Using the RDoC Approach to Understand Thought Disorder: A Linguistic Corpus-Based Approach

联系PD/PI：Corcoran、Cheryl M 精神障碍中的思维障碍及其风险状态通常使用临床评估 评分量表，偶尔还有劳动密集型的人工语言分析方法。我们建议， 使用一种新的基于自动语言语料库的方法来进行语言分析， 智能该方法通过利用一个大型语料库， 文本，类似于人类如何赋予语言意义，并导致语义连贯性的措施， 一句接一句它还通过“词性”标注和分析来评估句法复杂性。 语音图这些分析产生语音语义和句法的细粒度索引， 准确地捕捉思维障碍。 使用这些自动化的语音分析方法，与IBM的计算机科学家合作， 我们在一个小的队列研究中确定了一个对精神病发作具有高准确性的分类器，其中包括 减少短语之间的语义连贯性，降低句法复杂性，包括 缩短了短语长度，减少了限定代词（“which”、“what”、“that”）的使用。这些特征 与前驱症状相关，但在分类准确性方面优于它们。他们还歧视 精神分裂症患者的正常言语我们在第二个小规模的实验中进一步交叉验证了这种自动化方法。 ·识别在两个队列中对精神病结果进行分类的语义/句法分类器，以及 近期发作的精神病患者的言语与正常言语的区别。 这些自动化语言分析方法具有很大的前景，但到目前为止，它们的使用一直是 局限于少数旨在将精神分裂症与正常人区分开来的小型研究， 工作预测精神病在我们对语言机制的理解上， 思维障碍为了解决这一差距，响应PAR-16-136，我们建议使用RDoC结构： 语言产生及其基于语料库的分析范式，对思维障碍进行维度研究 在一个由150名健康志愿者、150名COPD患者和150名 近期发病的精神病患者我们期望潜在语义分析将产生语义的度量， 连贯性，指数积极的思维障碍（切线，出轨），而部分的讲话（POS） 标记/语音图将产生衡量句法复杂性的指标，消极思维障碍 （具体性，内容贫乏）。 该大型语言数据集将从两个PSYSCAN/HARMONY站点获得， 语言数据将可用于PSYSCAN/HARMONY成像和EEG数据的二次分析， 在电路和生理水平上研究语言产生。这一大型语言和临床数据集将 也在NIH存档，供其他研究人员进行进一步的语言分析。 项目摘要/摘要第7页

Contact PD/PI: Corcoran, Cheryl M Thought disorder in psychotic disorders and their risk states has typically been evaluated using clinical rating scales, and occasionally labor-intensive manual methods of linguistic analysis. We propose instead to use a novel automated linguistic corpus-based approach to language analysis informed by artificial intelligence. The method derives the semantic meaning of words and phrases by drawing on a large corpus of text, similar to how humans assign meaning to language, and leads to measures of semantic coherence from one phrase to the next. It also evaluates syntactic complexity through “part-of-speech” tagging and analysis of speech graphs. These analyses yield fine-grained indices of speech semantics and syntax that may more accurately capture thought disorder. Using these automated methods of speech analysis, in collaboration with computer scientists from IBM, we identified a classifier with high accuracy for psychosis onset in a small CHR cohort, which included decreased semantic coherence from phrase to phrase, and decreased syntactic complexity, including shortened phrase length and decreased use of determiner pronouns (“which”, “what”, “that”). These features correlated with prodromal symptoms but outperformed them in classification accuracy. They also discriminated schizophrenia from normal speech. We further cross-validated this automated approach in a second small CHR cohort, identifying a semantics/syntax classifier that classified psychosis outcome in both cohorts, and discriminated speech in recent-onset psychosis patients from normal speech. These automated linguistic analytic methods hold great promise, but their use thus far has been circumscribed to only a few small studies that aim to discriminate schizophrenia from the norm, and in our own work, predict psychosis. There is a critical gap in our understanding of the linguistic mechanisms that underlie thought disorder. To address this gap, in response to PAR-16-136, we propose to use the RDoC construct of language production, and its linguistic corpus-based analytic paradigm, to study thought disorder dimensionally and transdiagnostically, in a large cohort of 150 putatively healthy volunteers, 150 CHR patients, and 150 recent-onset psychosis patients. We expect that latent semantic analysis will yield measures of semantic coherence that index positive thought disorder (tangentiality, derailment), whereas part-of-speech (POS) tagging/speech graphs will yields measures of syntactic complexity that index negative thought disorder (concreteness, poverty of content). This large language dataset will be obtained from two PSYSCAN/HARMONY sites, such that these language data will be available for secondary analyses with PSYSCAN/HARMONY imaging and EEG data to study language production at the circuit and physiological levels. This large language and clinical dataset will also be archived at NIH for further linguistic analyses by other investigators. Project Summary/Abstract Page 7