Using computational analyses to understand cognitive impairment in multiple sclerosis

使用计算分析来了解多发性硬化症的认知障碍

• 批准号: 2899848
• 金额: --
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2899848
• 关键词: Using; computational; analyses; understand; cognitive

Multiple sclerosis (MS) is the most common cause of neurological disability in young adults. Cognitive impairment is common and is strongly associated with adverse outcomes including unemployment. Recent studies across a number of neurodegenerative conditions demonstrate that pathology typically occurs along networks of connected brain regions. These regions may be selectively vulnerable due to specific metabolic and cytoarchitectural characteristics. MS, long considered a multi-focal condition characterised by random pathology in the central nervous system, has recently been re-evaluated, in light of similar, network-based pathology. The present study will examine how changes in the connectivity of brain networks leads to the emergence of cognitive impairment in people with MS using a number of complementary approaches. First, we will apply Machine Learning algorithms to clinical MRI datasets to understand whether there are specific structural features that reliably characterise those with MS (generally) and those with cognitive symptoms (specifically). Second, we will examine whether these regions demonstrate changes in levels of cortical connectivity at early stages in MS. Third, we will examine whether similar changes occur in the specific pathways linking these hub regions. These findings will offer substantial new insight into the mechanisms of pathology in MS and the factors that affect employment, social function and quality of life. Throughout this project, the successful candidate will have the opportunity to work in a thriving academic environment, but also to spend time with our industrial collaborator, AInostics, developing neuroimaging analysis algorithms for the medical imaging services industry.

多发性硬化症（MS）是年轻人神经功能障碍的最常见原因。认知障碍很常见，并且与包括失业在内的不良后果密切相关。最近对许多神经退行性疾病的研究表明，病理通常发生在连接的大脑区域网络上。由于特定的代谢和细胞结构特征，这些区域可能选择性地脆弱。MS长期以来被认为是一种以中枢神经系统随机病理为特征的多灶性疾病，最近根据类似的基于网络的病理重新评估。目前的研究将使用一些互补的方法来研究大脑网络连接的变化是如何导致多发性硬化症患者出现认知障碍的。首先，我们将机器学习算法应用于临床MRI数据集，以了解是否存在特定的结构特征，这些特征可以可靠地表征MS（一般）和认知症状（具体）。其次，我们将研究这些区域在ms早期是否表现出皮层连通性水平的变化。第三，我们将研究连接这些中枢区域的特定通路是否发生类似的变化。这些发现将为MS的病理机制以及影响就业、社会功能和生活质量的因素提供实质性的新见解。在整个项目中，成功的候选人将有机会在一个蓬勃发展的学术环境中工作，但也有机会与我们的工业合作伙伴AInostics一起为医疗成像服务行业开发神经成像分析算法。