语言特征的关系从自发言语到多发性硬化症患者的客观和主观认知
Leila Simani1, Martin Subert2, Tomas Uher3
1Cognitive Neuroscience Division, Department of Neurology, Columbia University Irving Medical Center, NY, NY, USA.
Multiple sclerosis and related disorders
|November 13, 2025
概括
自动语音分析揭示了语言特征与多发性硬化症 (MS) 的认知功能相关. 这种技术可能有助于早期检测和管理MS患者的认知变化.
科学领域:
- 神经学 神经学
- 计算语言学 计算语言学
- 认知科学 认知科学
背景情况:
- 语言功能障碍在多发性硬化症 (MS) 中很普遍.
- 自动语音分析提供了一种新的方法来检测微妙的认知变化.
- 自发语音分析可以作为一般认知功能的代理.
研究的目的:
- 研究MS中语言特征与认知功能之间的关联.
- 探索语言特征与情绪/疲劳之间的关系.
- 评估语言特征的能力,以区分患有MS的个体与对照个体.
主要方法:
- 患有多发性硬化和对照组的参与者提供了自发的语音样本.
- 自然语言处理用于分析语言特征.
- 计算了语言特征与认知,情绪和疲劳指标之间的相关性.
主要成果:
- 语言特征 (N-grams,内容词,函数词) 与完整样本中的客观认知表现相关.
- 在MS组中,语言特征与主观认知和客观测试相关,但不是情绪或疲劳.
- 与对照组相比,患有多发性硬化症的个体产生的副词较少.
结论:
- 半自动语音分析可以识别语言变量,表明MS的认知变化.
- 这种方法可以改善MS的早期诊断和患者管理策略.
- 语音分析为MS的认知评估提供了一种非侵入性方法.
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