Modelling dementia progression based on machine learning and simulations

神经疾病和精神疾病加起来占英国国家疾病负担的约30%，每年造成英国经济损失700-1000亿英镑(占国内生产总值的4.5%-5%)，其中包括230亿英镑的痴呆症(OECD 2014)。随着人口迅速老龄化，这一负担正在增加。在韩国，老年人的患病率将从现在的10%上升到2050年的15%。阿尔茨海默病(AD)是最常见的痴呆形式，年龄是导致AD的主要危险因素中最有影响力的。AD的特征是一个持续的退化过程，包括临床前阶段，然后是轻度认知障碍(MCI)阶段，一旦认知功能障碍开始对日常功能产生显著影响，就会过渡到痴呆。实验证据表明，在临床衰退之前十多年，大脑就发生了病理生理变化。因此，为早期诊断和疾病修正治疗的开发寻找生物标记物是一项持续的和具有挑战性的努力。神经原纤维缠结和淀粉样斑块的存在是AD的主要病理特征。关于阿尔茨海默病进展的一个新的假说认为，这些有毒蛋白起源于特定的区域，并以类似于普里恩的方式在神经纤维中传播。类似的病因学传播假说也被提出用于路易体痴呆。网络神经科学已被证明有助于理解精神和神经疾病对全脑网络的影响。特别是，已经表明AD强烈干扰节点之间的连接，以及那些在网络中占据中心角色的节点(集线器节点)。因此，网络变化对预测疾病进展可能至关重要。最理想的干预时间是在重大的神经退行性改变和神经元丢失发生之前进行疾病修正治疗。然而，另一个高度相关的考虑是亚型诊断的改进，即确定导致痴呆的神经退变过程的类型。对于路易体痴呆(DLB)，第三种最常见的神经退行性痴呆，目前很难诊断，因为在疾病的早期阶段，症状类似于AD。然而，区分是至关重要的，因为每种疾病都有不同的管理轨迹；例如，在DLB组中，给予AD的抗精神病药物可能是致命的。然而，如果我们有一个足够精确的预测模型，我们可能会在非常早期的阶段和亚型诊断患者。有希望的初步数据，使用疾病进展的模拟，表明即使在目前的机器学习方法无法检测到细微变化的情况下，我们也可能做出早期诊断。总而言之，对潜在的神经变性原因进行准确的早期和亚型诊断正变得越来越重要，以确保未来的疾病修改治疗可以在实质性神经变性缺陷发生之前针对个人进行。除了机器学习亚型分类，我们的研究旨在开发一种基于模拟的疾病进展模型，该模型可以成为预测个别患者未来疾病进展的标准临床工具，并促进疾病的早期治疗，从而改善患者的预后并降低整体医疗成本。

Neurological disease and mental illness together account for ~30% of the national disease burden in the UK, costing the UK economy £70-100 billion per year (4.5-5% of the Gross Domestic Product) including £23 billion for dementia (OECD 2014). With a rapidly ageing population, this burden is increasing. For Korea, prevalence rates in the elderly will rise from 10% now to 15% by 2050. Alzheimer's disease (AD) is the most common form of dementia where age is the most influential of the main risk factors for developing AD. AD is characterized by a continuous process of degradation involving a preclinical stage, followed by a phase of mild cognitive impairment (MCI), which transitions into dementia once the cognitive dysfunction begins to impact significant on day to day function. Experimental evidence indicates that pathophysiological alterations take place in the brain more than a decade before clinical decline. Therefore, the search for biomarkers for early diagnosis and development of disease-modifying treatments is an ongoing and challenging endeavor. The presence of neurofibrillary tangles and amyloid plaques are the main pathological hallmarks of AD. One emerging hypothesis about the progression of AD posits that these toxic proteins originate in a particular area and propagate throughout neural fibers in a prion-like manner. Similar aetiological spread hypotheses have been proffered for Lewy body dementia. Network neuroscience has proven useful for understanding the impact of psychiatric and neurological disorders on brain-wide networks. In particular, it has been shown that AD strongly disturbs connections between nodes, as well as those nodes occupying a central role in the network (hub nodes). Therefore, network changes could be crucial to predict disease progression. The most ideal time to intervene with disease-modifying treatment is early on before significant neurodegenerative change and neuronal loss has occurred. However, another highly relevant consideration is improvements in subtype diagnosis i.e. determination of the type of neurodegenerative process giving rise to dementia. For Dementia with Lewy Bodies (DLB), the third most common neurodegenerative dementia, diagnosis is currently difficult as symptoms are similar to AD during the early stages of the disease. However, differentiation is crucial as there are different management trajectories for each disease; for example, neuroleptic drugs which are given to AD can be fatal in the DLB group. However, if we have a precise enough predictive model, we may diagnose patients at a very early stage and subtype. Promising preliminary data, using simulation of disease progression, suggest that we may be able to make an early diagnosis even when subtle changes cannot be detected with the current machine learning approach. In summary accurate early and subtype diagnosis of the underlying neurodegenerative cause is becoming increasingly important for ensuring that future disease-modifying treatments can be targeted in individuals before substantive neurodegenerative deficits have occurred. Going beyond machine learning subtype classification, our study aims to develop a simulation-based model of disease progression that can become a standard clinical tool to predict future disease progression of individual patients and to facilitate early treatment of the disease leading to improved outcomes for patients and reduced overall healthcare costs.