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: 9331744
• 负责人: CHERYL MARY CORCORAN
• 金额: $ 19.37万
• 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 2017-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9331744
• 关键词: Adolescence; Age; Antipsychotic Agents; Artificial Intelligence; Auditory; Behavior; Behavioral; Biological Assay; Brain; Characteristics; Chronic; Clinical; Cognitive; Cognitive deficits; Cognitive remediation; Collaborations; Data; Deltastab; Development; Disease; EEG-based imaging; Electrophysiology (science); Emotional; 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; 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; Production; Prospective Studies; Psychotic Disorders; Public Health; Research; Rest; Risk; Schizophrenia; Semantics; Sensory; Severities; Social Functioning; Speech; Stimulus; Symptoms; Testing; Text; Visual; Voice; Withdrawal; Work; auditory processing; 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; predictive modeling; prevent; prognostic tool; prognostic value; prospective; relating to nervous system; remediation; social; social cognition; syntax; 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 为了在精神病发作前进行干预并预防发病， 精神分裂症的研究一直是确定年轻人在一个假定的前驱期，所以， 制定安全有效的干预措施，以改变疾病进程。在过去的十年里， 哥伦比亚和其他地方已经评估了临床高风险（风险）的个人在广泛的认知， 试图识别精神分裂症发病前明显的核心缺陷的过程。阈下思维 情绪识别障碍和受损的情绪识别已经成为一种严重的缺陷，这种缺陷早于，而不是跟随， 精神病发作，因此可能是精神分裂症易感性的指标，与其他风险研究一致。 群体，包括遗传高风险。此外，阈下思维障碍和情绪识别缺陷是 显着相关，这表明共享的神经基板在颞顶区。 本研究旨在确定阈下思维障碍的神经机制， 对125名精神病患者的情绪识别缺陷进行前瞻性随访。CHR队列 精神分裂症的早期病例丰富，因为20-25%的人发展为精神分裂症和相关的精神病 1-2年内的疾病。队列可能是研究疾病核心特征的最佳选择，因为它们 在其他方面，症状轻微，慢性病较少，服用抗精神病药物的机会最少。25 包括精神分裂症患者和50名健康志愿者用于比较。 阈下思维障碍和情绪识别缺陷将在行为， 生理和电路水平。对于思维障碍，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组成障碍，以及听力 在语言回路中激发可靠激活的任务。我们的语音自动化机器学习方法 人工智能提供的分析，通过利用 一个大型的文本语料库，类似于人类如何为他们所读或所听的内容赋予意义。情感识别 将使用标准任务、具有动态面部刺激的自然主义任务和参数面部变形来测量 区分感知和评价的任务;与任务相关的BOLD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 消极的范式。电路级将使用静止状态功能连接性（RSFC）方法 分析不同疾病阶段的语言产生和情感识别，以确定独特的， 在早期精神分裂症中这些结构的共同底物。如果成功的话，这个建议将识别神经 治疗认知障碍的目标。