The Neural Bases of he Semantic Structure of Words and Concepts

描述（由申请人提供）：本项目旨在发现一些简单词汇和概念的神经表征。它使用新开发的机器学习技术和降维方法应用于fMRI脑激活数据，这些数据将在新的实验范式中获得。这种研究方法首次成功地根据大脑活动的模式识别了人类个体思想的内容。最初发表的研究已经在具体名词和物理对象的情况下证明了这种能力。本项目建议将该方法扩展到更多不同类型的概念，并检查概念呈现方式的效果（例如书面或口头单词，或图片）。我们的目标是发展一个全面的理论，说明意义的神经表征是如何从用于相互作用或考虑概念的各种大脑系统中产生的。一个重要的次要目标是确定人与人之间神经表征的共性程度。这些研究建议在三个领域检查意义的神经维度：(a)物理对象；(b)人类特征、情感和人际交往；(c)小数值量。这组语义域有望提供足够的广度来揭示语义表示的一些主要神经基础。与“语义学”领域（研究单词与其含义之间的关系）相比，该项目将有助于建立一个新的研究领域——神经语义学，即研究单词、思想及其神经表征之间的关系。关键的假设是，人类大脑中意义表征的潜在维度来源于基本的神经系统。例如，物理对象表示的一个维度是如何与它进行物理交互或处理它。这种表征的维度是由一个皮层区域网络支撑的，当一个人思考一个物理物体时，以及当一个人实际处理这个物理物体时，这些区域会共同激活。当遇到这个概念时，神经表征的其他维度也会类似地出现。这些研究将共同确定概念表征的主要维度，并将它们与共同激活的大脑区域网络联系起来。从完成的项目中积累的知识将为大脑系统如何映射到概念意义的表示提供理论框架。该理论将适用于理解和设计治疗概念意义扭曲的神经系统疾病，如阿尔茨海默病、皮克病、语义性痴呆和自闭症。由此产生的理论将是将意义表征与大脑功能联系起来的基础。

DESCRIPTION (provided by applicant): This project proposes to discover the neural representation of some simple words and concepts. It uses newly developed machine learning techniques and dimension reduction methods applied to fMRI brain activation data that will be acquired in new experimental paradigms. This research approach has for the first time succeeded in identifying the content of individual human thoughts based on the pattern of brain activity. The initial published studies have demonstrated this capability in the case of concrete nouns and physical objects. This project proposes to expand the approach to a much larger set of different types of concepts and to examine the effect of the way the concept is presented (e.g. a written or spoken word, or a picture). The goal is to develop a comprehensive theory of how neural representations of meaning arise from the various brain systems that are used in interacting or considering the concept. An important secondary goal is to determine the degree of commonality of the neural representations across people. The studies propose to examine the neural dimensions of meaning in three domains: (a) physical objects; (b) human traits, emotions, and interpersonal interactions; and (c) small numerical quantities. This set of semantic domains is expected to provide sufficient breadth to reveal some of the principle neural bases of semantic representation. In contrast to the field of "semantics" (the study of the relation between words and their meanings), this project will help establish a new research area, neurosemantics, which is the study of the relation between words, thoughts, and their neural representations. The key assumption is that the underlying dimensions of meaning representation in the human brain are derived from basic neural systems. For example, one of the dimensions of representation of a physical object is how one physically interacts with or handles it. This dimension of representation is underpinned by a network of cortical areas that co-activate when one thinks about a physical object, and also when one actually handles the physical object. Other dimensions of neural representation similarly emerge when the concept is encountered. The studies will collectively identify the major dimensions of concept representation and relate them to networks of co-activating brain areas. The cumulative knowledge from the completed project will provide the framework of a theory of how brain systems map onto the representation of the meaning of concepts. The theory will be applicable to understanding and designing therapies for neurological conditions in which the meanings of concepts are distorted, such as Alzheimer's Disease, Pick's Disease, semantic dementia, and autism. The resulting theory will be foundational in relating the representation of meaning to brain function. PUBLIC HEALTH RELEVANCE: The research has application to a number of neurological disorders, where the new approach is capable of (1) determining whether and how the neural representation of a particular set of concepts is distorted; (2) tracking the progressive loss of neurosemantic components in disorders affecting the representation of meaning (including Alzheimer's Disease, Pick's Disease, semantic dementia, and anomic aphasia); (3) identifying the particular neurosemantic dimensions that are affected by the disorder; and hence (4) pointing the way to the design of a therapy. More generally, as a recent review (Bray et al., 2009) indicates, the new approach provides a good match to the brain characteristics of several brain disorders, and it is beginning to be applied for health-related purposes in many domains.