Thought disorder and social cognition in clinical risk states for schizophrenia

精神分裂症临床危险状态下的思维障碍和社会认知

• 批准号: 9176279
• 负责人: CHERYL MARY CORCORAN
• 金额: $ 57.5万
• 依托单位: NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-15 至 2021-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9176279
• 关键词: Adolescence; Age; Antipsychotic Agents; Artificial Intelligence; Auditory; Behavior; Behavioral; Biological Assay; Brain; Characteristics; Clinical; Cognitive; Cognitive deficits; Collaborations; Data; Deltastab; Development; Disease; Electroencephalography; Electrophysiology (science); Emotions; Event-Related Potentials; Exposure to; Face; Frequencies; Functional Magnetic Resonance Imaging; Genetic; Goals; Hearing; Human; Impaired cognition; Impairment; Individual; Inferior; Language; Language Disorders; Link; Logistic Regressions; Logistics; Machine Learning; Measures; Mentored Patient-Oriented Research Career Development Award; Methods; Modeling; Morbidity - disease rate; Natural Language Processing; Neurocognitive; Neurodevelopmental Disorder; Neuronal Dysfunction; Outcome; Parietal; Pattern; Perception; Phase; Phenotype; Physiological; Predictive Value; Prevalence; Prevention strategy; Preventive Intervention; Process; Production; Psychotic Disorders; Public Health; Reading; Research; Rest; Risk; Schizophrenia; Semantics; Sensory; Sensory Process; Severities; Social Functioning; Speech; Staging; Stimulus; Symptoms; Testing; Text; Visual; Voice; Withdrawal; Work; career; clinical risk; cognitive process; cohort; connectome; deviant; effective intervention; healthy volunteer; high risk; indexing; insight; language impairment; language processing; natural language; neural patterning; neuroimaging; neuromechanism; novel; phrases; prevent; prognostic tool; relating to nervous system; remediation; social; social cognition; syntax; visual process; visual processing; young adult

Project Summary In an effort to intervene before psychosis onset and prevent morbidity, a major recent focus in schizophrenia research has been the identification of young people during a putative prodromal period, so as to develop safe and effective interventions to modify disease course. Over the past decade, studies at Columbia and elsewhere have evaluated clinical high-risk (CHR) individuals across a wide range of cognitive processes to try to identify core deficits of schizophrenia evident before psychosis onset. Subthreshold thought disorder and impaired emotion recognition have emerged as profound deficits that predate, rather than follow, psychosis onset and thus may be indicators of schizophrenia liability, consistent with studies in other risk cohorts, including genetic high risk. Further, subthreshold thought disorder and emotion recognition deficit are significantly correlated, suggesting shared neural substrates in temporoparietal regions. This study aims to identify the neural mechanisms that underlie subthreshold thought disorder and emotion recognition deficit in 125 CHR individuals followed prospectively for psychosis outcome. CHR cohorts are enriched with early cases of schizophrenia, as 20-25% develop schizophrenia and related psychotic disorders within 1-2 years. CHR cohorts may be optimal for studying core characteristics of illness as they otherwise have low-level symptoms, less illness chronicity and minimum exposure to antipsychotics. 25 individuals with schizophrenia and 50 healthy volunteers are included for comparison. Subthreshold thought disorder and emotion recognition deficits will be studied across behavioral, physiological and circuit levels. For thought disorder, we will use automated speech analysis approaches developed in collaboration with IBM to identify constituent impairments in semantics and syntax, and a listening task that elicits reliable activation in language circuits. Our automated machine-learning approach to speech analysis, informed by artificial intelligence, derives the semantic meaning of words and phrases by drawing on a large corpus of text, similar to how humans assign meaning to what they read or hear. Emotion recognition will be measured using standard tasks, naturalistic tasks with dynamic face stimuli and parametric face morph tasks that discriminate between perception and appraisal; task-related BOLD activity will be used to identify relevant circuits. Associations with basic sensory impairment will be tested, including novel auditory mismatch negativity paradigms. Resting state functional connectivity (RSFC) methods will be used for circuit-level analysis of language production and emotion recognition across stages of illness, to determine unique and shared substrates of these constructs in early schizophrenia. If successful, this proposal will identify neural targets for remediation of cognitive impairments.

项目摘要 为了在精神病发作之前进行干预并预防发病率，最近重点是 精神分裂症研究一直是在假定的前驱时期对年轻人的识别，因此 制定安全有效的干预措施以修改疾病病程。在过去的十年中，在 哥伦比亚和其他地方已经评估了广泛认知范围内的临床高风险（CHR）个体 试图识别精神分裂症的核心缺陷的过程在精神病开始前明显。亚阈值思想 情绪识别的混乱和情绪受损已出现为早期的赤字，而不是遵循，而不是遵循 精神病发作，因此可能是精神分裂症责任的指标，与其他风险的研究一致 队列，包括遗传高风险。此外，亚阈值思想障碍和情绪识别赤字是 显着相关，表明在颞叶区域中共有神经底物。 这项研究旨在确定基于亚阈值思想障碍和 125个CHR个人的情绪识别赤字前瞻性地进行了精神病结果。 CHR群体 精神分裂症早期的病例丰富，因为20-25％的精神分裂症和相关的精神病患者 1 - 2年内的疾病。 CHR群体可能是研究疾病的核心特征的最佳选择 否则，患有低水平的症状，较小的疾病慢性和最小暴露于抗精神病药。 25 包括精神分裂症和50名健康志愿者的人进行比较。 阈值思想障碍和情绪识别缺陷将在行为中进行研究， 生理和电路水平。对于思想障碍，我们将使用自动语音分析方法 与IBM合作开发，以识别语义和语法中的构成障碍，并聆听 在语言电路中引起可靠激活的任务。我们自动化的机器学习方法 由人工智能告知的分析，通过吸引单词和短语的语义含义 大量的文本类似于人类为他们阅读或听到的内容分配意义的方式。情绪识别 将使用标准任务，具有动态面部刺激的自然任务和参数性面部变形来测量 区分感知和评估的任务；与任务相关的粗体活动将用于识别 相关电路。将测试与基本感觉障碍的关联，包括新的听觉不匹配 消极范式。静止状态功能连接（RSFC）方法将用于电路级别 分析跨疾病阶段的语言产生和情感识别，以确定独特的和 这些构造的共同基质在早期精神分裂症中。如果成功，该建议将确定神经 修复认知障碍的目标。