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着信息？项目3地址 使用人类直接大脑记录来感知这一基本问题（皮层电图； ECoG）来评估两种感觉获取模型。主动传感模型假设高级输入 有节奏地对感官词进行采样并滤除噪音。相关预测编码模型理论 假设先验知识可以增强大脑对即将到来的刺激的预测的感知 提高低水平的感觉处理能力。我们认为这两个过程共享相似的神经基质 - 基于神经节律的额叶、前运动、运动和感觉皮层网络的参与，使 对世界进行主动和预测采样以增强感知。我们使用 ECoG 来测量神经 振荡和高频活动（HG；70-200 Hz；皮质内 SUA 活动的替代品）并采用 网络分析方法定义主动传感和预测编码自上而下控制的作用 在人脑中。项目 4 中的两项或我们提议的人类 ECoG 研究是在猴子身上进行的 允许对感觉获取的神经基质进行丰富的物种间比较。 AIM 1 测试 假设运动/前运动系统控制听觉采样节律并主动抑制 分散注意力的信息。该目标还探讨了外侧前额叶区域是否提供额外的控制 运动/前运动听觉主动传感网络。 AIM 2 解决了如何增强先验知识 言语感知和“填充”会降低听觉皮层的言语表征。鉴于使用 言语刺激这项研究仅在人类身上进行。该目标直接测试预测编码 模型并检查类似的神经基质是否支持预测编码和主动感知。目标3 将我们的 ECoG 数据与层流 LFP/CSD 和 MUA 配置文件以及网络参数进行比较 平行猴听觉项目4。这些独特的跨物种数据将用于识别细胞 猴子和人类中产生 ECoG 成分的种群和生理过程 为人类皮质生理学提供前所未有的见解。我们预测主动传感 机制是独立于模态的，并且还将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主动传感研究。核心 C 提供关键的 DTI 和 静息态 fMRI 与我们的 ECoG 网络和 HG 数据相关联，并且 Core B 提供数据 标准化和共享。最后，项目 5 提供计算和建模基础设施 对建立和完善世界的细胞和系统级模型所必需的进行采样。主动传感和 预测编码可能在许多精神、神经和发育障碍中受到损害 使得理解这些过程成为 NIMH 使命的核心。