Longitudinal neural fingerprinting of opioid-use trajectories

阿片类药物使用轨迹的纵向神经指纹图谱

• 批准号: 10805031
• 负责人: Sarah Yip
• 金额: $ 25.13万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-30 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10805031
• 关键词: Abstinence; Address; Award; Basic Science; Behavior Therapy; Behavioral; Big Data; Brain; Buprenorphine; Cessation of life; Clinical; Costs and Benefits; Data; Data Set; Decision Making; Development; Disease; Disease remission; Dropout; Engineering; Epidemic; Fingerprint; Foundations; Functional disorder; Goals; HIV/HCV; Hepatitis C Transmission; Human; Impulsivity; Individual; Individual Differences; International; Intervention; Link; Machine Learning; Magnetic Resonance Imaging; Matched Group; Medicine; Mental disorders; Methadone; Methodology; Network-based; Neurobiology; Neuronal Plasticity; Neurosciences; Opioid; Outcome; Overdose; Overdose reduction; Patients; Pattern; Persons; Pharmaceutical Preparations; Phase; Phenotype; Pilot Projects; Population; Process; Psychiatry; Public Health; Recovery; Relapse; Research; Resources; Risk; Risk Factors; Sample Size; Sampling; Scanning; Therapeutic; Time; Translating; Translations; United States; United States National Academy of Sciences; Work; addiction; brain based; case control; clinical care; clinically relevant; computational neuroscience; criminal behavior; design; illicit opioid; improved; innovation; insight; medication for opioid use disorder; methadone treatment; mortality; neural; neurofeedback; neuroimaging; neuromechanism; novel; novel strategies; opioid epidemic; opioid use; opioid use disorder; overdose risk; patient retention; predict clinical outcome; programs; protective factors; resilience; response; skills; temporal measurement; therapy development; willingness

ABSTRACT Opioid use disorder (OUD) is a significant public health problem in the United States, with overdoses and deaths currently maintaining at epidemic levels. As risk of overdose is highest following relapse and treatment dropout, improved mechanistic understanding of risk and protective factors in individuals currently receiving medications for OUD (MOUD) is urgently needed. To address this, this Cutting-Edge, Basic Science Award (CEBRA) application moves beyond the limitations of traditional case-control designs to rapidly advance our understanding of the neural mechanisms of early MOUD treatment. For decades, clinical neuroimaging has relied on case-control designs in which individuals with a given psychiatric disorder are compared to a group of matched healthy ‘control’ individuals, or a group of individuals distinguished by another individual difference feature. While informative, these approaches by definition focus on group average deviations from a presumed normative population and thus may have relatively limited real world clinical utility. For example, recent findings from machine learning studies of addictions and other disorders indicate that brain networks which distinguish patients from controls are often distinct from brain networks that predict specific clinical outcomes within-group. This striking distinction suggests that person-specific neurobiology is dissociable from group-specific patterns, and thus group-specific findings are unlikely to translate to an improved understanding of person-specific pathophysiology or to treatment. Our innovation, the characterization of neural trajectories during MOUD treatment using dense sampling (i.e., repeated longitudinal assessments of the same individual) will provide unprecedented mechanistic insight into the neurobiological basis of OUD remission. Dense sampling is an emerging methodology that aims to overcome limitations with cross-sectional research that inherently assumes the brain is static and unchanging. This approach is particularly relevant to studying MOUD treatment: MOUD is multiphasic, comprised of medication induction, stabilization, ongoing treatment and eventual discontinuation phases. However, with a few small exceptions, existing neuroimaging efforts are almost exclusively single time-point assessments which, by definition, fail to capture dynamic trajectories of individual risk and resilience that can be used to mechanistically inform treatment advancements. This pilot project therefore applies dense sampling to characterize early trajectories of MOUD recovery at unprecedented temporal resolution—i.e., bi-weekly over three months. To maximize mechanistic insight, complementary clinical and behavioral computational data will also be acquired longitudinally. Borrowing from basic human neuroscience, our dense sampling neuroimaging approach represents a paradigm shift for psychiatry research in general, and holds enormous potential to inform understanding of opioid use disorder remission specifically.

抽象的 阿片类药物使用障碍（OUD）在美国是一个重大的公共卫生问题，过量服药和死亡 目前保持流行水平。由于继电器和治疗辍学后的过量风险最高，因此 对当前接受药物的个人的风险和保护因素的机械理解提高了 迫切需要OUD（MOUD）。为了解决这个问题，这个尖端的基础科学奖（CEBRA） 应用程序超出了传统案例对照设计的局限性，以迅速提高我们的理解 早期moud治疗的神经力学。 几十年来，临床神经影像学一直依赖于病例对照设计，其中具有给定的个体 将精神疾病与一组匹配的健康“对照”个体或一组个人进行比较 由另一个个体差异特征区别。虽然有益，但这些方法定义为重点 在群体平均出发的正常人群中，实际上可能相对有限 世界临床实用程序。例如，来自成瘾和其他疾病的机器学习研究的最新发现 表明将患者与对照区分开的大脑网络通常与大脑网络不同 预测特定的临床结果。这种罢工的区别表明特定于人 神经生物学与特定组的模式可以分离，因此特定于组的发现不太可能翻译 对人类特异性病理生理学或治疗有了深入的了解。 我们的创新，使用致密采样的植物处理过程中神经轨迹的表征 （即，对同一个人的重复纵向评估）将提供前所未有的机械洞察力 成为OUD缓解的神经生物学基础。密集抽样是一种新兴方法，旨在 通过横断面研究来克服局限性，该研究本质地假设大脑是静态且不变的。 这种方法与研究MOUD治疗特别相关：Moud是多相，完成的 药物诱导，稳定，正在进行的治疗和最终中断阶段。但是，有几个 小例外，现有的神经影像学工作几乎是单个时间点评估， 定义，无法捕获可用于机械的个人风险和弹性的动态轨迹 告知治疗进展。因此，该试点项目适用着密集的采样以提早表征 前所未有的临时解决方案以莫德恢复的轨迹 - 即三个月以来，每两周一次。到 还将获取最大化机械洞察力，完整的临床和行为计算数据 纵向。从基本的人类神经科学借用我们的密集采样神经影像学方法 一般来说，代表精神病学研究的范式转变，并具有巨大的潜力来告知 特别了解蛋白质使用障碍的缓解。