Towards a unified, computationally-implemented neural network for understanding semantic cognition and its disorders.

建立一个统一的、计算实现的神经网络来理解语义认知及其障碍。

• 批准号: MR/J004146/1
• 负责人: Matthew Lambon Ralph
• 金额: $ 214.35万
• 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: 2012
• 资助国家: 英国
• 起止时间: 2012 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FJ004146%2F1
• 关键词: Towards; unified; computationally; implemented; neural

Semantic memory refers to the rich database of knowledge we have about the meanings of words, objects, people and all the stimuli present in our environment. We activate this information when we comprehend a word or recognise an object. We use the same knowledge to initiate speech or non-verbal activities such as object use. The aim of communication, itself, is for meaning to be conveyed between people. It is evident, therefore, that semantic knowledge is crucial for many everyday activities both at work and at home. When this type of knowledge disintegrates or becomes inaccessible after brain damage, patients become significantly disabled in many aspects of their lives. Imagine, for example, being able to comprehend only a small proportion of the words in everyday conversation, a letter or newspaper; being stuck with significant word-finding problems; or being unable to understand everyday symbols or road signs. Sadly, these kinds of problems are a common feature of many types of brain disease. Semantic impairment is a characteristic of certain types of dementia, brain infections and after stroke. In a past study of patients with language problems after stroke, we found measurable problems with semantic processing in around 1/3 of them. The core aims of our continuing research programme, therefore, are (a) to build up a formal, working model of how the brain supports semantic processing; (b) to catalogue and investigate all types of patient with semantic impairment; and (c) to use our "brain model" of semantic processing to understand the causes of the patients' different impairments. These steps will be used to improve: detection of semantic deficits; differential diagnosis; clinical management; and evidence-based interventions. In our last research programme we discovered important qualitative variations in the nature of semantic impairment in different patient groups. In addition to these clinical investigations, we developed and applied new basic science methods to probe the functioning of various specific brain regions. This allowed us to check the patient results in normally-functioning brains and then to use the methods to extend our understanding of semantic processes and which specific brain subregions are involved. In the next phase of our research, we will use these important findings and methods as a foundation for a new series of studies. Our ultimate aim is to build up a complete picture and model of the network of brain regions that support semantic processing. We will use this model to reproduce each patient group's pattern of performance, which will be catalogued with high precision. This will be achieved by adopting a variety of clinical and basic science methods. These will include: (a) further clinical investigations of existing and new patient groups - leading to a complete catalogue of the semantic processing problems in all types of relevant patient groups (covering dementia, stroke and other forms of brain damage); (b) brain stimulation studies to mimic patient-like deficits and to test key ideas about semantic functioning; (c) functional brain imaging in order to map out the semantic processing, in both healthy participants and various patient groups; (d) to amalgamate the information from all these lines of enquiry through a new form of mathematical modelling which incorporates information about brain regions and their 'wiring' into the model - such that we can simulate not only the patients' behaviour but also the underlying pattern of brain damage. We will then be able to use this model not only to understand the nature of semantic problems across all these different patient groups but also to use the model to gain new insights about minimising these problems and for generating new interventions that could be used by speech therapists with these patient groups.

语义记忆是指我们所拥有的丰富的知识数据库，其中包括我们所处环境中的单词、物体、人和所有刺激物的含义。当我们理解一个单词或识别一个物体时，我们会激活这个信息。我们使用相同的知识来启动语言或非语言活动，如物体使用。沟通的目的本身就是为了在人与人之间传递意义。因此，很明显，语义知识对于工作和家庭中的许多日常活动都是至关重要的。当这种类型的知识在脑损伤后瓦解或无法获得时，患者在生活的许多方面都会严重残疾。想象一下，例如，能够理解日常对话，信件或报纸中的一小部分单词；被严重的找词问题困住；或者无法理解日常符号或路标。可悲的是，这些问题是许多类型的脑部疾病的共同特征。语义障碍是某些类型的痴呆、脑感染和中风后的特征。在过去对中风后出现语言问题的患者进行的一项研究中，我们发现其中约三分之一的患者存在可测量的语义处理问题。因此，我们持续研究计划的核心目标是：(a)建立一个正式的、工作的大脑如何支持语义处理的模型；(b)对各类语义障碍患者进行分类和调查；(c)使用我们的语义处理“大脑模型”来理解患者不同损伤的原因。这些步骤将用于改进：语义缺陷的检测；鉴别诊断;临床管理;以及基于证据的干预措施。在我们上一个研究项目中，我们发现了不同患者群体语义障碍本质上的重要质的变化。除了这些临床研究外，我们还开发和应用了新的基础科学方法来探测大脑各个特定区域的功能。这使我们能够在正常运作的大脑中检查患者的结果，然后使用这些方法扩展我们对语义过程的理解，以及涉及哪些特定的大脑子区域。在下一阶段的研究中，我们将利用这些重要的发现和方法作为一系列新研究的基础。我们的最终目标是建立一个支持语义处理的大脑区域网络的完整图像和模型。我们将使用这个模型来重现每个病人组的表现模式，这将以高精度编目。这将通过采用各种临床和基础科学方法来实现。这些将包括：(a)对现有和新的患者群体进行进一步的临床调查——对所有类型的相关患者群体（包括痴呆、中风和其他形式的脑损伤）的语义处理问题进行完整的分类；(b)脑刺激研究，模拟类似病人的缺陷，测试语义功能的关键思想；(c)在健康参与者和不同患者群体中进行功能性脑成像，以绘制语义加工图；(d)通过一种新形式的数学模型将所有这些调查的信息合并起来，这种模型将有关大脑区域及其“线路”的信息纳入模型，这样我们不仅可以模拟患者的行为，还可以模拟脑损伤的潜在模式。然后，我们不仅可以使用这个模型来理解所有这些不同患者群体的语义问题的本质，还可以使用这个模型来获得最小化这些问题的新见解，并产生新的干预措施，这些干预措施可以被语言治疗师用于这些患者群体。

项目成果

The behavioural patterns and neural correlates of concrete and abstract verb processing in aphasia: A novel verb semantic battery.

• DOI: 10.1016/j.nicl.2017.12.009
• 发表时间: 2018
• 期刊: NeuroImage. Clinical
• 作者: Alyahya RSW;Halai AD;Conroy P;Lambon Ralph MA
• 通讯作者: Lambon Ralph MA

The tract terminations in the temporal lobe: Their location and associated functions.

• DOI: 10.1016/j.cortex.2016.03.013
• 发表时间: 2017-12
• 期刊: Cortex; a journal devoted to the study of the nervous system and behavior
• 作者: Bajada CJ;Haroon HA;Azadbakht H;Parker GJM;Lambon Ralph MA;Cloutman LL
• 通讯作者: Cloutman LL

Noun and verb processing in aphasia: Behavioural profiles and neural correlates.

• DOI: 10.1016/j.nicl.2018.01.023
• 发表时间: 2018
• 期刊: NeuroImage. Clinical
• 作者: Alyahya RSW;Halai AD;Conroy P;Lambon Ralph MA
• 通讯作者: Lambon Ralph MA

A graded tractographic parcellation of the temporal lobe.

• DOI: 10.1016/j.neuroimage.2017.04.016
• 发表时间: 2017-07-15
• 期刊: NeuroImage
• 影响因子: 5.7
• 作者: Bajada CJ;Jackson RL;Haroon HA;Azadbakht H;Parker GJM;Lambon Ralph MA;Cloutman LL
• 通讯作者: Cloutman LL