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：
10434013
负责人：
Xiaosi Gu
金额：
$ 59.27万
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 2024-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10434013
关键词：
Abstinence Address Adult Affect Alcohols American Animal Model Bayesian Modeling Behavior Big Data Binge Eating Brain Brain imaging Brain region Cannabis Child Cognition Common Core Computer Analysis Computer Models 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 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 obese person relapse prediction relating to nervous system response reward processing substance use 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.

抽象的药物滥用障碍 (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，其中 7860 万成年人和 12.7 百万儿童肥胖。提示引发的渴望是毒瘾和暴饮暴食的核心症状饮食和复发的强烈预测因素。与其他 SUD 症状相比，渴望也更多对治疗有抵抗力。不幸的是，我们对提示诱发渴望的神经生物学基础的理解是仍然有限，特别是与现有的丰富的人类神经影像数据相比。这部分是由于缺乏大数据集体（即功能磁共振成像研究大多是相互独立进行的）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。这该项目的总体目标是使用多层次、基于模型的计算方法来重新分析六种现有的功能磁共振成像数据集检查了总共 954 名个体的线索反应性和对物质的渴望使用或暴食（59 名吸烟者、254 名大麻使用者、598 名暴饮者和 43 名暴食成年人）。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 - 基于分析来检查药物和食物渴望的常见和独特的计算机制跨不同群体； 2）使用动态因果模型来量化神经区域之间的定向耦合涉及不同物质使用和暴食群体共有或独特的提示反应； 3）探索物质使用和暴饮暴食的严重程度如何调节线索引发的渴望模型。发现这个项目将极大地增强我们对神经和计算机制的理解药物成瘾和暴饮暴食的潜在渴望和提示反应。这些结果的意义可能是影响深远，因为 1) 渴望是不同物质使用和使用过程中常见的核心表型暴食人群； 2）这些先进的建模方法可以应用于许多其他相关的病理学渴望和奖励处理功能失调； 3）这些机制在更严重的情况下有何不同（例如 SUD）和不太严重（例如非 SUD）的个人可以提供可能保护来自开发 SUD 的个人。

项目成果

期刊论文数量（8）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Addiction beyond pharmacological effects: The role of environment complexity and bounded rationality.

超越药理作用的成瘾：环境复杂性和有限理性的作用。

DOI：
发表时间：
2019-08
期刊：
影响因子：
0
作者：
Ognibene, Dimitri;Fiore, Vincenzo G;Gu, Xiaosi
通讯作者：
Gu, Xiaosi

An Interpretable and Predictive Connectivity-Based Neural Signature for Chronic Cannabis Use.

针对慢性大麻使用的可解释和可预测的基于连接的神经特征。

DOI：
发表时间：
2023-03
期刊：
影响因子：
0
作者：
Kulkarni, Kaustubh R;Schafer, Matthew;Berner, Laura A;Fiore, Vincenzo G;Heflin, Matt;Hutchison, Kent;Calhoun, Vince;Filbey, Francesca;Pandey, Gaurav;Schiller, Daniela;Gu, Xiaosi
通讯作者：
Gu, Xiaosi

Aberrant neural computation of social controllability in nicotine-dependent humans.

尼古丁依赖人类社会可控性的异常神经计算。

DOI：
发表时间：
2024-01-24
期刊：
影响因子：
0
作者：
Gu, Xiaosi;McLaughlin, Caroline;Fu, Qixiu;Na, Soojung;Heflin, Matthew;Fiore, Vincenzo
通讯作者：
Fiore, Vincenzo

Anterior insular cortex plays a critical role in interoceptive attention.

前岛叶皮质在内感受注意力中起着至关重要的作用。

DOI：
发表时间：
2019
期刊：
影响因子：
7.7
作者：
Wang, Xingchao;Wu, Qiong;Egan, Laura;Gu, Xiaosi;Liu, Pinan;Gu, Hong;Yang, Yihong;Luo, Jing;Wu, Yanhong;Gao, Zhixian;Fan, Jin
通讯作者：
Fan, Jin

Modeling subjective belief states in computational psychiatry: interoceptive inference as a candidate framework.

计算精神病学中的主观信念状态建模：内感受推理作为候选框架。