Neuroimaging and neurochemical ageing biomarkers for optimising prognosis in motor neurone disease

1)简要描述研究背景，包括潜在影响肌萎缩性侧索硬化症（ALS）是一种进行性神经退行性疾病，目前尚无治愈方法。从发病到疾病末期的时间因患者而异。肌萎缩侧索硬化症患者会经历迅速而广泛的脑组织损失，而这一过程在老年人中通常进展得更快。因此，ALS被认为与衰老过程相互作用，而衰老过程本身通常会导致脑组织的逐渐丧失。可能的情况是，与年龄相关的组织损失较大的个体，如果患上神经退行性疾病，如肌萎缩侧索硬化症，预后可能更致命（生存时间更短）。为此，机器学习算法与磁共振成像（MRI）数据相结合，已被用于预测神经退行性疾病的疾病进展。这是通过“大脑年龄”指数来实现的，这是一种对大脑健康有帮助的指标。此外，从血液样本中提取的神经丝数据对脑萎缩有帮助。在健康情况下，神经丝通常存在于神经元的细胞质中，并在神经元死亡后释放到血液中。随着个体年龄的增长和正常脑组织的丧失，血液中神经丝的含量也会增加。MRI和神经丝的数据对预测ALS的预后很有用，但以前没有一起使用过。目前的研究旨在结合两种模式的数据，以开发有助于ALS预测的技术。这将通过应用计算方法来完成，特别关注机器学习。这些统计/机器学习模型将对与年龄相关的脑组织加速损失以及血液中神经纤维的增加敏感。最终，这样的模型将能够识别ALS在单个患者身上的进展速度。这将提高ALS干预临床试验的速度和成本效益，并使有针对性的治疗成为可能。2)目标和目标-具体目标是：-开发多模式（MRI和血液）机器学习和其他统计模型，这些模型对大脑与年龄相关的加速变化敏感。-使用这些模型来正确分类ALS在个体内的进展是快还是慢。-应用这些方法来帮助开发靶向治疗和更有效的ALS药物治疗临床试验。3)研究方法的新颖性：您的研究方法是新颖的，主要是由于将开发的机器学习/统计模型的多模态性质。核磁共振成像和血液样本数据已经独立显示出预测ALS预后的希望，但它们的结合应该为预测模型提供更大的力量。此外，使用“大脑年龄”和机器学习技术来帮助预测神经退行性疾病是最近的一项发展。因此，该项目将能够释放这些技术已经显示出的更多潜力。4)与EPSRC的战略和研究领域保持一致本项目与EPSRC的医疗技术战略保持一致。其中，该项目与医学成像、临床技术和分析科学研究领域保持一致。5)任何涉及的公司或合作者

1) Brief description of the context of the research including potential impactAmyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease with no known cure. The time between disease onset and the end stages of disease vary widely from patient to patient. Patients with ALS endure rapid widespread brain tissue loss and this process typically progresses quicker in elder people. ALS is therefore thought to interact with the ageing process, which by itself usually causes gradual loss of brain tissue. It may be the case that individuals with larger amounts of age-related tissue loss may have a more fatal (shorter survival time) prognosis if they develop neurodegenerative diseases like ALS. To this end, machine learning algorithms in tandem with magnetic resonance imaging (MRI) data, have been used to predict disease progression in neurodegenerative diseases. This is done through the 'brain age' index, which is a measure that is informative for brain health. Additionally, neurofilaments data taken from blood samples, are informative for brain atrophy. In health, neurofilaments typically reside in the cytoplasm of neurons, and are released into the blood after neurons die. As individuals age and normal brain tissue loss occurs the levels of neurofilaments in blood also increases. Both MRI and neurofilaments data have been useful for predicting outcomes in ALS but have not previously been used together. The present research aims to combine data from both modalities in-order to develop techniques which aid ALS prognostication. This will be done through the application of computational methods, with a particular focus on machine learning. These statistical/machine learning models will be sensitive to accelerated age-related brain tissue loss as well as increased neurofilaments in the blood. Ultimately, such models will be able to recognise the rate at which ALS will progress in an individual patient. This will increase the speed and cost effectiveness of clinical trials for ALS interventions, as well as enabling targeted treatments. 2) Aims and Objectives -The specific objectives are to:- Develop multimodal (MRI and blood) machine learning and other statistical models that are sensitive to accelerated age-related changes to the brain. - Use these models to correctly classify whether ALS will progress quickly or slowly within an individual. - Apply these approaches to aid in the development of targeted treatments and more efficient clinical trials for ALS drug treatments. 3) Novelty of Research MethodologyOur research methodology is novel primarily due to the multi-modal nature of the machine learning/statistical models that will be developed. MRI and blood samples data have independently shown promise in predicting ALS prognosis, but their combination should provide even greater power to predictive models. Also, the use of 'brain age' as well as, machine learning techniques in aiding prognostication of neurodegenerative diseases is a recent development. Therefore, this project will be able to unlock more of the potential these techniques have already shown. 4) Alignment to EPSRC's strategies and research areasThis project is aligned with the EPSRC's healthcare technology strategy. Within that, the project is aligned with the medical imaging, clinical technologies and analytic science research areas. 5) Any companies or collaborators involvedN/A