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:
- 金额:$ 54.96万
- 依托单位:
- 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:
- 关键词:AbstinenceAddressAdultAffectAlcohol or Other Drugs useAlcoholsAmericanAnimal ModelBayesian ModelingBehaviorBig DataBinge EatingBrainBrain imagingBrain regionCannabisChildCognitionCommon CoreComputer AnalysisComputer SimulationComputing MethodologiesCorpus striatum structureCouplingCuesDataData SetDopamineDrug AddictionDrug abuseDrug usageFailureFoodFunctional Magnetic Resonance ImagingGoalsHumanImage AnalysisImaging DeviceIncubatedIndividualInsula of ReilLearningMethodsMidbrain structureModalityModelingNatureNeurobiologyNicotineObesityPathologyPerceptionPharmaceutical PreparationsPhenotypePsychiatryPublic HealthResearchResistanceRewardsRoleSample SizeSeveritiesSubgroupSubstance Use DisorderSubstantia nigra structureSymptomsTimeUnited StatesUnited States National Institutes of HealthUpdateVentral Tegmental AreaWorkaddictionbasebench to bedsidebinge drinkercausal modelcravingcue reactivitydrug cravingfood cravinghuman modelimaging studyinterestmarijuana usemarijuana usermultilevel analysisneural circuitneural modelneuroimagingnovelrelapse predictionrelating to nervous systemresponsereward processingtobacco 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.
摘要
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Xiaosi Gu其他文献
Xiaosi Gu的其他文献
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{{ truncateString('Xiaosi Gu', 18)}}的其他基金
Neural, computational and behavioral characterization of dynamic social behavior in borderline and avoidant personality disorder
边缘型和回避型人格障碍动态社会行为的神经、计算和行为特征
- 批准号:
10579939 - 财政年份:2021
- 资助金额:
$ 54.96万 - 项目类别:
Neural, computational and behavioral characterization of dynamic social behavior in borderline and avoidant personality disorder
边缘型和回避型人格障碍动态社会行为的神经、计算和行为特征
- 批准号:
10400100 - 财政年份:2021
- 资助金额:
$ 54.96万 - 项目类别:
Computational and electrochemical substrates of social decision-making in humans
人类社会决策的计算和电化学基础
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10059060 - 财政年份:2020
- 资助金额:
$ 54.96万 - 项目类别:
Computational and electrochemical substrates of social decision-making in humans
人类社会决策的计算和电化学基础
- 批准号:
10428547 - 财政年份:2020
- 资助金额:
$ 54.96万 - 项目类别:
Computational and electrochemical substrates of social decision-making in humans
人类社会决策的计算和电化学基础
- 批准号:
10640947 - 财政年份:2020
- 资助金额:
$ 54.96万 - 项目类别:
Computational and electrochemical substrates of social decision-making in humans
人类社会决策的计算和电化学基础
- 批准号:
10227238 - 财政年份:2020
- 资助金额:
$ 54.96万 - 项目类别:
Neurocomputational Mechanisms for Addiction Heterogeneity, Impulsivity and Perseverance
成瘾异质性、冲动性和毅力的神经计算机制
- 批准号:
9980853 - 财政年份:2019
- 资助金额:
$ 54.96万 - 项目类别:
Neurocomputational Mechanisms for Addiction Heterogeneity, Impulsivity and Perseverance
成瘾异质性、冲动性和毅力的神经计算机制
- 批准号:
9809076 - 财政年份:2019
- 资助金额:
$ 54.96万 - 项目类别:
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:
10197070 - 财政年份:2018
- 资助金额:
$ 54.96万 - 项目类别:
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:
10434013 - 财政年份:2018
- 资助金额:
$ 54.96万 - 项目类别:
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