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

使用 RDoC 方法理解思维障碍：基于语言语料库的方法

• 批准号: 9903990
• 负责人: CHERYL MARY CORCORAN
• 金额: $ 8.92万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-06-05 至 2021-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9903990
• 关键词: Address; Archives; Artificial Intelligence; Classification; Clinical; Collaborations; Computers; Data; Data Set; Dimensions; Disease; Electroencephalography; Grain; Graph; Human; Image; Individual; International; Language; Length; Linguistics; Manuals; Measures; Methods; Mind; Outcome; Patients; Physiological; Poverty; Production; Psychotic Disorders; Research Domain Criteria; Research Personnel; Resources; Risk; Schizophrenia; Scientist; Semantics; Site; Speech; Symptoms; Text; Thinking; United States National Institutes of Health; Work; analytical method; base; cohort; healthy volunteer; indexing; novel; phrases; response; secondary analysis; syntax

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

联系PD/PI：Corcoran，Cheryl M 精神病性障碍的思维障碍及其风险状态通常是通过临床评估的 评价表，偶尔还有劳动密集型的语言分析手工方法。相反，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，该方法由人工智能提供信息 智慧。该方法通过利用大量的语料库来获得单词和短语的语义 语篇，类似于人类赋予语言意义的方式，并导致从 一句接一句。它还通过词性标注和词性分析来评估句法复杂性 语音图。这些分析产生了语音语义和句法的细粒度索引，可能会 准确捕捉思维障碍。 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CHR队列中确定了一个对精神病发病具有高准确性的分类器，其中包括 降低了短语之间的语义连贯性，降低了句法复杂性，包括 缩短短语长度，减少限定代词(“What”、“What”、“That”)的使用。这些功能 与前驱症状相关，但在分类准确性上优于它们。他们还歧视 精神分裂症来自正常的语言。我们在第二个小范围内进一步交叉验证了这种自动化方法 CHR队列，标识对两个队列中的精神病结果进行分类的语义/句法分类器，以及 新发精神病患者言语与正常言语的辨别。 这些自动化的语言分析方法前景看好，但到目前为止，它们的使用一直是 仅限于几项旨在区分精神分裂症与正常人和我们自己的小型研究 工作，预测精神病。在我们对语言机制的理解上存在着严重的差距 思维障碍。为了解决这一差距，为了响应PAR-16-136，我们建议使用RDoC结构 语言生成及其基于语料库的分析范式，从维度上研究思维障碍 在一个由150名假定健康的志愿者、150名慢性阻塞性肺病患者和150名 最近发病的精神病患者。我们期望潜在语义分析将产生语义的度量 指示积极思维障碍(切线、脱轨)的连贯，而词性(POS) 标记/语音图将产生句法复杂性的度量，以索引负面思维障碍 (具体性，内容贫乏)。 这个大型语言数据集将从两个PSYSCAN/Harmonity站点获得，这样这些站点 语言数据将用于二次分析，PSYSCAN/Harmonity成像和EEG数据将用于 在回路和生理水平上研究语言的产生。这一庞大的语言和临床数据集将 也被存档在NIH，供其他调查人员进一步进行语言分析。 项目摘要/摘要第7页

项目成果

Subjective experience and meaning of delusions in psychosis: a systematic review and qualitative evidence synthesis.

精神病妄想的主观经验和意义：系统评价和定性证据综合。

• DOI: 10.1016/s2215-0366(22)00104-3
• 发表时间: 2022
• 期刊: The lancet. Psychiatry
• 作者: Ritunnano,Rosa;Kleinman,Joshua;WhyteOshodi,Danniella;Michail,Maria;Nelson,Barnaby;Humpston,ClaraS;Broome,MatthewR
• 通讯作者: Broome,MatthewR