Advancing understanding of neural representations of threat perception through a novel predictive coding framework

通过新颖的预测编码框架增进对威胁感知的神经表征的理解

• 批准号: 10240282
• 负责人: Kent M Lee
• 金额: $ 6.64万
• 依托单位: NORTHEASTERN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10240282
• 关键词: Address; Affect; Amygdaloid structure; Animal Model; Animals; Anterior; Anxiety; Anxiety Disorders; Area; Behavior; Bipolar Disorder; Brain; Brain region; Categories; Code; Data Analyses; Data Collection; Development; Dimensions; Disease; Dissociation; Emotions; Expectancy; Face; Fright; Functional Magnetic Resonance Imaging; Human Subject Research; Insula of Reil; Knowledge; Laboratory Research; Lead; Machine Learning; Mathematics; Measures; Mental Depression; Mental disorders; Mind; Modeling; Mood Disorders; Nature; Neurosciences; Participant; Patient Self-Report; Perception; Peripheral; Physiological; Physiology; Placebo Effect; Psychophysiology; Research; Research Personnel; Rewards; Schizophrenia; Science; Specificity; Stimulus; Techniques; Testing; Tissues; Training; Treatment Effectiveness; Ventral Striatum; Work; affective neuroscience; anxiety treatment; base; cingulate cortex; cognitive function; cognitive neuroscience; computational neuroscience; design; effective intervention; effective therapy; expectation; experience; fusiform face area; improved; indexing; insight; neural model; neurobiological mechanism; neuromechanism; novel; pain processing; predictive modeling; psychologic; relating to nervous system; training project; translational neuroscience

PROJECT SUMMARY Anxiety disorders affect an estimated 6.7% to 7.3% of people globally each year and incur a large burden on people’s lives. To address this urgent problem, it is necessary to develop a better understanding of the neural bases of subjective experiences in anxiety, including threat perception. Translational neuroscience has focused on animal models of defensive behavior involving a core set of regions. Although these animal models and the human subjects research they inspired have led to advances in treatment of anxiety, the mapping between neural mechanisms and subjective experience remains poorly understood. The set of regions found to support defensive behavior in animal models does not appear to be involved in all instances of fear or anxiety. The current project overcomes this barrier by integrating current models of anxiety with predictive coding models of the mind and brain. Incorporating predictive coding into models of anxiety will offer a better understanding of how neural activity relates to subjective experiences important to anxiety (e.g., threat perception). The project tests two parallel hypotheses about neural representation of threat perception suggested by predictive coding models: that neural representations of threat perception are content-specific (Aim 1) and that neural representations of threat perception depend on expectations (Aim 2). Using a single design, we manipulate content-specificity and expectations to test these two hypotheses in parallel. We use fMRI to measure brain activity and use self-report and peripheral psychophysiology to measure subjective experiences of threat perception. To the extent that participants find stimuli threatening (as indexed by self-report and psychophysiology), we hypothesize that we will observe relatively content-specific neural representations of threat. We also hypothesize that neural representations of threat will differ under conditions of expectation vs. expectancy violation. The effect of expectation may impact content-specific neural activity or activity in a core set of regions. The knowledge gained from the proposed project has the potential improve understanding of the mapping between neural activity and subjective experiences. Relevant for translational neuroscience, a better understanding of the psychological and neurobiological mechanisms of anxiety will be critical to closing the gap between laboratory research and more effective treatments for anxiety. More broadly, predictive coding models are models of basic brain function. Thus, the predictive coding model we propose offers a new theoretical framework that is generalizable to psychiatric illnesses involving disordered threat perception (e.g., schizophrenia) and other affective disorders (e.g., depression, bipolar disorder).

项目摘要 据估计，焦虑症每年影响全球6.7%至7.3%的人，并给他们带来巨大的负担。 人们生活为了解决这个紧迫的问题，有必要更好地了解神经系统。 焦虑的主观经验基础，包括威胁感知。转化神经科学已经聚焦于 涉及一组核心区域的防御行为的动物模型。虽然这些动物模型和 受他们启发的人类受试者研究已经导致了焦虑治疗的进步，神经元之间的映射， 机制和主观经验仍然知之甚少。发现支持的区域集 动物模型中的防御行为似乎并不涉及所有的恐惧或焦虑情况。的 目前的项目克服了这一障碍，将当前的焦虑模型与预测编码模型相结合， 思想和大脑。将预测编码纳入焦虑模型将有助于更好地理解 神经活动如何与对焦虑重要的主观体验相关（例如，威胁感知）。项目 测试两个平行假设的神经表征的威胁知觉提出的预测编码 模型：威胁感知的神经表征是特定于内容的（目标1）， 威胁感知的表征取决于预期（目标2）。使用单一的设计，我们操纵 内容特异性和期望来并行测试这两个假设。我们用功能磁共振成像来测量大脑 活动，并使用自我报告和外围心理生理学来衡量主观经验的威胁 perception.在某种程度上，参与者认为刺激具有威胁性（如自我报告和 心理生理学），我们假设，我们将观察到相对内容特定的神经表征， 威胁我们还假设，神经表征的威胁将不同的条件下的期望与。 违反预期。期望的效果可能影响内容特异性神经活动或核心中的活动 一组区域。从拟议项目中获得的知识有可能提高对 神经活动和主观体验之间的映射。与转化神经科学相关， 了解焦虑的心理和神经生物学机制对于缩小差距至关重要 实验室研究和更有效的焦虑治疗之间的联系。更广泛地说，预测编码模型 是大脑基本功能的模型因此，我们提出的预测编码模型提供了一个新的理论 可推广到涉及无序威胁感知的精神疾病的框架（例如， 精神分裂症）和其它情感障碍（例如，抑郁症、躁郁症）。