Deconstructing Psychoses Based on Patterns of Abnormal Brain Activity

描述(申请人提供)：主要的精神障碍，精神分裂症(SZ)和双相情感障碍(BD)，仍然是世界范围内导致残疾的主要原因之一，主要是因为目前的临床症状定义不足以用于治疗和预后，因为它们与潜在的病理生理学不够一致。这项建议使用研究领域标准(RDoC)框架，以定义和验证精神障碍的生物信息和临床相关的神经表型。具体地说，对SZ和BD患者的神经成像研究表明，与RDoC知觉、认知控制和面部情感处理相关的神经网络内的连接障碍是精神病病理生理学的核心。此外，这些领域的异常也被提出用来解释与精神障碍相关的临床症状和认知缺陷。因此，我们的总体假设是，在感知、认知控制和面部情感识别的领域通用神经网络内，有效连接的异常将检测到与精神病生物和临床相关的神经表型。我们提供的初步数据表明，SZ或BD患者可以由他们的神经网络结构定义的亚组来分类，并且这些神经表型可以映射到临床维度。我们的结果是基于工作记忆中参与感知和认知控制的域-一般网络的动态因果模型对有效连通性的估计。我们鉴定的神经表型在SZ和BD之间部分重叠，并与症状严重程度和临床病程相关。基于这一证据，这项建议的目的是：(A)扩大我们的初步结果，以便基于从知觉、认知控制和面部情感加工的领域一般网络的动态因果模型得出的有效连接参数来确定精神病的神经表型，并在两个独立的样本中测试它们的重复性；(B)确定所识别的神经表型与症状和病程的临床维度之间的关联；以及(C)确定它们对治疗反应的预测价值。该提案得益于动态因果模型的使用，该模型可以推断网络动态变化背后的大脑区域之间的因果交互作用，通过基于结果的重复性测试我们结果的有效性，以及通过评估已识别的神经表型的治疗相关性。本申请中建议的研究的成功完成将提高我们对精神病患者脑连接异常的临床和预后意义的理解，为治疗计划提供科学依据，并促进有针对性的病因学研究和新治疗方法的开发。

DESCRIPTION (provided by applicant): The main psychotic disorders, schizophrenia (SZ) and bipolar disorder (BD), continue to rank amongst the leading causes of disability worldwide largely because current clinical syndromal definitions are insufficient for treatment and prognosis because they are inadequately aligned with underlying pathophysiology. This proposal uses the Research Domains Criteria (RDoC) framework in order to define and validate biologically informed and clinically relevant neural phenotypes for psychotic disorders. Specifically, neuroimaging studies in patients with SZ and BD suggest that dysconnectivity within neural networks linked to the RDoC domains of perception, cognitive control and facial affect processing is central to the pathophysiology of psychosis. Further, abnormalities in these domains have been proposed to explain the clinical symptoms and cognitive deficits associated with psychotic disorders. Accordingly, our overall hypothesis is that abnormalities in effective connectivity, within domain-general neural networks of perception, cognitive control and facial affect identification, will detect biologically and clinically relevant neural phenotypes for psychosis. We present preliminary data that show that patients with SZ or BD can be classified into subgroups defined by their neural network architecture and that these neural phenotypes can be mapped onto clinical dimensions. Our results are based on estimates of effective connectivity from a dynamic causal model of the domain-general networks engaged in perception and cognitive control during working memory. The neural phenotypes we identified showed partial overlap between SZ and BD and were associated with symptom severity and clinical course. Based on this evidence, the aims of this proposal are (a) to expand our preliminary results in order to identify neural phenotypes for psychosis based on effective connectivity parameters derived from dynamic causal models of domain-general networks of perception, cognitive control and facial affect processing and test their reproducibility in two independent samples, (b) to define the association between the identified neural phenotypes and clinical dimensions of symptomatology and course, and (c) to determine their predictive value for treatment response. The proposal benefits from the use of dynamic causal modelling, which can infer causal interactions between brain regions underlying altered network dynamics, from testing the validity of our results based on their reproducibility and from assessing the therapeutic relevance of the identified neural phenotypes. Successful completion of the studies proposed in this application will improve our understanding of the clinical and prognostic significance of abnormal brain connectivity in psychosis, provide a scientific basis for therapeutic planning, and facilitate targeted etiological investigations and the development of new therapeutic approaches.