从连接的语音中自动提取的形态语法测量区分非流利/语法和logopenic的PPA变体
Sladjana Lukic1, Zekai Fan2, Adolfo M García3
1University of California, San Francisco Memory and Aging Center, CA, USA; Ruth S. Ammon College of Education and Health Sciences, Department of Communication Sciences and Disorders, Adelphi University, Garden City, NY, USA.
语音模式的自动化分析有效地区分非流利/语法变体初级渐进性失言症 (nfvPPA) 和其他疾病. 这种方法识别了与 nfvPPA 中大脑缩相关的关键语言标记,有助于诊断.
科学领域:
- 神经语言学是一种神经语言学.
- 计算语言学 计算语言学
- 神经成像是一种神经成像.
背景情况:
- 形态语法评估对于诊断非流利/语法变异初级渐进性失言症 (nfvPPA) 是至关重要的.
- 目前对nfvPPA的诊断测试容易产生偏见,并且很难将其与逻辑变异PPA (lvPPA) 区分开来.
- 在nfvPPA中的形态合成缺陷的神经支需要进一步的研究.
研究的目的:
- 开发和验证使用自然语言处理 (NLP) 来分析 nfvPPA 中的形态语法障碍的自动化方法.
- 确定特定的形态语法标记,可以可靠地将nfvPPA与健康对照 (HC) 和lvPPA区分开来.
- 探索 nfvPPA 中最重要的形态合成标记物的神经解剖学相关性.
主要方法:
- 利用NLP工具对35名nfvPPA患者,10名HC患者和26名lvPPA患者的图片描述进行部分语音标签.
- 提取的特征包括连接,时态标记和代词类型.
- 采用梯度增强机来进行分类和特征重要性分析,随后使用基于voxel的形态测量来进行神经成像相关性.
主要成果:
- 与lvPPA和HC组相比,患有nfvPPA的个体表现出明显较少的形态合成元素.
- 自动化分析在区分nfvPPA与lvPPA (AUC=0.95) 和HC (AUC=0.82) 方面取得了很高的准确性.
- 附属连接成为最有区别的特征,与左后下额头回形的皮质缩相关 (pFWE <0.05).
结论:
- 自动化形态语法分析提供了一种有效和客观的方法来区分nfvPPA和lvPPA.
- 关键的形态语法标志物,特别是附属关节缺陷,与 nfvPPA 中的核心大脑缩区域有关.
- 这种NLP驱动的方法可以显著帮助初级渐进性失语症的诊断挑战.
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