Quantifying and predicting cognitive impact of cerebral small vessel disease lesions

量化和预测脑小血管疾病病变的认知影响

• 批准号: 2407568
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2407568
• 关键词: Quantifying; predicting; cognitive; impact; cerebral

Brief description of the context of the research including potential impact Hallmarks of cerebral small vessel disease (CSVD) are white matter neurovascular lesions commonly observed on brain MRI of the ageing population. These markers have been associated with a wide range of cognitive changes including problems with executive function, attention, memory, processing speed and verbal ability. A hypothesis for this wide range of cognitive phenotypes is that size, severity and location of the lesions play an important role in the impact on the white matter tracts. However, little is known about the quantification of the damage to the tracts and how much the lesion locations can be predictive of cognitive decline. It is thus crucial to be able to predict how the pattern of lesion severity and location affects cognitive function.Being able to predict risks of such cognitive decline from imaging could be applied as inclusion criteria for clinical trials but also be used for improved personalised care. The scope of the project thus starts with the quantification of damage on white matter tracts in age-related CSVD to ultimately develop a method for predicting the impact of CSVD lesions on cognition.Aims and Objectives The aim of the project is to develop a method to improve cognitive lesion mapping in CSVD.The specific objectives are to:1. Quantify CSVD tissue damage severity on white matter tracts from brain MRI images to later assess the possible impact on cognition2. Model the local and distant impact of lesions on cognition since lesions represent local tissue damage, but the associated function may depend on the compromised links between brain regions3. Assess the impact of CSVD lesions on the brain structural network as a whole4. Develop a graph deep learning model to predict the possible impact of CSVD on cognitionNovelty of Research MethodologyThis is a highly challenging topic that will address open research questions related to:- white matter lesion mapping in CSVD including accounting for different types and severity- linking image analysis and statistical methods to study the bi-directional association between cognition and CSVD, where cognitive development predicts CSVD, which in turn predicts cognitive impairment - the local and global impact of small lesions on the brain's structural network and its connectivity- adaptation of image analysis methods for CSVD- lesion location patterns and their effect on cognitive function and impairmentAlignment to EPSRC's strategies and research areasThis project lies within the healthcare technology strategy aligning with the grand challenges of 'Optimising Treatment' and 'Transforming Community and Healthcare'. Hereby the essential cross-cutting areas of research are 'Novel computational and mathematical sciences' and 'Novel imaging technologies'.Any companies or collaborators involved none

简要描述研究背景​​，包括潜在影响 脑小血管疾病 (CSVD) 的标志是老年人脑部 MRI 上常见的白质神经血管病变。这些标记与广泛的认知变化有关，包括执行功能、注意力、记忆力、处理速度和语言能力的问题。对于这种广泛的认知表型的假设是，病变的大小、严重程度和位置在对白质束的影响中发挥着重要作用。然而，人们对神经束损伤的量化以及损伤位置在多大程度上可以预测认知能力下降知之甚少。因此，能够预测病变严重程度和位置的模式如何影响认知功能至关重要。能够通过影像学预测认知能力下降的风险，可以作为临床试验的纳入标准，也可以用于改善个性化护理。因此，该项目的范围从量化与年龄相关的 CSVD 中白质束的损伤开始，最终开发一种预测 CSVD 病变对认知影响的方法。 目的和目标 该项目的目的是开发一种方法来改进 CSVD 中的认知病变绘图。具体目标是：1。从大脑 MRI 图像中量化 CSVD 组织损伤对白质束的严重程度，以便随后评估对认知可能产生的影响2。模拟病变对认知的局部和远处影响，因为病变代表局部组织损伤，但相关功能可能取决于大脑区域之间的受损链接。评估 CSVD 病变对整个大脑结构网络的影响4。开发一个图深度学习模型来预测 CSVD 对认知的可能影响研究方法的新颖性这是一个极具挑战性的主题，将解决与以下相关的开放研究问题：- CSVD 中的白质病变映射，包括考虑不同类型和严重程度- 连接图像分析和统计方法来研究认知与 CSVD 之间的双向关联，其中认知发展预测 CSVD，进而预测认知 损伤 - 小病变对大脑结构网络及其连接性的局部和全局影响 - CSVD 图像分析方法的适应 - 病变位置模式及其对认知功能和损伤的影响 与 EPSRC 的战略和研究领域保持一致 该项目属于医疗保健技术战略，与“优化治疗”和“转变社区和医疗保健”的重大挑战相一致。因此，研究的基本交叉领域是“新颖的计算和数学科学”和“新颖的成像技术”。任何涉及的公司或合作者都没有