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如何在大脑中处理语音的洞察力来提高我们对人言语和语言处理的理解。当前的大多数功能磁共振成像研究都缺乏解决以分布式活动模式编码的复杂信息的统计和描述能力，这使得它们适合现实世界中语音感知的复杂性。相比之下，使用新型的多元方法进行fMRI分析的最新研究揭示了神经活动的分级分布模式，这些模式有望提供大脑中知觉分类的详细定量描述。分类感知需要增强不同类别的刺激之间的对比度，并提高同一类别的刺激之间的相似性。该提案的第一个具体目的是发现与此过程相关的活动模式。高分辨率，与事件相关的FMRI数据将被收集，而受试者被动地聆听许多独特的自然意义重新合成的音节。将识别可用于识别刺激类别的活动模式，并使用多维缩放分析分析，以探索其定义的知觉相似性空间。然后，相同类型的分析将应用于行为数据，从而导致一种探索脑行为关系的新方法。第二个具体目的集中在探索通过语音分类的多元分析提出的方法论问题，特别是：确定包含有关刺激身份信息的神经活动模式的最佳方法是什么？将训练神经网络分类器，以根据对该刺激的神经反应在每个试验中确定哪个音节。然后将进行一系列测试，以确定这种方法是否提供了比标准单变量技术的优势，或者是否可以将基于分类器和单变量方法的互补优势组合在一起。将详细探讨有关这些分析的许多技术细节，以便获得一系列“最佳实践”，这些实践将使该技术最佳地集成到更大的研究计划中，包括多模式神经影像学和行为研究。公共卫生相关性如果成功，则拟议的研究将为研究语音感知的神经基础提供一组新的分析工具。这些技术将适用于成人处理，典型发展和一系列沟通障碍（包括阅读障碍和特定语言障碍）的研究，其中语音感知缺陷可能起着核心作用。具体而言，它将提供一种手段，以更好地表征语音类别表示的个体差异，并探讨与当前主要技术相比，不同疾病中缺陷性质的更详细的假设。