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.

项目概要 为了在精神病发作前进行干预并预防发病，最近的一个主要焦点是 精神分裂症研究是在假定的前驱期对年轻人进行识别，因此 制定安全有效的干预措施来改变病程。在过去的十年里，学习于 哥伦比亚大学和其他地方对临床高危 (CHR) 个体进行了广泛的认知评估 试图识别精神病发作前明显的精神分裂症核心缺陷的过程。阈下思维 障碍和情绪识别受损已经成为严重的缺陷，早于而不是随后出现， 精神病发作，因此可能是精神分裂症倾向的指标，与其他风险的研究一致 队列，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，表明颞顶区域具有共享的神经基质。 本研究旨在确定阈下思维障碍背后的神经机制 对 125 名 CHR 个体的情绪识别缺陷进行了前瞻性随访，以了解精神病结果。 CHR 队列 富含早期精神分裂症病例，20-25% 患有精神分裂症和相关精神病 1-2年内出现疾病。 CHR 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，症状较轻，慢性病较少，抗精神病药物的接触量也最少。 25 包括精神分裂症患者和 50 名健康志愿者进行比较。 阈下思维障碍和情绪识别缺陷将在行为、行为等方面进行研究。 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，用于识别语义和语法的构成障碍，以及听力 引起语言回路可靠激活的任务。我们的自动化机器学习语音方法 在人工智能的指导下进行分析，通过借鉴单词和短语的语义来推导出单词和短语的语义。 大型文本语料库，类似于人类如何为他们读到或听到的内容赋予含义。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化面部变形来测量 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 消极范式。静息态功能连接（RSFC）方法将用于电路级 分析疾病各个阶段的语言产生和情绪识别，以确定独特的和 早期精神分裂症中这些结构的共同底物。如果成功，该提案将识别神经网络 认知障碍的补救目标。