Development of digital voice biomarkers and associations with cognition, cerebrospinal biomarkers, and neural representation in early Alzheimer's disease.
Development of digital voice biomarkers and associations with cognition, cerebrospinal biomarkers, and neural representation in early Alzheimer's disease.
复制标题
DOI：
10.1002/dad2.12393
复制
发表时间：
2023-01
期刊：
影响因子：
--
通讯作者：
中科院分区：
文献类型：
--
作者：
自然语言处理（NLP）、语音识别和机器学习（ML）的进步允许探索以前难以测量的语言和声学变化。我们开发了提取词汇语义和声学测量作为阿尔茨海默病（AD）数字语音生物标志物的过程。我们收集了92名认知未受损（40名Aβ+）和114名认知受损（63名Aβ+）参与者的连接语音、神经心理学、神经影像学和脑脊液（CSF） AD生物标志物数据。声学和词汇语义特征是使用ML方法从录音中获得的。与波士顿命名测试（AUC = 0.66）相比，词汇语义（曲线下面积[AUC] = 0.80）和声学（AUC = 0.77）得分在检测MCI方面表现出更高的诊断性能。只有词汇-语义评分检测到淀粉样蛋白- β状态（p = 0.0003）。声学评分与海马体积相关（p = 0.017），而词汇语义评分与脑脊液淀粉样蛋白β相关（p = 0.007）。两项指标均与2年疾病进展显著相关。这些初步研究结果表明，衍生的数字生物标志物可以识别临床前和前驱AD的认知障碍，并可以预测疾病进展。本研究将词汇语义和声学特征作为阿尔茨海默病（AD）的数字生物标志物。这些特征是使用机器学习方法从录音中获得的。语音生物标志物检测早期AD患者的认知障碍和淀粉样蛋白β状态。语音生物标志物可以预测阿尔茨海默病的进展。这些标记与阿尔茨海默病易感大脑区域的功能连接密切相关。
Advances in natural language processing (NLP), speech recognition, and machine learning (ML) allow the exploration of linguistic and acoustic changes previously difficult to measure. We developed processes for deriving lexical‐semantic and acoustic measures as Alzheimer's disease (AD) digital voice biomarkers. We collected connected speech, neuropsychological, neuroimaging, and cerebrospinal fluid (CSF) AD biomarker data from 92 cognitively unimpaired (40 Aβ+) and 114 impaired (63 Aβ+) participants. Acoustic and lexical‐semantic features were derived from audio recordings using ML approaches. Lexical‐semantic (area under the curve [AUC] = 0.80) and acoustic (AUC = 0.77) scores demonstrated higher diagnostic performance for detecting MCI compared to Boston Naming Test (AUC = 0.66). Only lexical‐semantic scores detected amyloid‐β status (p = 0.0003). Acoustic scores associated with hippocampal volume (p = 0.017) while lexical‐semantic scores associated with CSF amyloid‐β (p = 0.007). Both measures were significantly associated with 2‐year disease progression. These preliminary findings suggest that derived digital biomarkers may identify cognitive impairment in preclinical and prodromal AD, and may predict disease progression. This study derived lexical‐semantic and acoustics features as Alzheimer's disease (AD) digital biomarkers. These features were derived from audio recordings using machine learning approaches. Voice biomarkers detected cognitive impairment and amyloid‐β status in early stages of AD. Voice biomarkers may predict Alzheimer's disease progression. These markers significantly mapped to functional connectivity in AD‐susceptible brain regions.
影响因子：
5.7
作者：
Pakhomov SV;Jones DT;Knopman DS
通讯作者：
Knopman DS