Advancing Computational Linguistic Biomarkers of Disorganized Speech in Psychosis

推进精神病言语混乱的计算语言生物标志物

• 批准号: 10686264
• 负责人: Sunny Xiaojing Tang
• 金额: $ 19.55万
• 依托单位: FEINSTEIN INSTITUTE FOR MEDICAL RESEARCH
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10686264
• 关键词: Address; Area; Biological; Biological Markers; Biology; Brain; Characteristics; Classification; Clinic; Clinical; Cognition; Cognitive; Complex; Computational Linguistics; Computer Models; Computing Methodologies; Data Set; Development; Diagnosis; Diagnostic; Dimensions; Ensure; Equity; Failure; Foundations; Future; Genetic Load; Genetic Risk; Goals; Impaired cognition; Individual; Knowledge; Label; Lead; Learning; Linguistics; Machine Learning; Measurement; Measures; Mentorship; Methods; Modeling; Monitor; Mood Disorders; Outcome; Participant; Persons; Phenotype; Physicians; Population; Poverty; Psychiatric therapeutic procedure; Psychoses; Relapse; Reproducibility; Research; Research Personnel; Resources; Sampling; Scientist; Severity of illness; Speech; Structure; Symptoms; Training; Work; analytical method; behavioral phenotyping; biomarker validation; clinical predictors; clinically relevant; cost effective; deep learning; deep learning model; early onset; experience; functional disability; improved; individual variation; innovation; interest; learning strategy; machine learning method; machine learning model; precision medicine; psychotic; schizophrenia spectrum disorder; symposium; synergism; treatment response

Project Summary Disorganization in psychosis has important clinical implications but is under-studied. Several lines of evidence suggest that disorganization reflects higher genetic loading and worse outcomes, and is sensitive to treatment response and relapse. We will use computational linguistics to measure disorganization in a sensitive, objective, efficient, reproducible, and repeatable way. Speech will be elicited with open-ended and structured tasks from 270 people with schizophrenia spectrum disorders and mood disorders with psychotic features, generating ~30,000 sentences across the sample. Findings will be validated in an existing independent dataset. We will measure psychosis symptoms, functioning, and cognition in both samples. Incoherence and inefficiency will be labeled for individual sentences and rated for the overall participant. Our Specific Aims are as follows: (1) Develop deep-learning methods to classify sentence-level disorganization; (2) Integrate across computational features to predict participant-level disorganization; (3) Predict key participant characteristics using linguistic features. An integrated training plan will combine hands-on experience through these research aims with mentorship, coursework, self-study, seminars, and conferences to achieve the following Training Goals: (1) Proficiency in computational linguistics and machine learning methods, (2) Expertise in validating clinically-relevant biomarkers, and (3) Development as a physician-scientist. This work provides the foundation needed to develop cutting-edge computational methods into biomarkers of disorganization and key psychosis outcomes. We lay the groundwork for future studies that leverage these features as early markers of treatment response and relapse, and that use these features to connect behavioral phenotypes with underlying biology. The proposed project builds on my existing expertise to develop the technical proficiency and expertise in psychosis biomarker research I need to lead new discoveries in this area as an independent physician- investigator.

项目摘要 精神病的紊乱具有重要的临床意义，但研究不足。若干条证据 这表明，组织解体反映了较高的遗传负荷和较差的结果，并对治疗敏感 反应和复发。我们将使用计算语言学来衡量一个敏感的， 客观、高效、可复制、可重复的方式。演讲将以开放式和结构化的方式进行 来自270名精神分裂症谱系障碍和具有精神病特征的心境障碍患者的任务， 在整个样本中生成约30，000个句子。调查结果将在现有的独立 数据集。我们将测量两个样本的精神病症状、功能和认知。不一致和 效率低下将被标记为单个句子，并对整体参与者进行评级。我们的具体目标是 具体如下：（1）开发深度学习方法来分类层次混乱;（2）整合跨 预测参与者层面混乱的计算特征;（3）预测关键参与者特征 使用语言特征。综合培训计划将通过这些研究结合联合收割机的实际经验 旨在通过指导、课程、自学、研讨会和会议来实现以下培训 目标：（1）熟练掌握计算语言学和机器学习方法，（2）验证方面的专业知识 临床相关的生物标志物，和（3）作为一个医生，科学家的发展。这项工作提供了基础 需要将尖端的计算方法开发成混乱和关键精神病的生物标志物 结果。我们为将来利用这些特征作为早期治疗标志物的研究奠定了基础 反应和复发，并使用这些特征将行为表型与潜在的生物学联系起来。 建议的项目建立在我现有的专业知识，以发展技术熟练程度和专业知识， 精神病生物标志物研究作为一名独立的医生，我需要在这一领域领导新的发现- 调查员

项目成果

Characterizing and detecting delirium with clinical and computational measures of speech and language disturbance.

• DOI: 10.1503/jpn.230026
• 发表时间: 2023-07
• 期刊: JOURNAL OF PSYCHIATRY & NEUROSCIENCE
• 影响因子: 4.3
• 作者: Tang, Sunny X. X.;Cong, Yan;Mercep, Gwenyth;Bhatti, Mutahira;Serpe, Grace;Gromova, Valeria;Berretta, Sarah;John, Majnu;Liberman, Mark Y.;Sinvani, Liron
• 通讯作者: Sinvani, Liron