Using Control Systems to Predict Individualized Dynamics of Nicotine Cravings

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性成分，使用它会显著增加患癌症、心脏病、哮喘、流产和婴儿死亡率的风险。成瘾被认为主要是由两个组成部分的交叉引起的：1)药物对多巴胺反应动力学的影响，2)大脑奖励回路的失调。虽然“失调”一词往往在神经科学文献中被定性地使用，但在控制系统工程中，调节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尚未以定量的方式解决这一问题。目前的神经成像方法主要集中在识别感兴趣的中观电路中的节点和因果联系，但尚未采取下一步措施，将这些节点和连接视为一个随时间进化的自交互动力系统。这样的方法对于提高我们的理解和预测的轨迹至关重要

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.