approach towards addressing the NIDA Strategic Plan objective. Findings stand to inform medical decision-making regarding pain care and opioid prescription, as well as risk mitigation strategies. The research, training and results obtained will provide a platform for applicant’s long-term scientific research goal of becoming an independent R01-funded, faculty-level principal investigator performing translational pain research aimed at developing neuroimaging tools to have clinical application.