Computational and Neural Modeling of Cue Reactivity in Addiction

成瘾中提示反应的计算和神经建模

• 批准号: 9769690
• 负责人: Xiaosi Gu
• 金额: $ 54.96万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9769690
• 关键词: Abstinence; Address; Adult; Affect; Alcohol or Other Drugs use; Alcohols; American; Animal Model; Bayesian Modeling; Behavior; Big Data; Binge Eating; Brain; Brain imaging; Brain region; Cannabis; Child; Cognition; Common Core; Computer Analysis; Computer Simulation; Computing Methodologies; Corpus striatum structure; Coupling; Cues; Data; Data Set; Dopamine; Drug Addiction; Drug abuse; Drug usage; Failure; Food; Functional Magnetic Resonance Imaging; Goals; Human; Image Analysis; Imaging Device; Incubated; Individual; Insula of Reil; Learning; Methods; Midbrain structure; Modality; Modeling; Nature; Neurobiology; Nicotine; Obesity; Pathology; Perception; Pharmaceutical Preparations; Phenotype; Psychiatry; Public Health; Research; Resistance; Rewards; Role; Sample Size; Severities; Subgroup; Substance Use Disorder; Substantia nigra structure; Symptoms; Time; United States; United States National Institutes of Health; Update; Ventral Tegmental Area; Work; addiction; base; bench to bedside; binge drinker; causal model; craving; cue reactivity; drug craving; food craving; human model; imaging study; interest; marijuana use; marijuana user; multilevel analysis; neural circuit; neural model; neuroimaging; novel; relapse prediction; relating to nervous system; response; reward processing; tobacco smokers

Abstract Substance use disorders (SUD) and obesity are both major public health concerns in the United States, with an estimated 20.8 million Americans struggling with at least one SUD in 2015 and 78.6 million adults and 12.7 million children who are obese. Cue-elicited craving is a central symptom of both drug addiction and binge eating and a strong predictor of relapse. Compared to other SUD symptoms, craving is also much more resistant to treatment. Unfortunately, our understanding of the neurobiological basis of cue-induced craving is still limited, especially compared to the wealth of existing human neuroimaging data. This is partially due to the lack of big data collectives (i.e. fMRI studies have mostly been conducted in isolation from each other) as well as the scarcity of model-based computational analysis in neuroimaging studies on addiction and obesity. The overarching goal of this project is to use multi-level, model-based computational methods to re-analyze six existing fMRI datasets that examine cue reactivity and craving across a total of 954 individuals with substance use or binge eating (59 tobacco smokers, 254 cannabis users, 598 binge drinkers, and 43 binge eating adults). We will address three timely aims using novel computational modeling methods: 1) conduct Bayesian model- based analyses to examine the common and distinct computational mechanisms of drug and food craving across different groups; 2) use dynamic causal modeling to quantify directed coupling between neural regions involved in cue reactivity shared by or unique to different substance using and binge eating groups; 3) explore how models of cue-elicited craving are modulated by the severity of substance use and binge eating. Findings from this project will greatly enhance our understanding of the neural and computational mechanisms underlying craving and cue reactivity in drug addiction and binge eating. The implication of these results could be far-reaching, because 1) craving is a common and core phenotype across different substance use and binge eating groups; 2) these advanced modeling methods could be applied to many other pathologies related to dysfunctional craving and reward processing; and 3) how these mechanisms differ between more severe (e.g. SUD) and less severe (e.g. non-SUD) individuals could provide mechanisms that might protect an individual from developing SUD.

摘要 物质使用障碍（SUD）和肥胖都是美国主要的公共卫生问题， 据估计，2015年有2080万美国人至少患有一种SUD，7860万成年人和1270万成年人 百万肥胖儿童。线索引发的渴望是药物成瘾和狂欢的中心症状 饮食和复发的强烈预测。与其他SUD症状相比，渴望也更多 抵抗治疗。不幸的是，我们对线索诱导渴望的神经生物学基础的理解是， 仍然是有限的，特别是与现有的人类神经成像数据的财富相比。这部分是由于 缺乏大数据集合（即功能磁共振成像研究大多是在相互隔离的情况下进行的） 在成瘾和肥胖的神经影像学研究中缺乏基于模型的计算分析。的 该项目的首要目标是使用多层次，基于模型的计算方法来重新分析六个 现有的功能磁共振成像数据集，研究了总共954个个体的线索反应和渴望， 使用或暴食（59名吸烟者，254名大麻使用者，598名酗酒者和43名暴食成年人）。 我们将使用新的计算建模方法解决三个及时的目标：1）进行贝叶斯模型- 基于分析来研究药物和食物渴望的共同和不同的计算机制 2）使用动态因果模型来量化神经区域之间的定向耦合 参与不同物质使用和暴饮暴食群体共享或独特的线索反应; 3）探索 线索引发的渴望模型如何受到物质使用和暴饮暴食的严重程度的调节。结果 这个项目将大大提高我们对神经和计算机制的理解 潜在的渴望和线索反应在药物成瘾和暴饮暴食。这些结果的含义可能 影响深远，因为1）渴望是不同物质使用的共同和核心表型， 暴饮暴食组; 2）这些先进的建模方法可以应用于许多其他病理相关 功能失调的渴望和奖励处理;以及3）这些机制在更严重的 (e.g. SUD）和不太严重（例如非SUD）的个体可以提供可能保护 个人发展SUD。