To identify mechanisms of predictive processing across the distributed thalamocortical circuit

确定分布式丘脑皮质回路的预测处理机制

• 批准号: 10740356
• 负责人: Nicholas Audette
• 金额: $ 13.31万
• 依托单位: NEW YORK UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-16 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10740356
• 关键词: Acoustics; Animals; Area; Auditory; Auditory area; Augmented Reality; Axon; Behavior; Behavioral; Brain; Brain region; Cells; Communication; Computational Technique; Data; Disease; Electrophysiology (science); Elements; Environment; Forelimb; Functional disorder; Funding; Goals; Head; Health; Hearing; Home; Individual; Influentials; Intervention; Knowledge; Label; Learning; Link; Location; Maps; Mentors; Mentorship; Modeling; Motor; Motor Cortex; Movement; Mus; Neurons; New York; Optics; Outcome; Pathway interactions; Pattern; Physiological; Play; Positioning Attribute; Resources; Scientist; Sensory; Shapes; Signal Transduction; Specificity; Synapses; Technology; Testing; Thalamic structure; Tracer; Training; Transgenic Mice; Translating; Universities; Work; auditory processing; auditory thalamus; behavior prediction; career; expectation; experience; experimental study; flexibility; insight; neural; neurotransmission; optogenetics; patch clamp; sensory cortex; sensory input; sensory integration; skills; sound; transmission process; wireless

Project Summary Many of the sounds that animals hear are created by their own actions and being able to correctly differentiate these sounds is critical to a range of behaviors. An influential idea is that the brain uses sensory-motor predictions to anticipate sounds generated by movement, and identifying the circuit mechanisms that learn and implement these predictions is critical to our understanding of cortical function in health and disease. Since predictive computations involve the interaction of sensory and non-sensory signals, identifying underlying circuit mechanisms will require understanding how distributed but interconnected brain regions work together. While the thalamus is often perceived as a simple conduit of sensory information, the second-order thalamus is tightly linked with both the sensory and motor cortex, positioning it to play a key role in integrating sensory and non- sensory information. This proposal will test the hypothesis that the auditory second-order thalamus shapes predictive processing throughout the auditory cortex. First, I will use a transgenic mouse line that specifically labels second-order thalamic neurons to map the precise functional connections of the second-order auditory thalamus (Aim 1, K99). Next, I will develop an acoustic augmented reality home cage environment where mice can rapidly learn multiple predictive behaviors. I will perform wireless recordings while freely moving mice make multiple sound-generating movements to determine the sensory, movement, and prediction information encoded in the second-order auditory thalamus (Aim 2, K99). Finally, I will perform simultaneous multi-area recordings and targeted neural interventions in the thalamus and cortex of behaving mice to determine how predictive computations are carried out across the thalamocortical circuit (Aim 3, R00). With the guidance of my mentorship team, I have developed a training plan at New York University that will provide me the technological skills needed to complete these aims and make important discoveries about how distributed circuits integrate sensory and non-sensory information during predictive processing. The proposed training plan will also provide me with the conceptual framework and professional skills to achieve my long-term career goal: to investigate how distributed circuits work together mechanistically to enable context-dependent auditory processing in health and disease as an independent scientist.

项目摘要 动物听到的许多声音都是由自己的行为创造的，并且能够正确区分 这些声音对于一系列行为至关重要。一个有影响力的想法是大脑使用感觉运动预测 预测运动产生的声音，并确定学习和实施的电路机制 这些预测对于我们对健康和疾病中皮质功能的理解至关重要。自预测 计算涉及感觉和非感官信号的相互作用，识别基础电路 机制将需要了解分布式但相互联系的大脑区域如何共同工作。尽管 丘脑通常被认为是感觉信息的简单管道，二阶丘脑紧密 与感觉皮层和运动皮层都相关，将其定位为在整合感觉和非 - 感官信息。该建议将检验听觉二阶丘脑形状的假设 整个听觉皮层的预测处理。首先，我将使用专门的转基因鼠标线 标记二阶丘脑神经元以绘制二阶听觉的精确功能连接 丘脑（AIM 1，K99）。接下来，我将开发一个声学增强的现实笼子环境 可以迅速学习多种预测行为。我将在自由移动老鼠的同时执行无线录音 多次发声的动作以确定编码的感官，运动和预测信息 在二阶听觉丘脑中（AIM 2，K99）。最后，我将执行同时的多区域录音 并针对行为小鼠的丘脑和皮层中的靶向神经干预措施，以确定如何预测性 计算在丘脑皮质回路上进行（AIM 3，R00）。在我的指导下 团队，我已经在纽约大学制定了一项培训计划，该计划将为我提供所需的技术技能 完成这些目标，并就分布式电路如何整合感官和 预测处理过程中的非感官信息。拟议的培训计划还将为我提供 概念框架和专业技能以实现我的长期职业目标：调查分布式的方式 电路机械地共同努力，以使健康和疾病中的上下文依赖性听觉加工为 独立科学家。