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且嘈杂的环境中提取显着信息？项目3地址 使用人类中直接脑记录的感知中的这个基本问题（皮质学； ECOG）评估两种感觉获取模型。主动传感模型认为高级输入 行动以节奏地采样感官单词并过滤掉噪声。相关的预测编码模型理论 假定先验知识可以通过大脑对即将到来的刺激做出预测来增强感知 锐化低级感觉处理。我们建议两个过程共享相似的神经底物 - 额叶，前运动和感觉皮质网络基于神经元节律的参与 对世界的积极和预测性抽样，以增强感知。我们采用ECOG来测量神经 振荡和高频活性（HG； 70-200 Hz；替代物质SUA活性）并采用 网络分析方法来定义自上而下控制主动传感和预测编码的作用 在人脑中。在项目4的猴子中进行了两个或我们提出的人类ECOG研究 允许对感觉采集的神经底物进行丰富的种间比较。 AIM 1测试 假设运动/前系统控制听觉采样节奏并积极抑制 分散注意力的信息。此目的还探讨了前侧前额叶区域是否为 电动机/前审计运动感应网络。 AIM 2解决了先验知识如何增强 语音感知和听觉皮层中的“填充”降解语音表示。考虑到 语音刺激该研究仅在人类中进行。此目标直接测试预测性编码 模型并检查相似的神经底物是否支持预测性编码和主动传感。目标3 将我们的ECOG数据与层流LFP/CSD和MUA配置文件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用于识别单元格 在猴子和人类中产生ECOG成分的人群和生理过程 提供对人类皮质生理的前所未有的见解。我们预测主动感应 机制是独立的方式，也将使我们的发现从听觉猴子和 在项目1和2中的视觉人和猴子主动传感研究。核心C提供了关键的DTI和 静止状态fMRI与我们的ECOG网络以及HG数据和核心B提供了数据 标准化和共享。最后，项目5提供了计算和建模基础架构 采样了建立和完善世界级别模型所必需的。主动感应和 预测性编码可能会在许多致残的精神病，神经和发展中受到损害 使对这些过程的理解是NIMH使命的核心。