Identification of Mild Cognitive Impairment using Machine Learning from Language and Behavior Markers

项目摘要 最近的估计表明，阿尔茨海默病(AD)可能是导致死亡的第三大原因 对于美国的老年人来说，仅次于心脏病和癌症。虽然科学家们知道 AD涉及进行性脑细胞衰竭，细胞衰竭的原因尚不清楚。要了解 本病的进展，关键之一是要了解轻症患者的认知改变。 认知障碍(MCI)。即使发现了成像和临床功能等生物标记物 研究表明，在区分AD患者和认知正常(NC)患者方面表现突出 他们在早期MCI中的辨别能力相当有限。检测区分的信号 由于MCI的低敏感性和高变异性，来自NC的MCI的受试者具有挑战性 目前的临床措施，如每年评估神经心理测试结果和自我报告 功能测量。此外，即使体内的生物标记物，如β-淀粉样蛋白和tau可以 作为AD病理进展的指标，筛选生物标记物有 在门诊环境中广泛应用于无症状个体的成本高得令人望而却步。 我们假设，来自MCI的渐进性认知影响已经在方式上引起了可检测的变化 人们的谈话和行为，可以通过廉价和可访问的传感器来感知并利用 通过机器学习(ML)算法来构建预测模型，以量化MCI的风险。我们的 一小群人的初步结果表明，MCI和MCI之间存在显著差异 NC主题在半结构化对话期间，ML算法可以将这种差异用于 以优异的性能区分MCI和NC。我们在行为监测方面的初步结果 还建议使用行为信号的时间模式进行高度预测性的表现。在父级中 项目，我们正在初步成功的基础上，对语言和语言进行全面研究 更大规模队列中的行为标记，以构建高性能和可解释的ML模型 筛查MCI。本附录建立在我们目前在数字生物标记物方面的工作基础上，并将重点放在 进一步细化数字生物标志物的预测能力。最近，MRI数据的可用性来自 I-CONECT研究为我们提供了意想不到的机会来显著提高 数字生物标志物。为了实现这一目标，在目标S1中，我们建议开发一种数据驱动的算法 使用高质量成像信息作为辅助信息以提高预测性的框架 语言标记的表现；在目标S2中，我们建议开发一个计算框架来使用 公共语言数据库，提高语言标记语的质量。这一副刊，如果得到资助，将 显著提高数字生物标记物的预测性能，从而提高 早期发现MCI的预测力。

Project Summary Recent estimates indicate that Alzheimer’s disease (AD) may rank as the third leading cause of death for older people in the United States, just behind heart disease and cancer. While scientists know that AD involves a progressive brain cell failure, the reason why cells fail is still not clear. To understand the progression of the disease, one of the keys is to investigate the cognitive changes in patients with mild cognitive impairment (MCI). Even though biomarkers such as imaging and clinical functions are found to be outstanding in differentiating AD patients from those with normal cognition (NC), studies suggest that their discriminative power in early-stage MCI are rather limited. Detecting signals which distinguish subjects with MCI from those with NC is challenging due to the low sensitivity and high variability of current clinical measures such as annually assessed neuropsychological test results and self-reported functional measurements. Moreover, even though in-vivo biomarkers such as beta-amyloid and tau can be used as indicators of pathological progression towards AD, the screening of biomarkers are prohibitively expensive to be widely used among pre-symptomatic individuals in the outpatient setting. We hypothesize that progressive cognitive impact from MCI has elicited detectable changes in the way people talk and behave, which can be sensed by inexpensive and accessible sensors and leveraged by machine learning (ML) algorithms to build predictive models for quantifying the risk of MCI. Our preliminary results on a small cohort indicated that there are significant differences between MCI and NC subjects during a semi-structured conversation, and ML algorithms can use such differences for differentiating MCI and NC with promising performance. Our preliminary results in behavior monitoring also suggest highly predictive performance using temporal patterns of behavior signals. In the parent project, we are building upon our initial success and conduct comprehensive studies on language and behavior markers in larger-scale cohorts to build high-performance and interpretable ML models for screening MCI. This supplement builds on our current work on digital biomarkers and will focus on further refining the prediction capability of digital biomarkers. Recently, the availability of MRI data from I-CONECT study has provided Unanticipated Opportunity for us to dramatically improve the quality of digital biomarkers. To achieve this goal, in Aim S1 we propose to develop a data-driven algorithms framework that uses high-quality imaging information as auxiliary information to increase the predictive performance of language markers; in Aim S2 we propose to develop a computational framework to use public language databases to improve the quality of language markers. This supplement, if funded, will significant predictive performance improvements of digital biomarkers and therefore improve the predictive power of early detection of MCI.