Refining a Taxonomy for Externalizing Psychopatholgy Using Genomic Imaging

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and occasionally redundant list of psychiatric outcome variables that is appealed to, which is largely based on an outdated, descriptive taxonomy of psychopathology and personality traits originally developed without the aid of modern neuroscientific techniques (Insel et al.,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