Modelling dementia progression based on machine learning and simulations

神经系统疾病和精神疾病共占英国国家疾病负担的约30%，每年花费英国经济700 - 1000亿英镑（占国内生产总值的4.5-5%），其中包括230亿英镑用于痴呆症（OECD 2014）。随着人口的迅速老龄化，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）是痴呆症的最常见形式，其中年龄是发展AD的主要风险因素中最有影响力的。AD的特征在于涉及临床前阶段的连续降解过程，随后是轻度认知障碍（MCI）阶段，一旦认知功能障碍开始对日常功能产生显著影响，该阶段就转变为痴呆。实验证据表明，在临床衰退之前十多年，大脑中就发生了病理生理学变化。因此，寻找用于早期诊断和开发疾病改善治疗的生物标志物是一项持续且具有挑战性的奋进。神经元缠结和淀粉样斑块的存在是AD的主要病理标志。关于AD进展的一个新兴假设假定这些毒性蛋白质起源于特定区域，并以朊病毒样方式在整个神经纤维中传播。路易体痴呆也有类似的病因学传播假说。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。特别地，已经表明AD强烈地干扰节点之间的连接，以及在网络中占据中心角色的那些节点（集线器节点）。因此，网络变化可能对预测疾病进展至关重要。最理想的干预时间是在发生显著的神经退行性变化和神经元丢失之前的早期。然而，另一个高度相关的考虑是亚型诊断的改进，即确定引起痴呆的神经退行性过程的类型。路易体痴呆（DLB）是第三种最常见的神经退行性痴呆，目前诊断困难，因为在疾病的早期阶段症状与AD相似。然而，分化是至关重要的，因为每种疾病有不同的管理轨迹;例如，给予AD的神经安定药物在DLB组中可能是致命的。但是，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。使用模拟疾病进展的有希望的初步数据表明，即使当前的机器学习方法无法检测到细微的变化，我们也可以做出早期诊断。总之，对潜在神经退行性疾病原因的准确早期和亚型诊断对于确保在发生实质性神经退行性缺陷之前可以在个体中靶向未来的疾病修饰治疗变得越来越重要。除了机器学习亚型分类之外，我们的研究旨在开发一种基于模拟的疾病进展模型，该模型可以成为标准的临床工具，用于预测个体患者未来的疾病进展，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.