Computational models of neurodegenerative disease progression

神经退行性疾病进展的计算模型

• 批准号: EP/J020990/1
• 负责人: Daniel Alexander
• 金额: $ 75.55万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2013
• 资助国家: 英国
• 起止时间: 2013 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FJ020990%2F1
• 关键词: Computational; models; neurodegenerative; disease; progression

The project develops new computer science technology for modelling the progression of a disease or developmental process. It pioneers the use of state-of-the-art generative modelling and learning techniques to address this problem. It demonstrates the new approach by addressing questions of intense current interest in neurology: what is the sequence of clinical and pathological decline in two important diseases, Alzheimer's disease (AD) and fronto-temporal dementia (FTD), and how does it vary over the population? The methodological development introduces new and general-purpose techniques in computer science and the experimental work adds fundamental new knowledge in neurology.The progression is the sequence of events that occurs as the disease or process advances. All diseases have an associated set of symptoms and pathologies. For example, AD causes loss of memory, personality changes, brain shrinkage, and deposits of abnormal proteins. However, other neurological diseases share many of these same occurrences. An additional fundamental characteristic that distinguishes diseases is the order in which the symptoms and pathologies appear. Knowledge, or a model, of this disease progression supports early diagnosis, which can maximize the effect of a treatment. It also provides insight into disease mechanisms that can accelerate development of the treatments. Furthermore, an effective model helps construct robust staging systems, which enable clinicians to tailor treatment and care plans for individual patients: so called "personalized medicine".Modelling disease progression, however, is a major challenge. First, the sequence of events can vary substantially among patients; monitoring a few individuals closely does not capture the variation over the larger population. Second, such close monitoring is often impossible, because the necessary examinations are too expensive or invasive to perform regularly. Thus, models must come from more cross-sectional data obtained from many patients each making a few irregular visits to a clinic. Very large data sets of this kind are available and contain a wealth of information, but current techniques for mining that information remain crude and do not exploit the available data effectively.The investigators on this project recently introduced a new computational approach to disease progression modelling: the event-based model. Unlike standard models, it learns the sequence of events directly from a large cross-sectional data set without requiring a-priori staging or ordering of the patients. Preliminary results using small data sets from genetically confirmed disease cohorts demonstrate the uniquely rich description of disease progression the new approach can provide. However, application to larger and less-controlled data sets, where the real interest lies, presents major new challenges.This project develops the event-based model from proof-of-concept to practical research tool. It then demonstrates the tool focussing on applications in neurological disease, although long-term applicability is much wider. In particular, we construct detailed models of the progression of AD and FTD, their variability over the population, and the influence of factors such as genetic profile. Finally, the project initiates exploration of the wider family of computational models of disease progression and their potential to extract new and fundamental information. For example, we introduce new models that potentially reveal disease subtypes, provide disease-staging systems, and highlight potential causal relationships among events.The new model-based approach has the potential to revolutionize the way we think about disease progression and thus to make a major impact in diagnosis, disease management, and treatment development for some of the most devastating and widespread medical problems facing us today. The project initiates a long-term effort towards these ends.

