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: 10418765
• 负责人: Kent M Lee
• 金额: $ 6.98万
• 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/10418765
• 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; Persons; 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 焦虑症每年影响全球6.7％至7.3％ 人们的生活。为了解决这个紧急问题，有必要更好地了解神经 焦虑中主观经历的基础，包括威胁感知。翻译神经科学集中 关于涉及一组核心区域的防御行为模型。尽管这些动物模型和 他们启发的人类受试者的研究导致了治疗焦虑的进步，神经之间的映射 机制和主题经验仍然很少理解。被发现支持的一组地区 动物模型中的防御行为似乎并未参与所有恐惧或动画的情况。这 当前的项目通过将当前动画模型与预测性编码模型相结合，从而克服了这一障碍 思想和大脑。将预测性编码纳入动画模型将提供更好的理解 神经活动与对焦虑重要的主题经历的关系（例如，威胁感知）。项目 测试有关预测编码提出的关于威胁感知神经的神经表示的两个平行假设 模型：威胁感知的神经元表示特定于内容（AIM 1）和神经元 威胁感知的表示取决于期望（目标2）。使用单个设计，我们操纵 内容特异性和期望并行检验这两个假设。我们使用fMRI测量大脑 活动并使用自我报告和周围心理生理学来衡量威胁的主题经验 洞察力。在某种程度上，参与者发现刺激威胁性（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示 威胁。我们还假设，威胁的神经表示在期望条件下会有所不同。 违反期望。期望的影响可能会影响核心中内容特定的神经活动或活动 一组地区。从拟议项目中获得的知识具有对该项目的潜在改进理解 神经元活动和主题经验之间的映射。与翻译神经科学相关，更好 了解动画的心理和神经生物学机制对于缩小差距至关重要 在实验室研究和动画的更有效治疗之间。更广泛的预测编码模型 是基本大脑功能的模型。那就是我们建议的预测编码模型提供了新的理论 可以推广到涉及不当威胁感知的精神病的框架（例如 精神分裂症）和其他情感障碍（例如抑郁症，躁郁症）。