Physiology of cognitive changes in ageing and dementia

衰老和痴呆症认知变化的生理学

• 批准号: MR/X020274/1
• 负责人: Peter Zeidman
• 金额: $ 149.59万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Fellowship
• 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=MR%2FX020274%2F1
• 关键词: Physiology; cognitive; changes; ageing; dementia

The brain changes as we age, but which changes should we consider normal, and which are warning signs that cognitive problems such as memory impairment may develop in future? How do we know whether someone is deviating from the course of healthy ageing and is on a path that will eventually lead to dementia, the most common diagnosis being Alzheimer's disease (AD)? These are pressing issues, not least because the UK has a rapidly ageing population and cases of AD are set to double in the next 10-20 years worldwide.My research is about identifying the earliest changes in brain physiology that give rise to cognitive impairment in later life. Research in animal models has identified many candidate mechanisms of brain ageing at the cellular level, while large-scale epidemiological studies have identified the cognitive changes that people typically experience as they age. To connect these findings, we need a mechanistic understanding of how age-related changes in the brain give rise to cognitive decline. To address this, I will tackle a key technical challenge. Ageing and AD can simultaneously affect neurons, the brain's blood supply (vasculature) and the interface between them (neurovascular coupling). How can we disentangle these effects, and identify which of them are associated with cognitive dysfunction? No single medical imaging method can achieve this. A standard approach for measuring the function of the brain, BOLD fMRI (Blood-Oxygen-Level-Dependent functional Magnetic Resonance Imaging), can localise where in the brain there are effects of ageing with high spatial precision, however the signals it measures reflect a mixture of neural and vascular contributions. Integrating other kinds of measurements could help to resolve this ambiguity. Arterial Spin Labelling (ASL) MRI provides a direct measure of blood perfusion (delivery of blood to the capillary bed), but it is slower than BOLD fMRI and therefore affords lower sensitivity. Direct electromagnetic recordings of the brain (electroencephalography, EEG or magnetoencephalography, MEG) provide insights into neural activity with exquisite temporal precision, but they are not sensitive to vasculature. These methods - BOLD fMRI, ASL MRI and EEG/MEG - provide complementary perspectives on the brain, raising the question of how to integrate them to build a cohesive picture of ageing.My solution, which I have developed in the lead up to this proposal, is to take a 'best of all worlds' approach. I analyse data from large-scale clinical studies conducted by my collaborators, where volunteers have undergone multiple kinds of neuroimaging. For example, they may start by having their neural activity measured with MEG while performing a simple task, and then they will perform the same task while undergoing MRI, to measure their blood oxygen and blood flow. The novelty of my approach lies in the way these different kinds of data are integrated. For each volunteer, a biologically detailed mathematical model is specified that describes how their data were generated. This model includes unknown quantities, such as the strength of neural connections, which are estimated from the combined neuroimaging data. The model thereby acts like a "mathematical microscope", for inferring the biological processes in the volunteer's brain that gave rise to their data. I will apply this approach to identify the particular mixture of medical and health factors that are associated with age-related brain dysfunction. Using methods similar to weather forecasting, I will investigate which of these factors affect the long-term trajectory of cognitive decline. This could, in future, enable targeted early interventions to prevent or slow cognitive decline in ageing.

随着年龄的增长，大脑会发生变化，但哪些变化应该被视为正常，哪些是未来可能出现记忆障碍等认知问题的警告信号？我们如何知道某人是否偏离了健康衰老的进程，并最终走向了痴呆症（最常见的诊断是阿尔茨海默病（AD））？这些都是紧迫的问题，尤其是因为英国人口迅速老龄化，并且全球 AD 病例在未来 10-20 年内将翻一番。我的研究是要确定大脑生理学的最早变化，这些变化会导致晚年认知障碍。动物模型研究已经在细胞水平上确定了许多大脑衰老的候选机制，而大规模流行病学研究已经确定了人们随着年龄增长通常会经历的认知变化。为了将这些发现联系起来，我们需要从机制上理解大脑中与年龄相关的变化如何导致认知能力下降。为了解决这个问题，我将解决一个关键的技术挑战。衰老和 AD 会同时影响神经元、大脑的血液供应（脉管系统）以及它们之间的界面（神经血管耦合）。我们如何理清这些影响，并确定哪些影响与认知功能障碍有关？没有任何一种医学成像方法可以实现这一点。测量大脑功能的标准方法 BOLD fMRI（血氧水平依赖性功能磁共振成像）可以以高空间精度定位大脑中存在衰老影响的位置，但它测量的信号反映了神经和血管贡献的混合。整合其他类型的测量可能有助于解决这种模糊性。动脉自旋标记 (ASL) MRI 可以直接测量血液灌注（将血液输送到毛细血管床），但它比 BOLD fMRI 慢，因此灵敏度较低。大脑的直接电磁记录（脑电图、EEG 或脑磁图、MEG）可以以精确的时间精度深入了解神经活动，但它们对脉管系统不敏感。这些方法——BOLD fMRI、ASL MRI 和 EEG/MEG——提供了关于大脑的互补视角，提出了如何将它们整合起来以构建一个有凝聚力的衰老图景的问题。我在提出这一提议时制定的解决方案是采取“世界上最好的”方法。我分析了我的合作者进行的大规模临床研究的数据，其中志愿者接受了多种神经影像学检查。例如，他们可能首先在执行一项简单任务时用 MEG 测量他们的神经活动，然后他们将在进行 MRI 时执行相同的任务，以测量他们的血氧和血流量。我的方法的新颖之处在于这些不同类型的数据的集成方式。对于每个志愿者，都会指定一个详细的生物学数学模型来描述他们的数据是如何生成的。该模型包括未知量，例如神经连接的强度，这是根据组合的神经影像数据估计的。因此，该模型就像一个“数学显微镜”，用于推断志愿者大脑中产生数据的生物过程。我将应用这种方法来识别与年龄相关的脑功能障碍相关的医疗和健康因素的特定组合。我将使用类似于天气预报的方法来调查哪些因素会影响认知能力下降的长期轨迹。这可能在未来实现有针对性的早期干预，以预防或减缓衰老过程中的认知能力下降。