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，其成瘾强度与可卡因、海洛因和酒精相当。它是烟草的主要成瘾成分，其使用显著增加癌症、心脏病、哮喘、流产和婴儿死亡的风险。成瘾被认为主要是由两个部分的交叉引起的：1）药物药代动力学对多巴胺反应动力学的影响，以及2）大脑奖励回路的失调。虽然术语“失调”倾向于在神经科学文献中定性使用，但调节在控制系统工程中具有精确和可测试的含义，但尚未通过当前的神经成像方法或成瘾模型以定量方式解决。目前的神经成像方法主要集中在识别感兴趣的中间回路内的节点和因果连接，但尚未采取下一步措施，将这些节点和连接视为随时间演变的自交互动力系统。这种方法对于改善我们对地球轨道的理解和预测至关重要， 成瘾和康复。这些轨迹可能是非线性的（例如，涉及阈值、饱和度和自我强化），以及对每个个体的高度特异性。Ou的研究旨在为解决这一差距迈出第一步：将超高场（7T）和超快（<1s）fMRI与计算建模相结合，为介观电路调节的动态和人类成瘾行为的动态之间提供桥梁。我们建议测试的假设，控制系统的监管，功能磁共振成像数据的动态分析，可以预测，在个人的基础上，正是当一个上瘾的吸烟者会想把他的下一个烟。这将通过首先验证MR兼容的尼古丁输送系统来实现，通过将其神经生物学和自主神经效应与香烟和电子烟进行比较。一旦实现了这一点，我们将从吸烟成瘾者那里获得功能磁共振成像数据。“利用个体受试者的神经成像数据，我们将推导出控制系统的耦合微分方程，该控制系统预测个体的渴望和行为反应。使用独立的数据集来估计参数并对其进行测试，我们将评估模型在预测每个个体受试者的渴望方面的准确性，这些渴望通过每个吸烟者自我管理尼古丁的频率进行行为测量。如果成功的话，这种方法可以通过确定尼古丁替代疗法中最不可能引发渴望的个体特定幅度，持续时间和给药频率来开发个性化的成瘾预防和治疗。更一般地说，我们提出的方法有可能首次严格检查成瘾的全系统失调，为探索人类其他基于大脑的失调疾病打开了大门。