Computational and Neural Modeling of Cue Reactivity in Addiction

成瘾中提示反应的计算和神经建模

基本信息

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
项目类别：
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

项目摘要

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.

抽象的药物使用障碍（SUD）和肥胖症都是美国的主要公共卫生问题估计有2080万美国人在2015年至少有一名SUD和7860万成年人和12.7 肥胖的百万儿童。提示引用的渴望是吸毒和暴饮暴食的核心症状饮食和强大的复发预测指标。与其他SUD症状相比，渴望也更多抗治疗。不幸的是，我们对提示引起的渴望神经生物学基础的理解是仍然有限，特别是与现有的人类神经影像数据的财富相比。这部分是由于还缺乏大数据集体（即fMRI研究大多是彼此孤立进行的）作为对成瘾和肥胖的神经影像学研究中基于模型的计算分析的稀缺。这该项目的总体目标是使用基于模型的计算方法重新分析六个现有的fMRI数据集检查了1154个具有物质的人的提示反应性和渴望使用或暴饮暴食（59名吸烟者，254位大麻使用者，598个暴饮暴食者和43个暴饮暴食者）。我们将使用新颖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，以检查药物和食物的常见和不同的计算机制跨不同的群体； 2）使用动态因果建模来量化神经区域之间的定向耦合参与使用和暴饮暴食组的不同物质共享或独特的提示反应性； 3）探索提示渴望的模型如何通过使用和暴饮暴食的严重性来调节。发现来自该项目将大大增强我们对神经和计算机制的理解在药物成瘾和暴饮暴食中的潜在渴望和提示反应性。这些结果的含义可能要深远，因为1）渴望是不同物质使用和暴饮暴食组； 2）这些高级建模方法可以应用于许多其他与病理相关的病理使功能失调的渴望和奖励处理； 3）这些机制在更严重的情况下如何有所不同（例如SUD）和不太严重（例如非SUD）个体可以提供可能保护一个机制来自发展SUD的个人。