A Novel Approach to Measuring Neural Tuning to Written Words

项目说明 书面语在大脑中的表现方式并不统一。相反，不同的功能 被认为是以相同或潜在的不同方式表示的 邻近但不同的皮质区域。例如，皮质区域主要在，但不是 局限于左侧枕颞叶腹侧皮质(VOTC)被认为代表不同的特征 例如整个单词单位(例如[mint])、二元语法([MI]、[IN]、[NT]或的子词法映射 发音字母([M]-/m/，[i]-/ɪ/，[N]-/n/，[T]-/t/)。为了发展一种更机械化的观点 在熟练和有缺陷的读者中，书面单词的哪些方面得到了有效的处理？ 重要的是能够分析这些不同特征的依赖经验的神经调节 印刷文字。例如，个人可能有调谐不佳的字母发音单元，但- 调整了二元语法单位或词汇单位。这是发展性阅读障碍研究中的一个中心问题。 以及阅读障碍。该项目的具体目标是通过以下方式解决这一问题 系统研究大脑皮层表征的多变量性质 在具有一定范围的个体中同时调整对这些特征的皮质反应 阅读能力的提高。在这样做的过程中，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 以依赖经验的方式书写单词的特征；以及c)如何调整这些特征 不同的拼写特征可以预测不同个体的阅读成绩。要完成 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 体素具有基于稀疏编码理论的假设，即高度调谐的特征 表示在体素之间具有更多不同的(即，唯一的)神经反应。这个 目标1的主要重点将是验证代表性相似性分析(RSA)在 结合一种新的多元分析方法在功能磁共振成像中的异质性回归 (HREG)以形成调谐相似性分析(TSA)的度量。目标2是确定这是否 一组新的指标可以预测在通常实现的范围内的阅读性能 读者以及阅读能力不佳或受损的表示。这将证实这一点 基于异质性的方法可以对熟练词汇中表征的内容和质量进行索引 阅读以及阅读失败的潜在根源。如果成功，更广泛的影响 这种方法可以用来确定代表性完整性，而不仅仅是困难 在视觉单词识别中，也用于口语处理和其他领域 多维感知学习与行为诊断相辅相成。

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.