Thought disorder and social cognition in clinical risk states for schizophrenia

项目摘要 为了在精神病发作之前进行干预并预防发病率，最近的一个主要焦点是 精神分裂症的研究一直是对年轻人在假定的前驱病期的识别，因此 制定安全有效的干预措施，改变病程。在过去的十年里，研究在 哥伦比亚大学和其他地方对临床高危(CHR)患者进行了广泛的认知评估。 试图识别精神分裂症的核心缺陷的过程在精神病发作之前就很明显了。亚阈值思维 障碍和情绪识别受损已经成为严重的缺陷，早于而不是跟随， 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，包括遗传高危人群。此外，阈值下思维障碍和情绪识别缺陷 显著相关，表明在颞顶区有共同的神经底物。 本研究旨在确定亚阈值思维障碍的神经机制和 125名CHR受试者的情绪识别缺陷前瞻性地追踪了精神病的结果。CHR队列 丰富了精神分裂症的早期病例，因为20%-25%的人会发展成精神分裂症和相关的精神病 在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 否则有低水平的症状，较少的疾病慢性病和最低限度的抗精神病药物暴露。25 精神分裂症患者和50名健康志愿者作为比较对象。 阈值下思维障碍和情绪识别缺陷将通过行为， 生理水平和电路水平。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成分缺陷，并进行监听 在语言回路中引发可靠激活的任务。我们的语音自动机器学习方法 在人工智能的启发下，分析通过借鉴 一个庞大的文本语料库，类似于人类为他们读到或听到的东西赋予意义的方式。情感识别 将使用标准任务、带有动态面部刺激和参数面部变形的自然任务进行测量 区分感知和评价的任务；与任务相关的大胆活动将用于确定 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 消极主义范式。对于电路级，将使用休眠状态功能连接(RSFC)方法 分析不同疾病阶段的语言产生和情绪识别，以确定独特的和 在早期精神分裂症中，这些结构的共同底物。如果成功，这项提议将识别神经细胞 治疗认知障碍的目标。

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.