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) 的特点是几十年前就开始出现进行性神经病理变化 认知和功能症状，因此一直致力于开发创新工具 以及用于早期识别痴呆前期阶段的生物标志物。迄今为止，临床能力能够识别那些患有 AD 的痴呆前期阶段受到限制，需要昂贵的（淀粉样蛋白 PET）或侵入性的（腰椎 穿刺）测试。然而，连接语音的细微变化可能会在明显的变化之前几年就被发现。 出现疾病症状。我们的团队开发了一种使用机器学习和自然的方法 语言处理与先进的声学语音和词汇语义分析相结合。 初步数据显示在识别 AD 生物标志物状态和预测 2 年认知能力方面有希望 进展。在拟议的研究中，我们利用我们在收集脑脊液 (CSF) 方面的成功 生物标志物、神经影像学和详细的认知表型结合录音 脑压力、高血压和衰老研究计划队列的参与者。这个群体，现在正处于 第三年的随访，由 400 名 50 岁或以上认知正常或轻度认知的个体组成 认知障碍。我们计划将这 400 名参与者的队列再延长 3 年，以收集 额外的语音记录、认知评估、后续脑脊液生物标志物和 神经影像学。我们的首要假设是，衍生的新颖特征反映了糟糕的词汇- 生物标志物阳性之间的语义关联性或声学扰动存在显着差异 -阴性参与者在 ATN 框架方面具有比 传统的认知测试可以跟踪疾病进展。具体目标是： 1) 确定 衍生的数字生物标志物在检测体内 AD 病理学和 B 中 ATN 分类中的准确性 夏普队列； 2) 研究衍生的数字生物标志物与疾病进展的关联 和认知能力下降； 3) 研究重复测量数字生物标志物的能力 追踪疾病进展。该项目将为非侵入性数字技术的使用提供所需的见解 生物标志物可提高检测和跟踪认知和功能状态纵向变化的能力 AD 并将为未来更大规模的关键研究奠定基础。