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

基本信息

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
项目类别：
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

项目摘要

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.

项目描述书面文字在大脑中并不以统一的方式表示，而是以不同的特征表示。书面文字的不同部分被认为以相同或潜在的方式有差别地表示例如，相邻但不同的皮质区域主要位于但不是。仅限于左腹侧枕颞叶皮层（vOTC）被认为代表了不同的特征例如整个单词单元（例如 [MINT]）、二元组（[MI]、[IN]、[NT] 或子词法映射字母到声音（[M]-/m/、[I]-/ɪ/、[N]-/n/、[T]-/t/）。熟练读者和受损读者能够有效处理书面文字的哪些方面能够解析这些不同特征的依赖于经验的神经调整很重要例如，人们的字母发音单位可能调整不佳，但调整的二元词单位或词汇单位这是发展性阅读障碍研究的核心问题。该项目的具体目标是解决这个问题。本质上是研究皮层表征的多元性质以及在具有一定范围的个体中同时调整皮质对这些特征的反应在此过程中，我们将解决a）单词形式的特征如何分布。 “阅读网络”；b）不同的神经群体如何适应这些不同的以依赖于经验的方式描述书面文字的特征；以及 c) 如何调整这些特征；不同的拼写特征可以预测个体的阅读表现。为此，我们采用了一套创新方法来量化神经反应异质性基于稀疏编码理论的体素假设是高度调整的特征表示在体素之间具有更多异质（即独特）的神经反应。目标 1 的主要重点是验证表征相似性分析 (RSA) 在与功能磁共振成像中称为异质性回归的新型多变量分析方法相结合（Hreg）形成调优相似性分析（TSA）的度量目标2是确定这是否如此。一组新颖的指标可以预测通常达到的范围内的阅读表现读者以及阅读能力较差或受损的人，这将证实这一点。基于异质性的方法可以索引熟练单词中表示的内容和质量阅读以及阅读失败的潜在原因如果成功，将产生更广泛的影响。这种方法不仅可以用来确定表征的完整性，还可以用来确定困难用于视觉单词识别，还用于口语处理和其他领域多维感知学习补充行为诊断。