Browsing by Author "Lewis, Michael"
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- Fear Conditioning as an Intermediate Phenotype: An RDoC Inspired Methodological AnalysisLewis, Michael (Virginia Tech, 2018-04-20)Due to difficulties in elucidating neurobiological aspects of psychological disorders, the National Institute of Mental Health (NIMH) created the Research Domain Criteria (RDoC), which encourages novel conceptualizations of the relationship between neurobiological circuitry and clinical difficulties. This approach is markedly different from the Diagnostic and Statistical Manual of Mental Disorders (DSM) based approach that has dominated clinical research to date. Thus, RDoC necessitates exploration of novel experimental and statistical approaches. Fear learning paradigms represent a promising methodology for elucidating connections between acute threat (“fear”) circuitry and fear-related clinical difficulties. However, traditional analytical approaches rely on central tendency statistics, which are tethered to a priori categories and assume homogeneity within groups. Growth Mixture Modeling (GMM) methods such as Latent Class Growth Analysis (LCGA) may be uniquely suited for examining fear learning phenotypes. However, just three extant studies have applied GMM to fear learning and only one did so in a human population. Thus, the degree to which classes identified in known studies represent characteristics of the general population and to which GMM methodology is applicable across populations and paradigms is unclear. This preliminary study applied LCGA to a fear learning lab study in an attempt to identify heterogeneity in fear learning patterns based on a posteriori classification. The findings of this investigation may inform efforts to move toward a trans-diagnostic conceptualization of fear learning. Consistent with the goals laid out in RDoC, explication of fear learning phenotypes may eventually provide critical information needed to spur innovation in psychotherapeutic and psychopharmacological treatment.
- Genetic Risk Factors for PTSD: A Gene-Set Analysis of Neurotransmitter ReceptorsLewis, Michael (Virginia Tech, 2020-07-08)PTSD is a moderately heritable disorder that causes intense and chronic suffering in many afflicted individuals. The pathogenesis of PTSD is not well understood, and genetic mechanisms are particularly elusive. Neurotransmitter systems are thought to contribute to PTSD etiology and are the targets of most pharmacotherapies used to treat PTSD, including the only two FDA approved options and a wide array of off-label options. However, the degree to which variation in genes which encode for and regulate neurotransmitter receptors increase risk of developing PTSD is unclear. Recently, large collaborative groups of PTSD genetics researchers have completed genome-wide association studies (GWAS) using massive sample sizes and have made summary statistics available for public use. In 2018, a new technique for high-powered analysis of GWAS summary statistics called GSA-SNP2 was introduced. In order to explore the relationship between PTSD and genetic variants in widely theorized molecular targets, this study applied GSA-SNP2 to manually curated neurotransmitter receptor gene-sets. Curated gene-sets included nine total "neurotransmitter receptor group" gene-sets and 45 total "receptor subtype" gene-sets. Each "neurotransmitter receptor group" gene-sets was designed to capture concentration of genetic risk factors for PTSD within genes which encode for all receptor subtypes that are activated by a given neurotransmitter. In contrast, "receptor subtype" gene-sets focused on specific subtypes and also accounted for intracellular signaling; each was designed to capture concentration of genetic risk factors for PTSD within genes which encode for specific receptor subtypes and the intracellular signaling proteins through which they exert their effects. Due to practical considerations, this work used summary statistics derived from a GWAS with far fewer participants (2,424 cases; 7,113 controls) than initially planned (23,212 cases; 151,447 controls). Prior to controlling for multiple comparisons, 7 of the investigated gene-sets reached statistical significance at the p ≤ .05 level. However, after controlling for multiple comparisons, none of the investigated gene-sets reached statistical significance. Due to limited statistical power of the current work, these results should be interpreted very cautiously. The current study is best interpreted as a preliminary study and is most informative in relation to refining study design. Implications for next steps are emphasized in discussion and nominally significant results are synthesized with the literature to demonstrate the types of research questions that might be addressed by applying a refined version of this study design to a larger sample.