Date of Award

Spring 1-1-2016

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Integrative Physiology

First Advisor

Matthew B. McQueen

Second Advisor

Tor D. Wager

Third Advisor

Soo Rhee

Fourth Advisor

Alaa Ahmed

Fifth Advisor

Robert S. Mazzeo

Abstract

Thoughts, feelings and complex behavioral patterns are represented through neural patterns. These neural patterns have molecular and genetic underpinnings, but the connection between the two isn’t always clear. In this manuscript, we evaluate a possible neuroendophenotype for behavioral disinhibition in a sample collected from the Center on Antisocial Drug Dependence, who were selected based upon their degree of behavioral disinhibition. We obtained genome-wide data on the 1,901 participants, and generated a composite polygenic risk score for each of the 80 subjects from 1,876 single nucleotide polymorphisms shown to be associated with the behavioral disinhibition phenotype. We then use this PGRS to determine how genetic contributors drive activation in the brain during a risky and cautious decision making paradigm through functional MRI (fMRI).

To expand on this work, we focus on the full behavioral disinhibition GWAS.

In this study, we collect data on behavioral measures encompassing novelty seeking, conduct disorder symptoms and substance dependence vulnerability. These measures collectively define the “BD” phenotype, and we inquire into whether adolescent BD is a predictor for later life outcomes regarding BMI, drug vulnerability, continuing to higher education after high school, engagement in risky sexual behaviors and experience of depressive symptoms. The average time between the first assessment and subsequent follow-up was on average 9.2 years after initial BD measurements were obtained.

We then discuss the utility of using a meta-analytic approach to combine neuroimaging data across 256 studies on pain and touch perception. This database includes 4,665 subjects’ functional neuroimaging data from studies published from 1993 to 2015. Here, we use a Multi-Level Kernel Density Analysis inquiry into the most commonly activated brain regions during the presentation of pain, non-painful touch, and the relative differences between the two. We make additional comparisons on smaller sections of this database. Lastly, we will compare the MKDA maps of pain and touch to 7 resting-state default networks and then briefly discuss possibilities for the future with regards to complex neurophysiological and behavioral data and the importance of data sharing initiatives.

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