Pathfound: Revealing the neural basis of semantic memory and its breakdown in semantic dementia and stroke aphasia

探路：揭示语义记忆的神经基础及其在语义痴呆和中风失语症中的崩溃

• 批准号: G0501632/1
• 负责人: Matthew Lambon Ralph
• 金额: $ 98.76万
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2006
• 资助国家: 英国
• 起止时间: 2006 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=G0501632%2F1
• 关键词: Pathfound; Revealing; neural; basis; semantic

We have lots of knowledge about the world. We know about familiar and famous people. We know what objects are for and how they are used. We know the meanings of words. Collectively, this is called semantic memory. Certain types of brain damage in patients with stoke and dementia can specifically affect semantic memory. These patients? impairments impact significantly on every aspect of their lives. They are often unable to work, have problems with everyday tasks and chores, and cannot maintain their interests and hobbies. It is critically important, therefore, to improve our understanding of how the brain encodes all of this knowledge and how this breaks down after brain damage. The research proposed here will use five different methods to investigate this topic. We will use simple tests of comprehension and language to reveal the nature and qualities of the patients? problems. We will also take brain images to look at which brain parts are used when we comprehend and also which of the remaining brain parts the patients can still use. We will adopt a new technique which uses a magnetic coil to temporarily suppress activity in a small part of the brain for a short period of time. We can use this method to test which regions in the normal brain contribute to semantic memory. In addition, we will build small mathematical models which, like brains, are built up from a network of simple units (like brain cells). The construction of these models can be made to reflect the organisation of real brains. At the same time, the models can be trained to perform the various tasks we use with the patients. By damaging the models, we can then see if they mimic the patients? impairments. In effect, the models help us to bridge the gap between brain (the network of units) and mind (the behaviour of the model). Finally, we will use a new form of brain imaging that shows us how the different parts of the brain are connected together. By putting all of this information together, we will be able to work out how the brain supports our knowledge of the world and why it breaks down in patients.

我们有很多关于世界的知识。我们知道熟悉的和著名的人。我们知道物体是用来做什么的，以及它们是如何被使用的。我们知道单词的含义。总的来说，这被称为语义记忆。斯托克和痴呆患者的某些类型的脑损伤会特别影响语义记忆。这些病人？残疾严重影响他们生活的各个方面。他们经常无法工作，对日常任务和家务有问题，不能保持他们的兴趣和爱好。因此，提高我们对大脑如何编码所有这些知识以及大脑损伤后这些知识如何分解的理解至关重要。本研究将使用五种不同的方法来研究这个问题。我们将使用简单的理解和语言测试来揭示病人的性质和素质？问题我们还将拍摄大脑图像，看看当我们理解时使用了哪些大脑部分，以及患者仍然可以使用哪些剩余的大脑部分。我们将采用一种新的技术，使用一个磁线圈在短时间内暂时抑制大脑一小部分的活动。我们可以使用这种方法来测试正常大脑中的哪些区域对语义记忆有贡献。此外，我们将建立小型数学模型，这些模型就像大脑一样，是由简单单元（如脑细胞）组成的网络构建的。这些模型的构建可以反映真实的大脑的组织。与此同时，可以训练模型来执行我们与患者一起使用的各种任务。通过破坏模型，我们可以看看他们是否模仿了病人？损伤实际上，这些模型帮助我们弥合了大脑（单元网络）和心智（模型行为）之间的差距。最后，我们将使用一种新形式的大脑成像，向我们展示大脑的不同部分是如何连接在一起的。通过将所有这些信息放在一起，我们将能够弄清楚大脑如何支持我们对世界的认识，以及为什么它在病人身上会崩溃。