Investigating early structural and functional changes to cholinergic and noradrenergic brain systems, and their relationship to APOE-e4 and Alzheimer'

痴呆症是一种与年龄有关的、高度衰弱的疾病。英国约有85万人患有痴呆症，每年造成的损失估计为260亿英镑。虽然研究帮助我们了解了不同形式的痴呆症背后的一些生物学变化，但目前还没有治愈方法，其神经退行性过程似乎在很大程度上是不可逆转的。因此，目前痴呆症研究的一个主要焦点是在目前用于诊断的认知缺陷之前几年甚至几十年发生的早期变化。该博士项目将研究早期痴呆相关现象的两个方面：(1)两种神经调节系统-去甲肾上腺素能和胆碱能-已知易受阿尔茨海默病（AD）和其他不常见形式痴呆的病理影响；(2)阿尔茨海默病和其他痴呆症的遗传风险因素——尤其是携带APOE-e4等位基因的人，这种等位基因会增加晚年患阿尔茨海默病的风险。我们将重点关注三个主要数据集：(1)人类连接组计划（HCP），它包含来自约1200名健康年轻参与者的横断面结构和功能神经成像、任务表现和全基因组数据。(2)剑桥老龄化与神经科学中心（Cam-CAN），该中心包含来自约650名参与者的丰富认知和成像数据，这些参与者在整个成人寿命中均匀抽样。(3)阿尔茨海默病神经成像计划（ADNI），包括约3500名老年人在项目的几个阶段的纵向数据，包括多模式神经成像数据、遗传学和其他评估，重点是MCI和阿尔茨海默病患者。候选人将分析这些数据集，以评估APOE-e4携带者和非携带者之间的结构，功能和多模式连接的差异，包括年轻和老年队列。分析将集中在两种感兴趣的神经调节系统对这些大范围模式的影响，特别是这些影响是否可以在早期观察到。多模态方法的使用将帮助我们更密切地关注连通性变化背后的生物学机制。他们将学习如何处理从大型、公开可用的数据集中获得的数据，包括神经影像学、神经心理学、临床和遗传信息。这将涉及使用Matlab和/或Python编程语言，专门的神经成像软件，包括FSL和SPM软件包，以及使用SLURM对数据进行高性能并行处理。

Dementia is an age-related, highly debilitating condition. Around 850,000 people in the UK suffer from dementia, at an estimated cost of £26 billion annually. While research has helped us understand some of the biological changes underlying different forms of dementia, there is presently no cure, and its neurodegenerative processes appear to be largely irreversible. As a result, a major focus of current dementia research is on the early changes that occur years or even decades prior to the cognitive deficits that are presently used for diagnosis. This PhD project will investigate two aspects of early dementia-related phenomena: (1) two neuromodulatory systems - noradrenergic and cholinergic - known to be susceptible to pathology found in Alzheimer's disease (AD) and other less common forms of dementia; and (2) genetic risk factors for AD and other dementias - in particular, carriers of the APOE-e4 allele, which confers a higher risk of developing AD in later life. We will focus on three major datasets: (1) The Human Connectome Project (HCP), which contains cross-sectional structural and functional neuroimaging, task performance, and whole-genome data from ~1200 healthy young participants. (2) The Cambridge Centre for Ageing and Neuroscience (Cam-CAN), which contains rich cognitive and imaging data from ~650 participants uniformly sampled across the adult lifespan. (3) The Alzheimer's Disease Neuroimaging Initiative (ADNI), which includes longitudinal data for ~3500 older adults across several stages of the project, with multimodal neuroimaging data, genetics, and other assessments-with an emphasis on MCI and Alzheimer's patients.The candidate will analyse these datasets in order to assess differences in the structure, function, and multimodal connectivity between APOE-e4 carriers and non-carriers, in both younger and older cohorts. Analyses will focus on the influence of the two neuromodulatory systems of interest on these large-scale patterns, and in particular whether these influences can be observed at early age ranges. The use of multimodal approaches will help us focus more closely on the biological mechanisms underlying connectivity changes. They will learn how to process data obtained from large, publicly available datasets, containing neuroimaging, neuropsychological, clinical, and genetic information. This will involve the use of Matlab and/or Python programming languages, specialized neuroimaging software including the FSL and SPM packages, and high-performance parallel processing of data using SLURM.