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Brain Imaging Studies of Negative Reinforcement in Humans

Brain Imaging Studies of Negative Reinforcement in Humans
人类负强化的脑成像研究
批准号:
7910718
负责人:
KEVIN S LABAR
金额:
$38.19万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2009
资助国家:
美国
项目状态:
已结题
起止时间:
2009-09-01 至 2014-07-31
关键词:
3-DimensionalAdoptedAffectiveAnimal ModelAnimalsAnxietyArousalAssociation LearningAvoidance LearningBehaviorBehavioralBehavioral inhibitionBrainBrain imagingClinical TrialsClipComputer GraphicsCorpus striatum structureCryingDevelopmentDivingDrug AddictionDrug usageEmerging TechnologiesEmotionalEnvironmentEnvironment and Public HealthExhibitsExploratory BehaviorFaceFigs - dietaryFloorFoodFrequenciesFrightFunctional Magnetic Resonance ImagingGlassGoalsGogglesHandHeadHippocampus (Brain)HumanImmersion Investigative TechniqueIncentivesIndividual DifferencesInvestigationKnowledgeLaboratoriesLeadLearningLeftLengthLifeLinkLiteratureLocationMagnetic Resonance ImagingMaintenanceMapsMeasuresMediatingMemoryMethodsModelingMonitorMoodsMovementNegative ReinforcementsNeurobiologyOperant ConditioningOutcomeParticipantPerformancePharmaceutical PreparationsPopulationPositive ReinforcerProcessPropertyPsychological reinforcementPsychophysiologyPublic HealthRelapseRelative (related person)ResearchRetrievalRewardsSeriesShockSideSimulateSpecific qualifier valueStimulusStressSystemTask PerformancesTechnologyTimeTrainingTranslatingUpdateWateraddictionavoidance behaviorbasecognitive neurosciencedrug addictenvironmental stressorexperienceindexinginnovationlong term memorymemory processmoviemultisensorynegative moodneural circuitneurobehavioralneuroimagingnovelpleasurepublic health relevancereinforcerrelating to nervous systemrepairedresearch studyresponsesimulationstereoscopicstressortraitvirtualvirtual reality

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中文摘要
翻译
描述(由申请人提供):治疗药物成瘾的一个主要挑战是了解学习到的与厌恶的增强剂的联系如何促进回避性行为,并触发药物使用的开始和复发。在非人类动物身上的研究已经开始阐明回避学习的神经行为机制,但很少有人努力将这些发现转化为人类群体。支持长期记忆形成的神经可塑性大脑机制被假设为改变刺激的表现形式,并加强上下文关联,作为其激励属性的函数。这项拟议的研究采用了认知神经科学的观点来描述动机大脑系统如何调节人类陈述性记忆的形成并导致行为回避。虽然人类的陈述性记忆传统上是通过列表学习范式来探索的,但计算机图形界面和沉浸式虚拟现实(VR)技术的最新进展使得新型导航回避任务的开发成为可能,这些任务提供了与动物文献更紧密的联系,并更紧密地模拟了吸毒者在应对环境应激源时表现出的现实世界回避行为。健康的参与者将接受一系列功能磁共振成像研究,在传统的列表学习和基于虚拟现实的新型导航学习和记忆任务的背景下呈现增强刺激。第一系列实验比较了欲望和厌恶工具强化对陈述性记忆系统的影响。第二个系列的实验确定了负面情绪状态如何放大工具性增强剂的激励属性的助记效应,并激发寻求奖励(“解脱”)作为情绪修复的一种形式。第三个系列的实验开发了一种多感官、沉浸式VR范式,模拟了在一项结合了导航和列表学习方法的自然记忆任务中由压力引发的回避和逃避。功能连接性模型,结合多元回归和独立成分分析,将表征动机和记忆系统的相互作用,以及它们与行为表现指数和回避特质标记物的个体差异的关系。因此,拟议的研究代表了一种系统和创新的人类回避学习方法,它结合了尖端的VR技术和功能神经成像方法。这些研究结果将在理解积极增强剂、情绪状态和压力源如何改变不良行为后果对学习和记忆系统的影响方面弥合翻译上的差距,这些影响有助于建立环境显著特征的内部地图。 公共卫生相关性:在吸毒成瘾中,环境应激源的经历和回忆可以触发吸毒的发作和复发,以此作为逃避生活挑战的一种手段。这项拟议的研究将通过促进对厌恶增强剂如何调节大脑记忆系统和促进回避行为的理解来影响公众健康。这项研究还开发了可被纳入临床试验的新范式,以评估奖励如何缓解负面情绪,并使用虚拟现实模拟评估在压力大的学习环境中的逃避行为。
英文摘要
DESCRIPTION (provided by applicant): A major challenge to treating drug addiction is understanding how learned associations to aversive reinforcers promote avoidant behavior and trigger the onset and relapse of drug use. Studies in non-human animals have begun to elucidate the neurobehavioral mechanisms of avoidance learning, but there have been few efforts to translate these findings to human populations. Neuroplastic brain mechanisms that support long-term memory formation are hypothesized to alter the representations of stimuli and strengthen contextual associations as a function of their incentive properties. The proposed research adopts a