Parola Alberto, Lin Jessica Mary, Simonsen Arndis, Bliksted Vibeke, Zhou Yuan, Wang Huiling, Inoue Lana, Koelkebeck Katja, Fusaroli Riccardo
Department of Linguistics, Semiotics and Cognitive Science, Aarhus University, Aarhus, Denmark; The Interacting Minds Centre, Institute of Culture and Society, Aarhus University, Aarhus, Denmark.
Department of Linguistics, Semiotics and Cognitive Science, Aarhus University, Aarhus, Denmark; The Interacting Minds Centre, Institute of Culture and Society, Aarhus University, Aarhus, Denmark.
Schizophr Res. 2023 Sep;259:59-70. doi: 10.1016/j.schres.2022.07.002. Epub 2022 Aug 1.
Language disorders - disorganized and incoherent speech in particular - are distinctive features of schizophrenia. Natural language processing (NLP) offers automated measures of incoherent speech as promising markers for schizophrenia. However, the scientific and clinical impact of NLP markers depends on their generalizability across contexts, samples, and languages, which we systematically assessed in the present study relying on a large, novel, cross-linguistic corpus.
We collected a Danish (DK), German (GE), and Chinese (CH) cross-linguistic dataset involving transcripts from 187 participants with schizophrenia (111DK, 25GE, 51CH) and 200 matched controls (129DK, 29GE, 42CH) performing the Animated Triangles Task. Fourteen previously published NLP coherence measures were calculated, and between-groups differences and association with symptoms were tested for cross-linguistic generalizability.
One coherence measure, i.e. second-order coherence, robustly generalized across samples and languages. We found several language-specific effects, some of which partially replicated previous findings (lower coherence in German and Chinese patients), while others did not (higher coherence in Danish patients). We found several associations between symptoms and measures of coherence, but the effects were generally inconsistent across languages and rating scales.
Using a cumulative approach, we have shown that NLP findings of reduced semantic coherence in schizophrenia have limited generalizability across different languages, samples, and measures. We argue that several factors such as sociodemographic and clinical heterogeneity, cross-linguistic variation, and the different NLP measures reflecting different clinical aspects may be responsible for this variability. Future studies should take this variability into account in order to develop effective clinical applications targeting different patient populations.
语言障碍，尤其是言语紊乱和语无伦次，是精神分裂症的显著特征。自然语言处理（NLP）提供了自动测量语无伦次言语的方法，有望作为精神分裂症的标志物。然而，NLP标志物的科学和临床影响取决于它们在不同情境、样本和语言中的通用性，我们在本研究中依靠一个大型的、新颖的跨语言语料库对其进行了系统评估。
我们收集了一个丹麦语（DK）、德语（GE）和中文（CH）的跨语言数据集，其中包括187名精神分裂症患者（111名丹麦患者、25名德国患者、51名中国患者）和200名匹配的对照组（129名丹麦对照组、29名德国对照组、42名中国对照组）在执行动画三角任务时的转录本。计算了14种先前发表的NLP连贯性指标，并测试了组间差异以及与症状的关联，以评估跨语言通用性。
一种连贯性指标，即二阶连贯性，在样本和语言中具有很强的通用性。我们发现了几种特定语言的效应，其中一些部分重复了先前的研究结果（德国和中国患者的连贯性较低），而另一些则没有（丹麦患者的连贯性较高）。我们发现症状与连贯性指标之间存在几种关联，但这些效应在不同语言和评分量表中通常不一致。
通过采用累积方法，我们表明精神分裂症中语义连贯性降低的NLP研究结果在不同语言、样本和测量方法中的通用性有限。我们认为，社会人口统计学和临床异质性、跨语言差异以及反映不同临床方面的不同NLP测量方法等几个因素可能导致了这种变异性。未来的研究应考虑到这种变异性，以便开发针对不同患者群体的有效临床应用。
