CRCNS: Bayesian Analysis of Neural-Behavioral Interactions in Mental Illness

描述(申请人提供)：精神分裂症是一种具有巨大公共卫生意义的疾病，影响大约1%的人口，并造成巨大的个人和经济损失。精神分裂症的治疗可能性仍然有限，至少部分原因是对其解剖学、神经、认知和遗传基础的了解不足。形态测量和功能神经成像技术表明，精神分裂症影响分布的大脑回路，但在如此丰富的数据源中识别重要回路是具有挑战性的。这项拟议项目的主要目标是确定精神分裂症患者发生改变的关键功能和解剖网络。为了实现这一目标，我们将开发新颖的、数据驱动的贝叶斯计算模型搜索技术，该技术可以自动定位丰富的多模式精神分裂症数据的重要和临床相关的网络描述。这些网络模型将告诉我们精神分裂症的特定神经和精神基础，将它们与外部临床评估和治疗结果相关联，并最终将指导未来对精神分裂症的研究和治疗过程。数据将从精神疾病和神经科学发现(Mind)研究所的临床成像联盟获得，他们正在进行一项史无前例的多地点、多模式的精神分裂症研究。这项研究将通过收集一套复杂的神经成像数据(包括结构MRI、功能磁共振成像、DTI、EEG和脑磁图数据)以及每个受试者的遗传、临床和精神变量来检查数百名精神分裂症患者和数量匹配的对照组。我们将使用动态贝叶斯网络(DBN)作为模型类，使用DBN结构搜索方法作为统计模型归纳方法。我们将把这些方法与严格的置信度测试、多假设评估的控制以及结果模型的专家评估结合起来。这种方法的优点是，它可以识别比线性技术更广泛的关系类别；得到的模型具有作为活动网络的直接解释；它允许将领域知识作为贝叶斯结构先验纳入；它可以自然扩展，以纳入外部变量或替代成像模式。我们的工作将对精神分裂症的神经网络基础及其与行为、遗传、临床特征和治疗结果的关系产生新的理解。这项工作将有助于改善精神分裂症的诊断和治疗，精神分裂症是一种影响数百万人的疾病。

DESCRIPTION (provided by applicant): Schizophrenia is an illness with enormous public health significance, affecting approximately 1% of the population and inflicting immense personal and economic cost. Treatment possibilities for schizophrenia are still limited, at least in part because of poor understanding of its anatomical, neural, cognitive, and genetic substrates. Morphometric and functional neuroimaging technologies suggest that schizophrenia affects distributed brain circuits, but identifying significant circuits in such rich data sources is challenging. The primary goal of this proposed project is to identify key functional and anatomical networks that are altered in schizophrenia. To accomplish this, we will develop novel, data-driven Bayesian computational model search techniques that can automatically locate significant and clinically relevant network descriptions of rich, multimodal schizophrenia data. These network models will inform us about the specific neural and mental substrates of schizophrenia, will correlate them with exogenous clinical assessments and treatment outcomes, and will ultimately guide both future investigations of schizophrenia and treatment courses. Data will be obtained from the Clinical Imaging Consortium of the Mental Illness and Neuroscience Discovery (MIND) Institute, who are performing an unprecedented multi-site, multi-modality study of schizophrenia. This study will examine hundreds of schizophrenic patients and a matched number of controls by collecting a sophisticated suite of neuroimaging data (including structural MRI, fMRI, DTI, EEG and MEG data) and genetic, clinical and psychiatric variables from each subject. We will use dynamic Bayesian networks (DBNs) as our model class and DBN structure search methods as the statistical model induction method. We will couple these methods to rigorous confidence testing, controls for multiple hypothesis evaluation, and expert evaluation of the resulting models. The advantages of this approach are that it can identify a wider class of relationships than can linear techniques; the resulting models have a straightforward interpretation as activity networks; it allows the incorporation of domain knowledge as Bayesian structural priors; and it can be naturally extended to incorporate exogenous variables or alternate imaging modalities. Our work will yield novel understanding of the neural network substrates of schizophrenia and their relationships to behavioral, genetic, and clinical features and treatment outcomes. This work will contribute toward improved diagnostics and therapies for schizophrenia, a disease that affects millions of people.