A unified cognitive network model of language

统一的语言认知网络模型

• 批准号: 9355244
• 负责人: NATHAN E CRONE
• 金额: $ 99.63万
• 依托单位: UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-25 至 2019-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9355244
• 关键词: Anatomy; Area; Auditory; Behavior; Biological Models; Biological Neural Networks; Brain; Cognitive; Collaborations; Complex; Cues; Data; Data Set; Databases; Descriptor; Development; Electrodes; Electroencephalography; Electrophysiology (science); Epilepsy; Feedback; Frequencies; Functional Magnetic Resonance Imaging; Functional disorder; Goals; Human; Individual; Interlobar; Intervention; Language; Lateral; Letters; Measures; Medical center; Mental disorders; Methods; Modality; Modeling; Motor; Names; Neurologic; Orthography; Output; Pathologic; Patients; Pattern; Pattern Recognition; Perception; Physiologic pulse; Physiological; Play; Population; Process; Psycholinguistics; Reading; Recruitment Activity; Resolution; Role; Sampling; Semantics; Signal Transduction; Site; Speech; Stream; Structure; System; Techniques; Temporal Lobe; Testing; Texas; Visual; Visual Pattern Recognition; Visual Perception; Work; base; cognitive load; cognitive process; cognitive task; cohort; computer framework; data modeling; experimental study; feeding; improved; indexing; information processing; innovation; insight; lexical; motor control; network models; neural model; novel; phonology; programs; relating to nervous system; response; semantic processing; signal processing; spatiotemporal; syntax; visual motor

Most current approaches to understanding the neural basis of cognitive processes are severely limited in two respects. First, most commonly used methods do not have the temporal (e.g., fMRI) or spatial (e.g., MEG/ EEG) resolution to capture the relevant dynamics. Second, even methods with high spatio-temporal resolution (intracranial EEG - icEEG) typically approach target cognitive processes in a fragmentary, un- integrated way. For instance, language is typically studied as a conglomeration of separate subsystems: perception, pattern recognition, categorization, semantically/syntactically appropriate response selection, cross-modal integration, motor control and sensorimotor integration. The present proposal aims to remedy both limitations by using icEEG to study a model system, reading/speech/language, from an integrative and unified perspective. We focus on reading, a complex task that involves visual pattern recognition, visual- auditory and visuo-motor integration, semantic, syntactic and phonological access, and (in reading aloud) - response selection and motor sequencing. Reading allows for easy, yet ecologically valid manipulations of cognitive load in the language system. The neuro-computational framework we propose to test is that computation is achieved not by information passing through a sequence of discrete processing stages in individual modules but via state transitions of a distributed network. We will recruit a large cohort of 80 patients in whom we will quantify both local as well as inter-regional cortical dynamics during word reading - from early primary visual perception, through selection, to word output. We will leverage our established techniques for precise co-localization and analysis of grouped icEEG data, circumventing the sparse sampling problem inherent to human icEEG experiments. The combined use of sub-dural grid electrodes and stereo-electroencephalographic depth electrodes will enable the study of not only classic peri-sylvian regions, but also of deep sulci (and regions such as the planum temporale). We will then characterize dynamic network interactions using linear and non linear measures of amplitude covariance in high frequencies, following analyses we have developed previously. Critical nodes and critical transitions in network states will then be perturbed using closed-loop activity-triggered direct cortical stimulation. To achieve these goals we have set up a collaboration between the Texas Comprehensive Epilepsy Program and Johns Hopkins Medical Center - both centers have a proven record of studying language with icEEG. Our team has expertise in all aspects language, reading, icEEG signal analysis, population level network modeling from intracranial recordings; and neural networks. This work will dramatically improve our understanding of language systems and test and develop a new way to model neural computation generally.

目前，大多数理解认知过程神经基础的方法都受到严重限制， 两个方面。首先，最常用的方法不具有时间（例如，fMRI）或空间（例如， MEG/EEG）分辨率来捕获相关动态。第二，即使是具有高时空分辨率的方法， 分辨率（颅内EEG-icEEG）通常以片段的，不完整的方式接近目标认知过程， 综合方式。例如，语言通常被研究为独立子系统的集合： 感知、模式识别、分类、语义/句法上适当的响应选择， 跨模态整合、运动控制和感觉运动整合。本提案旨在补救 使用icEEG从综合和综合角度研究模型系统（阅读/言语/语言）的局限性 统一的视角。我们专注于阅读，这是一项复杂的任务，涉及视觉模式识别，视觉- 听觉和视觉运动整合，语义，句法和语音的访问，和（在阅读）- 响应选择和电机排序。阅读允许简单，但生态有效的操纵， 语言系统中的认知负荷。我们提出要测试的神经计算框架是， 计算不是通过信息通过一系列离散的处理阶段来实现的， 单个模块，但通过分布式网络的状态转换。我们将招募一大批80人 我们将在患者中量化文字阅读过程中的局部和区域间皮质动力学- 从早期的初级视觉感知，通过选择，到文字输出。我们将利用现有的 用于精确共定位和分析分组icEEG数据的技术， 采样问题固有的人类icEEG实验。硬膜下栅极电极的联合使用 立体脑电图深度电极不仅可以研究典型的大脑侧裂周围 区域，但也深沟（和区域，如颞平面）。然后我们将描述 动态网络相互作用使用的线性和非线性措施的幅度协方差在高 频率，根据我们以前开发的分析。中的关键节点和关键过渡 然后使用闭环活动触发的直接皮层刺激来扰乱网络状态。到 为了实现这些目标，我们已经建立了一个合作， 和约翰霍普金斯医学中心-这两个中心都有用icEEG研究语言的证明记录。 我们的团队在语言、阅读、icEEG信号分析、人群水平网络等方面都有专业知识。 从颅内记录建模;和神经网络。这项工作将大大改善我们的 理解语言系统，测试和开发一种新的方法来模拟神经计算 一般来说。