Using Control Systems to Predict Individualized Dynamics of Nicotine Cravings

描述(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，它的使用显著增加了患癌症、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因素共同引起的：1)药物药代动力学对多巴胺反应动力学的影响，2)大脑奖赏回路的失调。虽然在神经科学文献中，“调控失调”一词往往被定性地使用，但调控在控制系统工程中具有精确和可测试的含义，目前的神经成像方法或成瘾模型还没有定量地解决这一问题。目前的神经成像方法主要集中在识别感兴趣的中观回路内的节点和因果联系，但尚未采取下一步行动，将这些节点和联系视为一个随时间演变的自我相互作用的动力系统。这种方法对于提高我们对轨道的理解和预测至关重要。 上瘾和康复一样重要。这些轨迹很可能是非线性的(例如，涉及阈值、饱和度和自我强化)，并且高度特定于每个人。OU的研究旨在提供解决这一差距的第一步：将超高场(7T)和超快(1S)功能磁共振与计算建模相结合，在中观电路调节的动力学和人类成瘾行为的动力学之间提供一座桥梁。我们建议测试这样一个假设，即通过对功能磁共振数据的动态分析来衡量控制系统的监管，可以在个人的基础上准确地预测上瘾的吸烟者将在什么时候想要吸下一口烟。这将通过首先验证与MR兼容的尼古丁递送系统来实现，方法是将其神经生物学和自主效应与香烟和电子烟的影响进行比较。一旦实现了这一点，我们将获得吸烟者在吸烟时的fMRI数据。利用个体受试者的神经成像数据，我们将推导出预测个体渴望和行为反应的控制系统的耦合微分方程式。使用独立的数据集来估计参数并对其进行测试，我们将评估该模型在预测每个个体受试者对尼古丁的渴望方面的准确性，这是通过每个吸烟者自我服用尼古丁的频率来衡量的。如果成功，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有的幅度、持续时间和频率，来开发个性化的成瘾预防和治疗。更广泛地说，我们提出的方法有可能首次严格检查成瘾的系统范围的调节失调，为探索人类其他调节失调的脑部疾病打开了大门。

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.