Multivariate Modeling of the Neural Mechanisms of Treatment Response in Opioid Addiction

项目总结/摘要 拟议的K 01项目将使用多模态磁共振成像（MRI）和机器学习（ML） 阐明阿片类药物使用障碍年轻成人治疗失败的神经认知过程 （OUD）。年轻人患OUD和致命阿片类药物过量的风险特别高。每月注射 缓释阿片拮抗剂纳洛酮（XR-NTX）是一种高效的OUD治疗， 非常适合年轻人。然而，XR-NTX依从性和复发显示出相当大的个体差异性， 并且与这种变异性相关的行为和临床因素仍然是不确定的。以前的研究 已经证明了多模态MRI和ML技术阐明神经认知因素的潜力 这有助于治疗反应超越行为和临床措施。该项目将利用 最先进的MRI和ML方法来模拟预测XR-NTX治疗的大脑结构和功能 年轻的成年人与OUD。该研究将在治疗前和治疗期间对18-34岁的OUD患者进行评估。 XR-NTX治疗的前三个月，这是与治疗脱落率最高相关的时期。 主要结局将是通过每周尿液毒理学和自我报告证实的阿片类药物复发。次级 结果将是不依从，定义为未能完成前三次注射。该项研究将集中 脑结构和功能的五个基线测量值可能预测治疗反应：1） 灰质体积; 2）与腹侧纹状体的功能连接; 3）对阿片类线索的反应性; 4）抑制性 （5）自我评价。ML技术将用于揭示大脑结构/功能的模式 与每一个结果变量相关联。根据文献和初步研究结果，我们预计， 将MRI与行为和临床评估相结合将更好地解释XR-NTX的个体差异 与单独使用行为和临床变量相比，OUD年轻成人的治疗结果。数据 将揭示导致这一关键人群治疗失败风险的新大脑机制。的 该项目还将作为一个培训工具，为博士Zhenhao石，以提高他的临床和计算技能 并促进其独立的职业发展。具体而言，将使施博士实现五项培养 目标：1）提高他在临床研究方法学方面的知识; 2）获得实践经验， 领先的临床项目; 3）掌握ML和多变量方法; 4）应用多模态MRI 技术转化和临床研究;和5）提高他的一般独立研究技能 包括领导力，网络，协作，科学写作和语法。通过结合 通过教学和实践活动，该项目将满足施博士的培训需求，并使他能够过渡到一个 成功和独立的研究生涯中应用先进的计算方法， 物质使用障碍及其治疗的神经科学研究。

PROJECT SUMMARY/ABSTRACT The proposed K01 project will use multimodal magnetic resonance imaging (MRI) and machine learning (ML) to elucidate the neurocognitive processes underlying treatment failure in young adults with opioid use disorder (OUD). Young adults are at particularly high risk of OUD and fatal opioid overdose. The monthly injectable extended-release opioid antagonist naltrexone (XR-NTX) is a highly effective OUD treatment and is particularly well suited for young adults. However, XR-NTX adherence and relapse show considerable individual variability, and the behavioral and clinical factors associated with such variability remain inconclusive. Previous research has demonstrated the potential for multimodal MRI and ML techniques to elucidate the neurocognitive factors that contribute to treatment response beyond behavioral and clinical measures. This project will take advantage of the cutting-edge MRI and ML methods to model brain structures and functions that predict XR-NTX treatment outcomes in young adults with OUD. The study will evaluate 18–34 year-old OUD patients before and during the first three months of XR-NTX treatment, a period associated with the highest rate of dropout from treatment. The primary outcome will be opioid relapse confirmed by weekly urine toxicology and self-report. The secondary outcome will be non-adherence defined as failure to complete the first three injections. The study will focus on five baseline measures of brain structures and functions that are potentially predictive of treatment response: 1) grey matter volume; 2) functional connectivity with the ventral striatum; 3) reactivity to opioid cues; 4) inhibitory control; and 5) self-evaluation. ML techniques will be used to reveal the patterns of brain structures/functions that are associated with each outcome variable. Based on literature and preliminary findings, we anticipate that combining MRI with behavioral and clinical assessments will better account for individual variability in XR-NTX treatment outcomes in young adults with OUD, than using the behavioral and clinical variables alone. The data will unveil novel brain mechanisms that contribute to the risk of treatment failure in this critical population. The project will also serve as a training vehicle for Dr. Zhenhao Shi to improve his clinical and computational skills and facilitate his independent career development. Specifically, it will enable Dr. Shi to achieve five training goals: 1) to advance his knowledge in the methodology of clinical research; 2) to gain hands-on experience in leading clinical projects; 3) to master ML and multivariate methodologies; 4) to apply multimodal MRI techniques to translational and clinical research; and 5) to advance his general independent research skills including leadership, networking, collaboration, scientific writing and grantsmanship. Through a combination of didactic and hands-on activities, the project will fulfill Dr. Shi's training needs and enable his transition to a successful and independent research career in applying advanced computational approaches to the neuroscience research of substance use disorders and their treatments.