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Distributed Phonetic Representations in the Brain

大脑中的分布式语音表征

基本信息

批准号：
7752570
负责人：
Jason D Zevin
金额：
$ 20.91万
依托单位：
WEILL MEDICAL COLL OF CORNELL UNIV
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-01-01 至 2011-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7752570
关键词：
Adult Basic Science Behavioral Biological Neural Networks Brain Categories Classification Communication impairment Complex Data Data Analyses Development Dimensions Disease Dyslexia Event Functional Magnetic Resonance Imaging Goals Human Image Impairment Individual Differences Judgment Language Maps Measures Methods Mining Modality Modeling Multivariate Analysis Nature Neural Network Simulation Pattern Perception Phonetics Play Preparation Process Property Recovery Research Resolution Role Scanning Series Source Speech Speech Perception Speech Sound Stimulus Techniques Testing Training Translational Research Variant base brain behavior data acquisition design insight interest language processing neural patterning neuroimaging novel novel strategies programs public health relevance relating to nervous system research study response specific language impairment tool

项目摘要

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.

描述（由申请人提供）：神经影像学研究通过提供有关大脑如何处理语音的见解，促进了我们对人类语音和语言处理的理解。目前大多数功能磁共振成像研究缺乏统计和描述能力来解析编码在分布式活动模式中的复杂信息，这使得它们与真实的世界中复杂的言语感知不太吻合。与此相反，最近的研究使用新的多变量方法来功能磁共振成像分析揭示了分级，分布的神经活动模式，承诺提供详细的，定量的描述在大脑中的感知分类。类别感知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。本提案的第一个具体目标是发现与这一进程有关的活动模式。高分辨率，事件相关的功能磁共振成像数据将被收集，而受试者被动地听许多独特的，自然重新合成的音节。将识别可用于识别刺激类别的活动模式，并使用多维尺度分析进行分析，以探索它们定义的感知相似性空间。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。第二个具体的目标是集中在探索语音分类的多变量分析提出的方法问题，特别是：什么是最好的方式来识别包含刺激身份信息的神经活动模式？神经网络分类器将被训练以基于对该刺激的神经响应来确定在每次试验中呈现哪个音节。然后将进行一系列测试，以确定这种方法是否优于标准单变量技术，或者是否可以结合基于分类器和单变量方法的互补优势。一些关于这些分析的技术细节将详细探讨，以达到一套“最佳实践”，将允许这种技术被最佳地整合到一个更大的研究计划，包括多模态神经成像和行为研究。公共卫生相关性如果成功，拟议的研究将提供一套新的分析工具，研究的神经基础的语音感知。这些技术将适用于研究成人处理，典型的发展，以及一系列的沟通障碍-包括诵读困难和特定的语言障碍-其中言语感知缺陷可能发挥核心作用。具体而言，它将提供一种手段，以更好地表征语音类别的表示中的个体差异，并探索更详细的假设，在不同的障碍的性质的赤字比目前占主导地位的技术是可能的。