Using Control Systems to Predict Individualized Dynamics of Nicotine Cravings

使用控制系统预测尼古丁渴望的个性化动态

• 批准号: 8787852
• 负责人: LILIANNE R MUJICA-PARODI
• 金额: $ 20.65万
• 依托单位: STATE UNIVERSITY NEW YORK STONY BROOK
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-07-01 至 2016-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8787852
• 关键词: Acute; Addictive Behavior; Address; Alcohols; Asthma; Base of the Brain; Behavioral; Biological; Blood; Blood Glucose; Bolus Infusion; Boxing; Brain; Cells; Cigarette; Clinical Research; Cocaine; Computer Simulation; Corpus striatum structure; Coupled; Data; Data Set; Development; Diabetic Coma; Differential Equation; Disease; Dopamine; Dose; Drug Addiction; Drug Kinetics; Eating; Electrocardiogram; Emergency Situation; Engineering; Environment; Event; Feedback; Food; Frequencies; Functional Magnetic Resonance Imaging; Galvanic Skin Response; Glucose; Glycemic Index; Head; Heart Diseases; Heroin; Homeostasis; Human; Hyperglycemia; Hypoglycemia; Individual; Infant Mortality; Insulin; Literature; Measures; Metabolic; Metabolic Control; Methods; Modeling; Near-Infrared Spectroscopy; Nebulizer; Neurobiology; Neurosciences; Nicotine; Noise; Nucleus Accumbens; Oxygen; Pharmaceutical Preparations; Physiologic pulse; Physiological; Physiology; Plasma; Preparation; Prevention; Psychological reinforcement; Reactive hypoglycemia; Recovery; Regulation; Reliance; Resolution; Rewards; Self-Administered; Signal Transduction; Slice; Smoke; Smoker; Spontaneous abortion; System; Techniques; Testing; Time; Tobacco; United States; Ventral Tegmental Area; addiction; base; cancer risk; craving; design; dosage; drug of abuse; executive function; experience; improved; interest; neuroimaging; nicotine craving; nicotine replacement; prevent; public health relevance; response; statistics; sugar

DESCRIPTION (provided by applicant): Nicotine is the most common drug of abuse in the United States, and has addiction strength comparable to cocaine, heroin, and alcohol. It is the primary addictive component of tobacco, and its use markedly increases risk for cancer, heart disease, asthma, miscarriage, and infant mortality. Addiction is thought to be caused primarily by the intersection of two components: 1) the impact of drug pharmacokinetics on the dynamics of dopamine response, and 2) dysregulation of the brain's reward circuit. While the term 'dysregulated' tends to be used qualitatively within the neuroscience literature, regulation has a precise and testable meaning in control systems engineering, which has yet to be addressed in a quantitative manner by current neuroimaging methods or models of addiction. Current approaches to neuroimaging have primarily focused on identifying nodes and causal connections within the meso-circuit of interest, but have yet to take the next step in treating these nodes and connection as a self-interacting dynamical system evolving over time. Such an approach is critical for improving our understanding, and therefore prediction, of trajectories for addiction as well as recovery. These trajectories are likely to be nonlinear (e.g., involving thresholds, saturation, and self- reinforcement), as well as highly specific to each individual. Ou study is designed to provide the first step towards addressing this gap: integrating ultra-high-field (7T) and ultra-fast (<1s) fMRI with computational modeling, to provide a bridge between the dynamics of meso-circuit regulation and the dynamics of human addictive behavior. We propose to test the hypothesis that control systems regulation, measured by dynamic analyses of fMRI data, can predict-on an individual basis-exactly when an addicted smoker will want to take his next puff. This will be achieved by first validating a MR-compatible nicotine delivery system, by comparing its neurobiological and autonomic effects against those of a cigarette and e-cigarette. Once this is achieved, we then will acquire fMRI data from addicted smokers while they 'smoke.' Using individual subjects' neuroimaging data, we will derive coupled differential equations for a control system that predicts craving and behavioral response for that individual. Using independent data sets to estimate the parameters and to test them, we will assess the model's accuracy in predicting each individual subject's cravings, as measured behaviorally by the frequency at which each smoker self-administers nicotine. If successful, this approach could then be exploited to develop individualized prevention and treatment of addiction by identifying individual-specific amplitude, duration, and frequency of dosing in nicotine replacement therapy that is least likely to trigger cravings. More generally, the methods we propose have the potential to rigorously examine system-wide dysregulation in addiction for the first time, opening the door to exploration of other dysregulatory brain-based disease in humans.

描述（由申请人提供）：尼古丁是美国最常见的滥用药物，并且具有与可卡因，海洛因和酒精相当的成瘾力量。它是烟草的主要成瘾成分，其使用显着增加了患癌症，心脏病，哮喘，流产和婴儿死亡率的风险。人们认为成瘾主要是由两个组成部分的交点引起的：1）药物药代动力学对多巴胺反应动力学的影响，以及2）大脑奖励回路失调。尽管“失调”一词倾向于在神经科学文献中定性地使用，但调节在控制系统工程中具有精确且可检验的含义，尚未通过当前的神经影像学方法或成瘾模型以定量的方式来解决。当前的神经影像学方法主要集中于识别感兴趣的中索环境中的节点和因果关系，但尚未下一步将这些节点和连接作为一种自我相互作用的动力学系统，随着时间的流逝而演变。这种方法对于改善我们对轨迹的理解，因此是至关重要的 成瘾和恢复。这些轨迹可能是非线性的（例如，涉及阈值，饱和度和自我增强），并且对每个人高度具体。 OU研究旨在提供解决这一差距的第一步：整合超高场（7T）和超快速（<1s）fMRI与计算建模，以在中循环调节的动力学与人类成瘾行为的动力学之间提供桥梁。我们建议检验以下假设：通过fMRI数据的动态分析来衡量的控制系统调节可以在上瘾的吸烟者想要接受下一个吹气时，可以预测单个基础。这将通过首先验证MR兼容的尼古丁递送系统来实现，并通过将其神经生物学和自主性效应与香烟和电子烟的效应进行比较。一旦实现这一目标，我们将在“吸烟”时从上瘾的吸烟者那里获取fMRI数据。使用单个受试者的神经成像数据，我们将为控制系统提供耦合的微分方程，以预测该个人的渴望和行为响应。使用独立的数据集估算参数并测试它们，我们将评估模型在预测每个受试者的渴望方面的准确性，这是通过每个吸烟者自我辅助尼古丁的频率在行为上衡量的。如果成功，则可以利用这种方法来开发个性化的预防和对成瘾的治疗，通过识别尼古丁替代疗法中的个体特异性振幅，持续时间和剂量频率，这最不可能引发人们的渴望。更笼统地，我们提出的方法有可能首次严格检查成瘾的全系统失调，为人类其他基于大脑的其他失调性脑部疾病打开了大门。