Computational PLatform for Assessment of Cognition In Dementia (C-PLACID)

认知障碍是痴呆症的标志。认知问题，如记忆、语言和推理方面的困难，是大多数神经退行性疾病最明显、最令人沮丧和最虚弱的方面。因此，对个人认知的评估是诊断服务和研究调查的重要组成部分，也是判断潜在药物和非药物疗法有效性的最常见结果衡量标准。然而，许多传统的纸笔认知评估都有一些局限性，包括不同测试之间缺乏独立性，认知侧写的定性性质，练习效果的影响，未能捕捉到表现的一些关键方面，动态范围有限，一些测试说明的复杂性，以及它们无法充分评估某些认知领域。虽然复杂的计算技术现在经常被用来分析关于大脑形状变化的神经成像数据，但很少有人尝试使用类似的技术来理解复杂的认知数据集。在这里，我们试图通过利用工程学、计算统计学和数学来改善痴呆症患者或有痴呆症风险的人的认知评估，以纠正这种不平衡。目前的项目旨在开发一个计算平台，以支持在复杂认知数据集的分析和可视化方面的实质性改进，以及用于获取认知数据的技术和设备的自动化、优化和创新。这项研究的具体目标代表了一系列相互关联的工程解决方案，以解决临床医生强调的长期存在的认知评估问题。第一组计算目标是通过使用多变量机器学习算法来生成不同痴呆的多维认知特征，并通过实施基于事件的模型来预测认知缺陷的演变。第二组目标与试图改善现有认知测试有关，方法是设计自动测量语音反应时间的方法，实施心理物理学原理，以及利用眼神追踪来捕捉任务表现的额外敏感指标。第三套目标涉及开发新的测试范例，包括适用于不同类型和严重程度的痴呆症患者的“无指导”认知测试，以及构建传感器和虚拟现实场景来测量社会认知。该项目的一个关键方面是提供四个具有特殊特征的、经过纵向研究的痴呆症患者或有痴呆风险的个人队列，以开发和评估新的模型和算法，并试验改进的和新的测试范例。临床队列包括患有家族性阿尔茨海默病基因突变的个人及其非携带者兄弟姐妹，患有阿尔茨海默病典型和非典型变异体的人，包括进行性视觉综合征后皮质萎缩，以及额颞部痴呆的行为或语言表型的患者。此外，来自MRC 1946出生队列的500名成员的数据也将被评估，他们的认知在一生中被跟踪，现在他们的年龄将有一部分处于神经退化的早期阶段。该项目涉及一个丰富的跨学科团队，既有已建立的合作，也有新的合作伙伴，令人兴奋。这项工作将世界领先的痴呆症单位之一(痴呆症研究中心)与其他三个备受瞩目的伦敦大学学院系结合在一起，即伦敦大学学院计算机科学、医学图像分析中心和伦敦大学学院互动中心。这些中心的专家将与合作者以及患者和照顾者支持团体合作，改进研究并实施研究结果。

Cognitive impairment is the hallmark of dementia. Cognitive problems, such as difficulties with memory, language and reasoning, are the most obvious, frustrating and debilitating aspects of most neurodegenerative diseases. As a result, assessment of a person's cognition is a vital component of both diagnostic services and research investigations, and is the most common outcome measure by which the effectiveness of potential pharmaceutical and non-pharmaceutical therapies is judged. However, many traditional paper-and-pencil cognitive assessments have a number of limitations, including the lack of independence across tests, the qualitative nature of cognitive profiling, the influence of practice effects, a failure to capture some critical aspects of performance, a limited dynamic range, the complexity of some test instructions, and their inability to adequately assess some domains of cognition. Whilst sophisticated computational techniques are now used routinely to analyze neuroimaging data about changes in the shape of the brain, there have been few attempts to use comparable techniques to understand complex cognitive datasets. Here we attempt to redress that imbalance by harnessing engineering, computational statistics and mathematics to improve the cognitive assessment of people with or at risk from dementia. The current project aims to develop a computational platform to support substantial improvements in the analysis and visualisation of complex cognitive datasets, and the automatization, optimization and innovation of techniques and devices used to acquire cognitive data. The specific aims of the study represent an interlinked series of engineering solutions to the longstanding cognitive assessment problems highlighted by clinicians. The first set of computational goals are to generate multidimensional cognitive profiles for different dementias by using multivariate machine learning algorithms, and to predict the evolution of cognitive deficits through the implementation of event-based models. The second set of goals relate to attempts to improve existing cognitive tests either by devising ways to measure voice reaction times automatically, implementing psychophysical principles, and utilizing eyetracking to capture additional sensitive metrics of task performance. The third set of goals involve the development of novel testing paradigms including 'instruction-less' tests of cognition suitable for patients with different types and severities of dementia, and the construction of sensors and virtual reality scenarios to measure social cognition.A critical aspect of the project is the availability of four exceptionally well-characterized, longitudinally studied cohorts of individuals with or at risk of dementia in whom to develop and evaluate the new models and algorithms and pilot the improved and novel testing paradigms. The clinical cohorts include individuals with a Familial Alzheimer's disease gene mutation and their non-carrier siblings, people with typical and atypical variants of Alzheimer's disease including the progressive visual syndrome Posterior Cortical Atrophy, and patients with behavioural or linguistic phenotypes of Frontotemporal Dementia. In addition, data from 500 members of the MRC 1946 Birth Cohort whose cognition has been tracked through life and who are now of an age whereby a proportion will be in the early stages of neurodegeneration will also be evaluated.The project involves a richly interdisciplinary team with an exciting blend of established collaborations and new partnerships. The work brings together one of the world's leading dementia units (Dementia Research Centre) with three other high profile UCL departments, namely UCL Computer Science, the Centre for Medical Image Analysis, and the UCL Interaction Centre. The experts from these centres will work together with collaborators and patient and carer support groups to improve the study and implement its findings.