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.

阅读涉及单词形式的复杂转换，视觉输入映射到词汇，语义和 语音系统不到一秒钟。虽然已经了解了阅读的神经解剖学 从功能成像和病变研究中，该系统的动态和互动特性在很大程度上仍然是 未知。我们将研究快速计算，使我们能够从一个字符串的视觉输入转换 信函具有相关声音和含义的已知单词的信件，并使用我们既定的技术进行精确的共同 大量颅内记录的本地化和分析（75例患者），从而规避了稀疏 人类颅内实验固有的采样问题。在系统上的一系列实验中 不同的书面单词的不同属性，并调节必须参加哪种语言信息参与者 对于，我们将把大脑的全球阅读网络映射为单词。我们将评估神经计算体系结构 在腹侧视觉流中，使我们能够迅速识别书面单词，并探测动态互动 在各种行为任务中，更广泛的阅读网络偏向词汇，语音和语义 过程。然后，我们将使用自回旋模型在阅读过程中得出可稳定的大脑状态 在这些阶段表征动态网络级交互，并将其与可观察的行为联系起来。 详细说明阅读网络在单词学习中的作用，我们将跟踪调制 分布式阅读网络，可以成功地学习。这将涉及教患者新单词 并检查阅读网络的响应会发生多天的变化。关键节点和过渡 从记录中得出的网络状态将使用统一和多焦点直接皮质刺激进行验证。 为了实现我们的目标，我们建立了大型的多中心协作。我们的团队在所有人方面都有证明的专业知识 语言，阅读，颅内信号分析，人群水平网络建模和神经方面 网络。这项工作将极大地改善我们对书面语言系统的理解，并发展新的 建模神经计算的方法。这将大大增强我们对阅读障碍和语言障碍的理解 脑损伤或变性后，我们的实验重点是直接告知单词学习 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