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Refining a Taxonomy for Externalizing Psychopatholgy Using Genomic Imaging

使用基因组成像完善外化精神病理学的分类法

基本信息

批准号：
8813492
负责人：
Nathaniel Erik Anderson
金额：
$ 5.87万
依托单位：
LOVELACE BIOMEDICAL & ENVIRONMENTAL RESEARCH INSTITUTE
依托单位国家：
美国
项目类别：
财政年份：
2013
资助国家：
美国
起止时间：
2013-04-01 至 2016-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8813492
关键词：
Alleles Anatomy Anxiety Area Behavior Behavioral Behavioral Genetics Biological Biological Sciences Brain Candidate Disease Gene Categories Characteristics Classification Scheme Clinical Complex Corpus striatum structure DRD2 gene DRD4 gene Data Data Set Databases Depression and Suicide Detection Development Diagnostic Dimensions Emotional Ensure Environment Esthesia Etiology Exhibits Forensic Medicine Functional disorder Funding Genes Genetic Genetic Markers Genetic Polymorphism Genetic Variation Genetic study Genomics Goals Heredity Heritability Image Impulsivity Intervention Investigation Literature Measures Mental Depression Mental Health Mental disorders Methaqualone Methods Molecular Genetics Moods National Institute of Mental Health Neurobiology Neurosciences Outcome Personality Traits Physiological Physiology Plant Roots Prefrontal Cortex Psychiatric Diagnosis Psychopathology Public Health Reporting Research Research Domain Criteria Resources Rewards Risk Sampling Scheme Secure Signal Pathway Source Stimulus Structure Substance Addiction Substance abuse problem Symptoms System Taxonomy Techniques Training and Education Variant Violence Work anti social auditory stimulus base behavioral outcome dopamine transporter effective intervention endophenotype falls gray matter high risk independent component analysis neuroimaging novel psychologic public health relevance receptor serotonin transporter tool trait

项目摘要

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and occasionally redundant list of psychiatric outcome variables that is appealed to, which is largely based on an outdated, descriptive taxonomy of psychopathology and personality traits originally developed without the aid of modern neuroscientific techniques (Insel et al.,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

描述（由申请人提供）：在内化/外化精神病理学框架内表征的各种心理特征和症状对于几种精神病学结构是共同的。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学病因。随着生物科学能力的进步，心理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、偶尔冗余的列表，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法，最初是在没有现代神经科学技术的帮助下开发的（Insel et al.，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对基因与可观察到的精神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组中间生理特征（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密切。最终，这些内在表型可以被用作现代生物科学告知的对精神病结果变量进行分类和诊断的更客观的标准。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。使用世界上最大的现有法医数据集，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，帮助定义一个更生物学的精神病学结果的分类。这将通过从两个方面考察这些关系来实现。首先，我们研究了标准的基因组成像之间的候选基因多态性，这些候选基因多态性先前已与这些维度的结构。具体而言，5 HTT、MAOA、DAT、DRD 2和DRD 4在该文献中具有显著特征，并影响负责调节情绪和行为的单胺能信号传导途径。其次，我们将应用更多不可知的数据驱动方法来定义大规模遗传阵列和神经影像数据之间的相似关系。并行独立分量分析是一种技术，它可以识别复杂的噪声系统中的唯一方差源，而无需对这些系统的特性进行先验假设。这是一个理想的工具，可以用来比较我们现有的关于遗传贡献者的概念，精神病理学结果与通过建立中间神经影像学内表型定义的关系。最终，这将有助于提供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的破坏性影响。