Applying Computational Linguistics to Fundamental Components of Schizophrenia

将计算语言学应用于精神分裂症的基本组成部分

• 批准号: 8512143
• 负责人: MICHAEL T COMPTON
• 金额: $ 24.93万
• 依托单位: GEORGE WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-04-01 至 2014-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8512143
• 关键词: Acoustics; Address; Affect; Alcohol or Other Drugs use; Alzheimer' s Disease; Area; Artificial Intelligence; Automation; Back; Behavioral; Biological Markers; Chronic; Clinical; Clinical Assessment Tool; Clinical Research; Clinical assessments; Cognition; Cognitive; Collaborations; Comorbidity; Complement; Complex; Computer Analysis; Computer software; Computers; Data; Development; Discriminant Analysis; Disease; Disease remission; Exhibits; Exploratory/Developmental Grant; Future; Gender; Goals; Human; Impairment; Knowledge; Language; Linear Regressions; Linguistics; Link; Longevity; Measurable; Measures; Mental disorders; Methodology; Methods; Modeling; Monitor; Movement; National Institute of Mental Health; Neurobiology; Neurocognition; Neurocognitive; Neurocognitive Deficit; Oral cavity; Parkinson Disease; Patients; Pharmaceutical Preparations; Phonetics; Positioning Attribute; Predictive Value; Psycholinguistics; Public Health; Reading; Research; Research Personnel; Risk; SCAP2 gene; Sampling; Schizophrenia; Semantics; Sensitivity and Specificity; Sound Spectrography; Spectrum Analysis; Speech; Staging; Strategic Planning; Substance Use Disorder; Symptoms; Techniques; Thinking; Tongue; Translating; Universities; Variant; Voice; Washington; Work; Youth; base; biosignature; clinical application; computerized; density; design; disability; health disparity; high risk; indexing; innovation; lexical; neglect; predictive modeling; psychosocial; public health relevance; routine practice; social stigma; software development; syntax; tool; treatment response

DESCRIPTION (provided by applicant): Schizophrenia is a uniquely human disorder with specific effects on the uniquely human capacity of language. Indeed, the gross and subtle language abnormalities of schizophrenia can be seen as fundamental illness components, perhaps even as part of a "biosignature." Bringing modern linguistics knowledge and tools to this disorder is a promising approach. We have formed a unique, inter-disciplinary collaboration (Dr. Compton, a schizophrenia researcher; Dr. Covington, a computational linguist; Dr. Lunden, a linguist specializing in phonetics; Dr. Cleary, a statistician; and Dr. Blanchard, an expert in measuring negative symptoms) to study some of the most perplexing and disabling facets of schizophrenia, the language/speech abnormalities linked closely to disorganization and negative symptoms. We will analyze speech abnormalities in patients with schizophrenia and unaffected controls. Rather than examining a single linguistic parameter, we will assess speech in "syntactic," "semantic," "pragmatic," and "phonetic" domains of linguistics. We will introduce cutting- edge innovation to this area of study by assessing these indices using psycholinguistics software developed by Dr. Covington's group so that our ratings of speech abnormalities will be highly objective and ultra-reliable. Our long-term goal is to develop multivariable models, and new methods for clinical and research settings, based on computational linguistic indices with inherent reliability from automation and proven validity. In this exploratory/developmental study, we will collect detailed symptom ratings from 100 schizophrenia patients, as well as audio-recorded speech samples and neurocognition scores from these patients and 100 controls. This study involves early/conceptual stages of new tools and models that could have a major translational impact. We strive to acquire new knowledge and then put it into action. For example, our new methods could translate into advanced clinical applications (e.g., highly reliable, voice-based monitoring of symptom progression or remission). Furthermore, our new models and methods could be a first step toward promising predictive models (e.g., combinations of factors useful in risk prediction among at-risk youth). These objectives are highly aligned with the NIMH Strategic Plan. Our 4 aims are to: (1) examine syntactic, semantic, and pragmatic linguistic parameters using computer analysis of speech, and assess their relation to disorganized symptoms; (2) examine phonetic linguistic parameters using computerized Fourier spectrum analysis of speech, and assess their relation to negative symptoms; (3) determine the combination of psycholinguistic parameters that best predicts patient versus control status; and (4) determine the combination of psycholinguistic parameters that best predicts disorganization scores and negative symptom scores among patients. Given the rich data we will collect, we will also be able to covary the effects of medication and substance use; examine variation in findings based on neutral v. emotionally laden content and spontaneous v. read speech; assess variance in linguistic measures attributable to cognitive domains; and compare results in first-episode and chronic patients.

描述（由申请人提供）：精神分裂症是一种人类特有的疾病，对人类特有的语言能力有特定的影响。事实上，精神分裂症的严重和微妙的语言异常可以被视为基本疾病的组成部分，甚至可能被视为“生物特征”的一部分。将现代语言学知识和工具引入这种疾病是一种有前途的方法。我们已经形成了独特的跨学科合作（康普顿博士，精神分裂症研究员；科文顿博士，计算语言学家；伦登博士，语音学语言学家；克利里博士，统计学家；布兰查德博士，测量阴性症状的专家）研究精神分裂症的一些最令人困惑和最无力的方面，语言/言语异常与紊乱和阴性症状。我们将分析精神分裂症患者和未受影响的对照组的言语异常。我们将在语言学的“句法”、“语义”、“语用”和“语音”领域评估语音，而不是检查单个语言参数。我们将通过使用 Covington 博士小组开发的心理语言学软件评估这些指标，将尖端创新引入这一研究领域，以便我们对言语异常的评级将高度客观和极其可靠。 我们的长期目标是基于具有自动化固有可靠性和经过验证的有效性的计算语言指数，开发用于临床和研究环境的多变量模型和新方法。在这项探索性/发展性研究中，我们将收集 100 名精神分裂症患者的详细症状评分，以及这些患者和 100 名对照者的录音语音样本和神经认知评分。这项研究涉及可能产生重大转化影响的新工具和模型的早期/概念阶段。我们努力获取新知识，然后将其付诸行动。例如，我们的新方法可以转化为先进的临床应用（例如，高度可靠、基于语音的症状进展或缓解监测）。此外，我们的新模型和方法可能是迈向有前景的预测模型的第一步（例如，可用于高危青少年风险预测的因素组合）。这些目标与 NIMH 战略计划高度一致。我们的 4 个目标是：（1）使用计算机语音分析来检查句法、语义和语用语言参数，并评估它们与混乱症状的关系； (2) 使用计算机语音傅里叶谱分析检查语音语言参数，并评估它们与阴性症状的关系； (3) 确定最能预测患者与对照状态的心理语言参数组合； (4)确定最能预测患者紊乱评分和阴性症状评分的心理语言学参数组合。鉴于我们将收集的丰富数据，我们还能够共同改变药物和药物使用的影响；检查基于中性与情感丰富的内容和自发与阅读演讲的结果的差异；评估认知领域导致的语言测量差异；并比较首发患者和慢性患者的结果。