Deconstructing Psychoses Based on Patterns of Abnormal Brain Activity

根据异常大脑活动模式解构精神病

• 批准号: 9119103
• 负责人: SOPHIA FRANGOU
• 金额: $ 55.94万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-05 至 2018-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9119103
• 关键词: Acute; Affect; Age of Onset; Antipsychotic Agents; Biological Neural Networks; Bipolar Disorder; Brain; Brain region; Brief Psychiatric Rating Scale; Clinical; Cluster Analysis; Cognitive deficits; Data; Development; Dimensions; Disease; Disease remission; Ensure; Face; Functional Magnetic Resonance Imaging; Functional disorder; Goals; Health; Institutes; Investigation; Life; Link; Literature; Maps; Multivariate Analysis; Neural Network Simulation; Onset of illness; Outpatients; Pathway interactions; Patients; Pattern; Perception; Pharmaceutical Preparations; Phenotype; Predictive Value; Procedures; Process; Psychopathology; Psychotic Disorders; Regression Analysis; Reproducibility; Research; Rest; Role; Sampling; Scanning; Schizophrenia; Severities; Severity of illness; Short-Term Memory; Subgroup; Survival Analysis; Symptoms; Testing; Therapeutic; Therapeutic Trials; Universities; base; causal model; clinical phenotype; clinically relevant; clinically significant; cognitive control; density; disability; improved; insight; medical schools; network architecture; network models; neuroimaging; neuropsychiatry; novel strategies; novel therapeutic intervention; outcome forecast; phenomenological models; prognostic significance; relating to nervous system; response; symptomatology; treatment response

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.

描述（由申请人提供）：主要的精神病性障碍，精神分裂症（SZ）和双相情感障碍（BD），仍然是全球残疾的主要原因之一，这主要是因为目前的临床综合征定义不足以用于治疗和预后，因为它们与基础病理生理学不充分一致。该提案使用研究领域标准（RDoC）框架，以定义和验证精神障碍的生物学信息和临床相关的神经表型。具体而言，SZ和BD患者的神经影像学研究表明，与感知、认知控制和面部情感处理的RDoC域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，这些领域的异常已被提出来解释与精神病性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域一般神经网络内，有效连接的异常将检测精神病的生物学和临床相关神经表型。我们目前的初步数据表明，SZ或BD患者可以分为由其神经网络结构定义的亚组，这些神经表型可以映射到临床维度。我们的研究结果是基于有效连接的估计，从一个动态的因果模型的域一般网络在工作记忆中的感知和认知控制。我们确定的神经表型显示SZ和BD之间有部分重叠，并与症状严重程度和临床病程相关。基于这一证据，本提案的目的是（a）扩展我们的初步结果，以便基于来自感知、认知控制和面部情感处理的一般域网络的动态因果模型的有效连接参数来识别精神病的神经表型，并在两个独立样本中测试其可重复性，（B）确定所鉴定的神经表型与神经病学和病程的临床维度之间的关联，和（c）确定它们对治疗反应的预测值。该提案受益于使用动态因果建模，可以推断改变网络动态的大脑区域之间的因果相互作用，基于其可重复性测试我们结果的有效性，并评估所识别的神经表型的治疗相关性。本申请中提出的研究的成功完成将提高我们对精神病大脑连接异常的临床和预后意义的了解，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。