The Neural Code and Dynamics of the Reading Network

阅读网络的神经代码和动力学

• 批准号: 10668493
• 负责人: NITIN TANDON
• 金额: $ 79.37万
• 依托单位: UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10668493
• 关键词: Adult; Architecture; Area; Behavior; Behavioral; Brain; Brain Injuries; Brain Mapping; Child; Code; Collaborations; Complex; Data; Dyslexia; Educational process of instructing; Electrodes; Epilepsy; Functional Imaging; Goals; Human; Impairment; Injury; Knowledge; Language; Language Disorders; Learning; Lesion; Letters; Linguistics; Maps; Modeling; Neuroanatomy; Neurobiology; Participant; Patients; Population; Process; Property; Prosthesis; Prosthesis Design; Reading; Role; Sampling; Semantics; Series; Signal Transduction; Stream; System; Techniques; Visual; Work; Writing; experimental study; improved; insight; lexical; network models; neural; neural model; neural network; neurosurgery; operation; phonology; prevent; response; sound; word learning

Reading involves complex transformations of word forms, with visual input mapped to lexical, semantic and phonological systems in less than a second. While much has been learned about the neuroanatomy of reading from functional imaging and lesion studies, the dynamic and interactive properties of this system remain largely unknown. We will investigate the rapid computations that allow us to convert from the visual input of a string of letters to a known word with an associated sound and meaning using our established techniques for precise co- localization and analysis of a large population intracranial recordings (75 patients), thus circumventing the sparse sampling problems inherent to human intracranial experiments. In a series of experiments that systematically vary different properties of written words and modulate what kind of linguistic information participants must attend to, we will map the brain's global reading network for words. We will evaluate the neurocomputational architecture across the ventral visual stream that allows us to rapidly identify written words, and probe dynamic interactions with the broader reading network during a variety of behavioral tasks biasing lexical, phonological and semantic processes. We will then use autoregressive models to derive metastable brain states during reading and characterize dynamic network-level interactions during these stages and relate these to observable behavior. elaborate on the roles of nodes of the reading network in word learning, we will track the modulations in the distributed reading network that enable successful word learning. This will involve teaching patients new words and examining the reading network's response changes over a number of days. Critical nodes and transitions in network states derived from recordings will be validated using unifocal and multifocal direct cortical stimulation. To accomplish our goals we have set up a large multicenter collaboration. Our team has proven expertise in all aspects of language, reading, intracranial signal analysis, population level network modeling, and neural networks. This work will dramatically improve our understanding of written language systems and develop new ways to model neural computation. It will greatly enhance our understanding of dyslexia and language disorders following brain injury or degeneration, with our experimental focus on word learning directly informing neurobiological models of language.

阅读涉及复杂的词形转换，视觉输入映射到词汇，语义和语法。 语音系统在不到一秒钟。虽然人们对阅读的神经解剖学已经了解了很多 从功能成像和病变研究来看，该系统的动态和交互特性在很大程度上仍然存在， 未知我们将研究快速计算，使我们能够从视觉输入的一串 字母到一个已知的词与相关的声音和意义，使用我们建立的技术，精确的合作， 定位和分析大量颅内记录（75例患者），从而避免了稀疏的 人类颅内实验固有的采样问题。在一系列的实验中， 改变书面文字的不同属性，并调整参与者必须注意的语言信息类型 我们将绘制大脑的全球阅读网络。我们将评估神经计算架构 通过腹侧视觉流，我们可以快速识别书面文字， 随着更广泛的阅读网络在各种行为任务中偏向词汇，语音和语义 流程.然后，我们将使用自回归模型来推导阅读过程中的亚稳态大脑状态， 描述这些阶段中的动态网络级交互，并将其与可观察的行为相关联。 详细阐述了阅读网络节点在单词学习中的作用，我们将跟踪 分布式阅读网络，使成功的单词学习。这将涉及教病人新的单词 并检查阅读网络的响应在多天内的变化。中的关键节点和过渡 将使用单焦点和多焦点直接皮层刺激来验证从记录导出的网络状态。 为了实现我们的目标，我们建立了一个大型的多中心合作。我们的团队在所有领域都拥有专业知识 语言、阅读、颅内信号分析、群体水平网络建模和神经网络等方面 网络.这项工作将大大提高我们对书面语言系统的理解，并开发新的 模拟神经计算的方法它将大大增强我们对阅读障碍和语言障碍的理解 在脑损伤或退化后，我们的实验重点是单词学习， 语言的神经生物学模型

项目成果

Spatiotemporally distributed frontotemporal networks for sentence reading.

• DOI: 10.1073/pnas.2300252120
• 发表时间: 2023-04-25
• 期刊: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• 影响因子: 11.1
• 作者: Woolnough, Oscar;Donos, Cristian;Murphy, Elliot;Rollo, Patrick S.;Roccaforte, Zachary J.;Dehaene, Stanislas;Abf, Nitin Tandon
• 通讯作者: Abf, Nitin Tandon