Distributed Phonetic Representations in the Brain

描述(由申请人提供)：神经成像研究通过提供有关语音在大脑中如何处理的见解，促进了我们对人类语音和语言处理的理解。大多数目前的功能磁共振研究缺乏统计和描述能力来解析以分布式活动模式编码的复杂信息，这使得它们很难适应现实世界中语音感知的复杂性。相比之下，最近使用新的多变量方法进行功能磁共振分析的研究揭示了神经活动的分级、分布模式，这有望提供对大脑知觉分类的详细、定量的描述。范畴知觉要求增强不同类别刺激之间的对比度，增强同一类别刺激之间的相似性。这项提议的第一个具体目标是发现与这一进程有关的活动模式。当受试者被动地听许多独特的、自然地重新合成的音节时，将收集高分辨率的、与事件相关的功能磁共振数据。可用于识别刺激类别的活动模式将被识别，并使用多维尺度分析进行分析，以探索它们定义的知觉相似空间。然后，同样类型的分析将应用于行为数据，从而产生一种探索大脑-行为关系的新方法。第二个具体目标集中在探索语音分类的多变量分析提出的方法学问题，特别是：识别包含刺激同一性信息的神经活动模式的最佳方法是什么？神经网络分类器将经过训练，根据神经对刺激的反应来确定每个试验中出现的是哪个音节。然后将进行一系列测试，以确定该方法是否比标准单变量方法具有优势，或者是否可以将基于分类器的方法和基于单变量的方法的互补优势结合起来。将详细探讨与这些分析有关的一些技术细节，以便得出一套“最佳实践”，使这项技术能够以最佳方式整合到更大的研究计划中，包括多模式神经成像和行为研究。如果成功，这项拟议的研究将为研究言语感知的神经基础提供一套新的分析工具。这些技术将适用于成人加工、典型发育和一系列沟通障碍--包括阅读障碍和特定语言障碍--的研究，在这些障碍中，言语感知缺陷可能起到核心作用。具体地说，它将提供一种手段，以更好地表征言语类别表征中的个体差异，并探索关于不同障碍中缺陷性质的更详细的假设，而不是目前的主要技术。

DESCRIPTION (provided by applicant): Neuroimaging research has advanced our understanding of human speech and language processing by providing insights about how speech sounds are processed in the brain. Most current fMRI studies lack the statistical and descriptive power to resolve complex information encoded in distributed patterns of activity, making them an awkward fit to the complexities of speech perception in the real world. In contrast, recent studies using novel multivariate approaches to fMRI analysis have revealed graded, distributed patterns of neural activity that promise to provide detailed, quantitative descriptions of perceptual categorization in the brain. Categorical perception requires enhancing contrast between stimuli of different categories and enhancing similarity between stimuli from the same category. The first specific aim of this proposal is to discover patterns of activity related to this process. High-resolution, event-related fMRI data will be collected while subjects passively listen to many unique, naturalistically resynthesized syllables. Patterns of activity that can be used to identify stimulus categories will be identified, and analyzed using multidimensional scaling analyses to explore the perceptual similarity space they define. The same type of analysis will then be applied to behavioral data, resulting in a novel means of exploring brain-behavior relationships. The second specific aim is focused on exploring methodological issues presented by multivariate analysis of speech categorization, in particular: What is the best way to identify patterns of neural activity that contain information about stimulus identity? A neural network classifier will be trained to determine which syllable was presented on each trial based on the neural response to that stimulus. A series of tests will then be conducted to determine whether this approach provides advantages over standard univariate techniques, or whether the complementary strengths of classifier- and univariate-based methods can be combined. A number of technical details regarding these analyses will be explored in detail, in order to arrive at a set of "best practices" that will permit this technique to be optimally integrated into a larger program of research including multi-modality neuroimaging and behavioral studies. PUBLIC HEALTH RELEVANCE If successful, the proposed research will provide a new set of analytic tools for the study of the neural basis of speech perception. These techniques will be applicable to research on adult processing, typical development, and a range of communication disorders -- including dyslexia and specific language impairment -- in which speech perception deficits may play a central role. Specifically, it will provide a means to better characterize individual differences in the representation of speech categories, and to explore more detailed hypotheses about the nature of deficits in different disorders than is possible with currently predominant techniques.