Bayesian inference of neurodegenerative spreading models

神经退行性传播模型的贝叶斯推断

• 批准号: 2578358
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2578358
• 关键词: Bayesian; inference; neurodegenerative; spreading; models

Project Summary (Please complete the summary in the following format with headings present. Should be 4,000 characters Maximum):1) Brief description of the context of the research including potential impactNeurodegenerative diseases may be the biggest challenge facing 21st Century healthcare. Delaying Alzheimer's onset by five years would relieve untold suffering and save £10B annually in the UK. This may come from either treatments that slow disease (none are yet proven) or avoiding risk associated behaviour. Both require deeper understanding of disease initiation and progression.Neurodegenerative diseases exhibit characteristic patterns of spread over the brain that provide clues to the underlying mechanisms of disease aetiology and propagation. This enables us to implement computational models encoding various hypotheses of disease propagation to predict the evolution of brain pathology using large collections of imaging data.This research reveals key unknowns on the biology of neurodegenerative disease like when and where it starts; how it propagates over the brain; how it varies among diseases, subtypes, and individuals; how risk factors influence mechanism. Thus, it will enhance disease understanding, prevention strategies, etc.2) Aims and Objectives -The specific objectives are to:1: Implement a new Bayesian inference framework for evaluation of candidate models of neurodegenerative disease.2. Work with researchers developing novel connectivity mapping and disease progression modelling to incorporate their models within the Bayesian framework.3. Implement models of neurodegenerative pathology spreading. 4. Use the new framework to evaluate the models against state-of-art imaging data sets. 3) Novelty of Research MethodologyThe primary novelty of the research is a new Bayesian inference framework to deal with current limitations in existing computational models. This will underpin new quantitative results evaluating the ability of models of neurodegenerative disease against measured patient data sets thereby shedding light on which models are likely given evidence and which are not. The new framework enables several further advances in the models themselves as well as new approaches to connectivity mapping and disease progression modelling, as well as new insights into the biology of neurodegenerative disease. The project interacts closely with other projects developing these ideas to ensure the framework is compatible with models and algorithms emerging in those areas and vice versa.4) Alignment to EPSRC's strategies and research areasThe project aligns with EPSRC's Healthcare technologies theme and the following research areas:- Artificial intelligence technologies- Clinical technologies- Image and vision computing- Medical imaging- Statistics and applied probability5) Any companies or collaborators involved. To be updated in due course

项目摘要（请按以下格式填写摘要，并注明标题。最多4,000个字符）：1）简要描述研究背景，包括潜在影响神经退行性疾病可能是21世纪世纪医疗保健面临的最大挑战。在英国，将阿尔茨海默氏症的发病时间推迟五年将减轻无数的痛苦，每年节省100亿英镑。这可能来自减缓疾病的治疗（尚未得到证实）或避免风险相关行为。这两种疾病都需要对疾病的发生和发展有更深入的了解。神经退行性疾病表现出在大脑中扩散的特征模式，为疾病病因学和传播的潜在机制提供线索。这使我们能够实现编码疾病传播的各种假设的计算模型，以使用大量的成像数据来预测大脑病理学的演变。这项研究揭示了神经退行性疾病生物学上的关键未知因素，如何时何地开始;它如何在大脑中传播;它如何在疾病，亚型和个体之间变化;风险因素如何影响机制。因此，它将提高疾病的理解，预防策略等。2）目的和目标-具体目标是：1：实施一个新的贝叶斯推理框架，用于评估神经退行性疾病的候选模型。与研究人员合作开发新的连接映射和疾病进展建模，将其模型纳入贝叶斯框架。实施神经退行性疾病传播模型。4.使用新的框架来评估模型对国家的最先进的成像数据集。3)研究方法的新奇该研究的主要新颖之处是一个新的贝叶斯推理框架，以处理现有计算模型中的当前限制。这将支持新的定量结果，评估神经退行性疾病模型对测量的患者数据集的能力，从而阐明哪些模型可能提供证据，哪些不是。新的框架使模型本身的一些进一步进展以及连接映射和疾病进展建模的新方法，以及对神经退行性疾病生物学的新见解。该项目与开发这些想法的其他项目密切互动，以确保框架与这些领域出现的模型和算法兼容。4）与EPSRC的战略和研究领域保持一致该项目与EPSRC的医疗保健技术主题和以下研究领域保持一致：-人工智能技术-临床技术-图像和视觉计算-医学成像-统计和应用概率5）任何参与的公司或合作者。将适时更新