A Novel Approach to Measuring Neural Tuning to Written Words

测量对书面文字的神经调节的新方法

• 批准号: 10673192
• 负责人: Donald J Bolger
• 金额: $ 19.42万
• 依托单位: UNIV OF MARYLAND, COLLEGE PARK
• 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 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10673192
• 关键词: Academic achievement; Address; Adult; Area; Auditory; Behavioral; Benchmarking; Brain; Brain region; Characteristics; Child; Code; Complement; Complex; Data; Development; Developmental reading disorder; Diagnostic; Dimensions; Dyslexia; Failure; Frequencies; Functional Magnetic Resonance Imaging; Goals; Health; Heterogeneity; Impairment; Imprisonment; Income; Individual; Individual Differences; Inferior; Inferior frontal gyrus; Knowledge; Language; Language Disorders; Learning; Left; Letters; Link; Literature; Lobule; Maps; Measures; Methodology; Methods; Modality; Nature; Neurons; Orthography; Parietal; Participant; Pattern; Perceptual Disorders; Perceptual learning; Performance; Population; Printing; Probability; Process; Reader; Reading; Reading Disorder; Role; Semantics; Source; Superior temporal gyrus; Temporal Lobe; Testing; Visual; Writing; analytical method; behavior measurement; experience; indexing; individual variation; innovation; language processing; lexical; neural; neurotransmission; novel; novel strategies; phonology; reading ability; response; skills; sound; spelling; success; theories; visual map

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.

项目描述 书面词在大脑中并不统一地表示。相反，不同的功能 书面的单词被认为在相同或可能的情况下是不同的代表 相邻但独特的皮质区域。例如，皮质区域主要是在 限制为左腹枕骨皮层（fotc）被认为代表不同的特征 例如整个单词单元（例如[Mint]），Biogrics（[mi]，[In]，[nt]或 给声音的信（[m] - /m/，[i] - /ɪ/，[n] - /n/，[t] - /t/）。为了发展更机械的观点 书面单词的哪些方面在熟练和受损的读者中有效地处理了，这是 重要的是要分解这些不同功能的依赖经验的神经调整 印刷单词。例如，个人可能的字母单元调整了很差，却很好 调谐的大量单元或词汇单元。这是发育阅读障碍研究的核心问题 和阅读障碍。该项目的具体目的是通过 系统地研究皮质和代表性的多元性质 调整对这些特征的皮质响应，同时遍及具有范围的个体 阅读技能。在这样做的过程中，我们将解决a）单词表单的特征是如何分布的 “阅读网络”； b）不同的神经元种群如何使这些不同 书面单词的特征以经验依赖的方式； c）如何调整这些 不同的拼写特征可以预测个人的阅读表现。完成 这，我们采用了一组创新方法来量化整个跨越神经反应异质性 基于稀疏编码理论，具有假设的体素，高度调整的特征 表示范围内具有更异质的（即独特）神经反应。 AIM 1的主要重点是验证使用代表性相似性分析（RSA） 与fMRI的新型多元分析方法的结合称为异质性回归 （HREG）形成调整相似性分析（TSA）的度量。目标2是确定是否 新颖的指标集可以预测通常成就范围内的阅读性能 读者以及不良阅读能力的表明。这将确认这 基于异质性的方法可以索引熟练词中表示的内容和质量 阅读以及潜在的阅读失败来源。如果成功，则更广泛的影响 这种方法不仅可以用来确定代表性完整性 在视觉单词识别中，以及语言处理和其他域 多维感知学习完成行为诊断。