生物标志物 生物标志物
Casper de Boer1,2, Rosanne L van den Berg1,2,3, Marissa D Zwan1,2
1Amsterdam Neuroscience, Neurodegeneration, Amsterdam, Netherlands.
Alzheimer's & dementia : the journal of the Alzheimer's Association
|December 24, 2025
概括
自动语音分析显示了认知正常的成年人与粉样β病理学的微妙变化. 这些发现表明,连接式语音可以成为早期发现阿尔茨海默病的数字生物标志物.
科学领域:
- 神经科学是一个神经科学.
- 数字健康数字健康
- 生物标志物 生物标志物
背景情况:
- 连接语音分析是阿尔茨海默病 (AD) 的新兴数字生物标志物.
- 细粒度语音分析可以在AD早期阶段检测到微妙的认知缺陷.
- 这项研究调查了与认知正常个体的粉样β (Aβ) 病理学相关的语言特征.
研究的目的:
- 识别可靠的声学和语言语音特征.
- 为了检查粉样β (Aβ) 病理和纵向连接语音之间的关联.
- 探索连接语音作为临床前AD的数字生物标志物的潜力.
主要方法:
- 包括50个认知正常的成年人 (23个Aβ阳性).
- 使用了为期五天的远程语音评估,在24个月内重复进行.
- 使用线性混合模型分析了声学和语言特征.
主要成果:
- 确定了10个声学和8个语言特征,具有良好的测试-重新测试可靠性.
- 在Aβ阳性个体中观察到更高的停顿到单词和代词到名词比率的趋势.
- 在暂停措施中发现了纵向差异,随着时间的推移,Aβ阳性个体的暂停更多.
结论:
- 粉样β病理与健康成年人改变的连接语音指标有关.
- 远程,多天的语音评估显示出作为临床前AD的数字生物标志物的潜力.
- 这些发现支持在观察性研究和分散试验中使用语音分析来早期检测AD.
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