该项目开发了新的计算机科学技术，用于模拟疾病或发育过程的进展。它率先使用最先进的生成建模和学习技术来解决这个问题。它展示了新的方法，通过解决神经学中当前强烈关注的问题：两种重要疾病，阿尔茨海默病（AD）和额颞叶痴呆（FTD）的临床和病理学下降的顺序是什么，以及它如何在人群中变化？方法学的发展引入了计算机科学中的新的和通用的技术，实验工作增加了神经学中的基本新知识。进展是随着疾病或过程的进展而发生的事件的顺序。所有疾病都有一系列相关的症状和病理。例如，AD导致记忆丧失、性格改变、大脑萎缩和异常蛋白质沉积。然而，其他神经系统疾病也有许多相同的发生。区分疾病的另一个基本特征是症状和病理出现的顺序。这种疾病进展的知识或模型支持早期诊断，这可以最大限度地提高治疗效果。它还提供了对疾病机制的深入了解，可以加速治疗的发展。此外，有效的模型有助于构建强大的分期系统，使临床医生能够为个体患者定制治疗和护理计划：所谓的“个性化医疗”。然而，建模疾病进展是一个重大挑战。首先，事件的顺序在患者之间可能有很大差异;密切监测少数个体并不能捕捉到更大人群的变化。其次，这种密切监测往往是不可能的，因为必要的检查过于昂贵或侵入性太大，无法定期进行。因此，模型必须来自更多的横截面数据，这些数据来自许多患者，每个患者都不定期地去诊所就诊。这类非常大的数据集是可用的，并包含了丰富的信息，但目前的技术挖掘的信息仍然粗糙，并没有有效地利用现有的data.The该项目的研究人员最近介绍了一种新的计算方法来疾病进展建模：基于事件的模型。与标准模型不同，它直接从大型横截面数据集中学习事件序列，而不需要对患者进行先验分期或排序。使用来自遗传学证实的疾病队列的小数据集的初步结果表明，新方法可以提供独特丰富的疾病进展描述。然而，应用到更大和更少的控制数据集，那里的真实的兴趣所在，提出了重大的新挑战。然后，它展示了专注于神经系统疾病应用的工具，尽管长期适用性要广泛得多。特别是，我们构建了AD和FTD进展的详细模型，其在人群中的变异性，以及遗传特征等因素的影响。最后，该项目开始探索更广泛的疾病进展计算模型及其提取新的基本信息的潜力。例如，我们引入了新的模型，这些模型可能揭示疾病亚型，提供疾病分期系统，并突出事件之间的潜在因果关系。基于模型的新方法有可能彻底改变我们对疾病进展的看法，从而对我们今天面临的一些最具破坏性和最广泛的医疗问题的诊断，疾病管理和治疗开发产生重大影响。该项目为实现这些目标开展了长期努力。

项目成果

Detailed volumetric analysis of the hypothalamus in behavioral variant frontotemporal dementia.

• DOI: 10.1007/s00415-015-7885-2
• 发表时间: 2015-12
• 期刊: Journal of neurology
• 影响因子: 6
• 作者: Bocchetta M;Gordon E;Manning E;Barnes J;Cash DM;Espak M;Thomas DL;Modat M;Rossor MN;Warren JD;Ourselin S;Frisoni GB;Rohrer JD
• 通讯作者: Rohrer JD

Antiphospholipid antibodies and neurological manifestations in acute COVID-19: A single-centre cross-sectional study.

• DOI: 10.1016/j.eclinm.2021.101070
• 发表时间: 2021-09
• 期刊: EClinicalMedicine
• 影响因子: 15.1
• 作者: Benjamin LA;Paterson RW;Moll R;Pericleous C;Brown R;Mehta PR;Athauda D;Ziff OJ;Heaney J;Checkley AM;Houlihan CF;Chou M;Heslegrave AJ;Chandratheva A;Michael BD;Blennow K;Vivekanandam V;Foulkes A;Mummery CJ;Lunn MP;Keddie S;Spyer MJ;Mckinnon T;Hart M;Carletti F;Jäger HR;Manji H;Zandi MS;Werring DJ;Nastouli E;Simister R;Solomon T;Zetterberg H;Schott JM;Cohen H;Efthymiou M;UCLH Queen Square COVID-19 Biomarker Study group
• 通讯作者: UCLH Queen Square COVID-19 Biomarker Study group

Hippocampal Subfield Volumetry: Differential Pattern of Atrophy in Different Forms of Genetic Frontotemporal Dementia.

• DOI: 10.3233/jad-180195
• 发表时间: 2018
• 期刊: Journal of Alzheimer's disease : JAD
• 作者: Bocchetta M;Iglesias JE;Scelsi MA;Cash DM;Cardoso MJ;Modat M;Altmann A;Ourselin S;Warren JD;Rohrer JD
• 通讯作者: Rohrer JD

An optimized framework for quantitative magnetization transfer imaging of the cervical spinal cord in vivo.

• DOI: 10.1002/mrm.26909
• 发表时间: 2018-05
• 期刊: Magnetic resonance in medicine
• 影响因子: 3.3
• 作者: Battiston M;Grussu F;Ianus A;Schneider T;Prados F;Fairney J;Ourselin S;Alexander DC;Cercignani M;Gandini Wheeler-Kingshott CAM;Samson RS
• 通讯作者: Samson RS