Horizon CDT Year 1

2020年，全球痴呆症患病率超过5500万人（世界卫生组织，2024年）。研究发现，轻度认知障碍（MCI）是痴呆症的前兆（janoutov<e:1>等人，2015），内侧颞叶是阿尔茨海默病最先恶化的部位之一（Pasquini等人，2019）。痴呆症影响健康。检测痴呆症不仅能让患者过上更好的生活，还能减轻NHS的负担。尽管如此，在八年的时间框架内，MCI发展为痴呆症的风险很高（Dang等人，2019）。因此，本博士旨在预测MCI患者的认知能力下降，而不是痴呆症患者。先前的研究表明，一项任务可能能够预测MCI导致痴呆的可能性（Zola et al., 2013）。然而，这个任务是模棱两可的，因为有两种记忆过程，即自我启动和检索生成启动（Wagner, 1981），两者都可能在这个任务中运行。一项针对健康参与者的研究通过操纵时间和发现对相对近距和现有物体的不同影响，证明了这一理论（Nitka et al., 2020）。这反映了自我启动和检索生成启动，并将它们区分开来。我的博士旨在探索一个类似的任务，因为Nitka等人（2020）的任务存在局限性，例如使用的图像都很容易口头区分（例如小提琴和手），这意味着它可以测试言语记忆和识别记忆。因此，我的一项研究将在健康个体中探索类似的任务，但每次试验中的6张图像将更加相似（例如，在一次试验中，所有6张图像都是月亮）。因此，本研究将评估自我启动和检索生成启动是否可以通过相对近代性和物体就位性来更准确地测量。与之前的方法相比，这种方法提供了一种更纯粹的识别记忆测量，特别是考虑到语言识别图像的难度增加。接下来的研究可能会探索这种计算机化的任务是否可以预测MCIs患者的认知能力下降。使用相对近代性和对象就位性可以检测到选择性缺陷。这意味着与之前的研究相比，这项任务可能对认知表现的下降更为敏感，因为一种受影响的启动形式可以被另一种形式补偿。为了进行这项研究，我将对ACE-3进行任务管理，并在一年后再次测量他们的认知功能和衰退。此外，本研究可能会探索眼动仪的眼动数据来预测MCI患者的认知能力下降。先前的研究发现，与健康对照组相比，阿尔茨海默病患者的眼动数据不同（Pereira et al., 2014）。如果结果显著，这项任务可以在全科医生的设置中使用，以帮助医务人员和患者了解谁在明年有认知能力下降的风险。早期发现将允许及时干预，并在治疗最有效地减缓进展时纳入临床试验。

In 2020, the global prevalence of dementia exceeded 55 million individuals (World Health Organization, 2024). Research has found that having a Mild Cognitive Impairment (MCI) is a precursor of dementia (Janoutová et al., 2015) and that the medial temporal lobe is one of the first to deteriorate with Alzheimer's disease (Pasquini et al., 2019). Dementia impacts wellbeing. Detecting dementia not only enables the patient to live a better life but also lessens the burden on the NHS. Nonetheless, there is an eight-year time frame where the risk of dementia developing from MCI is high (Dang et al., 2019). Therefore, this PhD aims to forecast cognitive decline specifically in individuals with MCI, rather than dementia.Previous research has shown that a task may be able to predict the likelihood of developing dementia from MCI (Zola et al., 2013). However, this task is ambiguous as there are suggested two memory processes which are self- and retrieval-generated priming (Wagner, 1981) and both could be operating within this task. One study with healthy participants has shown evidence of this theory by manipulating times and finding different effects on relative recency and objects in place (Nitka et al., 2020). This reflects self- and retrieval-generated priming and distinguishes them. My PhD aims to explore a similar task as there were limitations of Nitka et al's (2020) task for example the images used were all easy to verbally distinguish (e.g. violin and hand), which means it could be testing verbal memory as well as recognition memory. Therefore, one of my studies will explore a similar task to this with healthy individuals however the 6 images within each trial will be more similar (e.g. in one trial all of the 6 images will be of the moon). Hence, this study will assess whether self- and retrieval-generated priming can be more accurately measured through relative recency and object-in-place. This approach offers a purer gauge of recognition memory compared to prior methods, particularly considering the increased difficulty in verbally discriminating the images.The potential following study could explore whether this computerised task could predict cognitive decline in individuals with MCIs. The use of relative recency and object-in-place may detect a selective deficit. This would mean that this task would presumably be more sensitive to a decline in cognitive performance compared to previous studies because a single affected form of priming could be compensated by the other form. In order to conduct this research, I will administer the ACE-3 with the task and again one year later to measure their cognitive function and decline.Additionally, this research may explore the eye movement data from the eye trackers to predict cognitive decline in individuals with MCI. As previous research found individuals with Alzheimer's disease have different eye movement data compared to healthy controls (Pereira et al., 2014). If the results are significant, this task could be utilised within GP settings to help medical staff and the patient know who is at risk of cognitive decline in the next year. Earlier detection would allow timely interventions and enrolment into clinical trials when treatments are most effective at slowing progression.