Refining a Taxonomy for Externalizing Psychopatholgy Using Genomic Imaging

描述(由申请人提供)：在内化/外化精神病理学的框架内表现出的一系列广泛的心理特征和症状是几个精神病学概念的共同特征。这可能暗示了一些表面上截然不同的精神疾病的类似的病理生理学病因。随着生物科学能力的进步，精神健康研究进展的一个挥之不去的障碍是所呼吁的精神病学结果变量的广泛变化，经常重叠，有时是多余的，这在很大程度上是基于过时的，描述性的精神病理学和人格特征分类，最初没有现代神经科学技术的帮助(Insel等人，2010)。虽然我们越来越认识到这些结果变量部分依赖于遗传学，但遗传、环境和生理学之间的复杂关系使得对基因和可观察到的精神病学结果之间的直接关系的估计充其量是有限的。澄清这些关系的一种方法是定义一组中间的生理特征(内表型)，这些特征与遗传影响的关系比更广泛描述的人格特征和行为风格更接近。最终，在现代生物科学的指导下，这些内表型可能被用作更客观的分类和诊断精神病学结果变量的标准。在这里，我们将这些观点应用于精神病理学的内化/外化维度，包括冲动、反社会、药物滥用、抑郁和焦虑。利用世界上现有最大的法医数据集，其中包括遗传、结构和功能神经成像、生理、行为和精神病学措施，该项目的主要目标将是提供数据，帮助确定更具生物学意义的精神病学结果分类。这将通过两种方式检查这些关系来实现。首先，我们检查了候选基因多态之间的标准基因组成像关联，这些候选基因多态之前已经与许多这些维度结构相关联。具体地说，5HTT、MAOA、DAT、DRD2和DRD4在这篇文献中是突出的特征，并影响负责调节情绪和行为的单胺能信号通路。其次，我们将应用更不可知的、数据驱动的方法来定义大规模基因阵列和神经成像数据之间的相似关系。并行独立分量分析是一种在复杂、噪声系统中识别唯一的方差来源的技术，而不需要用关于这些系统的特征的先验假设来不必要地约束分析。它是比较我们现有的关于基因贡献者的概念的理想工具 精神病理学结果与通过建立中间神经成像内表型定义的关系。最终，这将有助于制定更具体和有效的干预战略，旨在减少这些相关精神疾病对公共卫生往往具有破坏性的影响。

DESCRIPTION (provided by applicant): A wide array of psychological traits and symptoms characterized within the framework of internalizing/externalizing psychopathology are common to several psychiatric constructs. This may suggest similar pathophysiological etiologies for some ostensibly distinct forms of mental illness. As the capabilities of biological science advance, a lingering impediment to progress in mental health research is the widely varied, often overlapping, and occasionally redundant list of psychiatric outcome variables that is appealed to, which is largely based on an outdated, descriptive taxonomy of psychopathology and personality traits originally developed without the aid of modern neuroscientific techniques (Insel et al., 2010).While we increasingly recognize that these outcome variables are partially dependent upon genetics, the complex relationships between heredity, environment, and physiology makes estimates of direct relationships between genes and observable psychiatric outcomes modest at best. A means of clarifying these relationships is by defining sets of intermediate physiological characteristics (endophenotypes) which are more proximally related to genetic influences than more broadly descriptive personality traits and behavioral styles. Ultimately, these endophenotypes may be used as more objective criteria for classifying and diagnosing psychiatric outcome variables, informed by modern biological science. Here we apply these perspectives to internalizing/externalizing dimensions of psychopathology including impulsivity, antisociality, substance abuse, depression, and anxiety. Using the world's largest existing forensic dataset which includes genetic, structural and functional neuroimaging, physiological, behavioral and psychiatric measures, the primary goal of this project will be to provide data which helps to define a more biologically informed taxonomy of psychiatric outcomes. This will be accomplished by examining these relationships in two ways. First, we examine standard genomic imaging associations between candidate gene polymorphisms which have been previously associated with a number of these dimensional constructs. Specifically, 5HTT, MAOA, DAT, DRD2, and DRD4 are featured prominently in this literature and impact monoaminergic signaling pathways responsible for modulating mood and behavior. Second, we will apply more agnostic, data-driven methods of defining similar relationships between large scale genetic arrays and neuroimaging data. Parallel Independent Components Analysis is a technique which identifies unique sources of variance in complex, noisy systems without unnecessarily constraining the analyses with a priori assumptions about the features of those systems. It is an ideal tool for comparing our existing notions about genetic contributors to psychopathological outcomes with relationships defined by establishing intermediate neuroimaging endophenotypes. Ultimately this will help to inform more specific and effective intervention strategies aimed at reducing the often devastating impact of these related mental illnesses on public health.