The Social Brain in Schizophrenia and Autism Spectrum Disorders

描述（由申请人提供）：虽然传统上认为精神分裂症（SZ）和自闭症谱系障碍（ASD）是基于临床症状和病程的2种独立实体，但它们是异质性神经发育障碍，具有社会功能的核心缺陷。然而，关于这些诊断中与这些异常相关的神经病理学或它们之间重叠的程度和性质知之甚少。本研究的目的是调查3个社会认知过程，心理理论（ToM），社会判断，共情，使用多模态方法结合功能磁共振成像（fMRI）和事件相关电位（ERP）的数据，与ASD和SZ的疾病分类学诊断及其临床和功能症状。80名SZ和80名高功能ASD患者，年龄18-30岁，以及匹配的健康对照（HC）将完成该方案。参与者的社会功能将通过自我报告问卷、计算机化测试、角色扮演和观察工具来表征。在神经元方面，ToM将通过2人交互式fMRI Domino任务进行评估，通过fMRI Trustworthiness任务进行社会判断，并通过ERP Empathy to Pain任务进行移情。我们将（1）基于社会功能和社会认知相关神经缺陷的分类诊断映射来识别组间差异。我们将进一步将患者的社会功能和症状与特定脑回路的异常激活联系起来;（2）基于与社会认知过程相关的综合多模态神经异常来识别患者亚组，而不依赖于基于脑电的诊断类别。我们将首先使用一种创新的数据驱动方法，联合独立成分分析（jICA），融合来自所有成像任务的数据，并根据不同社交任务期间的大脑活动，识别上30%受损患者（SZ或ASD）和30%与HC最相似的患者。将进一步评价这些亚组的临床特征。然后，jICA最具鉴别力的单个或集成分量将被输入到聚类伊卡（cICA）中，以独立于正式的临床诊断来识别自然患者亚组。我们假设，虽然SZ和ASD组与HC组相比将显示出异常的社会功能和相关的大脑活动，但一些发现将重叠，而另一些将是诊断特异性的。重要的是，我们预计基于非诊断的分析（目的2）将证明基于大脑的分类程序能够识别基于神经生物学的患者亚组，这些亚组在临床和行为特征方面比当前基于症状的类别更加同质。然后可以评价这些亚组的差异治疗反应和可能的病因，如遗传风险变量。如果成功的话，目前的研究将支持ASD和SZ的临床和研究范式的新转变，特别是精神疾病，朝着使用维度生物学（与分类行为）措施来识别有意义的患者群体。这反过来又将促进这些疾病的病因学和治疗研究。

DESCRIPTION (provided by applicant): Although traditionally considered 2 separate entities based on clinical symptoms and course, schizophrenia (SZ) and autism spectrum disorders (ASD) are heterogeneous, neurodevelopmental disorders with core deficits in social functions. Little is known, however, about the neuropathology associated with these abnormalities in these diagnoses or about the degree and nature of overlap between them. The aim of the current study is to investigate the neural correlates of 3 social cognitive processes, Theory of Mind (ToM), social judgment, and empathy, using a multimodal approach incorporating functional MRI (fMRI) and event related potential (ERP) data, in relation to the nosological diagnoses of ASD and SZ and their clinical and functional symptoms. Eighty SZ and 80 high-functioning ASD patients, ages 18-30, as well as matched healthy controls (HC) will complete the protocol. Participants' social functions will be characterized with self-report questionnaires, computerized tests, role-play and observational tools. Neuronally, ToM will be evaluated with a 2-player interactive fMRI Domino task, social judgment with an fMRI Trustworthiness task and empathy with an ERP Empathy to Pain task. We will (1) identify group differences based on categorical diagnostic mapping in social functions and social cognition-related neural deficits. We will further relate patients' social functions and symptoms to abnormal activation of specific brain circuits; (2) Identify patient sub-groups based on integrated multi-modal neural abnormalities related to social cognitive processes, independent of symptom-based diagnostic categories. We will first use an innovative data-driven method, joint independent component analysis (jICA), to fuse the data from all imaging tasks and to identify the upper 30% impaired patients (either SZ or ASD) and 30% of patients most similar to HC, based on brain activity during the different social tasks. The clinical characteristics of these subgroups will be further evaluated. Then the jICA most discriminative single or integrated components will be entered into a cluster ICA (cICA) to identify natural patient subgroups independently of formal clinical diagnosis. We hypothesize that while both SZ and ASD groups will show abnormal social functions and related brain activity compared to HC, some findings will overlap while others will be diagnosis specific. Importantly, we anticipate that the non-diagnostic based analyses (aim 2) will demonstrate the ability of brain based classifying procedures to identify neurobiologically-based patient subgroups that are more homogeneous than current symptom based categories with respect to clinical and behavioral characteristics. Such subgroups can then be evaluated with regard to differential treatment response and possible etiologies, such as genetic risk variables. If successful, the current study will support the emerging shift in clinical and research paradigms for ASD and SZ specifically and more generally for psychiatric illnesses, toward using dimensional biological (vs. categorical behavioral) measures to identify meaningful patient groups. This in turn will advance etiologic and treatment research for these illnesses.