Frontal/Prefrontal control of cortical rhythms during auditory active sensi

听觉主动感觉过程中皮质节律的额叶/前额叶控制

• 批准号: 10175035
• 负责人: Robert Thomas Knight
• 金额: $ 25.34万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-04-15 至 2023-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10175035
• 关键词: Address; Air Pressure; Auditory; Auditory area; Back; Brain; Brain region; Cells; Code; Complex; Computer Models; Data; Electrocorticogram; Environment; Frequencies; Functional Magnetic Resonance Imaging; Future; Human; Impairment; Infrastructure; Knowledge; Lateral; Measures; Mental disorders; Mission; Modality; Modeling; Monkeys; Motor; National Institute of Mental Health; Neurobiology; Neurons; Neurosciences; Noise; Pathway Analysis; Perception; Periodicity; Physiological Processes; Physiology; Population Process; Process; Rest; Role; Sampling; Semantics; Sensory; Series; Services; Signal Transduction; Speech; Speech Perception; Standardization; Stimulus; Stream; System; Testing; Time; Tympanic membrane; Visual; Visuospatial; Voice; Work; active control; auditory stimulus; base; computational network modeling; data sharing; data standards; developmental disease; experience; feature extraction; insight; man; nervous system disorder; neurophysiology; predictive modeling; predictive test; relating to nervous system; speech processing; theories; visual stimulus

How do we extract salient information from an ever-changing and noisy environment? Project 3 addresses this fundamental question in perception using direct brain recordings in humans (electrocorticography; ECoG) to assess two models of sensory acquisition. The Active Sensing model posits that high-level inputs act to rhythmically sample the sensory word and filter out noise. The related predictive coding model theory posits that prior knowledge enhances perception with the brain making predictions about upcoming stimuli to sharpen low-level sensory processing. We propose that both processes share similar neural substrates - neuronal rhythm-based engagement of frontal, premotor, motor and sensory cortical networks to enable active and predictive sampling of the world to enhance perception. We employ ECoG to measure neural oscillations and high frequency activity (HG; 70-200 Hz; surrogate for intracortical SUA activity) and employ network analysis approaches to define the role of top-down control of active sensing and predictive coding in the human brain. Two or our proposed human ECoG studies are performed in monkeys in Project 4 permitting a rich inter-species comparison of the neural substrates of sensory acquisition. AIM 1 tests the hypotheses that motor/premotor systems control auditory sampling rhythms and actively suppress distracting information. This aim also explores whether lateral prefrontal regions provide additional control to the motor/premotor-auditory active-sensing network. AIM 2 addresses how prior knowledge enhances speech perception and `fills-in' degraded speech representations in auditory cortices. Given the use of speech stimuli this study will only be performed in humans. This Aim directly tests the predictive coding model and examines if similar neural substrates support both predictive coding and active sensing. AIM 3 compares our ECoG data to the laminar LFP/CSD and MUA profiles and network parameters obtained in parallel monkey auditory Project 4. These unique cross-species data will be used to identify the cell populations and physiological processes that generate ECoG components in monkeys and humans providing unprecedented insights into cortical physiology in humans. We predict that active sensing mechanisms are modality independent and will also compare our finding from the auditory monkey-man to the visual human and monkey active sensing studies in Projects 1 and 2. Core C provides critical DTI and resting state fMRI to correlate with our ECoG network and HG data and Core B provides for data standardization and sharing. Finally, Project 5 provides the computational and modeling infrastructure necessary to build and refine cell and systems level models of the world is sampled. Active sensing and predictive coding are likely impaired in a host of disabling psychiatric, neurological and developmental disorders making the understanding of these processes central to the mission of the NIMH.

我们如何从不断变化和嘈杂的环境中提取显著信息？项目3涉及 使用人类的直接脑记录进行知觉的这一基本问题(皮质脑电； ECOG)评估两种感觉获得模型。主动感知模型假设高水平输入 采取行动，有节奏地采样感官单词并过滤噪音。相关预测编码模型理论 假设先前的知识通过大脑对即将到来的刺激做出预测来增强知觉 来强化低水平的感官处理。我们认为这两个过程共享相似的神经基础- 基于神经元节律的额叶、运动前、运动和感觉皮质网络的参与 对世界进行积极的和预测性的抽样，以增强感知。我们使用皮层脑电来测量神经 振荡和高频活动(HG；70-200赫兹；替代皮质内SUA活动)和使用 定义主动感知和预测编码的自上而下控制作用的网络分析方法 在人脑中。在项目4中，我们在猴子身上进行了两项或我们提议的人类ECoG研究 允许对感觉获得的神经底物进行丰富的物种间比较。AIM 1测试 假设运动/运动前系统控制听觉采样节律并主动抑制 分散注意力的信息。这一目标还探讨了外侧前额叶区域是否提供了额外的控制 运动/运动前-听觉主动感知网络。目标2阐述了先验知识如何增强 听觉皮层中的言语知觉和“填充”退化的言语表征。考虑到使用 言语刺激这项研究将只在人类身上进行。该目标直接测试预测编码 模型，并检查类似的神经底物是否支持预测编码和主动感知。目标3 将我们的ECoG数据与在中获得的层流LFP/CSD和MUA分布和网络参数进行比较 平行猴子听觉项目4。这些独特的跨物种数据将被用来识别细胞 猴和人产生皮层脑电成分的种群和生理过程 为人类的大脑皮层生理学提供了前所未有的见解。我们预测主动感测 机制是独立于通道的，也将把我们从听觉猴人的发现与 项目1和2中的视觉人类和猴子活动感知研究。核心C提供关键的DTI和 休眠状态功能磁共振成像与我们的ECoG网络和HG数据相关联，而核心B提供数据 标准化、共享化。最后，项目5提供了计算和建模基础设施 对建立和提炼世界的单元和系统级模型所需的数据进行了抽样。主动传感和 预测性编码可能在一系列精神、神经和发育障碍中受损 这些疾病使得对这些过程的理解成为NIMH使命的核心。