The Neural Code and Dynamics of the Reading Network

阅读涉及复杂的词形转换，视觉输入映射到词汇、语义和 在不到一秒的时间内完成语音系统。虽然关于阅读的神经解剖学已经学到了很多 从功能成像和损伤研究来看，该系统的动态和交互特性在很大程度上保持不变 未知。我们将研究快速计算，它允许我们从字符串的可视输入转换为 字母与相关的声音和意义的一个已知的单词使用我们建立的技术精确合作 大量人群颅内记录(75例患者)的定位和分析，从而绕过稀疏 人类颅内实验固有的采样问题。在一系列实验中，系统地 改变书面语的不同属性，调整参与者必须参加的语言信息 为此，我们将绘制大脑的全球单词阅读网络图。我们将评估神经计算架构 通过腹侧视觉流，使我们能够快速识别书面单词，并探索动态交互 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 流程。然后，我们将使用自回归模型得出阅读和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别交互，并将其与可观察到的行为相关联。 阐述阅读网络的节点在单词学习中的作用，我们将跟踪 分布式阅读网络，支持成功的单词学习。这将包括教病人新单词。 并检查阅读网络的反应在几天内的变化。关键节点和过渡 从录音中得出的网络状态将使用单焦点和多焦点直接皮质刺激进行验证。 为了实现我们的目标，我们建立了一个大型多中心协作。我们的团队在所有方面都有公认的专业知识 语言、阅读、颅内信号分析、人口水平网络建模和神经方面 网络。这项工作将极大地提高我们对书面语言系统的理解，并开发出新的 建立神经计算模型的方法。它将大大提高我们对阅读困难和语言障碍的理解 在大脑损伤或变性后，我们的实验重点是直接学习单词 语言的神经生物学模型。

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.