Digital Biomarkers for Alzheimers Disease

阿尔茨海默病(AD)的特点是几十年前就开始进行性的神经病变 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记物。到目前为止，临床上能够识别那些患有 阿尔茨海默病的痴呆前期阶段有限，需要昂贵的(淀粉样蛋白PET)或侵袭性(腰椎 穿孔、LP)测试。然而，联网语音的细微变化可能在公开之前几年就可以检测到 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语言的方法 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生物标记物状态和预测2年认知进展方面有希望。在 建议的研究，我们利用我们的成功收集脑脊液生物标记物，神经成像和详细的 认知表型与脑应激、高血压和高血压患者的录音相结合 老龄化研究计划队列。这一队列现在是第二年的后续行动，由400人组成 具有正常认知或MCI的50岁或以上的个人。我们计划将这400名参与者的范围扩大到 再花3年时间收集更多的语音记录、认知评估和后续脑脊液 生物标志物和神经成像。我们最重要的假设是，派生的新奇特征反映了贫穷 词汇-语义连接或声学扰动在不同生物标志物之间存在显著差异 与传统的认知测试相比，无论是阳性还是阴性参与者，诊断性能都更好(例如 对抗命名)，并与认知和AD相关生物标志物的纵向变化有关。 具体目的是：1)确定衍生数字生物标志物在体内AD检测中的准确性 B-Sharp队列中的病理学；2)纵向研究派生特征与认知的关联 3)使用静息状态功能磁共振成像，识别 大脑中的网络，映射到派生的词汇、语义和声学特征，在基线和 在随访期间。该项目将为非侵入性数字生物标记物的使用提供必要的见解 提高检测和跟踪AD患者认知和功能状态纵向变化的能力。

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.