cognitive neuroscience perspective to characterize how motivational brain systems modulate declarative memory formation in humans and lead to behavioral avoidance. Although human declarative memory is traditionally probed using list- learning paradigms, recent advances in computer graphics interfaces and immersive virtual reality (VR) technology permit the development of novel navigational avoidance tasks that provide a tighter link with the animal literature and more closely model real-world avoidant behaviors exhibited by drug addicts in response to environmental stressors. Healthy participants will undergo a series of functional magnetic resonance imaging studies that present reinforcing stimuli within the context of both traditional list-learning and novel VR-based navigational learning and memory tasks. The first series of experiments compares the influence of appetitive versus aversive instrumental reinforcers on declarative memory systems. The second series of experiments determines how negative mood states amplify the mnemonic effects of the incentive properties of instrumental reinforcers and motivate reward-seeking ("relief") as a form of mood repair. The third series of experiments develops a multisensory, immersive VR paradigm that simulates stress-induced avoidance and escape on a naturalistic memory task that combines navigational and list-learning approaches. Functional connectivity modeling, in combination with multiple regression and independent components analyses, will characterize the interactions of motivational and memory systems and their relationship to individual differences in behavioral performance indices and trait markers of avoidance. The proposed studies thus represent a systematic and innovative approach to human avoidance learning that combines cutting-edge VR technology and functional neuroimaging methods. The research findings will bridge a translational gap in understanding how positive reinforcers, mood states, and stressors modify the impact of aversive behavioral consequences on learning and memory systems that help establish internal maps of salient features of the environment. PUBLIC HEALTH RELEVANCE: In drug addiction, the experience and recall of environmental stressors can trigger the onset and relapse of drug use as a means to escape from life challenges. The proposed research will impact public health by advancing an understanding of how aversive reinforcers modulate memory systems in the brain and promote avoidant behavior. The research also develops new paradigms that could be incorporated into clinical trials to assess how rewards provide relief from negative moods, and to assess escape behavior in a stressful learning context using virtual reality simulations.
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Neurocomputational Approaches to Emotion Representation
  • 批准号:
    10421064
  • 项目类别:
  • 资助金额:
    $77.64万
  • 财政年份:
    2020
  • 负责人:
    KEVIN S LABAR
  • 依托单位:
Neurocomputational Approaches to Emotion Representation
  • 批准号:
    10059052
  • 项目类别:
  • 资助金额:
    $77.44万
  • 财政年份:
    2020
  • 负责人:
    KEVIN S LABAR
  • 依托单位:
Neurocomputational Approaches to Emotion Representation
  • 批准号:
    10626123
  • 项目类别:
  • 资助金额:
    $76.29万
  • 财政年份:
    2020
  • 负责人:
    KEVIN S LABAR
  • 依托单位:
Neurocomputational Approaches to Emotion Representation
  • 批准号:
    10227196
  • 项目类别:
  • 资助金额:
    $77.4万
  • 财政年份:
    2020
  • 负责人:
    KEVIN S LABAR
  • 依托单位:
海外基金