Digital Biomarkers for Alzheimer’s Disease

阿尔茨海默病的数字生物标志物

• 批准号: 10299379
• 负责人: IHAB M HAJJAR
• 金额: $ 15.89万
• 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: 2021-09-30 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10299379
• 关键词: Acoustics; Aging; Alzheimer disease detection; Alzheimer' s Disease; Alzheimer' s disease pathology; Alzheimer’s disease biomarker; Amyloid; Amyloid beta-Protein; Applications Grants; Area Under Curve; Artificial Intelligence; Autobiography; Autopsy; Biological Markers; Brain; Categories; Cerebrospinal Fluid; Classification; Clinical; Cognition; Cognitive; Complex; Consumption; Data; Dementia; Development; Diagnosis; Diagnostic; Disease; Disease Progression; Early identification; Elderly; Foundations; Framingham Heart Study; Future; Hippocampus (Brain); Hypertension; Image; Impaired cognition; Impairment; Individual; Light; Machine Learning; Magnetic Resonance Imaging; Measurement; Measures; Methodology; Methods; Natural Language Processing; Nerve Degeneration; Neurofibrillary Tangles; Neuropsychology; Online Systems; Participant; Pathologic; Performance; Phase; Phenotype; Positron-Emission Tomography; Predictive Value; Reporting; Research; Scanning; Semantics; Specificity; Speech; Spinal Puncture; Stress; Sum; Symptoms; Testing; Time; Voice; Weight; Work; automated speech recognition; base; biomarker signature; cerebral atrophy; clinical decision-making; cognitive testing; cohort; density; diagnostic accuracy; digital; early detection biomarkers; follow-up; functional status; hippocampal atrophy; improved; in vivo; innovation; insight; lexical; mild cognitive impairment; neurofilament; neuroimaging; novel; pre-clinical; programs; smartphone Application; success; tau Proteins; tau-1; technology validation; 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 puncture) 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 cerebrospinal fluid (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 third year of follow-up, consists of 400 individuals 50 years or older with normal cognition or mild cognitive impairment. We plan to extend this cohort of 400 participants for 3 more years to collect additional waves of voice recordings, cognitive assessments, 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 with regards to the ATN framework than traditional cognitive tests and can track disease progression. The Specific Aims are: 1) Determine the accuracy of the derived digital biomarkers in detecting in-vivo AD pathology and ATN classification in the B- SHARP cohort; 2) Investigate the association of the derived digital biomarkers with disease progression and cognitive decline; and 3) Investigate the ability of repeated measurement of the digital biomarkers to track disease progression. 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 and will set the foundation for a future larger pivotal study.

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（淀粉样蛋白PET）或侵入性的（腰椎 穿刺）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，在确定AD生物标志物状态和预测2年认知功能方面有希望。 进展在拟议的研究中，我们利用我们在收集脑脊液（CSF） 生物标志物，神经成像和详细的认知表型结合音频记录， 脑应激、高血压和衰老研究计划队列的参与者。这群人，现在在其 第三年随访，包括400名50岁或以上的认知正常或轻度 认知障碍我们计划将这个由400名参与者组成的队列再延长3年来收集 额外的语音记录波、认知评估、随访CSF生物标志物和 神经成像我们的总体假设是，派生的小说特征反映了糟糕的词汇- 语义连接性或声学扰动在生物标志物阳性之间显著不同， 和阴性的参与者，有更好的诊断性能方面的ATN框架比 传统的认知测试，可以跟踪疾病的进展。具体目标是：1）确定 在B中检测体内AD病理学和ATN分类中导出的数字生物标志物的准确性。 SHARP队列; 2）研究衍生的数字生物标志物与疾病进展的关联 和认知下降;和3）研究重复测量数字生物标志物的能力， 跟踪疾病进展。该项目将提供所需的洞察使用非侵入性数字 生物标志物，以提高检测和跟踪认知和功能状态纵向变化的能力 并将为未来更大的关键研究奠定基础。