A Novel Approach to Measuring Neural Tuning to Written Words

项目描述 书面文字在大脑中的表现方式并不统一。相反， 被认为是在同一个或潜在的差异表示 相邻但不同的皮层区域例如，大脑皮层区域主要位于 仅限于左腹侧枕颞皮层（vOTC）被认为代表不同的功能 例如整个单词单元（例如[MINT]）、二元组（[MI]、[IN]、[NT]）或 字母转换为音（[M]-/m/，[I]-//，[N]-/n/，[T]-/t/）。为了发展一种更机械的观点， 熟练的和有障碍的读者可以有效地处理书面文字的哪些方面，这是 重要的是能够解析这些不同特征的经验依赖性神经调谐 印刷的字。例如，个人可能有很差的调整字母的声音单位，但很好- 调谐的二元组单元或词汇单元。这是发展性阅读障碍研究中的一个中心问题 和阅读障碍。该项目的具体目标是通过以下方式解决这一问题： 系统地研究了大脑皮层中表征的多元性， 在个体之间同时调整皮层对这些特征的反应， 阅读技能。在这样做时，我们将解决a）单词形式的特征如何分布在 "阅读网络"; B）不同的神经元群体如何适应这些不同的 以经验依赖的方式的书面文字的特点;和c）如何调整这些 不同的正字法特征预测个体之间的阅读表现。完成 为此，我们采用了一套创新的方法来量化神经反应的异质性， 体素的假设，基于稀疏编码理论，高度调谐功能 表示具有更多的异质性（即，独特的）跨体素的神经反应。的 目标1的主要重点是验证表征相似性分析（RSA）在 结合功能磁共振成像中的一种新的多变量分析方法，称为异方差回归 （Hreg）以形成调谐相似性分析（TSA）的度量。目标2是确定这是否 一组新的度量是在通常实现的范围内的阅读性能的预测 阅读者以及指示差的或受损的阅读能力。这将证实， 基于异质性的方法可以索引熟练词汇中表示的内容和质量， 阅读以及阅读失败的潜在来源。如果成功的话， 这种方法不仅可以用来确定表示完整性， 在视觉单词识别中，也用于口语处理和其他领域， 多维感知学习补充行为诊断。

Project Description The written word is not represented in a uniform manner in the brain. Instead, different features of the written word are thought to be differentially represented in the same or potentially neighboring, but distinct cortical regions. For instance, cortical areas predominantly in, but not limited to left ventral occipital-temporal cortex (vOTC) are thought to represent different features such as entire word units (e.g. [MINT]), bigrams ([MI], [IN], [NT], or the sub-lexical mappings of letters to sounds ([M]-/m/, [I]-/ɪ/, [N]-/n/, [T]-/t/). In order to develop a more mechanistic view of what aspects of the written word are effectively processed in skilled and impaired readers, it is important to be able to parse apart experience-dependent neural tuning of these different features of printed words. For instance, individuals may have poorly tuned letter-sound units, yet well- tuned bigram units or lexical units. This is a central question in the study of developmental dyslexia and reading impairments. The Specific Aims of this project are to address this question by systematically investigating the multivariate nature of representation in cortex and the tuning of cortical responses to these features concurrently across individuals with a range of reading skill. In doing so we will address a) how features of word forms are distributed across the “reading network”; b) how different neuronal populations become attuned to these different features of the written word in an experience-dependent manner; and c) how the tuning of these different orthographic features predicts reading performance across individuals. To accomplish this, we employ a set of innovative methods for quantifying neural response heterogeneity across voxels with the assumption, based on sparse coding theory, that highly tuned feature representations have more heterogeneous (i.e., unique) neural responses across voxels. The main focus of Aim 1 will be to validate the use of Representational Similarity Analysis (RSA) in combination with a novel multivariate analytic method in fMRI termed Heterogeneity Regression (Hreg) to form a metric of Tuning Similarity Analysis (TSA). Aim 2 is to determine whether this novel set of metrics is predictive of reading performance across the range of typically achieving readers as well as indicative of poor or impaired reading ability. This would confirm that this heterogeneity based approach can index the content and quality of representations in skilled word reading as well as the potential source of reading failure. If successful, the Broader Impacts of this approach could be used to determine representational integrity not only to difficulties in visual word recognition, but also for spoken language processing and other domains of multidimensional perceptual learning complementing behavioral diagnostics.