Automated Web-Based Behavioral Diagnostics of Cognitive Impairment

描述（由申请人提供）：阿尔茨海默病目前影响着大约530万美国人，预计这一数字在未来十年将显著增长。阿尔茨海默病研究的一个关键目标是改善目前的诊断方法，以便患者可以更快地被识别，从而从现有的治疗中获得更大的优势。该项目的主要目标是开发和验证一种自动化的、基于网络的方法，用于认知衰退和记忆丧失的早期诊断。我们将建立在目前的研究，已经证明了视觉配对比较任务（VPC）的性能和MCI的诊断之间有明确的联系，我们将通过开发一套软件工具和分析方法来扩展这一发现的影响，这将提高准确性，并扩展认知诊断的可访问性与基于Web的VPC任务，可以广泛部署。虽然VPC任务作为诊断辅助是有前途的，但临床应用受到严重限制，因为需要使用眼动仪来精确监测受试者的眼动。不幸的是，眼动追踪器价格昂贵，需要训练有素的人员，并且不能广泛使用。然而，我们的初步研究结果表明，使用VPC任务的修改版本，可以在任何具有互联网连接的计算机上由任何人管理，可以产生类似于眼球运动的图片检查行为。此外，计算机科学中强大的机器学习技术可以帮助准确分析和诊断由此产生的行为。这项工作的一个重要贡献将是预测MCI患者即将到来的认知功能下降的可能性比现在更快，也就是说，在神经系统受损较少并且更有可能从治疗干预中受益的时候。为了支持这一目标，我们提出了三个目标：1）开发和研究眼动特征的适当表示和相应的基于机器学习的分类技术，以有效识别患者状态，2）开发和验证基于Web的VPC任务版本，以及3）探索我们的任务作为一般老年人群自动筛查工具的可行性。这些目标的成功完成有可能极大地改变目前的临床转化研究实践以及目前用于诊断认知缺陷的方法。这将使成千上万甚至可能是数百万的患者和潜在的研究对象能够将这项测试作为他们例行检查的一部分，只需要一台连接互联网的电脑。

DESCRIPTION (provided by applicant): Alzheimer's disease affects an estimated 5.3 million Americans currently, and this number is expected to grow significantly in the coming decade. A critical goal of Alzheimer's disease research is to improve current methods of diagnosis so that patients can be identified sooner and, therefore, obtain greater advantage from available therapies. The major goal of this project is to develop and validate an automated, web-based method for early diagnosis of cognitive decline and memory loss. We will build on current research that has demonstrated a clear link between performance on the Visual Paired- Comparison Task (VPC) and diagnosis of MCI, and we will extend the impact of this finding by developing a suite of software tools and analysis methods that will improve the accuracy and extend the accessibility of cognitive diagnostics with a web-based VPC task that can be widely deployed. Although the VPC task is promising as a diagnostic aid, clinical application is severely limited by the need to use an eye tracker to precisely monitor subjects' eye movements. Unfortunately, eye trackers are expensive, require trained personnel, and are not widely available. However, our preliminary findings show that it is possible to produce picture examination behavior that is similar to eye-movements, using modified versions of the VPC task that could be administered by anyone, on any computer with an internet connection. In addition, powerful machine learning techniques from computer science can help accurately analyze and diagnose the resulting behavior. An important contribution from this work will be the possibility of predicting oncoming cognitive decline in MCI patients sooner than is now possible, that is, at a time when the nervous system is less compromised and more likely to benefit from therapeutic intervention. In support of this objective, we propose three aims: 1) Develop and investigate appropriate representation of eye movement characteristics and corresponding machine learning-based classification techniques for effective identification of the patient status, 2) Develop and validate a web-based version of the VPC task, and 3) explore the feasibility of our task as an automatic screening instrument for the general elderly population. Successful completion of these aims has the potential to dramatically alter the current practice of clinical translational research as well as the current methods used for diagnosing cognitive deficits. This would enable thousands and potentially millions of patients and potential research subjects to take the test as part of their routine checkup, requiring nothing more than a computer with an internet connection.