Elvevåg Brita, Foltz Peter W, Rosenstein Mark, Delisi Lynn E
Clinical Brain Disorders Branch, National Institute of Mental Health, National Institutes of Health, Building 10, Room 4S235, MSC 1379, Bethesda MD 20892.
J Neurolinguistics. 2010 May 1;23(3):270-284. doi: 10.1016/j.jneuroling.2009.05.002.
Communication disturbances are prevalent in schizophrenia, and since it is a heritable illness these are likely present - albeit in a muted form - in the relatives of patients. Given the time-consuming, and often subjective nature of discourse analysis, these deviances are frequently not assayed in large scale studies. Recent work in computational linguistics and statistical-based semantic analysis has shown the potential and power of automated analysis of communication. We present an automated and objective approach to modeling discourse that detects very subtle deviations between probands, their first-degree relatives and unrelated healthy controls. Although these findings should be regarded as preliminary due to the limitations of the data at our disposal, we present a brief analysis of the models that best differentiate these groups in order to illustrate the utility of the method for future explorations of how language components are differentially affected by familial and illness related issues.
沟通障碍在精神分裂症中很常见,由于这是一种遗传性疾病,这些障碍很可能以一种不明显的形式存在于患者的亲属中。鉴于话语分析耗时且往往具有主观性,这些偏差在大规模研究中常常未被检测。计算语言学和基于统计的语义分析方面的最新研究表明了沟通自动分析的潜力和力量。我们提出了一种自动且客观的话语建模方法,该方法能检测先证者、其一级亲属和无关健康对照之间非常细微的偏差。尽管由于我们现有数据的局限性,这些发现应被视为初步的,但我们对最能区分这些组别的模型进行了简要分析,以说明该方法在未来探索语言成分如何受到家族和疾病相关问题不同影响方面的实用性。