数字语言标记可以区分正面与右前叶缩在前性痴呆症中
Jet M J Vonk1, Brittany T Morin1, Janhavi Pillai1
1Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA.
medRxiv : the preprint server for health sciences
|September 10, 2024
概括
自动语音分析准确区分前性痴呆症 (FTD) 亚型,基于前或右前叶缩. 这种非侵入性工具有助于FTD的早期诊断和监测,提供了一个可扩展的解决方案.
科学领域:
- 神经科学是一个神经科学.
- 计算语言学 计算语言学
- 医学诊断 医学诊断 医学诊断
背景情况:
- 前性痴呆 (FTD) 亚型,行为变体 (bvFTD) 和语义行为变体 (sbvFTD),由于重叠的症状和神经解剖学,存在诊断挑战.
- 准确的区分对于有针对性的临床试验至关重要,特别是那些专注于TDP-43蛋白病变的临床试验.
研究的目的:
- 调查自动语音分析在区分前额缩FTD与右前叶缩FTD之间的有效性.
- 探索自动语音分析作为FTD亚型的潜在的非侵入性诊断工具.
主要方法:
- 一项涉及40名FTD参与者 (16名前额缩,24名rATL缩) 和22名健康对照者的横截面研究.
- 从图片描述任务中提取的16个语言和声学特征的分析,使用逐步逻辑回归和ROC曲线分析.
- 基于voxel的形态测量用于检查区域缩和选定的语音特征之间的大脑行为关系.
主要成果:
- 自动语音分析使用三个特征将整体FTD组与高精度 (AUC=.973) 的对照区分开来.
- 在FTD组中,有五种言语特征可以区分为正面与rATL缩 (AUC=.943).
- 神经成像揭示了语义,语音/词汇和社会情感特征的独特神经相关性,复合得分与rATL缩有独特的关联.
结论:
- 自动语音分析有效地区分FTD亚型和健康对照,提供可扩展的,非侵入性的诊断方法.
- 语音特征分析为FTD变体中语言障碍的神经基础提供了洞察力.
- 这种方法可以增强早期FTD诊断和监测,特别是在资源有限的环境中.
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