生物标志物 生物标志物
Junhyuk Louis Kwak1, Mark Y Liberman1, Sunghye Cho1
1Linguistic Data Consortium, University of Pennsylvania, Philadelphia, PA, USA.
Alzheimer's & dementia : the journal of the Alzheimer's Association
|December 25, 2025
概括
健康个体的言语持续时间测量显示了与年龄相关的变化,老年人说话少,停顿多. 这为早期检测神经退行形成了基线.
科学领域:
- 神经学 神经学
- 语音科学 语言科学
- 老年学是指老年学的学科.
背景情况:
- 图像描述任务评估神经退行性患者的沟通.
- 语音持续时间的测量有助于区分健康个体与神经退行性疾病患者.
- 建立基于年龄的语音持续时间基线对于早期神经退行检测至关重要.
研究的目的:
- 在健康成年人中量化与年龄相关的语音持续时间基线.
- 识别语言中与健康衰老模式的偏差.
- 帮助早期发现神经退行性疾病.
主要方法:
- 收集了290名健康参与者 (年龄15-90岁) 的图片描述任务反应.
- 使用自动语音活动探测器进行音频细分.
- 应用线性混合效应模型来分析年龄对语音和停顿时间,语音百分比和停顿率的影响.
主要成果:
- 年龄较大的参与者表现出较低的语音百分比和平均语音持续时间.
- 年龄的增加与更高的休息率,平均休息时间和总休息时间相关.
- 总言语持续时间没有显示出与年龄相关的显著下降.
结论:
- 在语音持续时间指标中观察到与年龄相关的具体趋势.
- 结果为健康的衰老语言模式提供了可靠的基线.
- 这一基线有助于估计不同年龄段的神经退行症的偏差.
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