Corcoran Cheryl M, Mittal Vijay A, Bearden Carrie E, E Gur Raquel, Hitczenko Kasia, Bilgrami Zarina, Savic Aleksandar, Cecchi Guillermo A, Wolff Phillip
Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Department of Psychology, Northwestern University, Evanston, IL, USA.
Schizophr Res. 2020 Dec;226:158-166. doi: 10.1016/j.schres.2020.04.032. Epub 2020 Jun 1.
Human ratings of conceptual disorganization, poverty of content, referential cohesion and illogical thinking have been shown to predict psychosis onset in prospective clinical high risk (CHR) cohort studies. The potential value of linguistic biomarkers has been significantly magnified, however, by recent advances in natural language processing (NLP) and machine learning (ML). Such methodologies allow for the rapid and objective measurement of language features, many of which are not easily recognized by human raters. Here we review the key findings on language production disturbance in psychosis. We also describe recent advances in the computational methods used to analyze language data, including methods for the automatic measurement of discourse coherence, syntactic complexity, poverty of content, referential coherence, and metaphorical language. Linguistic biomarkers of psychosis risk are now undergoing cross-validation, with attention to harmonization of methods. Future directions in extended CHR networks include studies of sources of variance, and combination with other promising biomarkers of psychosis risk, such as cognitive and sensory processing impairments likely to be related to language. Implications for the broader study of social communication, including reciprocal prosody, face expression and gesture, are discussed.
在前瞻性临床高危（CHR）队列研究中，人类对概念紊乱、内容贫乏、指称连贯和逻辑思维的评分已被证明可预测精神病发作。然而，随着自然语言处理（NLP）和机器学习（ML）的最新进展，语言生物标志物的潜在价值得到了显著提升。此类方法能够快速、客观地测量语言特征，其中许多特征人类评估者并不容易识别。在此，我们回顾了关于精神病语言产生障碍的关键发现。我们还描述了用于分析语言数据的计算方法的最新进展，包括用于自动测量语篇连贯、句法复杂性、内容贫乏、指称连贯和隐喻性语言的方法。精神病风险的语言生物标志物目前正在进行交叉验证，并注重方法的统一。扩展CHR网络的未来方向包括对变异来源的研究，以及与其他有前景的精神病风险生物标志物相结合，如可能与语言相关的认知和感觉加工障碍。本文还讨论了对包括相互韵律、面部表情和手势在内的社会交流更广泛研究的意义。
