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语音数字生物标志物与流体生物标志物相结合,可以通过机器学习预测认知障碍

Jin-Tao Wang^1,2, Nan Zhi², Gang Xu³

¹Department of Neurology, Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No.197 Ruijin No.2 street, Shanghai, 200025, China.

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概括

语音分析与血清生物标志物相结合,显示出早期阿尔茨海默病检测的前景. 这种方法为诊断认知障碍和评估阿尔茨海默病的病理学提供了更容易获得和负担得起的方法.

科学领域:

神经科学是一个神经科学.
生物标志物生物标志物
语音分析语言分析

背景情况:

目前的阿尔茨海默病 (AD) 检测方法昂贵,侵入性和难以获得.
语音分析为早期认知障碍 (CI) 诊断提供了一个潜在的患者友好的替代方案.
对CI的语音生物标志物缺乏病理验证.

研究的目的:

探索语音数字生物标志物和AD核心生物标志物之间的相关性.
开发具有成本效益的机器学习模型来诊断CI和Aβ状态.

主要方法:

1223名参与者 (AD,aMCI,认知正常) 接受了神经心理测试,语音录音和生物标志物量化.
部分相关性分析和LOWESS探索了语音生物标志物和AD生物标志物之间的相关性.
机器学习模型 (XGBoost,物流回归) 使用语音,血清,APOE和人口统计数据构建.

主要成果:

语音生物标志物沉默持续时间 (PSD) 的百分比与认知水平,血清GFAP,NFL,p-Tau217和大脑粉样蛋白沉积相关.
PSD,p-Tau217和GFAP显示了Aβ沉积的两阶段变化模式.
机器学习模型实现了CI诊断 (AUC=0.928) 和Aβ状态分类 (AUC=0.845) 的高精度.

结论:

将语音数字标记与血清和其他生物标记物集成,可以提高CI识别.
这项研究为AD诊断中语音数字生物标志物的临床实用性提供了初步证据.

关键词:

阿尔茨海默氏症是阿尔茨海默氏症的一种疾病.粉样蛋白PET是一种PET.早期检测早期检测机器学习是机器学习.血清生物标志物的血清生物标志物语音测试语言测试语音测试结构性核磁共振成像 (MRI)

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Language and Cognition

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Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.