Refining a Taxonomy for Externalizing Psychopatholgy Using Genomic Imaging

描述（由申请人提供）：在内在化/外在化精神病理学框架内描述的一系列广泛的心理特征和症状是几种精神病学结构所共有的。这可能表明一些表面上不同形式的精神疾病有相似的病理生理病因。随着生物科学能力的进步，精神健康研究进展的一个长期障碍是广泛多样的，经常重叠的，偶尔重复的精神病学结果变量列表，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，最初是在没有现代神经科学技术的帮助下发展起来的（Insel等人，2010）。虽然我们越来越认识到这些结果变量部分取决于遗传，但遗传，环境和生理之间的复杂关系使得基因与可观察到的精神疾病结果之间的直接关系的估计充其量是适度的。澄清这些关系的一种方法是定义一系列中间生理特征（内表型），这些特征与遗传影响的关系比更广泛的描述性人格特征和行为风格更接近。最终，这些内表型可以作为分类和诊断精神结局变量的更客观的标准，由现代生物科学提供信息。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。利用世界上最大的现有法医数据集，包括基因、结构和功能神经成像、生理、行为和精神测量，这个项目的主要目标将是提供数据，帮助定义一个更生物学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，我们研究了候选基因多态性之间的标准基因组成像关联，这些基因多态性先前与许多这些维度结构相关。具体而言，5HTT、MAOA、DAT、DRD2和DRD4在本文献中具有突出的特点，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类似关系。并行独立分量分析是一种在复杂的、有噪声的系统中识别唯一方差来源的技术，它不会不必要地用对这些系统特征的先验假设来限制分析。它是比较我们现有的关于基因贡献者的概念的理想工具

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and occasionally redundant list of psychiatric outcome variables that is appealed to, which is largely based on an outdated, descriptive taxonomy of psychopathology and personality traits originally developed without the aid of modern neuroscientific techniques (Insel et al.,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.