Modelling dementia progression based on machine learning and simulations

点击翻译按钮获取中文摘要

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.