在情感和精神疾病中将语音模式与大脑结构联系起来:一种整合性的自然语言处理方法
Svenja Seuffert1,2, Rieke Roxanne Mülfarth3,4, Lea Teutenberg3,4
1Faculty of Medicine, Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany. svenja.seuffert@uni-marburg.de.
自然语言处理 (NLP) 客观地分析严重精神疾病中的语音,揭示与大脑结构相关的语言维度. 这些NLP指标为语言功能障碍提供了敏感的指标,并推进了精神病理学模型.
科学领域:
- 神经科学是一个神经科学.
- 精神病学是一个精神病学.
- 计算语言学 计算语言学
背景情况:
- 语言障碍是严重精神疾病的关键.
- 传统的语音评估是主观的,可能会忽略微妙的异常.
研究的目的:
- 使用自然语言处理 (NLP) 对有情感和精神障碍的个体客观分析自发言语.
- 识别潜在的语言维度及其与神经解剖学和临床症状的关联.
主要方法:
- 从一个跨诊断样本 (严重抑郁症,双极性障碍,精神分裂症,精神分裂症和对照) 转录自发言语.
- 使用NLP和因子分析分析语义和语法特征,以确定语言维度.
- 与临床评级和神经解剖学测量 (MRI) 相关的语言维度.
主要成果:
- 确定了三个语言维度:语法复杂性,词汇多样性和流利性,以及狭窄的主题重点.
- 语法复杂性和词汇多样性与前腔道白质完整性 (FA) 降低相关.
- 狭窄主题重点与右后侧岛屿灰色物质体积减少有关.
结论:
- 从NLP衍生的语音指标是精神疾病中语言功能障碍的敏感指标.
- 这些客观措施可以阐明心理病理学中的大脑行为关系.
- NLP提供了一种可扩展的方法来理解精神疾病中的语言异常.
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