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）：精神分裂症是一种独特的人类障碍，对人类独特的语言能力有特殊的影响。的确，精神分裂症的严重和微妙的语言异常可以看作是基本疾病成分，甚至可能是“生物签名”的一部分。将现代语言学知识和工具带入这种疾病是一种有前途的方法。 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混乱和负面症状。我们将分析精神分裂症患者和未受影响的对照患者的语音异常。我们将在“句法”，“语义”，“务实”和“语音”语言学领域中评估语音，而不是检查单个语言参数。我们将通过使用由Covington博士组开发的心理语言学软件评估这些指数来对这些研究领域进行削减创新，以便我们的语音异常评级将是高度客观且非常可靠的。 我们的长期目标是基于计算语言指数，开发多变量模型以及用于临床和研究环境的新方法，其自动化和可靠的有效性具有固有的可靠性。在这项探索性/发育研究中，我们将从100名精神分裂症患者中收集详细的症状等级，以及这些患者和100个对照组的音频录制的语音样本和神经认知评分。这项研究涉及可能产生重大翻译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，然后将其付诸实践。例如，我们的新方法可以转化为先进的临床应用（例如，高度可靠的，基于语音的症状进展或缓解）。此外，我们的新模型和方法可能是迈向有前途的预测模型的第一步（例如，对高危青年的风险预测有用的因素组合）。这些目标与NIMH战略计划高度一致。我们的4个目标是：（1）使用语音计算机分析检查句法，语义和务实的语言参数，并评估它们与混乱的症状的关系； （2）使用计算机化的傅立叶频谱分析来检查语音语言参数，并评估其与负面症状的关系； （3）确定最能预测患者与控制状态的心理语言参数的组合； （4）确定心理语言参数的组合，最能预测患者之间的混乱得分和负面症状评分。鉴于我们将收集的丰富数据，我们还将能够将药物和药物使用的影响归因；检查基于中性诉情感上载有内容和自发性v。阅读语音的发现的变化；评估归因于认知领域的语言措施的差异；并比较第一期和慢性患者的结果。