Digital Biomarkers for Alzheimers Disease

阿尔茨海默病的数字生物标志物

• 批准号: 10330044
• 负责人: IHAB M HAJJAR
• 金额: $ 80.15万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-15 至 2021-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10330044
• 关键词: Acoustics; Aging; Alzheimer' s Disease; Alzheimer' s disease pathology; Alzheimer’s disease biomarker; Amyloid; Amyloid beta-42; Amyloid beta-Protein; Applications Grants; Area Under Curve; Artificial Intelligence; Atrophic; Autobiography; Autopsy; Biological Markers; Brain; Clinical; Cognition; Cognitive; Complex; Consumption; Data; Dementia; Detection; Diagnosis; Diagnostic; Disease; Disease Progression; Early identification; Elderly; Functional Magnetic Resonance Imaging; Hippocampus (Brain); Hypertension; Image; Impaired cognition; Individual; Language; Linguistics; Link; Machine Learning; Magnetic Resonance Imaging; Maps; Measures; Methodology; Methods; Names; Natural Language Processing; Neurofibrillary Tangles; Neuropsychology; Participant; Performance; Phase; Phenotype; Positron-Emission Tomography; Predictive Value; Production; Reporting; Research; Research Personnel; Rest; Scanning; Semantics; Specificity; Speech; Spinal Puncture; Stress; Sum; Symptoms; Testing; Time; Voice; Weight; automated speech recognition; base; biomarker signature; cerebral atrophy; clinically significant; cognitive control; cognitive testing; cohort; density; diagnostic accuracy; digital; early detection biomarkers; follow-up; functional status; improved; in vivo; indexing; innovation; insight; lexical; mild cognitive impairment; motor control; neuroimaging; novel; pre-clinical; programs; relating to nervous system; success; tau Proteins; tau-1; tool

Alzheimer's disease (AD) is marked by progressive neuropathological changes that begin decades before cognitive and functional symptoms, and thus efforts have been focused on developing innovative tools and biomarkers for early identification of pre-dementia stages. To date, clinical ability to identify those with pre-dementia stages of AD has been limited and requires expensive (Amyloid PET) or invasive (Lumbar Punctures, LP) testing. However, subtle changes in connected speech may be detectable years before overt disease symptoms present. Our team has developed an approach that uses machine learning and natural language processing combined with advanced acoustic phonetic and lexical-semantic analyses. Preliminary data show promise in identifying AD biomarker status and predicting 2-year cognitive progression. In the proposed study, we leverage our success in collecting CSF biomarkers, neuroimaging and detailed cognitive phenotyping combined with audio-recordings of participants in the Brain Stress, Hypertension and Aging Research Program cohort. This cohort, now in its second year of follow-up, consists of 400 individuals 50 years or older with normal cognition or MCI. We plan to extend this cohort of 400 participants for 3 more years to collect additional waves of voice recordings, cognitive assessments, and follow-up CSF biomarkers and neuroimaging. Our overarching hypothesis is that the derived novel features reflecting poor lexical-semantic connectedness or acoustic perturbations are significantly different between biomarker positive and negative participants, have better diagnostic performance than traditional cognitive tests (e.g. confrontation naming), and are associated with a longitudinal change in cognition and AD-related biomarkers. The Specific Aims are: 1) Determine the accuracy of the derived digital biomarkers in detection of in-vivo AD pathology in the B-SHARP cohort; 2) Investigate longitudinally the association of the derived features with cognitive decline and their ability to reflect changes in AD biomarkers; and 3) using resting state functional MRI, identify the networks in the brain that map to derived lexical semantic and acoustic features with brain connectivity at baseline and during follow-up. This project will provide needed insight into the use of non-invasive digital biomarkers to improve the ability to detect and track longitudinal changes in cognitive and functional status in AD.

阿尔茨海默病（AD）是一种开始数十年的进行性神经病理学改变 认知和功能症状，因此努力集中在开发创新工具上 以及用于早期识别痴呆前期阶段的生物标志物。到目前为止，临床能力，以确定那些与 AD的痴呆前阶段是有限的，并且需要昂贵的（淀粉样PET）或侵入性的（腰椎 穿刺，LP）测试。然而，连接语音的微妙变化可能在公开之前几年就可以检测到。 疾病症状出现。我们的团队开发了一种方法，使用机器学习和自然 语言处理结合先进的声学语音和词汇语义分析。初步 数据显示在识别AD生物标志物状态和预测2年认知进展方面的前景。在 我们利用我们在收集CSF生物标志物、神经成像和详细的 认知表型结合参与者的录音，在脑应激，高血压和 老龄化研究计划队列。这个队列，现在在其第二年的后续行动，包括400 50岁或50岁以上认知正常或MCI的人。我们计划将这400名参与者的队列扩展到 3年以上，收集额外的语音记录波、认知评估和随访CSF 生物标记物和神经成像。我们的总体假设是，衍生的新特征反映了穷人的生活方式。 词汇-语义连接性或声学扰动在生物标志物之间显著不同 积极和消极的参与者，有更好的诊断性能比传统的认知测试（例如， 对抗命名），并与认知和AD相关生物标志物的纵向变化相关。 具体目的是：1）确定衍生的数字生物标志物在体内AD检测中的准确性 2）纵向研究衍生特征与认知功能的相关性， 下降和他们反映AD生物标志物变化的能力;和3）使用静息状态功能MRI， 大脑中的网络，映射到派生的词汇语义和声学特征，大脑连接处于基线， 在后续行动中。该项目将提供所需的深入了解使用非侵入性数字生物标志物， 提高检测和跟踪AD认知和功能状态纵向变化的能力。